From 05160385c390b5a40df47a9b0990750d6d5e3284 Mon Sep 17 00:00:00 2001 From: PommesPeter Date: Fri, 17 May 2024 14:11:26 +0800 Subject: [PATCH 01/92] init lumina-t2i pipeline --- src/diffusers/__init__.py | 2 ++ src/diffusers/pipelines/__init__.py | 2 ++ src/diffusers/pipelines/lumina/__init__.py | 1 + 3 files changed, 5 insertions(+) create mode 100644 src/diffusers/pipelines/lumina/__init__.py diff --git a/src/diffusers/__init__.py b/src/diffusers/__init__.py index 66c98804eadc..e2bc2f668d5f 100644 --- a/src/diffusers/__init__.py +++ b/src/diffusers/__init__.py @@ -259,6 +259,7 @@ "LDMTextToImagePipeline", "LEditsPPPipelineStableDiffusion", "LEditsPPPipelineStableDiffusionXL", + "d", "MusicLDMPipeline", "PaintByExamplePipeline", "PIAPipeline", @@ -637,6 +638,7 @@ LDMTextToImagePipeline, LEditsPPPipelineStableDiffusion, LEditsPPPipelineStableDiffusionXL, + LuminaText2ImgPipeline, MusicLDMPipeline, PaintByExamplePipeline, PIAPipeline, diff --git a/src/diffusers/pipelines/__init__.py b/src/diffusers/pipelines/__init__.py index c2dd7ac0d551..7ae2329c52ad 100644 --- a/src/diffusers/pipelines/__init__.py +++ b/src/diffusers/pipelines/__init__.py @@ -185,6 +185,7 @@ "LEditsPPPipelineStableDiffusionXL", ] ) + _import_structure["lumina"] = ["LuminaText2ImgPipeline"] _import_structure["musicldm"] = ["MusicLDMPipeline"] _import_structure["paint_by_example"] = ["PaintByExamplePipeline"] _import_structure["pia"] = ["PIAPipeline"] @@ -448,6 +449,7 @@ LEditsPPPipelineStableDiffusion, LEditsPPPipelineStableDiffusionXL, ) + from .lumina import LuminaText2ImgPipeline from .musicldm import MusicLDMPipeline from .paint_by_example import PaintByExamplePipeline from .pia import PIAPipeline diff --git a/src/diffusers/pipelines/lumina/__init__.py b/src/diffusers/pipelines/lumina/__init__.py new file mode 100644 index 000000000000..38ad13939515 --- /dev/null +++ b/src/diffusers/pipelines/lumina/__init__.py @@ -0,0 +1 @@ +from .pipeline_lumina import LuminaText2ImgPipeline \ No newline at end of file From bd2445ea110569254e71354b0081fd4eb9b9e8b3 Mon Sep 17 00:00:00 2001 From: PommesPeter Date: Fri, 17 May 2024 14:17:30 +0800 Subject: [PATCH 02/92] added pipeline code --- src/diffusers/pipelines/lumina/__init__.py | 2 +- .../pipelines/lumina/pipeline_lumina_t2i.py | 969 ++++++++++++++++++ 2 files changed, 970 insertions(+), 1 deletion(-) create mode 100644 src/diffusers/pipelines/lumina/pipeline_lumina_t2i.py diff --git a/src/diffusers/pipelines/lumina/__init__.py b/src/diffusers/pipelines/lumina/__init__.py index 38ad13939515..32503a6442c7 100644 --- a/src/diffusers/pipelines/lumina/__init__.py +++ b/src/diffusers/pipelines/lumina/__init__.py @@ -1 +1 @@ -from .pipeline_lumina import LuminaText2ImgPipeline \ No newline at end of file +from .pipeline_lumina_t2i import LuminaText2ImgPipeline \ No newline at end of file diff --git a/src/diffusers/pipelines/lumina/pipeline_lumina_t2i.py b/src/diffusers/pipelines/lumina/pipeline_lumina_t2i.py new file mode 100644 index 000000000000..3b6a5636bd58 --- /dev/null +++ b/src/diffusers/pipelines/lumina/pipeline_lumina_t2i.py @@ -0,0 +1,969 @@ +# Copyright 2024 Lumina-T2X Authors and The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import html +import inspect +import re +import urllib.parse as ul +from typing import Callable, List, Optional, Tuple, Union + +import torch +from transformers import T5EncoderModel, T5Tokenizer + +from ...image_processor import LuminaImageProcessor +from ...models import AutoencoderKL, Transformer2DModel +from ...schedulers import DPMSolverMultistepScheduler +from ...utils import ( + BACKENDS_MAPPING, + deprecate, + is_bs4_available, + is_ftfy_available, + logging, + replace_example_docstring, +) +from ...utils.torch_utils import randn_tensor +from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput + + +logger = logging.get_logger(__name__) # pylint: disable=invalid-name + +if is_bs4_available(): + from bs4 import BeautifulSoup + +if is_ftfy_available(): + import ftfy + +EXAMPLE_DOC_STRING = """ + Examples: + ```py + >>> import torch + >>> from diffusers import LuminaText2ImgPipeline + + >>> # You can replace the checkpoint id with "Alpha-VLLM/Lumina-Next-T2I" too. + >>> pipe = LuminaText2ImgPipeline.from_pretrained("Alpha-VLLM/Lumina-T2I", torch_dtype=torch.float16) + >>> # Enable memory optimizations. + >>> pipe.enable_model_cpu_offload() + + >>> prompt = "A small cactus with a happy face in the Sahara desert." + >>> image = pipe(prompt).images[0] + ``` +""" + +ASPECT_RATIO_1024_BIN = { + "0.25": [512.0, 2048.0], + "0.28": [512.0, 1856.0], + "0.32": [576.0, 1792.0], + "0.33": [576.0, 1728.0], + "0.35": [576.0, 1664.0], + "0.4": [640.0, 1600.0], + "0.42": [640.0, 1536.0], + "0.48": [704.0, 1472.0], + "0.5": [704.0, 1408.0], + "0.52": [704.0, 1344.0], + "0.57": [768.0, 1344.0], + "0.6": [768.0, 1280.0], + "0.68": [832.0, 1216.0], + "0.72": [832.0, 1152.0], + "0.78": [896.0, 1152.0], + "0.82": [896.0, 1088.0], + "0.88": [960.0, 1088.0], + "0.94": [960.0, 1024.0], + "1.0": [1024.0, 1024.0], + "1.07": [1024.0, 960.0], + "1.13": [1088.0, 960.0], + "1.21": [1088.0, 896.0], + "1.29": [1152.0, 896.0], + "1.38": [1152.0, 832.0], + "1.46": [1216.0, 832.0], + "1.67": [1280.0, 768.0], + "1.75": [1344.0, 768.0], + "2.0": [1408.0, 704.0], + "2.09": [1472.0, 704.0], + "2.4": [1536.0, 640.0], + "2.5": [1600.0, 640.0], + "3.0": [1728.0, 576.0], + "4.0": [2048.0, 512.0], +} + +ASPECT_RATIO_512_BIN = { + "0.25": [256.0, 1024.0], + "0.28": [256.0, 928.0], + "0.32": [288.0, 896.0], + "0.33": [288.0, 864.0], + "0.35": [288.0, 832.0], + "0.4": [320.0, 800.0], + "0.42": [320.0, 768.0], + "0.48": [352.0, 736.0], + "0.5": [352.0, 704.0], + "0.52": [352.0, 672.0], + "0.57": [384.0, 672.0], + "0.6": [384.0, 640.0], + "0.68": [416.0, 608.0], + "0.72": [416.0, 576.0], + "0.78": [448.0, 576.0], + "0.82": [448.0, 544.0], + "0.88": [480.0, 544.0], + "0.94": [480.0, 512.0], + "1.0": [512.0, 512.0], + "1.07": [512.0, 480.0], + "1.13": [544.0, 480.0], + "1.21": [544.0, 448.0], + "1.29": [576.0, 448.0], + "1.38": [576.0, 416.0], + "1.46": [608.0, 416.0], + "1.67": [640.0, 384.0], + "1.75": [672.0, 384.0], + "2.0": [704.0, 352.0], + "2.09": [736.0, 352.0], + "2.4": [768.0, 320.0], + "2.5": [800.0, 320.0], + "3.0": [864.0, 288.0], + "4.0": [1024.0, 256.0], +} + +ASPECT_RATIO_256_BIN = { + "0.25": [128.0, 512.0], + "0.28": [128.0, 464.0], + "0.32": [144.0, 448.0], + "0.33": [144.0, 432.0], + "0.35": [144.0, 416.0], + "0.4": [160.0, 400.0], + "0.42": [160.0, 384.0], + "0.48": [176.0, 368.0], + "0.5": [176.0, 352.0], + "0.52": [176.0, 336.0], + "0.57": [192.0, 336.0], + "0.6": [192.0, 320.0], + "0.68": [208.0, 304.0], + "0.72": [208.0, 288.0], + "0.78": [224.0, 288.0], + "0.82": [224.0, 272.0], + "0.88": [240.0, 272.0], + "0.94": [240.0, 256.0], + "1.0": [256.0, 256.0], + "1.07": [256.0, 240.0], + "1.13": [272.0, 240.0], + "1.21": [272.0, 224.0], + "1.29": [288.0, 224.0], + "1.38": [288.0, 208.0], + "1.46": [304.0, 208.0], + "1.67": [320.0, 192.0], + "1.75": [336.0, 192.0], + "2.0": [352.0, 176.0], + "2.09": [368.0, 176.0], + "2.4": [384.0, 160.0], + "2.5": [400.0, 160.0], + "3.0": [432.0, 144.0], + "4.0": [512.0, 128.0], +} + + +# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps +def retrieve_timesteps( + scheduler, + num_inference_steps: Optional[int] = None, + device: Optional[Union[str, torch.device]] = None, + timesteps: Optional[List[int]] = None, + sigmas: Optional[List[float]] = None, + **kwargs, +): + """ + Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles + custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`. + + Args: + scheduler (`SchedulerMixin`): + The scheduler to get timesteps from. + num_inference_steps (`int`): + The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps` + must be `None`. + device (`str` or `torch.device`, *optional*): + The device to which the timesteps should be moved to. If `None`, the timesteps are not moved. + timesteps (`List[int]`, *optional*): + Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed, + `num_inference_steps` and `sigmas` must be `None`. + sigmas (`List[float]`, *optional*): + Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed, + `num_inference_steps` and `timesteps` must be `None`. + + Returns: + `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the + second element is the number of inference steps. + """ + if timesteps is not None and sigmas is not None: + raise ValueError("Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values") + if timesteps is not None: + accepts_timesteps = "timesteps" in set(inspect.signature(scheduler.set_timesteps).parameters.keys()) + if not accepts_timesteps: + raise ValueError( + f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom" + f" timestep schedules. Please check whether you are using the correct scheduler." + ) + scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs) + timesteps = scheduler.timesteps + num_inference_steps = len(timesteps) + elif sigmas is not None: + accept_sigmas = "sigmas" in set(inspect.signature(scheduler.set_timesteps).parameters.keys()) + if not accept_sigmas: + raise ValueError( + f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom" + f" sigmas schedules. Please check whether you are using the correct scheduler." + ) + scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs) + timesteps = scheduler.timesteps + num_inference_steps = len(timesteps) + else: + scheduler.set_timesteps(num_inference_steps, device=device, **kwargs) + timesteps = scheduler.timesteps + return timesteps, num_inference_steps + + +class LuminaText2ImgPipeline(DiffusionPipeline): + r""" + Pipeline for text-to-image generation using Lumina-T2I. + + This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the + library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) + + Args: + vae ([`AutoencoderKL`]): + Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. + text_encoder ([`T5EncoderModel`]): + Frozen text-encoder. Lumina-T2I uses + [T5](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5EncoderModel), specifically the + [t5-v1_1-xxl](https://huggingface.co/Alpha-VLLM/tree/main/t5-v1_1-xxl) variant. + tokenizer (`T5Tokenizer`): + Tokenizer of class + [T5Tokenizer](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5Tokenizer). + transformer ([`Transformer2DModel`]): + A text conditioned `Transformer2DModel` to denoise the encoded image latents. + scheduler ([`SchedulerMixin`]): + A scheduler to be used in combination with `transformer` to denoise the encoded image latents. + """ + + bad_punct_regex = re.compile( + r"[" + + "#®•©™&@·º½¾¿¡§~" + + r"\)" + + r"\(" + + r"\]" + + r"\[" + + r"\}" + + r"\{" + + r"\|" + + "\\" + + r"\/" + + r"\*" + + r"]{1,}" + ) # noqa + + _optional_components = ["tokenizer", "text_encoder"] + model_cpu_offload_seq = "text_encoder->transformer->vae" + + def __init__( + self, + tokenizer: T5Tokenizer, + text_encoder: T5EncoderModel, + vae: AutoencoderKL, + transformer: Transformer2DModel, + scheduler: DPMSolverMultistepScheduler, + ): + super().__init__() + + self.register_modules( + tokenizer=tokenizer, text_encoder=text_encoder, vae=vae, transformer=transformer, scheduler=scheduler + ) + + self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) + self.image_processor = LuminaImageProcessor(vae_scale_factor=self.vae_scale_factor) + + # Adapted from diffusers.pipelines.deepfloyd_if.pipeline_if.encode_prompt + def encode_prompt( + self, + prompt: Union[str, List[str]], + do_classifier_free_guidance: bool = True, + negative_prompt: str = "", + num_images_per_prompt: int = 1, + device: Optional[torch.device] = None, + prompt_embeds: Optional[torch.Tensor] = None, + negative_prompt_embeds: Optional[torch.Tensor] = None, + prompt_attention_mask: Optional[torch.Tensor] = None, + negative_prompt_attention_mask: Optional[torch.Tensor] = None, + clean_caption: bool = False, + max_sequence_length: int = 120, + **kwargs, + ): + r""" + Encodes the prompt into text encoder hidden states. + + Args: + prompt (`str` or `List[str]`, *optional*): + prompt to be encoded + negative_prompt (`str` or `List[str]`, *optional*): + The prompt not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds` + instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). For + Lumina-T2I, this should be "". + do_classifier_free_guidance (`bool`, *optional*, defaults to `True`): + whether to use classifier free guidance or not + num_images_per_prompt (`int`, *optional*, defaults to 1): + number of images that should be generated per prompt + device: (`torch.device`, *optional*): + torch device to place the resulting embeddings on + prompt_embeds (`torch.Tensor`, *optional*): + Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not + provided, text embeddings will be generated from `prompt` input argument. + negative_prompt_embeds (`torch.Tensor`, *optional*): + Pre-generated negative text embeddings. For Lumina-T2I, it's should be the embeddings of the "" + string. + clean_caption (`bool`, defaults to `False`): + If `True`, the function will preprocess and clean the provided caption before encoding. + max_sequence_length (`int`, defaults to 120): Maximum sequence length to use for the prompt. + """ + + if "mask_feature" in kwargs: + deprecation_message = "The use of `mask_feature` is deprecated. It is no longer used in any computation and that doesn't affect the end results. It will be removed in a future version." + deprecate("mask_feature", "1.0.0", deprecation_message, standard_warn=False) + + if device is None: + device = self._execution_device + + if prompt is not None and isinstance(prompt, str): + batch_size = 1 + elif prompt is not None and isinstance(prompt, list): + batch_size = len(prompt) + else: + batch_size = prompt_embeds.shape[0] + + # See Section 3.1. of the paper. + max_length = max_sequence_length + + if prompt_embeds is None: + prompt = self._text_preprocessing(prompt, clean_caption=clean_caption) + text_inputs = self.tokenizer( + prompt, + padding="max_length", + max_length=max_length, + truncation=True, + add_special_tokens=True, + return_tensors="pt", + ) + text_input_ids = text_inputs.input_ids + untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids + + if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal( + text_input_ids, untruncated_ids + ): + removed_text = self.tokenizer.batch_decode(untruncated_ids[:, max_length - 1 : -1]) + logger.warning( + "The following part of your input was truncated because T5 can only handle sequences up to" + f" {max_length} tokens: {removed_text}" + ) + + prompt_attention_mask = text_inputs.attention_mask + prompt_attention_mask = prompt_attention_mask.to(device) + + prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=prompt_attention_mask) + prompt_embeds = prompt_embeds[0] + + if self.text_encoder is not None: + dtype = self.text_encoder.dtype + elif self.transformer is not None: + dtype = self.transformer.dtype + else: + dtype = None + + prompt_embeds = prompt_embeds.to(dtype=dtype, device=device) + + bs_embed, seq_len, _ = prompt_embeds.shape + # duplicate text embeddings and attention mask for each generation per prompt, using mps friendly method + prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1) + prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1) + prompt_attention_mask = prompt_attention_mask.view(bs_embed, -1) + prompt_attention_mask = prompt_attention_mask.repeat(num_images_per_prompt, 1) + + # get unconditional embeddings for classifier free guidance + if do_classifier_free_guidance and negative_prompt_embeds is None: + uncond_tokens = [negative_prompt] * batch_size + uncond_tokens = self._text_preprocessing(uncond_tokens, clean_caption=clean_caption) + max_length = prompt_embeds.shape[1] + uncond_input = self.tokenizer( + uncond_tokens, + padding="max_length", + max_length=max_length, + truncation=True, + return_attention_mask=True, + add_special_tokens=True, + return_tensors="pt", + ) + negative_prompt_attention_mask = uncond_input.attention_mask + negative_prompt_attention_mask = negative_prompt_attention_mask.to(device) + + negative_prompt_embeds = self.text_encoder( + uncond_input.input_ids.to(device), attention_mask=negative_prompt_attention_mask + ) + negative_prompt_embeds = negative_prompt_embeds[0] + + if do_classifier_free_guidance: + # duplicate unconditional embeddings for each generation per prompt, using mps friendly method + seq_len = negative_prompt_embeds.shape[1] + + negative_prompt_embeds = negative_prompt_embeds.to(dtype=dtype, device=device) + + negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1) + negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1) + + negative_prompt_attention_mask = negative_prompt_attention_mask.view(bs_embed, -1) + negative_prompt_attention_mask = negative_prompt_attention_mask.repeat(num_images_per_prompt, 1) + else: + negative_prompt_embeds = None + negative_prompt_attention_mask = None + + return prompt_embeds, prompt_attention_mask, negative_prompt_embeds, negative_prompt_attention_mask + + # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs + def prepare_extra_step_kwargs(self, generator, eta): + # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature + # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. + # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 + # and should be between [0, 1] + + accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) + extra_step_kwargs = {} + if accepts_eta: + extra_step_kwargs["eta"] = eta + + # check if the scheduler accepts generator + accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys()) + if accepts_generator: + extra_step_kwargs["generator"] = generator + return extra_step_kwargs + + def check_inputs( + self, + prompt, + height, + width, + negative_prompt, + callback_steps, + prompt_embeds=None, + negative_prompt_embeds=None, + prompt_attention_mask=None, + negative_prompt_attention_mask=None, + ): + if height % 8 != 0 or width % 8 != 0: + raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.") + + if (callback_steps is None) or ( + callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0) + ): + raise ValueError( + f"`callback_steps` has to be a positive integer but is {callback_steps} of type" + f" {type(callback_steps)}." + ) + + if prompt is not None and prompt_embeds is not None: + raise ValueError( + f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to" + " only forward one of the two." + ) + elif prompt is None and prompt_embeds is None: + raise ValueError( + "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined." + ) + elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)): + raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") + + if prompt is not None and negative_prompt_embeds is not None: + raise ValueError( + f"Cannot forward both `prompt`: {prompt} and `negative_prompt_embeds`:" + f" {negative_prompt_embeds}. Please make sure to only forward one of the two." + ) + + if negative_prompt is not None and negative_prompt_embeds is not None: + raise ValueError( + f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:" + f" {negative_prompt_embeds}. Please make sure to only forward one of the two." + ) + + if prompt_embeds is not None and prompt_attention_mask is None: + raise ValueError("Must provide `prompt_attention_mask` when specifying `prompt_embeds`.") + + if negative_prompt_embeds is not None and negative_prompt_attention_mask is None: + raise ValueError("Must provide `negative_prompt_attention_mask` when specifying `negative_prompt_embeds`.") + + if prompt_embeds is not None and negative_prompt_embeds is not None: + if prompt_embeds.shape != negative_prompt_embeds.shape: + raise ValueError( + "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but" + f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`" + f" {negative_prompt_embeds.shape}." + ) + if prompt_attention_mask.shape != negative_prompt_attention_mask.shape: + raise ValueError( + "`prompt_attention_mask` and `negative_prompt_attention_mask` must have the same shape when passed directly, but" + f" got: `prompt_attention_mask` {prompt_attention_mask.shape} != `negative_prompt_attention_mask`" + f" {negative_prompt_attention_mask.shape}." + ) + + # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline._text_preprocessing + def _text_preprocessing(self, text, clean_caption=False): + if clean_caption and not is_bs4_available(): + logger.warning(BACKENDS_MAPPING["bs4"][-1].format("Setting `clean_caption=True`")) + logger.warning("Setting `clean_caption` to False...") + clean_caption = False + + if clean_caption and not is_ftfy_available(): + logger.warning(BACKENDS_MAPPING["ftfy"][-1].format("Setting `clean_caption=True`")) + logger.warning("Setting `clean_caption` to False...") + clean_caption = False + + if not isinstance(text, (tuple, list)): + text = [text] + + def process(text: str): + if clean_caption: + text = self._clean_caption(text) + text = self._clean_caption(text) + else: + text = text.lower().strip() + return text + + return [process(t) for t in text] + + # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline._clean_caption + def _clean_caption(self, caption): + caption = str(caption) + caption = ul.unquote_plus(caption) + caption = caption.strip().lower() + caption = re.sub("", "person", caption) + # urls: + caption = re.sub( + r"\b((?:https?:(?:\/{1,3}|[a-zA-Z0-9%])|[a-zA-Z0-9.\-]+[.](?:com|co|ru|net|org|edu|gov|it)[\w/-]*\b\/?(?!@)))", # noqa + "", + caption, + ) # regex for urls + caption = re.sub( + r"\b((?:www:(?:\/{1,3}|[a-zA-Z0-9%])|[a-zA-Z0-9.\-]+[.](?:com|co|ru|net|org|edu|gov|it)[\w/-]*\b\/?(?!@)))", # noqa + "", + caption, + ) # regex for urls + # html: + caption = BeautifulSoup(caption, features="html.parser").text + + # @ + caption = re.sub(r"@[\w\d]+\b", "", caption) + + # 31C0—31EF CJK Strokes + # 31F0—31FF Katakana Phonetic Extensions + # 3200—32FF Enclosed CJK Letters and Months + # 3300—33FF CJK Compatibility + # 3400—4DBF CJK Unified Ideographs Extension A + # 4DC0—4DFF Yijing Hexagram Symbols + # 4E00—9FFF CJK Unified Ideographs + caption = re.sub(r"[\u31c0-\u31ef]+", "", caption) + caption = re.sub(r"[\u31f0-\u31ff]+", "", caption) + caption = re.sub(r"[\u3200-\u32ff]+", "", caption) + caption = re.sub(r"[\u3300-\u33ff]+", "", caption) + caption = re.sub(r"[\u3400-\u4dbf]+", "", caption) + caption = re.sub(r"[\u4dc0-\u4dff]+", "", caption) + caption = re.sub(r"[\u4e00-\u9fff]+", "", caption) + ####################################################### + + # все виды тире / all types of dash --> "-" + caption = re.sub( + r"[\u002D\u058A\u05BE\u1400\u1806\u2010-\u2015\u2E17\u2E1A\u2E3A\u2E3B\u2E40\u301C\u3030\u30A0\uFE31\uFE32\uFE58\uFE63\uFF0D]+", # noqa + "-", + caption, + ) + + # кавычки к одному стандарту + caption = re.sub(r"[`´«»“”¨]", '"', caption) + caption = re.sub(r"[‘’]", "'", caption) + + # " + caption = re.sub(r""?", "", caption) + # & + caption = re.sub(r"&", "", caption) + + # ip adresses: + caption = re.sub(r"\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}", " ", caption) + + # article ids: + caption = re.sub(r"\d:\d\d\s+$", "", caption) + + # \n + caption = re.sub(r"\\n", " ", caption) + + # "#123" + caption = re.sub(r"#\d{1,3}\b", "", caption) + # "#12345.." + caption = re.sub(r"#\d{5,}\b", "", caption) + # "123456.." + caption = re.sub(r"\b\d{6,}\b", "", caption) + # filenames: + caption = re.sub(r"[\S]+\.(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)", "", caption) + + # + caption = re.sub(r"[\"\']{2,}", r'"', caption) # """AUSVERKAUFT""" + caption = re.sub(r"[\.]{2,}", r" ", caption) # """AUSVERKAUFT""" + + caption = re.sub(self.bad_punct_regex, r" ", caption) # ***AUSVERKAUFT***, #AUSVERKAUFT + caption = re.sub(r"\s+\.\s+", r" ", caption) # " . " + + # this-is-my-cute-cat / this_is_my_cute_cat + regex2 = re.compile(r"(?:\-|\_)") + if len(re.findall(regex2, caption)) > 3: + caption = re.sub(regex2, " ", caption) + + caption = ftfy.fix_text(caption) + caption = html.unescape(html.unescape(caption)) + + caption = re.sub(r"\b[a-zA-Z]{1,3}\d{3,15}\b", "", caption) # jc6640 + caption = re.sub(r"\b[a-zA-Z]+\d+[a-zA-Z]+\b", "", caption) # jc6640vc + caption = re.sub(r"\b\d+[a-zA-Z]+\d+\b", "", caption) # 6640vc231 + + caption = re.sub(r"(worldwide\s+)?(free\s+)?shipping", "", caption) + caption = re.sub(r"(free\s)?download(\sfree)?", "", caption) + caption = re.sub(r"\bclick\b\s(?:for|on)\s\w+", "", caption) + caption = re.sub(r"\b(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)(\simage[s]?)?", "", caption) + caption = re.sub(r"\bpage\s+\d+\b", "", caption) + + caption = re.sub(r"\b\d*[a-zA-Z]+\d+[a-zA-Z]+\d+[a-zA-Z\d]*\b", r" ", caption) # j2d1a2a... + + caption = re.sub(r"\b\d+\.?\d*[xх×]\d+\.?\d*\b", "", caption) + + caption = re.sub(r"\b\s+\:\s+", r": ", caption) + caption = re.sub(r"(\D[,\./])\b", r"\1 ", caption) + caption = re.sub(r"\s+", " ", caption) + + caption.strip() + + caption = re.sub(r"^[\"\']([\w\W]+)[\"\']$", r"\1", caption) + caption = re.sub(r"^[\'\_,\-\:;]", r"", caption) + caption = re.sub(r"[\'\_,\-\:\-\+]$", r"", caption) + caption = re.sub(r"^\.\S+$", "", caption) + + return caption.strip() + + # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents + def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None): + shape = ( + batch_size, + num_channels_latents, + int(height) // self.vae_scale_factor, + int(width) // self.vae_scale_factor, + ) + if isinstance(generator, list) and len(generator) != batch_size: + raise ValueError( + f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" + f" size of {batch_size}. Make sure the batch size matches the length of the generators." + ) + + if latents is None: + latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype) + else: + latents = latents.to(device) + + # scale the initial noise by the standard deviation required by the scheduler + latents = latents * self.scheduler.init_noise_sigma + return latents + + @torch.no_grad() + @replace_example_docstring(EXAMPLE_DOC_STRING) + def __call__( + self, + prompt: Union[str, List[str]] = None, + negative_prompt: str = "", + num_inference_steps: int = 20, + timesteps: List[int] = None, + sigmas: List[float] = None, + guidance_scale: float = 4.5, + num_images_per_prompt: Optional[int] = 1, + height: Optional[int] = None, + width: Optional[int] = None, + eta: float = 0.0, + generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, + latents: Optional[torch.Tensor] = None, + prompt_embeds: Optional[torch.Tensor] = None, + prompt_attention_mask: Optional[torch.Tensor] = None, + negative_prompt_embeds: Optional[torch.Tensor] = None, + negative_prompt_attention_mask: Optional[torch.Tensor] = None, + output_type: Optional[str] = "pil", + return_dict: bool = True, + callback: Optional[Callable[[int, int, torch.Tensor], None]] = None, + callback_steps: int = 1, + clean_caption: bool = True, + use_resolution_binning: bool = True, + max_sequence_length: int = 120, + **kwargs, + ) -> Union[ImagePipelineOutput, Tuple]: + """ + Function invoked when calling the pipeline for generation. + + Args: + prompt (`str` or `List[str]`, *optional*): + The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`. + instead. + negative_prompt (`str` or `List[str]`, *optional*): + The prompt or prompts not to guide the image generation. If not defined, one has to pass + `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is + less than `1`). + num_inference_steps (`int`, *optional*, defaults to 100): + The number of denoising steps. More denoising steps usually lead to a higher quality image at the + expense of slower inference. + timesteps (`List[int]`, *optional*): + Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument + in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is + passed will be used. Must be in descending order. + sigmas (`List[float]`, *optional*): + Custom sigmas to use for the denoising process with schedulers which support a `sigmas` argument in + their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed + will be used. + guidance_scale (`float`, *optional*, defaults to 4.5): + Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). + `guidance_scale` is defined as `w` of equation 2. of [Imagen + Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > + 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, + usually at the expense of lower image quality. + num_images_per_prompt (`int`, *optional*, defaults to 1): + The number of images to generate per prompt. + height (`int`, *optional*, defaults to self.unet.config.sample_size): + The height in pixels of the generated image. + width (`int`, *optional*, defaults to self.unet.config.sample_size): + The width in pixels of the generated image. + eta (`float`, *optional*, defaults to 0.0): + Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to + [`schedulers.DDIMScheduler`], will be ignored for others. + generator (`torch.Generator` or `List[torch.Generator]`, *optional*): + One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html) + to make generation deterministic. + latents (`torch.Tensor`, *optional*): + Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image + generation. Can be used to tweak the same generation with different prompts. If not provided, a latents + tensor will ge generated by sampling using the supplied random `generator`. + prompt_embeds (`torch.Tensor`, *optional*): + Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not + provided, text embeddings will be generated from `prompt` input argument. + prompt_attention_mask (`torch.Tensor`, *optional*): Pre-generated attention mask for text embeddings. + negative_prompt_embeds (`torch.Tensor`, *optional*): + Pre-generated negative text embeddings. For Lumina-T2I this negative prompt should be "". If not + provided, negative_prompt_embeds will be generated from `negative_prompt` input argument. + negative_prompt_attention_mask (`torch.Tensor`, *optional*): + Pre-generated attention mask for negative text embeddings. + output_type (`str`, *optional*, defaults to `"pil"`): + The output format of the generate image. Choose between + [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. + return_dict (`bool`, *optional*, defaults to `True`): + Whether or not to return a [`~pipelines.stable_diffusion.IFPipelineOutput`] instead of a plain tuple. + callback (`Callable`, *optional*): + A function that will be called every `callback_steps` steps during inference. The function will be + called with the following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`. + callback_steps (`int`, *optional*, defaults to 1): + The frequency at which the `callback` function will be called. If not specified, the callback will be + called at every step. + clean_caption (`bool`, *optional*, defaults to `True`): + Whether or not to clean the caption before creating embeddings. Requires `beautifulsoup4` and `ftfy` to + be installed. If the dependencies are not installed, the embeddings will be created from the raw + prompt. + use_resolution_binning (`bool` defaults to `True`): + If set to `True`, the requested height and width are first mapped to the closest resolutions using + `ASPECT_RATIO_1024_BIN`. After the produced latents are decoded into images, they are resized back to + the requested resolution. Useful for generating non-square images. + max_sequence_length (`int` defaults to 120): Maximum sequence length to use with the `prompt`. + + Examples: + + Returns: + [`~pipelines.ImagePipelineOutput`] or `tuple`: + If `return_dict` is `True`, [`~pipelines.ImagePipelineOutput`] is returned, otherwise a `tuple` is + returned where the first element is a list with the generated images + """ + if "mask_feature" in kwargs: + deprecation_message = "The use of `mask_feature` is deprecated. It is no longer used in any computation and that doesn't affect the end results. It will be removed in a future version." + deprecate("mask_feature", "1.0.0", deprecation_message, standard_warn=False) + # 1. Check inputs. Raise error if not correct + height = height or self.transformer.config.sample_size * self.vae_scale_factor + width = width or self.transformer.config.sample_size * self.vae_scale_factor + if use_resolution_binning: + if self.transformer.config.sample_size == 128: + aspect_ratio_bin = ASPECT_RATIO_1024_BIN + elif self.transformer.config.sample_size == 64: + aspect_ratio_bin = ASPECT_RATIO_512_BIN + elif self.transformer.config.sample_size == 32: + aspect_ratio_bin = ASPECT_RATIO_256_BIN + else: + raise ValueError("Invalid sample size") + orig_height, orig_width = height, width + height, width = self.image_processor.classify_height_width_bin(height, width, ratios=aspect_ratio_bin) + + self.check_inputs( + prompt, + height, + width, + negative_prompt, + callback_steps, + prompt_embeds, + negative_prompt_embeds, + prompt_attention_mask, + negative_prompt_attention_mask, + ) + + # 2. Default height and width to transformer + if prompt is not None and isinstance(prompt, str): + batch_size = 1 + elif prompt is not None and isinstance(prompt, list): + batch_size = len(prompt) + else: + batch_size = prompt_embeds.shape[0] + + device = self._execution_device + + # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) + # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` + # corresponds to doing no classifier free guidance. + do_classifier_free_guidance = guidance_scale > 1.0 + + # 3. Encode input prompt + ( + prompt_embeds, + prompt_attention_mask, + negative_prompt_embeds, + negative_prompt_attention_mask, + ) = self.encode_prompt( + prompt, + do_classifier_free_guidance, + negative_prompt=negative_prompt, + num_images_per_prompt=num_images_per_prompt, + device=device, + prompt_embeds=prompt_embeds, + negative_prompt_embeds=negative_prompt_embeds, + prompt_attention_mask=prompt_attention_mask, + negative_prompt_attention_mask=negative_prompt_attention_mask, + clean_caption=clean_caption, + max_sequence_length=max_sequence_length, + ) + if do_classifier_free_guidance: + prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0) + prompt_attention_mask = torch.cat([negative_prompt_attention_mask, prompt_attention_mask], dim=0) + + # 4. Prepare timesteps + timesteps, num_inference_steps = retrieve_timesteps( + self.scheduler, num_inference_steps, device, timesteps, sigmas + ) + + # 5. Prepare latents. + latent_channels = self.transformer.config.in_channels + latents = self.prepare_latents( + batch_size * num_images_per_prompt, + latent_channels, + height, + width, + prompt_embeds.dtype, + device, + generator, + latents, + ) + + # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline + extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) + + # 6.1 Prepare micro-conditions. + added_cond_kwargs = {"resolution": None, "aspect_ratio": None} + if self.transformer.config.sample_size == 128: + resolution = torch.tensor([height, width]).repeat(batch_size * num_images_per_prompt, 1) + aspect_ratio = torch.tensor([float(height / width)]).repeat(batch_size * num_images_per_prompt, 1) + resolution = resolution.to(dtype=prompt_embeds.dtype, device=device) + aspect_ratio = aspect_ratio.to(dtype=prompt_embeds.dtype, device=device) + + if do_classifier_free_guidance: + resolution = torch.cat([resolution, resolution], dim=0) + aspect_ratio = torch.cat([aspect_ratio, aspect_ratio], dim=0) + + added_cond_kwargs = {"resolution": resolution, "aspect_ratio": aspect_ratio} + + # 7. Denoising loop + num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0) + + with self.progress_bar(total=num_inference_steps) as progress_bar: + for i, t in enumerate(timesteps): + latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents + latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) + + current_timestep = t + if not torch.is_tensor(current_timestep): + # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can + # This would be a good case for the `match` statement (Python 3.10+) + is_mps = latent_model_input.device.type == "mps" + if isinstance(current_timestep, float): + dtype = torch.float32 if is_mps else torch.float64 + else: + dtype = torch.int32 if is_mps else torch.int64 + current_timestep = torch.tensor([current_timestep], dtype=dtype, device=latent_model_input.device) + elif len(current_timestep.shape) == 0: + current_timestep = current_timestep[None].to(latent_model_input.device) + # broadcast to batch dimension in a way that's compatible with ONNX/Core ML + current_timestep = current_timestep.expand(latent_model_input.shape[0]) + + # predict noise model_output + noise_pred = self.transformer( + latent_model_input, + encoder_hidden_states=prompt_embeds, + encoder_attention_mask=prompt_attention_mask, + timestep=current_timestep, + added_cond_kwargs=added_cond_kwargs, + return_dict=False, + )[0] + + # perform guidance + if do_classifier_free_guidance: + noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) + noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) + + # learned sigma + if self.transformer.config.out_channels // 2 == latent_channels: + noise_pred = noise_pred.chunk(2, dim=1)[0] + else: + noise_pred = noise_pred + + # compute previous image: x_t -> x_t-1 + if num_inference_steps == 1: + # For DMD one step sampling: https://arxiv.org/abs/2311.18828 + latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).pred_original_sample + else: + latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0] + + # call the callback, if provided + if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0): + progress_bar.update() + if callback is not None and i % callback_steps == 0: + step_idx = i // getattr(self.scheduler, "order", 1) + callback(step_idx, t, latents) + + if not output_type == "latent": + image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0] + if use_resolution_binning: + image = self.image_processor.resize_and_crop_tensor(image, orig_width, orig_height) + else: + image = latents + + if not output_type == "latent": + image = self.image_processor.postprocess(image, output_type=output_type) + + # Offload all models + self.maybe_free_model_hooks() + + if not return_dict: + return (image,) + + return ImagePipelineOutput(images=image) From 65a6991c3daa65c63234e928d5d249f2e877b0ac Mon Sep 17 00:00:00 2001 From: PommesPeter Date: Sun, 26 May 2024 02:17:01 +0800 Subject: [PATCH 03/92] added flag-dit and next-dit model --- src/diffusers/pipelines/lumina/__init__.py | 2 +- .../pipelines/lumina/modeling_flag_dit.py | 999 ++++++++++++++++ .../pipelines/lumina/modeling_next_dit.py | 1032 +++++++++++++++++ ...eline_lumina_t2i.py => pipeline_lumina.py} | 302 +++-- 4 files changed, 2229 insertions(+), 106 deletions(-) create mode 100644 src/diffusers/pipelines/lumina/modeling_flag_dit.py create mode 100644 src/diffusers/pipelines/lumina/modeling_next_dit.py rename src/diffusers/pipelines/lumina/{pipeline_lumina_t2i.py => pipeline_lumina.py} (81%) diff --git a/src/diffusers/pipelines/lumina/__init__.py b/src/diffusers/pipelines/lumina/__init__.py index 32503a6442c7..38ad13939515 100644 --- a/src/diffusers/pipelines/lumina/__init__.py +++ b/src/diffusers/pipelines/lumina/__init__.py @@ -1 +1 @@ -from .pipeline_lumina_t2i import LuminaText2ImgPipeline \ No newline at end of file +from .pipeline_lumina import LuminaText2ImgPipeline \ No newline at end of file diff --git a/src/diffusers/pipelines/lumina/modeling_flag_dit.py b/src/diffusers/pipelines/lumina/modeling_flag_dit.py new file mode 100644 index 000000000000..e57b6fe1d445 --- /dev/null +++ b/src/diffusers/pipelines/lumina/modeling_flag_dit.py @@ -0,0 +1,999 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. +# -------------------------------------------------------- +# References: +# GLIDE: https://github.com/openai/glide-text2im +# MAE: https://github.com/facebookresearch/mae/blob/main/models_mae.py +# -------------------------------------------------------- + +import functools +import math +from typing import Optional, Tuple, List + +from .components import RMSNorm +from flash_attn import flash_attn_varlen_func +from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa +import torch +import torch.nn as nn +import torch.nn.functional as F + +from ...configuration_utils import ConfigMixin, register_to_config +from ...models import ModelMixin +from ...models.transformers.transformer_2d import Transformer2DModelOutput +from ...utils import logging + + +def modulate(x, shift, scale): + return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1) + + +class RMSNorm(torch.nn.Module): + def __init__(self, dim: int, eps: float = 1e-6): + """ + Initialize the RMSNorm normalization layer. + + Args: + dim (int): The dimension of the input tensor. + eps (float, optional): A small value added to the denominator for numerical stability. Default is 1e-6. + + Attributes: + eps (float): A small value added to the denominator for numerical stability. + weight (nn.Parameter): Learnable scaling parameter. + + """ + super().__init__() + self.eps = eps + self.weight = nn.Parameter(torch.ones(dim)) + + def _norm(self, x): + """ + Apply the RMSNorm normalization to the input tensor. + + Args: + x (torch.Tensor): The input tensor. + + Returns: + torch.Tensor: The normalized tensor. + + """ + return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps) + + def forward(self, x): + """ + Forward pass through the RMSNorm layer. + + Args: + x (torch.Tensor): The input tensor. + + Returns: + torch.Tensor: The output tensor after applying RMSNorm. + + """ + output = self._norm(x.float()).type_as(x) + return output * self.weight + + +class TimestepEmbedder(nn.Module): + """ + Embeds scalar timesteps into vector representations. + """ + + def __init__(self, hidden_size, frequency_embedding_size=256): + super().__init__() + self.mlp = nn.Sequential( + nn.Linear( + frequency_embedding_size, + hidden_size, + bias=True, + ), + nn.SiLU(), + nn.Linear( + hidden_size, + hidden_size, + bias=True, + ), + ) + self.frequency_embedding_size = frequency_embedding_size + + @staticmethod + def timestep_embedding(t, dim, max_period=10000): + """ + Create sinusoidal timestep embeddings. + :param t: a 1-D Tensor of N indices, one per batch element. + These may be fractional. + :param dim: the dimension of the output. + :param max_period: controls the minimum frequency of the embeddings. + :return: an (N, D) Tensor of positional embeddings. + """ + # https://github.com/openai/glide-text2im/blob/main/glide_text2im/nn.py + half = dim // 2 + freqs = torch.exp( + -math.log(max_period) + * torch.arange(start=0, end=half, dtype=torch.float32) + / half + ).to(device=t.device) + args = t[:, None].float() * freqs[None] + embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) + if dim % 2: + embedding = torch.cat( + [embedding, torch.zeros_like(embedding[:, :1])], dim=-1 + ) + return embedding + + def forward(self, t): + t_freq = self.timestep_embedding(t, self.frequency_embedding_size) + t_emb = self.mlp(t_freq.to(self.mlp[0].weight.dtype)) + return t_emb + + +class LabelEmbedder(nn.Module): + r"""Embeds class labels into vector representations. Also handles label + dropout for classifier-free guidance. + """ + + def __init__(self, num_classes, hidden_size, dropout_prob): + super().__init__() + use_cfg_embedding = int(dropout_prob > 0) + self.embedding_table = nn.Embedding(num_classes + use_cfg_embedding) + self.num_classes = num_classes + self.dropout_prob = dropout_prob + + def token_drop(self, labels, force_drop_ids=None): + """ + Drops labels to enable classifier-free guidance. + """ + if force_drop_ids is None: + drop_ids = ( + torch.rand(labels.shape[0], device=labels.device) < self.dropout_prob + ) + drop_ids = drop_ids.cuda() + drop_ids = drop_ids.to(labels.device) + else: + drop_ids = force_drop_ids == 1 + labels = torch.where(drop_ids, self.num_classes, labels) + return labels + + def forward(self, labels, train, force_drop_ids=None): + use_dropout = self.dropout_prob > 0 + if (train and use_dropout) or (force_drop_ids is not None): + labels = self.token_drop(labels, force_drop_ids) + embeddings = self.embedding_table(labels) + return embeddings + + +############################################################################# +# Core DiT Model # +############################################################################# + + +class Attention(nn.Module): + """Multi-head attention module.""" + + def __init__( + self, + dim: int, + n_heads: int, + n_kv_heads: Optional[int], + qk_norm: bool, + y_dim: int, + ): + """ + Initialize the Attention module. + + Args: + dim (int): Number of input dimensions. + n_heads (int): Number of heads. + n_kv_heads (Optional[int]): Number of kv heads, if using GQA. + + Attributes: + n_kv_heads (int): Number of key and value heads. + n_heads (int): Number of local query heads. + n_kv_heads (int): Number of local key and value heads. + n_rep (int): Number of repetitions for local heads. + head_dim (int): Dimension size of each attention head. + wq (nn.Linear): Linear transformation for queries. + wk (nn.Linear): Linear transformation for keys. + wv (nn.Linear): Linear transformation for values. + wo (nn.Linear): Linear transformation for output. + cache_k (torch.Tensor): Cached keys for attention. + cache_v (torch.Tensor): Cached values for attention. + + """ + super().__init__() + self.n_kv_heads = n_heads if n_kv_heads is None else n_kv_heads + self.n_heads = n_heads + self.n_kv_heads = n_kv_heads + self.n_rep = n_heads + self.head_dim = dim // n_heads + + self.wq = nn.Linear(dim, n_heads * self.head_dim, bias=False) + self.wk = nn.Linear(dim, self.n_kv_heads * self.head_dim, bias=False) + self.wv = nn.Linear(dim, self.n_kv_heads * self.head_dim, bias=False) + if y_dim > 0: + self.wk_y = nn.Linear(y_dim, self.n_kv_heads * self.head_dim, bias=False) + self.wv_y = nn.Linear(y_dim, self.n_kv_heads * self.head_dim, bias=False) + self.gate = nn.Parameter(torch.zeros([self.n_heads])) + + self.wo = nn.Linear(n_heads * self.head_dim, dim, bias=False) + + if qk_norm: + self.q_norm = nn.LayerNorm(self.n_heads * self.head_dim) + self.k_norm = nn.LayerNorm(self.n_kv_heads * self.head_dim) + if y_dim > 0: + self.ky_norm = nn.LayerNorm(self.n_kv_heads * self.head_dim) + else: + self.ky_norm = nn.Identity() + else: + self.q_norm = self.k_norm = nn.Identity() + self.ky_norm = nn.Identity() + + # for proportional attention computation + self.base_seqlen = None + self.proportional_attn = False + + @staticmethod + def reshape_for_broadcast(freqs_cis: torch.Tensor, x: torch.Tensor): + """ + Reshape frequency tensor for broadcasting it with another tensor. + + This function reshapes the frequency tensor to have the same shape as + the target tensor 'x' for the purpose of broadcasting the frequency + tensor during element-wise operations. + + Args: + freqs_cis (torch.Tensor): Frequency tensor to be reshaped. + x (torch.Tensor): Target tensor for broadcasting compatibility. + + Returns: + torch.Tensor: Reshaped frequency tensor. + + Raises: + AssertionError: If the frequency tensor doesn't match the expected + shape. + AssertionError: If the target tensor 'x' doesn't have the expected + number of dimensions. + """ + ndim = x.ndim + assert 0 <= 1 < ndim + assert freqs_cis.shape == (x.shape[1], x.shape[-1]) + shape = [d if i == 1 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)] + return freqs_cis.view(*shape) + + @staticmethod + def apply_rotary_emb( + xq: torch.Tensor, + xk: torch.Tensor, + freqs_cis: torch.Tensor, + ) -> Tuple[torch.Tensor, torch.Tensor]: + """ + Apply rotary embeddings to input tensors using the given frequency + tensor. + + This function applies rotary embeddings to the given query 'xq' and + key 'xk' tensors using the provided frequency tensor 'freqs_cis'. The + input tensors are reshaped as complex numbers, and the frequency tensor + is reshaped for broadcasting compatibility. The resulting tensors + contain rotary embeddings and are returned as real tensors. + + Args: + xq (torch.Tensor): Query tensor to apply rotary embeddings. + xk (torch.Tensor): Key tensor to apply rotary embeddings. + freqs_cis (torch.Tensor): Precomputed frequency tensor for complex + exponentials. + + Returns: + Tuple[torch.Tensor, torch.Tensor]: Tuple of modified query tensor + and key tensor with rotary embeddings. + """ + with torch.cuda.amp.autocast(enabled=False): + xq_ = torch.view_as_complex(xq.float().reshape(*xq.shape[:-1], -1, 2)) + xk_ = torch.view_as_complex(xk.float().reshape(*xk.shape[:-1], -1, 2)) + freqs_cis = Attention.reshape_for_broadcast(freqs_cis, xq_) + xq_out = torch.view_as_real(xq_ * freqs_cis).flatten(3) + xk_out = torch.view_as_real(xk_ * freqs_cis).flatten(3) + return xq_out.type_as(xq), xk_out.type_as(xk) + + # copied from huggingface modeling_llama.py + def _upad_input( + self, query_layer, key_layer, value_layer, attention_mask, query_length + ): + + def _get_unpad_data(attention_mask): + seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32) + indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten() + max_seqlen_in_batch = seqlens_in_batch.max().item() + cu_seqlens = F.pad( + torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0) + ) + return ( + indices, + cu_seqlens, + max_seqlen_in_batch, + ) + + indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask) + batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape + + key_layer = index_first_axis( + key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), + indices_k, + ) + value_layer = index_first_axis( + value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), + indices_k, + ) + if query_length == kv_seq_len: + query_layer = index_first_axis( + query_layer.reshape(batch_size * kv_seq_len, self.n_heads, head_dim), + indices_k, + ) + cu_seqlens_q = cu_seqlens_k + max_seqlen_in_batch_q = max_seqlen_in_batch_k + indices_q = indices_k + elif query_length == 1: + max_seqlen_in_batch_q = 1 + cu_seqlens_q = torch.arange( + batch_size + 1, dtype=torch.int32, device=query_layer.device + ) # There is a memcpy here, that is very bad. + indices_q = cu_seqlens_q[:-1] + query_layer = query_layer.squeeze(1) + else: + # The -q_len: slice assumes left padding. + attention_mask = attention_mask[:, -query_length:] + query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input( + query_layer, attention_mask + ) + + return ( + query_layer, + key_layer, + value_layer, + indices_q, + (cu_seqlens_q, cu_seqlens_k), + (max_seqlen_in_batch_q, max_seqlen_in_batch_k), + ) + + def forward( + self, + x: torch.Tensor, + x_mask: torch.Tensor, + freqs_cis: torch.Tensor, + y: torch.Tensor, + y_mask: torch.Tensor, + ) -> torch.Tensor: + """ + Forward pass of the attention module. + + Args: + x (torch.Tensor): Input tensor. + freqs_cis (torch.Tensor): Precomputed frequency tensor. + + Returns: + torch.Tensor: Output tensor after attention. + + """ + bsz, seqlen, _ = x.shape + xq, xk, xv = self.wq(x), self.wk(x), self.wv(x) + dtype = xq.dtype + + xq = self.q_norm(xq) + xk = self.k_norm(xk) + + xq = xq.view(bsz, seqlen, self.n_heads, self.head_dim) + xk = xk.view(bsz, seqlen, self.n_kv_heads, self.head_dim) + xv = xv.view(bsz, seqlen, self.n_kv_heads, self.head_dim) + + xq, xk = Attention.apply_rotary_emb(xq, xk, freqs_cis=freqs_cis) + xq, xk = xq.to(dtype), xk.to(dtype) + + if dtype in [torch.float16, torch.bfloat16]: + # begin var_len flash attn + ( + query_states, + key_states, + value_states, + indices_q, + cu_seq_lens, + max_seq_lens, + ) = self._upad_input(xq, xk, xv, x_mask, seqlen) + + cu_seqlens_q, cu_seqlens_k = cu_seq_lens + max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens + + if self.proportional_attn: + softmax_scale = math.sqrt( + math.log(seqlen, self.base_seqlen) / self.head_dim + ) + else: + softmax_scale = math.sqrt(1 / self.head_dim) + attn_output_unpad = flash_attn_varlen_func( + query_states, + key_states, + value_states, + cu_seqlens_q=cu_seqlens_q, + cu_seqlens_k=cu_seqlens_k, + max_seqlen_q=max_seqlen_in_batch_q, + max_seqlen_k=max_seqlen_in_batch_k, + dropout_p=0.0, + causal=False, + softmax_scale=softmax_scale, + ) + output = pad_input(attn_output_unpad, indices_q, bsz, seqlen) + # end var_len_flash_attn + + else: + output = ( + F.scaled_dot_product_attention( + xq.permute(0, 2, 1, 3), + xk.permute(0, 2, 1, 3), + xv.permute(0, 2, 1, 3), + attn_mask=x_mask.bool() + .view(bsz, 1, 1, seqlen) + .expand(-1, self.n_heads, seqlen, -1), + ) + .permute(0, 2, 1, 3) + .to(dtype) + ) + + if hasattr(self, "wk_y"): + yk = self.ky_norm(self.wk_y(y)).view( + bsz, -1, self.n_kv_heads, self.head_dim + ) + yv = self.wv_y(y).view(bsz, -1, self.n_kv_heads, self.head_dim) + n_rep = self.n_heads // self.n_kv_heads + if n_rep >= 1: + yk = yk.unsqueeze(3).repeat(1, 1, 1, n_rep, 1).flatten(2, 3) + yv = yv.unsqueeze(3).repeat(1, 1, 1, n_rep, 1).flatten(2, 3) + output_y = F.scaled_dot_product_attention( + xq.permute(0, 2, 1, 3), + yk.permute(0, 2, 1, 3), + yv.permute(0, 2, 1, 3), + y_mask.view(bsz, 1, 1, -1).expand(bsz, self.n_heads, seqlen, -1), + ).permute(0, 2, 1, 3) + output_y = output_y * self.gate.tanh().view(1, 1, -1, 1) + output = output + output_y + + output = output.flatten(-2) + + return self.wo(output) + + +class FeedForward(nn.Module): + def __init__( + self, + dim: int, + hidden_dim: int, + multiple_of: int, + ffn_dim_multiplier: Optional[float], + ): + """ + Initialize the FeedForward module. + + Args: + dim (int): Input dimension. + hidden_dim (int): Hidden dimension of the feedforward layer. + multiple_of (int): Value to ensure hidden dimension is a multiple + of this value. + ffn_dim_multiplier (float, optional): Custom multiplier for hidden + dimension. Defaults to None. + + Attributes: + w1 (nn.Linear): Linear transformation for the first + layer. + w2 (nn.Linear): Linear transformation for the second layer. + w3 (nn.Linear): Linear transformation for the third + layer. + + """ + super().__init__() + hidden_dim = int(2 * hidden_dim / 3) + # custom dim factor multiplier + if ffn_dim_multiplier is not None: + hidden_dim = int(ffn_dim_multiplier * hidden_dim) + hidden_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of) + + self.w1 = nn.Linear(dim, hidden_dim, bias=False) + self.w2 = nn.Linear(hidden_dim, dim, bias=False) + self.w3 = nn.Linear(dim, hidden_dim, bias=False) + + # @torch.compile + def _forward_silu_gating(self, x1, x3): + return F.silu(x1) * x3 + + def forward(self, x): + return self.w2(self._forward_silu_gating(self.w1(x), self.w3(x))) + + +class TransformerBlock(nn.Module): + def __init__( + self, + layer_id: int, + dim: int, + n_heads: int, + n_kv_heads: int, + multiple_of: int, + ffn_dim_multiplier: float, + norm_eps: float, + qk_norm: bool, + y_dim: int, + ) -> None: + """ + Initialize a TransformerBlock. + + Args: + layer_id (int): Identifier for the layer. + dim (int): Embedding dimension of the input features. + n_heads (int): Number of attention heads. + n_kv_heads (Optional[int]): Number of attention heads in key and + value features (if using GQA), or set to None for the same as + query. + multiple_of (int): + ffn_dim_multiplier (float): + norm_eps (float): + + Attributes: + n_heads (int): Number of attention heads. + dim (int): Dimension size of the model. + head_dim (int): Dimension size of each attention head. + attention (Attention): Attention module. + feed_forward (FeedForward): FeedForward module. + layer_id (int): Identifier for the layer. + attention_norm (RMSNorm): Layer normalization for attention output. + ffn_norm (RMSNorm): Layer normalization for feedforward output. + + """ + super().__init__() + self.dim = dim + self.head_dim = dim // n_heads + self.attention = Attention(dim, n_heads, n_kv_heads, qk_norm, y_dim) + self.feed_forward = FeedForward( + dim=dim, + hidden_dim=4 * dim, + multiple_of=multiple_of, + ffn_dim_multiplier=ffn_dim_multiplier, + ) + self.layer_id = layer_id + self.attention_norm = RMSNorm(dim, eps=norm_eps) + self.ffn_norm = RMSNorm(dim, eps=norm_eps) + + self.adaLN_modulation = nn.Sequential( + nn.SiLU(), + nn.Linear(min(dim, 1024), 6 * dim, bias=True), + ) + + self.attention_y_norm = RMSNorm(y_dim, eps=norm_eps) + + def forward( + self, + x: torch.Tensor, + x_mask: torch.Tensor, + y: torch.Tensor, + y_mask: torch.Tensor, + freqs_cis: torch.Tensor, + adaln_input: Optional[torch.Tensor] = None, + ): + """ + Perform a forward pass through the TransformerBlock. + + Args: + x (torch.Tensor): Input tensor. + freqs_cis (torch.Tensor): Precomputed cosine and sine frequencies. + mask (torch.Tensor, optional): Masking tensor for attention. + Defaults to None. + + Returns: + torch.Tensor: Output tensor after applying attention and + feedforward layers. + + """ + if adaln_input is not None: + shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = ( + self.adaLN_modulation(adaln_input).chunk(6, dim=1) + ) + + x = x + gate_msa.unsqueeze(1) * self.attention( + modulate(self.attention_norm(x), shift_msa, scale_msa), + x_mask, + freqs_cis, + self.attention_y_norm(y), + y_mask, + ) + x = x + gate_mlp.unsqueeze(1) * self.feed_forward( + modulate(self.ffn_norm(x), shift_mlp, scale_mlp), + ) + + else: + x = x + self.attention( + self.attention_norm(x), + x_mask, + freqs_cis, + self.attention_y_norm(y), + y_mask, + ) + x = x + self.feed_forward(self.ffn_norm(x)) + + return x + + +class FinalLayer(nn.Module): + """ + The final layer of DiT. + """ + + def __init__(self, hidden_size, patch_size, out_channels): + super().__init__() + self.norm_final = nn.LayerNorm( + hidden_size, + elementwise_affine=False, + eps=1e-6, + ) + self.linear = nn.Linear(hidden_size, patch_size * patch_size * out_channels) + self.adaLN_modulation = nn.Sequential( + nn.SiLU(), + nn.Linear(min(hidden_size, 1024), 2 * hidden_size), + ) + + def forward(self, x, c): + shift, scale = self.adaLN_modulation(c).chunk(2, dim=1) + x = modulate(self.norm_final(x), shift, scale) + x = self.linear(x) + return x + + +class FlagDiffuserTransformer(ModelMixin, ConfigMixin): + """ + Diffusion model with a Transformer backbone. + """ + + def __init__( + self, + patch_size: int = 2, + in_channels: int = 4, + dim: int = 4096, + n_layers: int = 32, + n_heads: int = 32, + n_kv_heads: Optional[int] = None, + multiple_of: int = 256, + ffn_dim_multiplier: Optional[float] = None, + norm_eps: float = 1e-5, + learn_sigma: bool = True, + qk_norm: bool = False, + cap_feat_dim: int = 5120, + rope_scaling_factor: float = 1.0, + ntk_factor: float = 1.0, + ) -> None: + super().__init__() + self.learn_sigma = learn_sigma + self.in_channels = in_channels + self.out_channels = in_channels * 2 if learn_sigma else in_channels + self.patch_size = patch_size + + self.x_embedder = nn.Linear( + in_features=patch_size * patch_size * in_channels, + out_features=dim, + bias=True, + ) + nn.init.constant_(self.x_embedder.bias, 0.0) + + self.t_embedder = TimestepEmbedder(min(dim, 1024)) + self.cap_embedder = nn.Sequential( + nn.LayerNorm(cap_feat_dim), + nn.Linear(cap_feat_dim, min(dim, 1024), bias=True), + ) + + self.layers = nn.ModuleList( + [ + TransformerBlock( + layer_id, + dim, + n_heads, + n_kv_heads, + multiple_of, + ffn_dim_multiplier, + norm_eps, + qk_norm, + cap_feat_dim, + ) + for layer_id in range(n_layers) + ] + ) + self.final_layer = FinalLayer(dim, patch_size, self.out_channels) + + self.freqs_cis = DiT_Llama.precompute_freqs_cis( + dim // n_heads, + 40000, + rope_scaling_factor=rope_scaling_factor, + ntk_factor=ntk_factor, + ) + self.dim = dim + self.n_heads = n_heads + self.rope_scaling_factor = rope_scaling_factor + self.ntk_factor = ntk_factor + self.eol_token = nn.Parameter(torch.empty(dim)) + self.pad_token = nn.Parameter(torch.empty(dim)) + nn.init.normal_(self.eol_token, std=0.02) + nn.init.normal_(self.pad_token, std=0.02) + + def unpatchify( + self, x: torch.Tensor, img_size: List[Tuple[int, int]], return_tensor=False + ) -> List[torch.Tensor]: + """ + x: (N, T, patch_size**2 * C) + imgs: (N, H, W, C) + """ + pH = pW = self.patch_size + if return_tensor: + H, W = img_size[0] + B = x.size(0) + L = (H // pH) * (W // pW + 1) # one additional for eol + x = x[:, :L].view(B, H // pH, W // pW + 1, pH, pW, self.out_channels) + x = x[:, :, :-1] + x = x.permute(0, 5, 1, 3, 2, 4).flatten(4, 5).flatten(2, 3) + return x + else: + imgs = [] + for i in range(x.size(0)): + H, W = img_size[i] + L = (H // pH) * (W // pW + 1) + imgs.append( + x[i][:L] + .view(H // pH, W // pW + 1, pH, pW, self.out_channels)[ + :, :-1, :, :, : + ] + .permute(4, 0, 2, 1, 3) + .flatten(3, 4) + .flatten(1, 2) + ) + return imgs + + def patchify_and_embed( + self, x: List[torch.Tensor] | torch.Tensor + ) -> Tuple[torch.Tensor, torch.Tensor, List[Tuple[int, int]]]: + if isinstance(x, torch.Tensor): + pH = pW = self.patch_size + B, C, H, W = x.size() + x = ( + x.view(B, C, H // pH, pH, W // pW, pW) + .permute(0, 2, 4, 1, 3, 5) + .flatten(3) + ) + x = self.x_embedder(x) + x = torch.cat( + [ + x, + self.eol_token.view(1, 1, 1, -1).expand(B, H // pH, 1, -1), + ], + dim=2, + ) + x = x.flatten(1, 2) + + mask = torch.ones( + x.shape[0], x.shape[1], dtype=torch.int32, device=x.device + ) + return x, mask, [(H, W)] * B + else: + pH = pW = self.patch_size + x_embed = [] + img_size = [] + l_effective_seq_len = [] + + for img in x: + C, H, W = img.size() + img_size.append((H, W)) + img = ( + img.view(C, H // pH, pH, W // pW, pW) + .permute(1, 3, 0, 2, 4) + .flatten(2) + ) + img = self.x_embedder(img) + img = torch.cat( + [ + img, + self.eol_token.view(1, 1, -1).expand(H // pH, 1, -1), + ], + dim=1, + ) + img = img.flatten(0, 1) + l_effective_seq_len.append(len(img)) + x_embed.append(img) + + max_seq_len = max(l_effective_seq_len) + mask = torch.zeros( + len(x), max_seq_len, dtype=torch.int32, device=x[0].device + ) + padded_x_embed = [] + for i, (item_embed, item_seq_len) in enumerate( + zip(x_embed, l_effective_seq_len) + ): + item_embed = torch.cat( + [ + item_embed, + self.pad_token.view(1, -1).expand( + max_seq_len - item_seq_len, -1 + ), + ], + dim=0, + ) + padded_x_embed.append(item_embed) + mask[i][:item_seq_len] = 1 + + x_embed = torch.stack(padded_x_embed, dim=0) + return x_embed, mask, img_size + + def forward(self, x, t, cap_feats, cap_mask): + """ + Forward pass of DiT. + t: (N,) tensor of diffusion timesteps + y: (N,) tensor of class labels + """ + x_is_tensor = isinstance(x, torch.Tensor) + x, mask, img_size = self.patchify_and_embed(x) + self.freqs_cis = self.freqs_cis.to(x.device) + + t = self.t_embedder(t) # (N, D) + cap_mask_float = cap_mask.float().unsqueeze(-1) + cap_feats_pool = (cap_feats * cap_mask_float).sum(dim=1) / cap_mask_float.sum( + dim=1 + ) + cap_feats_pool = cap_feats_pool.to(cap_feats) + cap_emb = self.cap_embedder(cap_feats_pool) + adaln_input = t + cap_emb + + cap_mask = cap_mask.bool() + for layer in self.layers: + x = layer( + x, + mask, + cap_feats, + cap_mask, + self.freqs_cis[: x.size(1)], + adaln_input=adaln_input, + ) + + x = self.final_layer(x, adaln_input) + x = self.unpatchify(x, img_size, return_tensor=x_is_tensor) + if self.learn_sigma: + if x_is_tensor: + x, _ = x.chunk(2, dim=1) + else: + x = [_.chunk(2, dim=0)[0] for _ in x] + return x + + def forward_with_cfg( + self, + x, + t, + cap_feats, + cap_mask, + cfg_scale, + rope_scaling_factor=None, + ntk_factor=None, + base_seqlen: Optional[int] = None, + proportional_attn: bool = False, + ): + """ + Forward pass of DiT, but also batches the unconditional forward pass + for classifier-free guidance. + """ + # https://github.com/openai/glide-text2im/blob/main/notebooks/text2im.ipynb + + if rope_scaling_factor is not None or ntk_factor is not None: + rope_scaling_factor = ( + rope_scaling_factor + if rope_scaling_factor is not None + else self.rope_scaling_factor + ) + ntk_factor = ntk_factor if ntk_factor is not None else self.ntk_factor + if ( + rope_scaling_factor != self.rope_scaling_factor + or ntk_factor != self.ntk_factor + ): + print( + f"override freqs_cis, rope_scaling {rope_scaling_factor}, ntk {ntk_factor}", + flush=True, + ) + self.freqs_cis = DiT_Llama.precompute_freqs_cis( + self.dim // self.n_heads, + 40000, + rope_scaling_factor=rope_scaling_factor, + ntk_factor=ntk_factor, + ) + self.rope_scaling_factor = rope_scaling_factor + self.ntk_factor = ntk_factor + + if proportional_attn: + assert base_seqlen is not None + for layer in self.layers: + layer.attention.base_seqlen = base_seqlen + layer.attention.proportional_attn = proportional_attn + else: + for layer in self.layers: + layer.attention.base_seqlen = None + layer.attention.proportional_attn = proportional_attn + + half = x[: len(x) // 2] + combined = torch.cat([half, half], dim=0) + model_out = self(combined, t, cap_feats, cap_mask) + # For exact reproducibility reasons, we apply classifier-free guidance on only + # three channels by default. The standard approach to cfg applies it to all channels. + # This can be done by uncommenting the following line and commenting-out the line following that. + # eps, rest = model_out[:, :self.in_channels], model_out[:, self.in_channels:] + eps, rest = model_out[:, :3], model_out[:, 3:] + cond_eps, uncond_eps = torch.split(eps, len(eps) // 2, dim=0) + half_eps = uncond_eps + cfg_scale * (cond_eps - uncond_eps) + eps = torch.cat([half_eps, half_eps], dim=0) + return torch.cat([eps, rest], dim=1) + + @staticmethod + def precompute_freqs_cis( + dim: int, + end: int, + theta: float = 10000.0, + rope_scaling_factor: float = 1.0, + ntk_factor: float = 1.0, + ): + """ + Precompute the frequency tensor for complex exponentials (cis) with + given dimensions. + + This function calculates a frequency tensor with complex exponentials + using the given dimension 'dim' and the end index 'end'. The 'theta' + parameter scales the frequencies. The returned tensor contains complex + values in complex64 data type. + + Args: + dim (int): Dimension of the frequency tensor. + end (int): End index for precomputing frequencies. + theta (float, optional): Scaling factor for frequency computation. + Defaults to 10000.0. + + Returns: + torch.Tensor: Precomputed frequency tensor with complex + exponentials. + """ + + theta = theta * ntk_factor + + print(f"theta {theta} rope scaling {rope_scaling_factor} ntk {ntk_factor}") + freqs = 1.0 / ( + theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float().cuda() / dim) + ) + t = torch.arange(end, device=freqs.device, dtype=torch.float) # type: ignore + t = t / rope_scaling_factor + freqs = torch.outer(t, freqs).float() # type: ignore + freqs_cis = torch.polar(torch.ones_like(freqs), freqs) # complex64 + return freqs_cis + + def parameter_count(self) -> int: + tensor_module_list = ( + nn.Linear, + nn.Linear, + nn.Embedding, + ) + total_params = 0 + + def _recursive_count_params(module): + nonlocal total_params + is_tp_module = isinstance(module, tensor_module_list) + for param in module.parameters(recurse=False): + total_params += param.numel() + for submodule in module.children(): + _recursive_count_params(submodule) + + _recursive_count_params(self) + return total_params + + def get_fsdp_wrap_module_list(self) -> List[nn.Module]: + return list(self.layers) + + +############################################################################# +# DiT Configs # +############################################################################# + + +def DiT_Llama_5B_patch2(**kwargs): + return DiT_Llama(patch_size=2, dim=3072, n_layers=32, n_heads=32, **kwargs) diff --git a/src/diffusers/pipelines/lumina/modeling_next_dit.py b/src/diffusers/pipelines/lumina/modeling_next_dit.py new file mode 100644 index 000000000000..2aadfe00c0df --- /dev/null +++ b/src/diffusers/pipelines/lumina/modeling_next_dit.py @@ -0,0 +1,1032 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. +# -------------------------------------------------------- +# References: +# GLIDE: https://github.com/openai/glide-text2im +# MAE: https://github.com/facebookresearch/mae/blob/main/models_mae.py +# -------------------------------------------------------- + +import logging +import math +from typing import Optional, Tuple, List + +from flash_attn import flash_attn_varlen_func +from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa +import torch +import torch.nn as nn +import torch.nn.functional as F + +from ...configuration_utils import ConfigMixin, register_to_config +from ...models import ModelMixin +from ...models.transformers.transformer_2d import Transformer2DModelOutput +from ...utils import logging + +logger = logging.getLogger(__name__) + + +def modulate(x, shift, scale): + return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1) + + +class RMSNorm(torch.nn.Module): + def __init__(self, dim: int, eps: float = 1e-6): + """ + Initialize the RMSNorm normalization layer. + + Args: + dim (int): The dimension of the input tensor. + eps (float, optional): A small value added to the denominator for numerical stability. Default is 1e-6. + + Attributes: + eps (float): A small value added to the denominator for numerical stability. + weight (nn.Parameter): Learnable scaling parameter. + + """ + super().__init__() + self.eps = eps + self.weight = nn.Parameter(torch.ones(dim)) + + def _norm(self, x): + """ + Apply the RMSNorm normalization to the input tensor. + + Args: + x (torch.Tensor): The input tensor. + + Returns: + torch.Tensor: The normalized tensor. + + """ + return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps) + + def forward(self, x): + """ + Forward pass through the RMSNorm layer. + + Args: + x (torch.Tensor): The input tensor. + + Returns: + torch.Tensor: The output tensor after applying RMSNorm. + + """ + output = self._norm(x.float()).type_as(x) + return output * self.weight + + +############################################################################# +# Embedding Layers for Timesteps and Class Labels # +############################################################################# + + +class TimestepEmbedder(nn.Module): + """ + Embeds scalar timesteps into vector representations. + """ + + def __init__(self, hidden_size, frequency_embedding_size=256): + super().__init__() + self.mlp = nn.Sequential( + nn.Linear(frequency_embedding_size, hidden_size, bias=True), + nn.SiLU(), + nn.Linear(hidden_size, hidden_size, bias=True), + ) + self.frequency_embedding_size = frequency_embedding_size + + @staticmethod + def timestep_embedding(t, dim, max_period=10000): + """ + Create sinusoidal timestep embeddings. + :param t: a 1-D Tensor of N indices, one per batch element. + These may be fractional. + :param dim: the dimension of the output. + :param max_period: controls the minimum frequency of the embeddings. + :return: an (N, D) Tensor of positional embeddings. + """ + # https://github.com/openai/glide-text2im/blob/main/glide_text2im/nn.py + half = dim // 2 + freqs = torch.exp( + -math.log(max_period) + * torch.arange(start=0, end=half, dtype=torch.float32) + / half + ).to(device=t.device) + args = t[:, None].float() * freqs[None] + embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) + if dim % 2: + embedding = torch.cat( + [embedding, torch.zeros_like(embedding[:, :1])], dim=-1 + ) + return embedding + + def forward(self, t): + t_freq = self.timestep_embedding(t, self.frequency_embedding_size) + t_emb = self.mlp(t_freq.to(self.mlp[0].weight.dtype)) + return t_emb + + +class ParallelLabelEmbedder(nn.Module): + r"""Embeds class labels into vector representations. Also handles label + dropout for classifier-free guidance. + """ + + def __init__(self, num_classes, hidden_size, dropout_prob): + super().__init__() + use_cfg_embedding = int(dropout_prob > 0) + self.embedding_table = nn.Embedding( + num_classes + use_cfg_embedding, hidden_size + ) + self.num_classes = num_classes + self.dropout_prob = dropout_prob + + def token_drop(self, labels, force_drop_ids=None): + """ + Drops labels to enable classifier-free guidance. + """ + if force_drop_ids is None: + drop_ids = ( + torch.rand(labels.shape[0], device=labels.device) < self.dropout_prob + ) + drop_ids = drop_ids.cuda() + drop_ids = drop_ids.to(labels.device) + else: + drop_ids = force_drop_ids == 1 + labels = torch.where(drop_ids, self.num_classes, labels) + return labels + + def forward(self, labels, train, force_drop_ids=None): + use_dropout = self.dropout_prob > 0 + if (train and use_dropout) or (force_drop_ids is not None): + labels = self.token_drop(labels, force_drop_ids) + embeddings = self.embedding_table(labels) + return embeddings + + +############################################################################# +# Core NextDiT Model # +############################################################################# + + +class Attention(nn.Module): + """Multi-head attention module.""" + + def __init__( + self, + dim: int, + n_heads: int, + n_kv_heads: Optional[int], + qk_norm: bool, + y_dim: int, + ): + """ + Initialize the Attention module. + + Args: + dim (int): Number of input dimensions. + n_heads (int): Number of heads. + n_kv_heads (Optional[int]): Number of kv heads, if using GQA. + + """ + super().__init__() + self.n_kv_heads = n_heads if n_kv_heads is None else n_kv_heads + self.n_heads = n_heads + self.n_rep = self.n_heads // self.n_kv_heads + self.head_dim = dim // n_heads + + self.wq = nn.Linear(dim, n_heads * self.head_dim, bias=False) + self.wk = nn.Linear(dim, self.n_kv_heads * self.head_dim, bias=False) + self.wv = nn.Linear(dim, self.n_kv_heads * self.head_dim, bias=False) + if y_dim > 0: + self.wk_y = nn.Linear(y_dim, self.n_kv_heads * self.head_dim, bias=False) + self.wv_y = nn.Linear(y_dim, self.n_kv_heads * self.head_dim, bias=False) + self.gate = nn.Parameter(torch.zeros([self.n_heads])) + + self.wo = nn.Linear(n_heads * self.head_dim, dim, bias=False) + + if qk_norm: + self.q_norm = nn.LayerNorm(self.n_heads * self.head_dim) + self.k_norm = nn.LayerNorm(self.n_kv_heads * self.head_dim) + if y_dim > 0: + self.ky_norm = nn.LayerNorm(self.n_kv_heads * self.head_dim) + else: + self.ky_norm = nn.Identity() + else: + self.q_norm = self.k_norm = nn.Identity() + self.ky_norm = nn.Identity() + + # for proportional attention computation + self.base_seqlen = None + self.proportional_attn = False + + @staticmethod + def reshape_for_broadcast(freqs_cis: torch.Tensor, x: torch.Tensor): + """ + Reshape frequency tensor for broadcasting it with another tensor. + + This function reshapes the frequency tensor to have the same shape as + the target tensor 'x' for the purpose of broadcasting the frequency + tensor during element-wise operations. + + Args: + freqs_cis (torch.Tensor): Frequency tensor to be reshaped. + x (torch.Tensor): Target tensor for broadcasting compatibility. + + Returns: + torch.Tensor: Reshaped frequency tensor. + + Raises: + AssertionError: If the frequency tensor doesn't match the expected + shape. + AssertionError: If the target tensor 'x' doesn't have the expected + number of dimensions. + """ + ndim = x.ndim + assert 0 <= 1 < ndim + assert freqs_cis.shape == (x.shape[1], x.shape[-1]) + shape = [d if i == 1 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)] + return freqs_cis.view(*shape) + + @staticmethod + def apply_rotary_emb( + x_in: torch.Tensor, + freqs_cis: torch.Tensor, + ) -> torch.Tensor: + """ + Apply rotary embeddings to input tensors using the given frequency + tensor. + + This function applies rotary embeddings to the given query 'xq' and + key 'xk' tensors using the provided frequency tensor 'freqs_cis'. The + input tensors are reshaped as complex numbers, and the frequency tensor + is reshaped for broadcasting compatibility. The resulting tensors + contain rotary embeddings and are returned as real tensors. + + Args: + x_in (torch.Tensor): Query or Key tensor to apply rotary embeddings. + freqs_cis (torch.Tensor): Precomputed frequency tensor for complex + exponentials. + + Returns: + Tuple[torch.Tensor, torch.Tensor]: Tuple of modified query tensor + and key tensor with rotary embeddings. + """ + with torch.cuda.amp.autocast(enabled=False): + x = torch.view_as_complex(x_in.float().reshape(*x_in.shape[:-1], -1, 2)) + freqs_cis = freqs_cis.unsqueeze(2) + x_out = torch.view_as_real(x * freqs_cis).flatten(3) + return x_out.type_as(x_in) + + # copied from huggingface modeling_llama.py + def _upad_input( + self, query_layer, key_layer, value_layer, attention_mask, query_length + ): + + def _get_unpad_data(attention_mask): + seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32) + indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten() + max_seqlen_in_batch = seqlens_in_batch.max().item() + cu_seqlens = F.pad( + torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0) + ) + return ( + indices, + cu_seqlens, + max_seqlen_in_batch, + ) + + indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask) + batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape + + key_layer = index_first_axis( + key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), + indices_k, + ) + value_layer = index_first_axis( + value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), + indices_k, + ) + if query_length == kv_seq_len: + query_layer = index_first_axis( + query_layer.reshape(batch_size * kv_seq_len, self.n_heads, head_dim), + indices_k, + ) + cu_seqlens_q = cu_seqlens_k + max_seqlen_in_batch_q = max_seqlen_in_batch_k + indices_q = indices_k + elif query_length == 1: + max_seqlen_in_batch_q = 1 + cu_seqlens_q = torch.arange( + batch_size + 1, dtype=torch.int32, device=query_layer.device + ) # There is a memcpy here, that is very bad. + indices_q = cu_seqlens_q[:-1] + query_layer = query_layer.squeeze(1) + else: + # The -q_len: slice assumes left padding. + attention_mask = attention_mask[:, -query_length:] + query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input( + query_layer, attention_mask + ) + + return ( + query_layer, + key_layer, + value_layer, + indices_q, + (cu_seqlens_q, cu_seqlens_k), + (max_seqlen_in_batch_q, max_seqlen_in_batch_k), + ) + + def forward( + self, + x: torch.Tensor, + x_mask: torch.Tensor, + freqs_cis: torch.Tensor, + y: torch.Tensor, + y_mask: torch.Tensor, + ) -> torch.Tensor: + """ + + Args: + x: + x_mask: + freqs_cis: + y: + y_mask: + + Returns: + + """ + bsz, seqlen, _ = x.shape + xq, xk, xv = self.wq(x), self.wk(x), self.wv(x) + dtype = xq.dtype + + xq = self.q_norm(xq) + xk = self.k_norm(xk) + + xq = xq.view(bsz, seqlen, self.n_heads, self.head_dim) + xk = xk.view(bsz, seqlen, self.n_kv_heads, self.head_dim) + xv = xv.view(bsz, seqlen, self.n_kv_heads, self.head_dim) + + xq = Attention.apply_rotary_emb(xq, freqs_cis=freqs_cis) + xk = Attention.apply_rotary_emb(xk, freqs_cis=freqs_cis) + + xq, xk = xq.to(dtype), xk.to(dtype) + + if dtype in [torch.float16, torch.bfloat16]: + # begin var_len flash attn + ( + query_states, + key_states, + value_states, + indices_q, + cu_seq_lens, + max_seq_lens, + ) = self._upad_input(xq, xk, xv, x_mask, seqlen) + + cu_seqlens_q, cu_seqlens_k = cu_seq_lens + max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens + + if self.proportional_attn: + softmax_scale = math.sqrt( + math.log(seqlen, self.base_seqlen) / self.head_dim + ) + else: + softmax_scale = math.sqrt(1 / self.head_dim) + + attn_output_unpad = flash_attn_varlen_func( + query_states, + key_states, + value_states, + cu_seqlens_q=cu_seqlens_q, + cu_seqlens_k=cu_seqlens_k, + max_seqlen_q=max_seqlen_in_batch_q, + max_seqlen_k=max_seqlen_in_batch_k, + dropout_p=0.0, + causal=False, + softmax_scale=softmax_scale, + ) + output = pad_input(attn_output_unpad, indices_q, bsz, seqlen) + # end var_len_flash_attn + + else: + output = ( + F.scaled_dot_product_attention( + xq.permute(0, 2, 1, 3), + xk.permute(0, 2, 1, 3), + xv.permute(0, 2, 1, 3), + attn_mask=x_mask.bool() + .view(bsz, 1, 1, seqlen) + .expand(-1, self.n_heads, seqlen, -1), + ) + .permute(0, 2, 1, 3) + .to(dtype) + ) + + if hasattr(self, "wk_y"): + # todo better flash_attn support + yk = self.ky_norm(self.wk_y(y)).view( + bsz, -1, self.n_kv_heads, self.head_dim + ) + yv = self.wv_y(y).view(bsz, -1, self.n_kv_heads, self.head_dim) + n_rep = self.n_heads // self.n_kv_heads + if n_rep >= 1: + yk = yk.unsqueeze(3).repeat(1, 1, 1, n_rep, 1).flatten(2, 3) + yv = yv.unsqueeze(3).repeat(1, 1, 1, n_rep, 1).flatten(2, 3) + output_y = F.scaled_dot_product_attention( + xq.permute(0, 2, 1, 3), + yk.permute(0, 2, 1, 3), + yv.permute(0, 2, 1, 3), + y_mask.view(bsz, 1, 1, -1).expand(bsz, self.n_heads, seqlen, -1), + ).permute(0, 2, 1, 3) + output_y = output_y * self.gate.tanh().view(1, 1, -1, 1) + output = output + output_y + + output = output.flatten(-2) + + return self.wo(output) + + +class FeedForward(nn.Module): + def __init__( + self, + dim: int, + hidden_dim: int, + multiple_of: int, + ffn_dim_multiplier: Optional[float], + ): + """ + Initialize the FeedForward module. + + Args: + dim (int): Input dimension. + hidden_dim (int): Hidden dimension of the feedforward layer. + multiple_of (int): Value to ensure hidden dimension is a multiple + of this value. + ffn_dim_multiplier (float, optional): Custom multiplier for hidden + dimension. Defaults to None. + + Attributes: + w1 (nn.Linear): Linear transformation for the first + layer. + w2 (nn.Linear): Linear transformation for the second layer. + w3 (nn.Linear): Linear transformation for the third + layer. + + """ + super().__init__() + hidden_dim = int(2 * hidden_dim / 3) + # custom dim factor multiplier + if ffn_dim_multiplier is not None: + hidden_dim = int(ffn_dim_multiplier * hidden_dim) + hidden_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of) + + self.w1 = nn.Linear(dim, hidden_dim, bias=False) + self.w2 = nn.Linear(hidden_dim, dim, bias=False) + self.w3 = nn.Linear(dim, hidden_dim, bias=False) + + # @torch.compile + def _forward_silu_gating(self, x1, x3): + return F.silu(x1) * x3 + + def forward(self, x): + return self.w2(self._forward_silu_gating(self.w1(x), self.w3(x))) + + +class TransformerBlock(nn.Module): + def __init__( + self, + layer_id: int, + dim: int, + n_heads: int, + n_kv_heads: int, + multiple_of: int, + ffn_dim_multiplier: float, + norm_eps: float, + qk_norm: bool, + y_dim: int, + ) -> None: + """ + Initialize a TransformerBlock. + + Args: + layer_id (int): Identifier for the layer. + dim (int): Embedding dimension of the input features. + n_heads (int): Number of attention heads. + n_kv_heads (Optional[int]): Number of attention heads in key and + value features (if using GQA), or set to None for the same as + query. + multiple_of (int): + ffn_dim_multiplier (float): + norm_eps (float): + + Attributes: + n_heads (int): Number of attention heads. + dim (int): Dimension size of the model. + head_dim (int): Dimension size of each attention head. + attention (Attention): Attention module. + feed_forward (FeedForward): FeedForward module. + layer_id (int): Identifier for the layer. + attention_norm (RMSNorm): Layer normalization for attention output. + ffn_norm (RMSNorm): Layer normalization for feedforward output. + + """ + super().__init__() + self.dim = dim + self.head_dim = dim // n_heads + self.attention = Attention(dim, n_heads, n_kv_heads, qk_norm, y_dim) + self.feed_forward = FeedForward( + dim=dim, + hidden_dim=4 * dim, + multiple_of=multiple_of, + ffn_dim_multiplier=ffn_dim_multiplier, + ) + self.layer_id = layer_id + self.attention_norm = RMSNorm(dim, eps=norm_eps) + self.attention_norm1 = RMSNorm(dim, eps=norm_eps) + self.ffn_norm = RMSNorm(dim, eps=norm_eps) + self.ffn_norm1 = RMSNorm(dim, eps=norm_eps) + + self.adaLN_modulation = nn.Sequential( + nn.SiLU(), + nn.Linear(min(dim, 1024), 6 * dim, bias=True), + ) + + self.attention_y_norm = RMSNorm(y_dim, eps=norm_eps) + + def forward( + self, + x: torch.Tensor, + x_mask: torch.Tensor, + freqs_cis: torch.Tensor, + y: torch.Tensor, + y_mask: torch.Tensor, + adaln_input: Optional[torch.Tensor] = None, + ): + """ + Perform a forward pass through the TransformerBlock. + + Args: + x (torch.Tensor): Input tensor. + freqs_cis (torch.Tensor): Precomputed cosine and sine frequencies. + + Returns: + torch.Tensor: Output tensor after applying attention and + feedforward layers. + + """ + if adaln_input is not None: + shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = ( + self.adaLN_modulation(adaln_input).chunk(6, dim=1) + ) + + x = x + self.attention_norm1( + gate_msa.unsqueeze(1) + * self.attention( + modulate(self.attention_norm(x), shift_msa, scale_msa), + x_mask, + freqs_cis, + self.attention_y_norm(y), + y_mask, + ) + ) + d = x.shape[-1] + x = x + self.ffn_norm1( + gate_mlp.unsqueeze(1) + * self.feed_forward( + modulate(self.ffn_norm(x), shift_mlp, scale_mlp).view(-1, d), + ).view(*x.shape) + ) + + else: + x = x + self.attention_norm1( + self.attention( + self.attention_norm(x), + x_mask, + freqs_cis, + self.attention_y_norm(y), + y_mask, + ) + ) + # for compatibility with torch.compile because the sequence length changes + B, L, D = x.shape + x = x.view(B * L, D) + x = x + self.ffn_norm1(self.feed_forward(self.ffn_norm(x))) + x = x.view(B, L, D) + + return x + + +class FinalLayer(nn.Module): + """ + The final layer of NextDiT. + """ + + def __init__(self, hidden_size, patch_size, out_channels): + super().__init__() + self.norm_final = nn.LayerNorm( + hidden_size, + elementwise_affine=False, + eps=1e-6, + ) + self.linear = nn.Linear(hidden_size, patch_size * patch_size * out_channels) + self.adaLN_modulation = nn.Sequential( + nn.SiLU(), + nn.Linear(min(hidden_size, 1024), 2 * hidden_size), + ) + + def forward(self, x, c): + shift, scale = self.adaLN_modulation(c).chunk(2, dim=1) + x = modulate(self.norm_final(x), shift, scale) + x = self.linear(x) + return x + + +class NextFlagDiffuserTransformer2DModel(ModelMixin, ConfigMixin): + """ + Diffusion model with a Transformer backbone. + """ + + @register_to_config + def __init__( + self, + patch_size: int = 2, + in_channels: int = 4, + dim: int = 4096, + n_layers: int = 32, + n_heads: int = 32, + n_kv_heads: Optional[int] = None, + multiple_of: int = 256, + ffn_dim_multiplier: Optional[float] = None, + norm_eps: float = 1e-5, + learn_sigma: bool = True, + qk_norm: bool = False, + cap_feat_dim: int = 5120, + rope_scaling_factor: float = 1.0, + ntk_factor: float = 1.0, + ) -> None: + super().__init__() + self.learn_sigma = learn_sigma + self.in_channels = in_channels + self.out_channels = in_channels * 2 if learn_sigma else in_channels + self.patch_size = patch_size + + self.x_embedder = nn.Linear( + in_features=patch_size * patch_size * in_channels, + out_features=dim, + bias=True, + ) + nn.init.constant_(self.x_embedder.bias, 0.0) + + self.t_embedder = TimestepEmbedder(min(dim, 1024)) + self.cap_embedder = nn.Sequential( + nn.LayerNorm(cap_feat_dim), + nn.Linear(cap_feat_dim, min(dim, 1024), bias=True), + ) + + self.layers = nn.ModuleList( + [ + TransformerBlock( + layer_id, + dim, + n_heads, + n_kv_heads, + multiple_of, + ffn_dim_multiplier, + norm_eps, + qk_norm, + cap_feat_dim, + ) + for layer_id in range(n_layers) + ] + ) + self.final_layer = FinalLayer(dim, patch_size, self.out_channels) + + assert (dim // n_heads) % 4 == 0, "2d rope needs head dim to be divisible by 4" + self.dim = dim + self.n_heads = n_heads + self.freqs_cis = NextDiT.precompute_freqs_cis( + dim // n_heads, + 384, + rope_scaling_factor=rope_scaling_factor, + ntk_factor=ntk_factor, + ) + self.rope_scaling_factor = rope_scaling_factor + self.ntk_factor = ntk_factor + # self.eol_token = nn.Parameter(torch.empty(dim)) + self.pad_token = nn.Parameter(torch.empty(dim)) + # nn.init.normal_(self.eol_token, std=0.02) + nn.init.normal_(self.pad_token, std=0.02) + + def unpatchify( + self, x: torch.Tensor, img_size: List[Tuple[int, int]], return_tensor=False + ) -> List[torch.Tensor]: + """ + x: (N, T, patch_size**2 * C) + imgs: (N, H, W, C) + """ + pH = pW = self.patch_size + if return_tensor: + H, W = img_size[0] + B = x.size(0) + L = (H // pH) * (W // pW) + x = x[:, :L].view(B, H // pH, W // pW, pH, pW, self.out_channels) + x = x.permute(0, 5, 1, 3, 2, 4).flatten(4, 5).flatten(2, 3) + return x + else: + imgs = [] + for i in range(x.size(0)): + H, W = img_size[i] + L = (H // pH) * (W // pW) + imgs.append( + x[i][:L] + .view(H // pH, W // pW, pH, pW, self.out_channels) + .permute(4, 0, 2, 1, 3) + .flatten(3, 4) + .flatten(1, 2) + ) + return imgs + + def patchify_and_embed( + self, x: List[torch.Tensor] | torch.Tensor + ) -> Tuple[torch.Tensor, torch.Tensor, List[Tuple[int, int]], torch.Tensor]: + self.freqs_cis = self.freqs_cis.to(x[0].device) + if isinstance(x, torch.Tensor): + pH = pW = self.patch_size + B, C, H, W = x.size() + x = ( + x.view(B, C, H // pH, pH, W // pW, pW) + .permute(0, 2, 4, 1, 3, 5) + .flatten(3) + ) + x = self.x_embedder(x) + x = x.flatten(1, 2) + + mask = torch.ones( + x.shape[0], x.shape[1], dtype=torch.int32, device=x.device + ) + # leave the first line for text + return ( + x, + mask, + [(H, W)] * B, + self.freqs_cis[: H // pH, : W // pW].flatten(0, 1).unsqueeze(0), + ) + else: + pH = pW = self.patch_size + x_embed = [] + freqs_cis = [] + img_size = [] + l_effective_seq_len = [] + + for img in x: + C, H, W = img.size() + item_freqs_cis = self.freqs_cis[: H // pH, : W // pW] + freqs_cis.append(item_freqs_cis.flatten(0, 1)) + img_size.append((H, W)) + img = ( + img.view(C, H // pH, pH, W // pW, pW) + .permute(1, 3, 0, 2, 4) + .flatten(2) + ) + img = self.x_embedder(img) + img = img.flatten(0, 1) + l_effective_seq_len.append(len(img)) + x_embed.append(img) + + max_seq_len = max(l_effective_seq_len) + mask = torch.zeros( + len(x), max_seq_len, dtype=torch.int32, device=x[0].device + ) + padded_x_embed = [] + padded_freqs_cis = [] + for i, (item_embed, item_freqs_cis, item_seq_len) in enumerate( + zip(x_embed, freqs_cis, l_effective_seq_len) + ): + item_embed = torch.cat( + [ + item_embed, + self.pad_token.view(1, -1).expand( + max_seq_len - item_seq_len, -1 + ), + ], + dim=0, + ) + item_freqs_cis = torch.cat( + [ + item_freqs_cis, + item_freqs_cis[-1:].expand(max_seq_len - item_seq_len, -1), + ], + dim=0, + ) + padded_x_embed.append(item_embed) + padded_freqs_cis.append(item_freqs_cis) + mask[i][:item_seq_len] = 1 + + x_embed = torch.stack(padded_x_embed, dim=0) + freqs_cis = torch.stack(padded_freqs_cis, dim=0) + return x_embed, mask, img_size, freqs_cis + + def forward( + self, + hidden_states, + timestep=None, + encoder_hidden_states=None, + encoder_attn_mask=None, + return_dict: bool = True, + hidden_states_is_tensor: bool = False, + unpatchify: bool = True, + ): + """ + Forward pass of NextDiT. + t: (N,) tensor of diffusion timesteps + y: (N,) tensor of class labels + """ + + # 0. Check inputs + if not unpatchify and return_dict: + raise ValueError( + f"Cannot both define `unpatchify`: {unpatchify} and `return_dict`: {return_dict} since when" + f" `unpatchify` is {unpatchify} the returned output is of shape (batch_size, seq_len, hidden_dim)" + " rather than (batch_size, num_channels, height, width)." + ) + + # 1. Patchify and Get embedding + hidden_states, mask, img_size, freqs_cis = self.patchify_and_embed(hidden_states) + freqs_cis = freqs_cis.to(hidden_states.device) + + # 2. Get timestep embedding + timestep = self.t_embedder(timestep) # (N, D) + + # 3. Get encoder hidden_state + encoder_attn_mask_float = encoder_attn_mask.float().unsqueeze(-1) + encoder_hidden_states_pool = (encoder_hidden_states * encoder_attn_mask_float).sum(dim=1) / encoder_attn_mask_float.sum( + dim=1 + ) + encoder_hidden_states_pool = encoder_hidden_states_pool.to(encoder_hidden_states) + cap_emb = self.cap_embedder(encoder_hidden_states_pool) + encoder_attn_mask = encoder_attn_mask.bool() + + adaln_input = timestep + cap_emb + # 4. Get hidden_state from transformer + for layer in self.layers: + hidden_states = layer(hidden_states, mask, freqs_cis, encoder_hidden_states, encoder_attn_mask, adaln_input=adaln_input) + + hidden_states = self.final_layer(hidden_states, adaln_input) + + if unpatchify: + hidden_states = self.unpatchify(hidden_states, img_size, return_tensor=hidden_states_is_tensor) + else: + output = hidden_states + + if self.learn_sigma: + if hidden_states_is_tensor: + hidden_states, _ = hidden_states.chunk(2, dim=1) + else: + hidden_states = [_.chunk(2, dim=0)[0] for _ in hidden_states] + + if not return_dict: + return (output,) + + return Transformer2DModelOutput(sample=output) + + def forward_with_cfg( + self, + x, + t, + cap_feats, + cap_mask, + cfg_scale, + rope_scaling_factor=None, + ntk_factor=None, + base_seqlen: Optional[int] = None, + proportional_attn: bool = False, + ): + # """ + # Forward pass of NextDiT, but also batches the unconNextditional forward pass + # for classifier-free guidance. + # """ + # # https://github.com/openai/glide-text2im/blob/main/notebooks/text2im.ipynb + # print(ntk_factor, rope_scaling_factor, self.ntk_factor, self.rope_scaling_factor) + if rope_scaling_factor is not None or ntk_factor is not None: + rope_scaling_factor = ( + rope_scaling_factor + if rope_scaling_factor is not None + else self.rope_scaling_factor + ) + ntk_factor = ntk_factor if ntk_factor is not None else self.ntk_factor + if ( + rope_scaling_factor != self.rope_scaling_factor + or ntk_factor != self.ntk_factor + ): + print( + f"override freqs_cis, rope_scaling {rope_scaling_factor}, ntk {ntk_factor}", + flush=True, + ) + self.freqs_cis = NextDiT.precompute_freqs_cis( + self.dim // self.n_heads, + 384, + rope_scaling_factor=rope_scaling_factor, + ntk_factor=ntk_factor, + ) + self.rope_scaling_factor = rope_scaling_factor + self.ntk_factor = ntk_factor + + if proportional_attn: + assert base_seqlen is not None + for layer in self.layers: + layer.attention.base_seqlen = base_seqlen + layer.attention.proportional_attn = proportional_attn + else: + for layer in self.layers: + layer.attention.base_seqlen = None + layer.attention.proportional_attn = proportional_attn + + half = x[: len(x) // 2] + combined = torch.cat([half, half], dim=0) + model_out = self.forward(combined, t, cap_feats, cap_mask) + # For exact reproducibility reasons, we apply classifier-free guidance on only + # three channels by default. The standard approach to cfg applies it to all channels. + # This can be done by uncommenting the following line and commenting-out the line following that. + # eps, rest = model_out[:, :self.in_channels], model_out[:, self.in_channels:] + eps, rest = model_out[:, :3], model_out[:, 3:] + cond_eps, uncond_eps = torch.split(eps, len(eps) // 2, dim=0) + half_eps = uncond_eps + cfg_scale * (cond_eps - uncond_eps) + eps = torch.cat([half_eps, half_eps], dim=0) + return torch.cat([eps, rest], dim=1) + + @staticmethod + def precompute_freqs_cis( + dim: int, + end: int, + theta: float = 10000.0, + rope_scaling_factor: float = 1.0, + ntk_factor: float = 1.0, + ): + """ + Precompute the frequency tensor for complex exponentials (cis) with + given dimensions. + + This function calculates a frequency tensor with complex exponentials + using the given dimension 'dim' and the end index 'end'. The 'theta' + parameter scales the frequencies. The returned tensor contains complex + values in complex64 data type. + + Args: + dim (int): Dimension of the frequency tensor. + end (int): End index for precomputing frequencies. + theta (float, optional): Scaling factor for frequency computation. + Defaults to 10000.0. + + Returns: + torch.Tensor: Precomputed frequency tensor with complex + exponentials. + """ + + theta = theta * ntk_factor + + logger.info( + f"theta {theta} rope scaling {rope_scaling_factor} ntk {ntk_factor}" + ) + freqs = 1.0 / ( + theta ** (torch.arange(0, dim, 4)[: (dim // 4)].float().cuda() / dim) + ) + t = torch.arange(end, device=freqs.device, dtype=torch.float) # type: ignore + t = t / rope_scaling_factor + freqs = torch.outer(t, freqs).float() # type: ignore + freqs_cis = torch.polar(torch.ones_like(freqs), freqs) # complex64 + + freqs_cis_h = freqs_cis.view(end, 1, dim // 4, 1).repeat(1, end, 1, 1) + freqs_cis_w = freqs_cis.view(1, end, dim // 4, 1).repeat(end, 1, 1, 1) + freqs_cis = torch.cat([freqs_cis_h, freqs_cis_w], dim=-1).flatten(2) + return freqs_cis + + def parameter_count(self) -> int: + tensor_parallel_module_list = ( + nn.Linear, + nn.Linear, + nn.Embedding, + ) + total_params = 0 + + def _recursive_count_params(module): + nonlocal total_params + is_tp_module = isinstance(module, tensor_parallel_module_list) + for param in module.parameters(recurse=False): + total_params += param.numel() + for submodule in module.children(): + _recursive_count_params(submodule) + + _recursive_count_params(self) + return total_params + + def get_fsdp_wrap_module_list(self) -> List[nn.Module]: + return list(self.layers) + + +############################################################################# +# NextDiT Configs # +############################################################################# +def NextDiT_2B_patch2(**kwargs): + return NextDiT(patch_size=2, dim=2304, n_layers=24, n_heads=32, **kwargs) diff --git a/src/diffusers/pipelines/lumina/pipeline_lumina_t2i.py b/src/diffusers/pipelines/lumina/pipeline_lumina.py similarity index 81% rename from src/diffusers/pipelines/lumina/pipeline_lumina_t2i.py rename to src/diffusers/pipelines/lumina/pipeline_lumina.py index 3b6a5636bd58..9b67aeab0173 100644 --- a/src/diffusers/pipelines/lumina/pipeline_lumina_t2i.py +++ b/src/diffusers/pipelines/lumina/pipeline_lumina.py @@ -19,10 +19,10 @@ from typing import Callable, List, Optional, Tuple, Union import torch -from transformers import T5EncoderModel, T5Tokenizer +from transformers import AutoModel, AutoTokenizer -from ...image_processor import LuminaImageProcessor -from ...models import AutoencoderKL, Transformer2DModel +from ...image_processor import VaeImageProcessor +from ...models import AutoencoderKL from ...schedulers import DPMSolverMultistepScheduler from ...utils import ( BACKENDS_MAPPING, @@ -34,6 +34,7 @@ ) from ...utils.torch_utils import randn_tensor from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput +from .modeling_next_dit import NextFlagDiffuserTransformer2DModel logger = logging.get_logger(__name__) # pylint: disable=invalid-name @@ -202,9 +203,13 @@ def retrieve_timesteps( second element is the number of inference steps. """ if timesteps is not None and sigmas is not None: - raise ValueError("Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values") + raise ValueError( + "Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values" + ) if timesteps is not None: - accepts_timesteps = "timesteps" in set(inspect.signature(scheduler.set_timesteps).parameters.keys()) + accepts_timesteps = "timesteps" in set( + inspect.signature(scheduler.set_timesteps).parameters.keys() + ) if not accepts_timesteps: raise ValueError( f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom" @@ -214,7 +219,9 @@ def retrieve_timesteps( timesteps = scheduler.timesteps num_inference_steps = len(timesteps) elif sigmas is not None: - accept_sigmas = "sigmas" in set(inspect.signature(scheduler.set_timesteps).parameters.keys()) + accept_sigmas = "sigmas" in set( + inspect.signature(scheduler.set_timesteps).parameters.keys() + ) if not accept_sigmas: raise ValueError( f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom" @@ -239,13 +246,13 @@ class LuminaText2ImgPipeline(DiffusionPipeline): Args: vae ([`AutoencoderKL`]): Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. - text_encoder ([`T5EncoderModel`]): + text_encoder ([`AutoModel`]): Frozen text-encoder. Lumina-T2I uses - [T5](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5EncoderModel), specifically the + [T5](https://huggingface.co/docs/transformers/model_doc/t5#transformers.AutoModel), specifically the [t5-v1_1-xxl](https://huggingface.co/Alpha-VLLM/tree/main/t5-v1_1-xxl) variant. - tokenizer (`T5Tokenizer`): + tokenizer (`AutoModel`): Tokenizer of class - [T5Tokenizer](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5Tokenizer). + [AutoModel](https://huggingface.co/docs/transformers/model_doc/t5#transformers.AutoModel). transformer ([`Transformer2DModel`]): A text conditioned `Transformer2DModel` to denoise the encoded image latents. scheduler ([`SchedulerMixin`]): @@ -273,20 +280,24 @@ class LuminaText2ImgPipeline(DiffusionPipeline): def __init__( self, - tokenizer: T5Tokenizer, - text_encoder: T5EncoderModel, + tokenizer: AutoTokenizer, + text_encoder: AutoModel, vae: AutoencoderKL, - transformer: Transformer2DModel, + transformer: NextFlagDiffuserTransformer2DModel, scheduler: DPMSolverMultistepScheduler, ): super().__init__() self.register_modules( - tokenizer=tokenizer, text_encoder=text_encoder, vae=vae, transformer=transformer, scheduler=scheduler + tokenizer=tokenizer, + text_encoder=text_encoder, + vae=vae, + transformer=transformer, + scheduler=scheduler, ) - self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) - self.image_processor = LuminaImageProcessor(vae_scale_factor=self.vae_scale_factor) + self.vae_scale_factor = 0.18215 + self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor) # Adapted from diffusers.pipelines.deepfloyd_if.pipeline_if.encode_prompt def encode_prompt( @@ -301,7 +312,7 @@ def encode_prompt( prompt_attention_mask: Optional[torch.Tensor] = None, negative_prompt_attention_mask: Optional[torch.Tensor] = None, clean_caption: bool = False, - max_sequence_length: int = 120, + max_sequence_length: int = 256, **kwargs, ): r""" @@ -345,26 +356,29 @@ def encode_prompt( else: batch_size = prompt_embeds.shape[0] - # See Section 3.1. of the paper. max_length = max_sequence_length - if prompt_embeds is None: prompt = self._text_preprocessing(prompt, clean_caption=clean_caption) text_inputs = self.tokenizer( prompt, - padding="max_length", + padding=True, + pad_to_multiple_of=8, max_length=max_length, truncation=True, - add_special_tokens=True, return_tensors="pt", ) - text_input_ids = text_inputs.input_ids - untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids - if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal( - text_input_ids, untruncated_ids - ): - removed_text = self.tokenizer.batch_decode(untruncated_ids[:, max_length - 1 : -1]) + text_input_ids = text_inputs.input_ids + untruncated_ids = self.tokenizer( + prompt, padding="longest", return_tensors="pt" + ).input_ids + + if untruncated_ids.shape[-1] >= text_input_ids.shape[ + -1 + ] and not torch.equal(text_input_ids, untruncated_ids): + removed_text = self.tokenizer.batch_decode( + untruncated_ids[:, max_length - 1 : -1] + ) logger.warning( "The following part of your input was truncated because T5 can only handle sequences up to" f" {max_length} tokens: {removed_text}" @@ -373,8 +387,10 @@ def encode_prompt( prompt_attention_mask = text_inputs.attention_mask prompt_attention_mask = prompt_attention_mask.to(device) - prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=prompt_attention_mask) - prompt_embeds = prompt_embeds[0] + prompt_embeds = self.text_encoder( + text_input_ids.to(device), attention_mask=prompt_attention_mask + ) + prompt_embeds = prompt_embeds.hidden_states[-2] if self.text_encoder is not None: dtype = self.text_encoder.dtype @@ -388,48 +404,51 @@ def encode_prompt( bs_embed, seq_len, _ = prompt_embeds.shape # duplicate text embeddings and attention mask for each generation per prompt, using mps friendly method prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1) - prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1) + prompt_embeds = prompt_embeds.view( + bs_embed * num_images_per_prompt, seq_len, -1 + ) prompt_attention_mask = prompt_attention_mask.view(bs_embed, -1) prompt_attention_mask = prompt_attention_mask.repeat(num_images_per_prompt, 1) # get unconditional embeddings for classifier free guidance - if do_classifier_free_guidance and negative_prompt_embeds is None: - uncond_tokens = [negative_prompt] * batch_size - uncond_tokens = self._text_preprocessing(uncond_tokens, clean_caption=clean_caption) - max_length = prompt_embeds.shape[1] - uncond_input = self.tokenizer( - uncond_tokens, - padding="max_length", - max_length=max_length, - truncation=True, - return_attention_mask=True, - add_special_tokens=True, - return_tensors="pt", - ) - negative_prompt_attention_mask = uncond_input.attention_mask - negative_prompt_attention_mask = negative_prompt_attention_mask.to(device) - - negative_prompt_embeds = self.text_encoder( - uncond_input.input_ids.to(device), attention_mask=negative_prompt_attention_mask - ) - negative_prompt_embeds = negative_prompt_embeds[0] - - if do_classifier_free_guidance: - # duplicate unconditional embeddings for each generation per prompt, using mps friendly method - seq_len = negative_prompt_embeds.shape[1] - - negative_prompt_embeds = negative_prompt_embeds.to(dtype=dtype, device=device) - - negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1) - negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1) - - negative_prompt_attention_mask = negative_prompt_attention_mask.view(bs_embed, -1) - negative_prompt_attention_mask = negative_prompt_attention_mask.repeat(num_images_per_prompt, 1) - else: - negative_prompt_embeds = None - negative_prompt_attention_mask = None - - return prompt_embeds, prompt_attention_mask, negative_prompt_embeds, negative_prompt_attention_mask + # if do_classifier_free_guidance and negative_prompt_embeds is None: + # uncond_tokens = [negative_prompt] * batch_size + # uncond_tokens = self._text_preprocessing(uncond_tokens, clean_caption=clean_caption) + # max_length = prompt_embeds.shape[1] + # uncond_input = self.tokenizer( + # uncond_tokens, + # padding="max_length", + # max_length=max_length, + # truncation=True, + # return_attention_mask=True, + # add_special_tokens=True, + # return_tensors="pt", + # ) + # negative_prompt_attention_mask = uncond_input.attention_mask + # negative_prompt_attention_mask = negative_prompt_attention_mask.to(device) + + # negative_prompt_embeds = self.text_encoder( + # uncond_input.input_ids.to(device), attention_mask=negative_prompt_attention_mask + # ) + # negative_prompt_embeds = negative_prompt_embeds[0] + + # if do_classifier_free_guidance: + # # duplicate unconditional embeddings for each generation per prompt, using mps friendly method + # seq_len = negative_prompt_embeds.shape[1] + + # negative_prompt_embeds = negative_prompt_embeds.to(dtype=dtype, device=device) + + # negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1) + # negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1) + + # negative_prompt_attention_mask = negative_prompt_attention_mask.view(bs_embed, -1) + # negative_prompt_attention_mask = negative_prompt_attention_mask.repeat(num_images_per_prompt, 1) + # else: + # negative_prompt_embeds = None + # negative_prompt_attention_mask = None + + # return prompt_embeds, prompt_attention_mask, negative_prompt_embeds, negative_prompt_attention_mask + return prompt_embeds, prompt_attention_mask # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs def prepare_extra_step_kwargs(self, generator, eta): @@ -438,13 +457,17 @@ def prepare_extra_step_kwargs(self, generator, eta): # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 # and should be between [0, 1] - accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) + accepts_eta = "eta" in set( + inspect.signature(self.scheduler.step).parameters.keys() + ) extra_step_kwargs = {} if accepts_eta: extra_step_kwargs["eta"] = eta # check if the scheduler accepts generator - accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys()) + accepts_generator = "generator" in set( + inspect.signature(self.scheduler.step).parameters.keys() + ) if accepts_generator: extra_step_kwargs["generator"] = generator return extra_step_kwargs @@ -462,10 +485,13 @@ def check_inputs( negative_prompt_attention_mask=None, ): if height % 8 != 0 or width % 8 != 0: - raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.") + raise ValueError( + f"`height` and `width` have to be divisible by 8 but are {height} and {width}." + ) if (callback_steps is None) or ( - callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0) + callback_steps is not None + and (not isinstance(callback_steps, int) or callback_steps <= 0) ): raise ValueError( f"`callback_steps` has to be a positive integer but is {callback_steps} of type" @@ -481,8 +507,12 @@ def check_inputs( raise ValueError( "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined." ) - elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)): - raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") + elif prompt is not None and ( + not isinstance(prompt, str) and not isinstance(prompt, list) + ): + raise ValueError( + f"`prompt` has to be of type `str` or `list` but is {type(prompt)}" + ) if prompt is not None and negative_prompt_embeds is not None: raise ValueError( @@ -497,10 +527,17 @@ def check_inputs( ) if prompt_embeds is not None and prompt_attention_mask is None: - raise ValueError("Must provide `prompt_attention_mask` when specifying `prompt_embeds`.") + raise ValueError( + "Must provide `prompt_attention_mask` when specifying `prompt_embeds`." + ) - if negative_prompt_embeds is not None and negative_prompt_attention_mask is None: - raise ValueError("Must provide `negative_prompt_attention_mask` when specifying `negative_prompt_embeds`.") + if ( + negative_prompt_embeds is not None + and negative_prompt_attention_mask is None + ): + raise ValueError( + "Must provide `negative_prompt_attention_mask` when specifying `negative_prompt_embeds`." + ) if prompt_embeds is not None and negative_prompt_embeds is not None: if prompt_embeds.shape != negative_prompt_embeds.shape: @@ -519,12 +556,16 @@ def check_inputs( # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline._text_preprocessing def _text_preprocessing(self, text, clean_caption=False): if clean_caption and not is_bs4_available(): - logger.warning(BACKENDS_MAPPING["bs4"][-1].format("Setting `clean_caption=True`")) + logger.warning( + BACKENDS_MAPPING["bs4"][-1].format("Setting `clean_caption=True`") + ) logger.warning("Setting `clean_caption` to False...") clean_caption = False if clean_caption and not is_ftfy_available(): - logger.warning(BACKENDS_MAPPING["ftfy"][-1].format("Setting `clean_caption=True`")) + logger.warning( + BACKENDS_MAPPING["ftfy"][-1].format("Setting `clean_caption=True`") + ) logger.warning("Setting `clean_caption` to False...") clean_caption = False @@ -612,13 +653,17 @@ def _clean_caption(self, caption): # "123456.." caption = re.sub(r"\b\d{6,}\b", "", caption) # filenames: - caption = re.sub(r"[\S]+\.(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)", "", caption) + caption = re.sub( + r"[\S]+\.(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)", "", caption + ) # caption = re.sub(r"[\"\']{2,}", r'"', caption) # """AUSVERKAUFT""" caption = re.sub(r"[\.]{2,}", r" ", caption) # """AUSVERKAUFT""" - caption = re.sub(self.bad_punct_regex, r" ", caption) # ***AUSVERKAUFT***, #AUSVERKAUFT + caption = re.sub( + self.bad_punct_regex, r" ", caption + ) # ***AUSVERKAUFT***, #AUSVERKAUFT caption = re.sub(r"\s+\.\s+", r" ", caption) # " . " # this-is-my-cute-cat / this_is_my_cute_cat @@ -636,10 +681,14 @@ def _clean_caption(self, caption): caption = re.sub(r"(worldwide\s+)?(free\s+)?shipping", "", caption) caption = re.sub(r"(free\s)?download(\sfree)?", "", caption) caption = re.sub(r"\bclick\b\s(?:for|on)\s\w+", "", caption) - caption = re.sub(r"\b(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)(\simage[s]?)?", "", caption) + caption = re.sub( + r"\b(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)(\simage[s]?)?", "", caption + ) caption = re.sub(r"\bpage\s+\d+\b", "", caption) - caption = re.sub(r"\b\d*[a-zA-Z]+\d+[a-zA-Z]+\d+[a-zA-Z\d]*\b", r" ", caption) # j2d1a2a... + caption = re.sub( + r"\b\d*[a-zA-Z]+\d+[a-zA-Z]+\d+[a-zA-Z\d]*\b", r" ", caption + ) # j2d1a2a... caption = re.sub(r"\b\d+\.?\d*[xх×]\d+\.?\d*\b", "", caption) @@ -657,7 +706,17 @@ def _clean_caption(self, caption): return caption.strip() # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents - def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None): + def prepare_latents( + self, + batch_size, + num_channels_latents, + height, + width, + dtype, + device, + generator, + latents=None, + ): shape = ( batch_size, num_channels_latents, @@ -671,7 +730,9 @@ def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype ) if latents is None: - latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype) + latents = randn_tensor( + shape, generator=generator, device=device, dtype=dtype + ) else: latents = latents.to(device) @@ -805,7 +866,9 @@ def __call__( else: raise ValueError("Invalid sample size") orig_height, orig_width = height, width - height, width = self.image_processor.classify_height_width_bin(height, width, ratios=aspect_ratio_bin) + height, width = self.image_processor.classify_height_width_bin( + height, width, ratios=aspect_ratio_bin + ) self.check_inputs( prompt, @@ -838,8 +901,8 @@ def __call__( ( prompt_embeds, prompt_attention_mask, - negative_prompt_embeds, - negative_prompt_attention_mask, + # negative_prompt_embeds, + # negative_prompt_attention_mask, ) = self.encode_prompt( prompt, do_classifier_free_guidance, @@ -853,9 +916,9 @@ def __call__( clean_caption=clean_caption, max_sequence_length=max_sequence_length, ) - if do_classifier_free_guidance: - prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0) - prompt_attention_mask = torch.cat([negative_prompt_attention_mask, prompt_attention_mask], dim=0) + # if do_classifier_free_guidance: + # prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0) + # prompt_attention_mask = torch.cat([negative_prompt_attention_mask, prompt_attention_mask], dim=0) # 4. Prepare timesteps timesteps, num_inference_steps = retrieve_timesteps( @@ -875,14 +938,18 @@ def __call__( latents, ) - # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline + # 6. Prepare extra step kwargs. TODO: Logigic should ideally just be moved out of the pipeline extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) # 6.1 Prepare micro-conditions. added_cond_kwargs = {"resolution": None, "aspect_ratio": None} if self.transformer.config.sample_size == 128: - resolution = torch.tensor([height, width]).repeat(batch_size * num_images_per_prompt, 1) - aspect_ratio = torch.tensor([float(height / width)]).repeat(batch_size * num_images_per_prompt, 1) + resolution = torch.tensor([height, width]).repeat( + batch_size * num_images_per_prompt, 1 + ) + aspect_ratio = torch.tensor([float(height / width)]).repeat( + batch_size * num_images_per_prompt, 1 + ) resolution = resolution.to(dtype=prompt_embeds.dtype, device=device) aspect_ratio = aspect_ratio.to(dtype=prompt_embeds.dtype, device=device) @@ -893,12 +960,18 @@ def __call__( added_cond_kwargs = {"resolution": resolution, "aspect_ratio": aspect_ratio} # 7. Denoising loop - num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0) + num_warmup_steps = max( + len(timesteps) - num_inference_steps * self.scheduler.order, 0 + ) with self.progress_bar(total=num_inference_steps) as progress_bar: for i, t in enumerate(timesteps): - latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents - latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) + latent_model_input = ( + torch.cat([latents] * 2) if do_classifier_free_guidance else latents + ) + latent_model_input = self.scheduler.scale_model_input( + latent_model_input, t + ) current_timestep = t if not torch.is_tensor(current_timestep): @@ -909,9 +982,15 @@ def __call__( dtype = torch.float32 if is_mps else torch.float64 else: dtype = torch.int32 if is_mps else torch.int64 - current_timestep = torch.tensor([current_timestep], dtype=dtype, device=latent_model_input.device) + current_timestep = torch.tensor( + [current_timestep], + dtype=dtype, + device=latent_model_input.device, + ) elif len(current_timestep.shape) == 0: - current_timestep = current_timestep[None].to(latent_model_input.device) + current_timestep = current_timestep[None].to( + latent_model_input.device + ) # broadcast to batch dimension in a way that's compatible with ONNX/Core ML current_timestep = current_timestep.expand(latent_model_input.shape[0]) @@ -928,7 +1007,9 @@ def __call__( # perform guidance if do_classifier_free_guidance: noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) - noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) + noise_pred = noise_pred_uncond + guidance_scale * ( + noise_pred_text - noise_pred_uncond + ) # learned sigma if self.transformer.config.out_channels // 2 == latent_channels: @@ -939,21 +1020,32 @@ def __call__( # compute previous image: x_t -> x_t-1 if num_inference_steps == 1: # For DMD one step sampling: https://arxiv.org/abs/2311.18828 - latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).pred_original_sample + latents = self.scheduler.step( + noise_pred, t, latents, **extra_step_kwargs + ).pred_original_sample else: - latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0] + latents = self.scheduler.step( + noise_pred, t, latents, **extra_step_kwargs, return_dict=False + )[0] # call the callback, if provided - if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0): + if i == len(timesteps) - 1 or ( + (i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0 + ): progress_bar.update() if callback is not None and i % callback_steps == 0: step_idx = i // getattr(self.scheduler, "order", 1) callback(step_idx, t, latents) if not output_type == "latent": - image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0] - if use_resolution_binning: - image = self.image_processor.resize_and_crop_tensor(image, orig_width, orig_height) + image = self.vae.decode( + latents / self.vae.config.scaling_factor, return_dict=False + ).sample + image = (image + 1.0) / 2.0 + image.clamp_(0.0, 1.0) + # real_image = to_pil_image(image[0].float()) + # if use_resolution_binning: + # image = self.image_processor.resize_and_crop_tensor(image, orig_width, orig_height) else: image = latents From a0f7e183630bcb3eb5a96d0f6e2fd6f77562fddf Mon Sep 17 00:00:00 2001 From: PommesPeter Date: Sun, 26 May 2024 22:36:49 +0800 Subject: [PATCH 04/92] fixed typo and added test code --- src/diffusers/__init__.py | 1 + .../pipelines/lumina/modeling_next_dit.py | 92 ++-- .../dummy_torch_and_transformers_objects.py | 15 + tests/pipelines/lumina/__init__.py | 0 tests/pipelines/lumina/test_lumina.py | 442 ++++++++++++++++++ 5 files changed, 505 insertions(+), 45 deletions(-) create mode 100644 tests/pipelines/lumina/__init__.py create mode 100644 tests/pipelines/lumina/test_lumina.py diff --git a/src/diffusers/__init__.py b/src/diffusers/__init__.py index e2bc2f668d5f..e377ae6755ff 100644 --- a/src/diffusers/__init__.py +++ b/src/diffusers/__init__.py @@ -256,6 +256,7 @@ "KandinskyV22PriorPipeline", "LatentConsistencyModelImg2ImgPipeline", "LatentConsistencyModelPipeline", + "LuminaText2ImgPipeline", "LDMTextToImagePipeline", "LEditsPPPipelineStableDiffusion", "LEditsPPPipelineStableDiffusionXL", diff --git a/src/diffusers/pipelines/lumina/modeling_next_dit.py b/src/diffusers/pipelines/lumina/modeling_next_dit.py index 2aadfe00c0df..0a9914b84338 100644 --- a/src/diffusers/pipelines/lumina/modeling_next_dit.py +++ b/src/diffusers/pipelines/lumina/modeling_next_dit.py @@ -24,7 +24,7 @@ from ...models.transformers.transformer_2d import Transformer2DModelOutput from ...utils import logging -logger = logging.getLogger(__name__) +logger = logging.get_logger(__name__) def modulate(x, shift, scale): @@ -127,7 +127,7 @@ def forward(self, t): return t_emb -class ParallelLabelEmbedder(nn.Module): +class LabelEmbedder(nn.Module): r"""Embeds class labels into vector representations. Also handles label dropout for classifier-free guidance. """ @@ -165,7 +165,7 @@ def forward(self, labels, train, force_drop_ids=None): ############################################################################# -# Core NextDiT Model # +# Core NextFlagDiffuserTransformer2DModel Model # ############################################################################# @@ -191,8 +191,8 @@ def __init__( """ super().__init__() self.n_kv_heads = n_heads if n_kv_heads is None else n_kv_heads - self.n_heads = n_heads - self.n_rep = self.n_heads // self.n_kv_heads + self.num_attention_heads = n_heads + self.n_rep = self.num_attention_heads // self.n_kv_heads self.head_dim = dim // n_heads self.wq = nn.Linear(dim, n_heads * self.head_dim, bias=False) @@ -201,12 +201,12 @@ def __init__( if y_dim > 0: self.wk_y = nn.Linear(y_dim, self.n_kv_heads * self.head_dim, bias=False) self.wv_y = nn.Linear(y_dim, self.n_kv_heads * self.head_dim, bias=False) - self.gate = nn.Parameter(torch.zeros([self.n_heads])) + self.gate = nn.Parameter(torch.zeros([self.num_attention_heads])) self.wo = nn.Linear(n_heads * self.head_dim, dim, bias=False) if qk_norm: - self.q_norm = nn.LayerNorm(self.n_heads * self.head_dim) + self.q_norm = nn.LayerNorm(self.num_attention_heads * self.head_dim) self.k_norm = nn.LayerNorm(self.n_kv_heads * self.head_dim) if y_dim > 0: self.ky_norm = nn.LayerNorm(self.n_kv_heads * self.head_dim) @@ -309,7 +309,7 @@ def _get_unpad_data(attention_mask): ) if query_length == kv_seq_len: query_layer = index_first_axis( - query_layer.reshape(batch_size * kv_seq_len, self.n_heads, head_dim), + query_layer.reshape(batch_size * kv_seq_len, self.num_attention_heads, head_dim), indices_k, ) cu_seqlens_q = cu_seqlens_k @@ -365,7 +365,7 @@ def forward( xq = self.q_norm(xq) xk = self.k_norm(xk) - xq = xq.view(bsz, seqlen, self.n_heads, self.head_dim) + xq = xq.view(bsz, seqlen, self.num_attention_heads, self.head_dim) xk = xk.view(bsz, seqlen, self.n_kv_heads, self.head_dim) xv = xv.view(bsz, seqlen, self.n_kv_heads, self.head_dim) @@ -418,7 +418,7 @@ def forward( xv.permute(0, 2, 1, 3), attn_mask=x_mask.bool() .view(bsz, 1, 1, seqlen) - .expand(-1, self.n_heads, seqlen, -1), + .expand(-1, self.num_attention_heads, seqlen, -1), ) .permute(0, 2, 1, 3) .to(dtype) @@ -430,7 +430,7 @@ def forward( bsz, -1, self.n_kv_heads, self.head_dim ) yv = self.wv_y(y).view(bsz, -1, self.n_kv_heads, self.head_dim) - n_rep = self.n_heads // self.n_kv_heads + n_rep = self.num_attention_heads // self.n_kv_heads if n_rep >= 1: yk = yk.unsqueeze(3).repeat(1, 1, 1, n_rep, 1).flatten(2, 3) yv = yv.unsqueeze(3).repeat(1, 1, 1, n_rep, 1).flatten(2, 3) @@ -438,7 +438,7 @@ def forward( xq.permute(0, 2, 1, 3), yk.permute(0, 2, 1, 3), yv.permute(0, 2, 1, 3), - y_mask.view(bsz, 1, 1, -1).expand(bsz, self.n_heads, seqlen, -1), + y_mask.view(bsz, 1, 1, -1).expand(bsz, self.num_attention_heads, seqlen, -1), ).permute(0, 2, 1, 3) output_y = output_y * self.gate.tanh().view(1, 1, -1, 1) output = output + output_y @@ -620,7 +620,7 @@ def forward( class FinalLayer(nn.Module): """ - The final layer of NextDiT. + The final layer of NextFlagDiffuserTransformer2DModel. """ def __init__(self, hidden_size, patch_size, out_channels): @@ -651,18 +651,20 @@ class NextFlagDiffuserTransformer2DModel(ModelMixin, ConfigMixin): @register_to_config def __init__( self, - patch_size: int = 2, - in_channels: int = 4, - dim: int = 4096, - n_layers: int = 32, - n_heads: int = 32, - n_kv_heads: Optional[int] = None, + patch_size: Optional[int] = 2, + in_channels: Optional[int] = None, + out_channels: Optional[int] = None, + hidden_size: int = 4096, + num_layers: int = 32, + num_attention_heads: int = 32, + num_kv_heads: Optional[int] = None, multiple_of: int = 256, ffn_dim_multiplier: Optional[float] = None, + norm_elementwise_affine: bool = True, norm_eps: float = 1e-5, learn_sigma: bool = True, qk_norm: bool = False, - cap_feat_dim: int = 5120, + text_dim: int = 5120, rope_scaling_factor: float = 1.0, ntk_factor: float = 1.0, ) -> None: @@ -671,51 +673,51 @@ def __init__( self.in_channels = in_channels self.out_channels = in_channels * 2 if learn_sigma else in_channels self.patch_size = patch_size + self.num_attention_heads = num_attention_heads + self.rope_scaling_factor = rope_scaling_factor + self.ntk_factor = ntk_factor self.x_embedder = nn.Linear( in_features=patch_size * patch_size * in_channels, - out_features=dim, + out_features=hidden_size, bias=True, ) nn.init.constant_(self.x_embedder.bias, 0.0) - self.t_embedder = TimestepEmbedder(min(dim, 1024)) + self.t_embedder = TimestepEmbedder(min(hidden_size, 1024)) self.cap_embedder = nn.Sequential( - nn.LayerNorm(cap_feat_dim), - nn.Linear(cap_feat_dim, min(dim, 1024), bias=True), + nn.LayerNorm(text_dim), + nn.Linear(text_dim, min(hidden_size, 1024), bias=True), ) self.layers = nn.ModuleList( [ TransformerBlock( layer_id, - dim, - n_heads, - n_kv_heads, + hidden_size, + num_attention_heads, + num_kv_heads, multiple_of, ffn_dim_multiplier, norm_eps, qk_norm, - cap_feat_dim, + text_dim, ) - for layer_id in range(n_layers) + for layer_id in range(num_layers) ] ) - self.final_layer = FinalLayer(dim, patch_size, self.out_channels) + self.final_layer = FinalLayer(hidden_size, patch_size, self.out_channels) - assert (dim // n_heads) % 4 == 0, "2d rope needs head dim to be divisible by 4" - self.dim = dim - self.n_heads = n_heads - self.freqs_cis = NextDiT.precompute_freqs_cis( - dim // n_heads, + assert (hidden_size // num_attention_heads) % 4 == 0, "2d rope needs head dim to be divisible by 4" + self.hidden_size = hidden_size + self.freqs_cis = NextFlagDiffuserTransformer2DModel.precompute_freqs_cis( + hidden_size // num_attention_heads, 384, rope_scaling_factor=rope_scaling_factor, ntk_factor=ntk_factor, ) - self.rope_scaling_factor = rope_scaling_factor - self.ntk_factor = ntk_factor # self.eol_token = nn.Parameter(torch.empty(dim)) - self.pad_token = nn.Parameter(torch.empty(dim)) + self.pad_token = nn.Parameter(torch.empty(hidden_size)) # nn.init.normal_(self.eol_token, std=0.02) nn.init.normal_(self.pad_token, std=0.02) @@ -839,7 +841,7 @@ def forward( unpatchify: bool = True, ): """ - Forward pass of NextDiT. + Forward pass of NextFlagDiffuserTransformer2DModel. t: (N,) tensor of diffusion timesteps y: (N,) tensor of class labels """ @@ -904,7 +906,7 @@ def forward_with_cfg( proportional_attn: bool = False, ): # """ - # Forward pass of NextDiT, but also batches the unconNextditional forward pass + # Forward pass of NextFlagDiffuserTransformer2DModel, but also batches the unconNextFlagDiffuserTransformer2DModelional forward pass # for classifier-free guidance. # """ # # https://github.com/openai/glide-text2im/blob/main/notebooks/text2im.ipynb @@ -924,8 +926,8 @@ def forward_with_cfg( f"override freqs_cis, rope_scaling {rope_scaling_factor}, ntk {ntk_factor}", flush=True, ) - self.freqs_cis = NextDiT.precompute_freqs_cis( - self.dim // self.n_heads, + self.freqs_cis = NextFlagDiffuserTransformer2DModel.precompute_freqs_cis( + self.hidden_size // self.num_attention_heads, 384, rope_scaling_factor=rope_scaling_factor, ntk_factor=ntk_factor, @@ -1026,7 +1028,7 @@ def get_fsdp_wrap_module_list(self) -> List[nn.Module]: ############################################################################# -# NextDiT Configs # +# NextFlagDiffuserTransformer2DModel Configs # ############################################################################# -def NextDiT_2B_patch2(**kwargs): - return NextDiT(patch_size=2, dim=2304, n_layers=24, n_heads=32, **kwargs) +def NextFlagDiffuserTransformer2DModel_2B_patch2(**kwargs): + return NextFlagDiffuserTransformer2DModel(patch_size=2, dim=2304, num_layers=24, n_heads=32, **kwargs) diff --git a/src/diffusers/utils/dummy_torch_and_transformers_objects.py b/src/diffusers/utils/dummy_torch_and_transformers_objects.py index 0583cf839ff7..09314e33bb1b 100644 --- a/src/diffusers/utils/dummy_torch_and_transformers_objects.py +++ b/src/diffusers/utils/dummy_torch_and_transformers_objects.py @@ -692,6 +692,21 @@ def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers"]) +class LuminaText2ImgPipeline(metaclass=DummyObject): + _backends = ["torch", "transformers"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch", "transformers"]) + + @classmethod + def from_config(cls, *args, **kwargs): + requires_backends(cls, ["torch", "transformers"]) + + @classmethod + def from_pretrained(cls, *args, **kwargs): + requires_backends(cls, ["torch", "transformers"]) + + class MusicLDMPipeline(metaclass=DummyObject): _backends = ["torch", "transformers"] diff --git a/tests/pipelines/lumina/__init__.py b/tests/pipelines/lumina/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/tests/pipelines/lumina/test_lumina.py b/tests/pipelines/lumina/test_lumina.py new file mode 100644 index 000000000000..59ddfe2916e4 --- /dev/null +++ b/tests/pipelines/lumina/test_lumina.py @@ -0,0 +1,442 @@ +# coding=utf-8 +# Copyright 2024 HuggingFace Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import gc +import tempfile +import unittest + +import numpy as np +import torch +from transformers import AutoTokenizer, AutoModel + +from diffusers import ( + AutoencoderKL, + DDIMScheduler, + LuminaText2ImgPipeline, + Transformer2DModel, +) +from diffusers.utils.testing_utils import ( + enable_full_determinism, + numpy_cosine_similarity_distance, + require_torch_gpu, + slow, + torch_device, +) + +from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS +from ..test_pipelines_common import PipelineTesterMixin, to_np + + +enable_full_determinism() + + +class LuminaText2ImgPipelineFastTests(PipelineTesterMixin, unittest.TestCase): + pipeline_class = LuminaText2ImgPipeline + params = TEXT_TO_IMAGE_PARAMS - {"cross_attention_kwargs"} + batch_params = TEXT_TO_IMAGE_BATCH_PARAMS + image_params = TEXT_TO_IMAGE_IMAGE_PARAMS + image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS + + required_optional_params = PipelineTesterMixin.required_optional_params + + def get_dummy_components(self): + torch.manual_seed(0) + transformer = Transformer2DModel( + sample_size=8, + num_layers=2, + patch_size=2, + attention_head_dim=8, + num_attention_heads=3, + caption_channels=32, + in_channels=4, + cross_attention_dim=24, + out_channels=8, + attention_bias=True, + activation_fn="gelu-approximate", + num_embeds_ada_norm=1000, + norm_type="ada_norm_single", + norm_elementwise_affine=False, + norm_eps=1e-6, + ) + torch.manual_seed(0) + vae = AutoencoderKL() + + scheduler = DDIMScheduler() + text_encoder = AutoModel.from_pretrained("google/gemma-2b") + + tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b") + + components = { + "transformer": transformer.eval(), + "vae": vae.eval(), + "scheduler": scheduler, + "text_encoder": text_encoder, + "tokenizer": tokenizer, + } + return components + + def get_dummy_inputs(self, device, seed=0): + if str(device).startswith("mps"): + generator = torch.manual_seed(seed) + else: + generator = torch.Generator(device=device).manual_seed(seed) + inputs = { + "prompt": "A painting of a squirrel eating a burger", + "generator": generator, + "num_inference_steps": 2, + "guidance_scale": 5.0, + "use_resolution_binning": False, + "output_type": "np", + } + return inputs + + def test_sequential_cpu_offload_forward_pass(self): + # TODO(PVP, Sayak) need to fix later + return + + def test_save_load_optional_components(self): + components = self.get_dummy_components() + pipe = self.pipeline_class(**components) + pipe.to(torch_device) + pipe.set_progress_bar_config(disable=None) + + inputs = self.get_dummy_inputs(torch_device) + + prompt = inputs["prompt"] + generator = inputs["generator"] + num_inference_steps = inputs["num_inference_steps"] + output_type = inputs["output_type"] + + ( + prompt_embeds, + prompt_attention_mask, + negative_prompt_embeds, + negative_prompt_attention_mask, + ) = pipe.encode_prompt(prompt) + + # inputs with prompt converted to embeddings + inputs = { + "prompt_embeds": prompt_embeds, + "prompt_attention_mask": prompt_attention_mask, + "negative_prompt": None, + "negative_prompt_embeds": negative_prompt_embeds, + "negative_prompt_attention_mask": negative_prompt_attention_mask, + "generator": generator, + "num_inference_steps": num_inference_steps, + "output_type": output_type, + "use_resolution_binning": False, + } + + # set all optional components to None + for optional_component in pipe._optional_components: + setattr(pipe, optional_component, None) + + output = pipe(**inputs)[0] + + with tempfile.TemporaryDirectory() as tmpdir: + pipe.save_pretrained(tmpdir) + pipe_loaded = self.pipeline_class.from_pretrained(tmpdir) + pipe_loaded.to(torch_device) + pipe_loaded.set_progress_bar_config(disable=None) + + for optional_component in pipe._optional_components: + self.assertTrue( + getattr(pipe_loaded, optional_component) is None, + f"`{optional_component}` did not stay set to None after loading.", + ) + + inputs = self.get_dummy_inputs(torch_device) + + generator = inputs["generator"] + num_inference_steps = inputs["num_inference_steps"] + output_type = inputs["output_type"] + + # inputs with prompt converted to embeddings + inputs = { + "prompt_embeds": prompt_embeds, + "prompt_attention_mask": prompt_attention_mask, + "negative_prompt": None, + "negative_prompt_embeds": negative_prompt_embeds, + "negative_prompt_attention_mask": negative_prompt_attention_mask, + "generator": generator, + "num_inference_steps": num_inference_steps, + "output_type": output_type, + "use_resolution_binning": False, + } + + output_loaded = pipe_loaded(**inputs)[0] + + max_diff = np.abs(to_np(output) - to_np(output_loaded)).max() + self.assertLess(max_diff, 1e-4) + + def test_inference(self): + device = "cpu" + + components = self.get_dummy_components() + pipe = self.pipeline_class(**components) + pipe.to(device) + pipe.set_progress_bar_config(disable=None) + + inputs = self.get_dummy_inputs(device) + image = pipe(**inputs).images + image_slice = image[0, -3:, -3:, -1] + + self.assertEqual(image.shape, (1, 8, 8, 3)) + expected_slice = np.array([0.6319, 0.3526, 0.3806, 0.6327, 0.4639, 0.483, 0.2583, 0.5331, 0.4852]) + max_diff = np.abs(image_slice.flatten() - expected_slice).max() + self.assertLessEqual(max_diff, 1e-3) + + def test_inference_non_square_images(self): + device = "cpu" + + components = self.get_dummy_components() + pipe = self.pipeline_class(**components) + pipe.to(device) + pipe.set_progress_bar_config(disable=None) + + inputs = self.get_dummy_inputs(device) + image = pipe(**inputs, height=32, width=48).images + image_slice = image[0, -3:, -3:, -1] + self.assertEqual(image.shape, (1, 32, 48, 3)) + + expected_slice = np.array([0.6493, 0.537, 0.4081, 0.4762, 0.3695, 0.4711, 0.3026, 0.5218, 0.5263]) + max_diff = np.abs(image_slice.flatten() - expected_slice).max() + self.assertLessEqual(max_diff, 1e-3) + + def test_inference_with_embeddings_and_multiple_images(self): + components = self.get_dummy_components() + pipe = self.pipeline_class(**components) + pipe.to(torch_device) + pipe.set_progress_bar_config(disable=None) + + inputs = self.get_dummy_inputs(torch_device) + + prompt = inputs["prompt"] + generator = inputs["generator"] + num_inference_steps = inputs["num_inference_steps"] + output_type = inputs["output_type"] + + prompt_embeds, prompt_attn_mask, negative_prompt_embeds, neg_prompt_attn_mask = pipe.encode_prompt(prompt) + + # inputs with prompt converted to embeddings + inputs = { + "prompt_embeds": prompt_embeds, + "prompt_attention_mask": prompt_attn_mask, + "negative_prompt": None, + "negative_prompt_embeds": negative_prompt_embeds, + "negative_prompt_attention_mask": neg_prompt_attn_mask, + "generator": generator, + "num_inference_steps": num_inference_steps, + "output_type": output_type, + "num_images_per_prompt": 2, + "use_resolution_binning": False, + } + + # set all optional components to None + for optional_component in pipe._optional_components: + setattr(pipe, optional_component, None) + + output = pipe(**inputs)[0] + + with tempfile.TemporaryDirectory() as tmpdir: + pipe.save_pretrained(tmpdir) + pipe_loaded = self.pipeline_class.from_pretrained(tmpdir) + pipe_loaded.to(torch_device) + pipe_loaded.set_progress_bar_config(disable=None) + + for optional_component in pipe._optional_components: + self.assertTrue( + getattr(pipe_loaded, optional_component) is None, + f"`{optional_component}` did not stay set to None after loading.", + ) + + inputs = self.get_dummy_inputs(torch_device) + + generator = inputs["generator"] + num_inference_steps = inputs["num_inference_steps"] + output_type = inputs["output_type"] + + # inputs with prompt converted to embeddings + inputs = { + "prompt_embeds": prompt_embeds, + "prompt_attention_mask": prompt_attn_mask, + "negative_prompt": None, + "negative_prompt_embeds": negative_prompt_embeds, + "negative_prompt_attention_mask": neg_prompt_attn_mask, + "generator": generator, + "num_inference_steps": num_inference_steps, + "output_type": output_type, + "num_images_per_prompt": 2, + "use_resolution_binning": False, + } + + output_loaded = pipe_loaded(**inputs)[0] + + max_diff = np.abs(to_np(output) - to_np(output_loaded)).max() + self.assertLess(max_diff, 1e-4) + + def test_inference_with_multiple_images_per_prompt(self): + device = "cpu" + + components = self.get_dummy_components() + pipe = self.pipeline_class(**components) + pipe.to(device) + pipe.set_progress_bar_config(disable=None) + + inputs = self.get_dummy_inputs(device) + inputs["num_images_per_prompt"] = 2 + image = pipe(**inputs).images + image_slice = image[0, -3:, -3:, -1] + + self.assertEqual(image.shape, (2, 8, 8, 3)) + expected_slice = np.array([0.6319, 0.3526, 0.3806, 0.6327, 0.4639, 0.483, 0.2583, 0.5331, 0.4852]) + max_diff = np.abs(image_slice.flatten() - expected_slice).max() + self.assertLessEqual(max_diff, 1e-3) + + def test_raises_warning_for_mask_feature(self): + device = "cpu" + + components = self.get_dummy_components() + pipe = self.pipeline_class(**components) + pipe.to(device) + pipe.set_progress_bar_config(disable=None) + + inputs = self.get_dummy_inputs(device) + inputs.update({"mask_feature": True}) + + with self.assertWarns(FutureWarning) as warning_ctx: + _ = pipe(**inputs).images + + assert "mask_feature" in str(warning_ctx.warning) + + def test_inference_batch_single_identical(self): + self._test_inference_batch_single_identical(expected_max_diff=1e-3) + + +@slow +@require_torch_gpu +class LuminaText2ImgPipelineIntegrationTests(unittest.TestCase): + ckpt_id_1024 = "PixArt-alpha/PixArt-XL-2-1024-MS" + ckpt_id_512 = "PixArt-alpha/PixArt-XL-2-512x512" + prompt = "A small cactus with a happy face in the Sahara desert." + + def setUp(self): + super().setUp() + gc.collect() + torch.cuda.empty_cache() + + def tearDown(self): + super().tearDown() + gc.collect() + torch.cuda.empty_cache() + + def test_pixart_1024(self): + generator = torch.Generator("cpu").manual_seed(0) + + pipe = LuminaText2ImgPipeline.from_pretrained(self.ckpt_id_1024, torch_dtype=torch.float16) + pipe.enable_model_cpu_offload() + prompt = self.prompt + + image = pipe(prompt, generator=generator, num_inference_steps=2, output_type="np").images + + image_slice = image[0, -3:, -3:, -1] + expected_slice = np.array([0.0742, 0.0835, 0.2114, 0.0295, 0.0784, 0.2361, 0.1738, 0.2251, 0.3589]) + + max_diff = numpy_cosine_similarity_distance(image_slice.flatten(), expected_slice) + self.assertLessEqual(max_diff, 1e-4) + + def test_pixart_512(self): + generator = torch.Generator("cpu").manual_seed(0) + + pipe = LuminaText2ImgPipeline.from_pretrained(self.ckpt_id_512, torch_dtype=torch.float16) + pipe.enable_model_cpu_offload() + + prompt = self.prompt + + image = pipe(prompt, generator=generator, num_inference_steps=2, output_type="np").images + + image_slice = image[0, -3:, -3:, -1] + expected_slice = np.array([0.3477, 0.3882, 0.4541, 0.3413, 0.3821, 0.4463, 0.4001, 0.4409, 0.4958]) + + max_diff = numpy_cosine_similarity_distance(image_slice.flatten(), expected_slice) + self.assertLessEqual(max_diff, 1e-4) + + def test_pixart_1024_without_resolution_binning(self): + generator = torch.manual_seed(0) + + pipe = LuminaText2ImgPipeline.from_pretrained(self.ckpt_id_1024, torch_dtype=torch.float16) + pipe.enable_model_cpu_offload() + + prompt = self.prompt + height, width = 1024, 768 + num_inference_steps = 2 + + image = pipe( + prompt, + height=height, + width=width, + generator=generator, + num_inference_steps=num_inference_steps, + output_type="np", + ).images + image_slice = image[0, -3:, -3:, -1] + + generator = torch.manual_seed(0) + no_res_bin_image = pipe( + prompt, + height=height, + width=width, + generator=generator, + num_inference_steps=num_inference_steps, + output_type="np", + use_resolution_binning=False, + ).images + no_res_bin_image_slice = no_res_bin_image[0, -3:, -3:, -1] + + assert not np.allclose(image_slice, no_res_bin_image_slice, atol=1e-4, rtol=1e-4) + + def test_pixart_512_without_resolution_binning(self): + generator = torch.manual_seed(0) + + pipe = LuminaText2ImgPipeline.from_pretrained(self.ckpt_id_512, torch_dtype=torch.float16) + pipe.enable_model_cpu_offload() + + prompt = self.prompt + height, width = 512, 768 + num_inference_steps = 2 + + image = pipe( + prompt, + height=height, + width=width, + generator=generator, + num_inference_steps=num_inference_steps, + output_type="np", + ).images + image_slice = image[0, -3:, -3:, -1] + + generator = torch.manual_seed(0) + no_res_bin_image = pipe( + prompt, + height=height, + width=width, + generator=generator, + num_inference_steps=num_inference_steps, + output_type="np", + use_resolution_binning=False, + ).images + no_res_bin_image_slice = no_res_bin_image[0, -3:, -3:, -1] + + assert not np.allclose(image_slice, no_res_bin_image_slice, atol=1e-4, rtol=1e-4) From dfb826e513fbc7b2e35423f8a32c7418efa415e2 Mon Sep 17 00:00:00 2001 From: PommesPeter Date: Fri, 17 May 2024 14:11:26 +0800 Subject: [PATCH 05/92] init lumina-t2i pipeline --- src/diffusers/pipelines/lumina/__init__.py | 1 + 1 file changed, 1 insertion(+) create mode 100644 src/diffusers/pipelines/lumina/__init__.py diff --git a/src/diffusers/pipelines/lumina/__init__.py b/src/diffusers/pipelines/lumina/__init__.py new file mode 100644 index 000000000000..38ad13939515 --- /dev/null +++ b/src/diffusers/pipelines/lumina/__init__.py @@ -0,0 +1 @@ +from .pipeline_lumina import LuminaText2ImgPipeline \ No newline at end of file From e516d507384fe7869934fde8aa0a028f47dd2bee Mon Sep 17 00:00:00 2001 From: PommesPeter Date: Fri, 17 May 2024 14:17:30 +0800 Subject: [PATCH 06/92] added pipeline code --- src/diffusers/pipelines/lumina/__init__.py | 2 +- .../pipelines/lumina/pipeline_lumina_t2i.py | 969 ++++++++++++++++++ 2 files changed, 970 insertions(+), 1 deletion(-) create mode 100644 src/diffusers/pipelines/lumina/pipeline_lumina_t2i.py diff --git a/src/diffusers/pipelines/lumina/__init__.py b/src/diffusers/pipelines/lumina/__init__.py index 38ad13939515..32503a6442c7 100644 --- a/src/diffusers/pipelines/lumina/__init__.py +++ b/src/diffusers/pipelines/lumina/__init__.py @@ -1 +1 @@ -from .pipeline_lumina import LuminaText2ImgPipeline \ No newline at end of file +from .pipeline_lumina_t2i import LuminaText2ImgPipeline \ No newline at end of file diff --git a/src/diffusers/pipelines/lumina/pipeline_lumina_t2i.py b/src/diffusers/pipelines/lumina/pipeline_lumina_t2i.py new file mode 100644 index 000000000000..3b6a5636bd58 --- /dev/null +++ b/src/diffusers/pipelines/lumina/pipeline_lumina_t2i.py @@ -0,0 +1,969 @@ +# Copyright 2024 Lumina-T2X Authors and The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import html +import inspect +import re +import urllib.parse as ul +from typing import Callable, List, Optional, Tuple, Union + +import torch +from transformers import T5EncoderModel, T5Tokenizer + +from ...image_processor import LuminaImageProcessor +from ...models import AutoencoderKL, Transformer2DModel +from ...schedulers import DPMSolverMultistepScheduler +from ...utils import ( + BACKENDS_MAPPING, + deprecate, + is_bs4_available, + is_ftfy_available, + logging, + replace_example_docstring, +) +from ...utils.torch_utils import randn_tensor +from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput + + +logger = logging.get_logger(__name__) # pylint: disable=invalid-name + +if is_bs4_available(): + from bs4 import BeautifulSoup + +if is_ftfy_available(): + import ftfy + +EXAMPLE_DOC_STRING = """ + Examples: + ```py + >>> import torch + >>> from diffusers import LuminaText2ImgPipeline + + >>> # You can replace the checkpoint id with "Alpha-VLLM/Lumina-Next-T2I" too. + >>> pipe = LuminaText2ImgPipeline.from_pretrained("Alpha-VLLM/Lumina-T2I", torch_dtype=torch.float16) + >>> # Enable memory optimizations. + >>> pipe.enable_model_cpu_offload() + + >>> prompt = "A small cactus with a happy face in the Sahara desert." + >>> image = pipe(prompt).images[0] + ``` +""" + +ASPECT_RATIO_1024_BIN = { + "0.25": [512.0, 2048.0], + "0.28": [512.0, 1856.0], + "0.32": [576.0, 1792.0], + "0.33": [576.0, 1728.0], + "0.35": [576.0, 1664.0], + "0.4": [640.0, 1600.0], + "0.42": [640.0, 1536.0], + "0.48": [704.0, 1472.0], + "0.5": [704.0, 1408.0], + "0.52": [704.0, 1344.0], + "0.57": [768.0, 1344.0], + "0.6": [768.0, 1280.0], + "0.68": [832.0, 1216.0], + "0.72": [832.0, 1152.0], + "0.78": [896.0, 1152.0], + "0.82": [896.0, 1088.0], + "0.88": [960.0, 1088.0], + "0.94": [960.0, 1024.0], + "1.0": [1024.0, 1024.0], + "1.07": [1024.0, 960.0], + "1.13": [1088.0, 960.0], + "1.21": [1088.0, 896.0], + "1.29": [1152.0, 896.0], + "1.38": [1152.0, 832.0], + "1.46": [1216.0, 832.0], + "1.67": [1280.0, 768.0], + "1.75": [1344.0, 768.0], + "2.0": [1408.0, 704.0], + "2.09": [1472.0, 704.0], + "2.4": [1536.0, 640.0], + "2.5": [1600.0, 640.0], + "3.0": [1728.0, 576.0], + "4.0": [2048.0, 512.0], +} + +ASPECT_RATIO_512_BIN = { + "0.25": [256.0, 1024.0], + "0.28": [256.0, 928.0], + "0.32": [288.0, 896.0], + "0.33": [288.0, 864.0], + "0.35": [288.0, 832.0], + "0.4": [320.0, 800.0], + "0.42": [320.0, 768.0], + "0.48": [352.0, 736.0], + "0.5": [352.0, 704.0], + "0.52": [352.0, 672.0], + "0.57": [384.0, 672.0], + "0.6": [384.0, 640.0], + "0.68": [416.0, 608.0], + "0.72": [416.0, 576.0], + "0.78": [448.0, 576.0], + "0.82": [448.0, 544.0], + "0.88": [480.0, 544.0], + "0.94": [480.0, 512.0], + "1.0": [512.0, 512.0], + "1.07": [512.0, 480.0], + "1.13": [544.0, 480.0], + "1.21": [544.0, 448.0], + "1.29": [576.0, 448.0], + "1.38": [576.0, 416.0], + "1.46": [608.0, 416.0], + "1.67": [640.0, 384.0], + "1.75": [672.0, 384.0], + "2.0": [704.0, 352.0], + "2.09": [736.0, 352.0], + "2.4": [768.0, 320.0], + "2.5": [800.0, 320.0], + "3.0": [864.0, 288.0], + "4.0": [1024.0, 256.0], +} + +ASPECT_RATIO_256_BIN = { + "0.25": [128.0, 512.0], + "0.28": [128.0, 464.0], + "0.32": [144.0, 448.0], + "0.33": [144.0, 432.0], + "0.35": [144.0, 416.0], + "0.4": [160.0, 400.0], + "0.42": [160.0, 384.0], + "0.48": [176.0, 368.0], + "0.5": [176.0, 352.0], + "0.52": [176.0, 336.0], + "0.57": [192.0, 336.0], + "0.6": [192.0, 320.0], + "0.68": [208.0, 304.0], + "0.72": [208.0, 288.0], + "0.78": [224.0, 288.0], + "0.82": [224.0, 272.0], + "0.88": [240.0, 272.0], + "0.94": [240.0, 256.0], + "1.0": [256.0, 256.0], + "1.07": [256.0, 240.0], + "1.13": [272.0, 240.0], + "1.21": [272.0, 224.0], + "1.29": [288.0, 224.0], + "1.38": [288.0, 208.0], + "1.46": [304.0, 208.0], + "1.67": [320.0, 192.0], + "1.75": [336.0, 192.0], + "2.0": [352.0, 176.0], + "2.09": [368.0, 176.0], + "2.4": [384.0, 160.0], + "2.5": [400.0, 160.0], + "3.0": [432.0, 144.0], + "4.0": [512.0, 128.0], +} + + +# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps +def retrieve_timesteps( + scheduler, + num_inference_steps: Optional[int] = None, + device: Optional[Union[str, torch.device]] = None, + timesteps: Optional[List[int]] = None, + sigmas: Optional[List[float]] = None, + **kwargs, +): + """ + Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles + custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`. + + Args: + scheduler (`SchedulerMixin`): + The scheduler to get timesteps from. + num_inference_steps (`int`): + The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps` + must be `None`. + device (`str` or `torch.device`, *optional*): + The device to which the timesteps should be moved to. If `None`, the timesteps are not moved. + timesteps (`List[int]`, *optional*): + Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed, + `num_inference_steps` and `sigmas` must be `None`. + sigmas (`List[float]`, *optional*): + Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed, + `num_inference_steps` and `timesteps` must be `None`. + + Returns: + `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the + second element is the number of inference steps. + """ + if timesteps is not None and sigmas is not None: + raise ValueError("Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values") + if timesteps is not None: + accepts_timesteps = "timesteps" in set(inspect.signature(scheduler.set_timesteps).parameters.keys()) + if not accepts_timesteps: + raise ValueError( + f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom" + f" timestep schedules. Please check whether you are using the correct scheduler." + ) + scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs) + timesteps = scheduler.timesteps + num_inference_steps = len(timesteps) + elif sigmas is not None: + accept_sigmas = "sigmas" in set(inspect.signature(scheduler.set_timesteps).parameters.keys()) + if not accept_sigmas: + raise ValueError( + f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom" + f" sigmas schedules. Please check whether you are using the correct scheduler." + ) + scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs) + timesteps = scheduler.timesteps + num_inference_steps = len(timesteps) + else: + scheduler.set_timesteps(num_inference_steps, device=device, **kwargs) + timesteps = scheduler.timesteps + return timesteps, num_inference_steps + + +class LuminaText2ImgPipeline(DiffusionPipeline): + r""" + Pipeline for text-to-image generation using Lumina-T2I. + + This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the + library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) + + Args: + vae ([`AutoencoderKL`]): + Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. + text_encoder ([`T5EncoderModel`]): + Frozen text-encoder. Lumina-T2I uses + [T5](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5EncoderModel), specifically the + [t5-v1_1-xxl](https://huggingface.co/Alpha-VLLM/tree/main/t5-v1_1-xxl) variant. + tokenizer (`T5Tokenizer`): + Tokenizer of class + [T5Tokenizer](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5Tokenizer). + transformer ([`Transformer2DModel`]): + A text conditioned `Transformer2DModel` to denoise the encoded image latents. + scheduler ([`SchedulerMixin`]): + A scheduler to be used in combination with `transformer` to denoise the encoded image latents. + """ + + bad_punct_regex = re.compile( + r"[" + + "#®•©™&@·º½¾¿¡§~" + + r"\)" + + r"\(" + + r"\]" + + r"\[" + + r"\}" + + r"\{" + + r"\|" + + "\\" + + r"\/" + + r"\*" + + r"]{1,}" + ) # noqa + + _optional_components = ["tokenizer", "text_encoder"] + model_cpu_offload_seq = "text_encoder->transformer->vae" + + def __init__( + self, + tokenizer: T5Tokenizer, + text_encoder: T5EncoderModel, + vae: AutoencoderKL, + transformer: Transformer2DModel, + scheduler: DPMSolverMultistepScheduler, + ): + super().__init__() + + self.register_modules( + tokenizer=tokenizer, text_encoder=text_encoder, vae=vae, transformer=transformer, scheduler=scheduler + ) + + self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) + self.image_processor = LuminaImageProcessor(vae_scale_factor=self.vae_scale_factor) + + # Adapted from diffusers.pipelines.deepfloyd_if.pipeline_if.encode_prompt + def encode_prompt( + self, + prompt: Union[str, List[str]], + do_classifier_free_guidance: bool = True, + negative_prompt: str = "", + num_images_per_prompt: int = 1, + device: Optional[torch.device] = None, + prompt_embeds: Optional[torch.Tensor] = None, + negative_prompt_embeds: Optional[torch.Tensor] = None, + prompt_attention_mask: Optional[torch.Tensor] = None, + negative_prompt_attention_mask: Optional[torch.Tensor] = None, + clean_caption: bool = False, + max_sequence_length: int = 120, + **kwargs, + ): + r""" + Encodes the prompt into text encoder hidden states. + + Args: + prompt (`str` or `List[str]`, *optional*): + prompt to be encoded + negative_prompt (`str` or `List[str]`, *optional*): + The prompt not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds` + instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). For + Lumina-T2I, this should be "". + do_classifier_free_guidance (`bool`, *optional*, defaults to `True`): + whether to use classifier free guidance or not + num_images_per_prompt (`int`, *optional*, defaults to 1): + number of images that should be generated per prompt + device: (`torch.device`, *optional*): + torch device to place the resulting embeddings on + prompt_embeds (`torch.Tensor`, *optional*): + Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not + provided, text embeddings will be generated from `prompt` input argument. + negative_prompt_embeds (`torch.Tensor`, *optional*): + Pre-generated negative text embeddings. For Lumina-T2I, it's should be the embeddings of the "" + string. + clean_caption (`bool`, defaults to `False`): + If `True`, the function will preprocess and clean the provided caption before encoding. + max_sequence_length (`int`, defaults to 120): Maximum sequence length to use for the prompt. + """ + + if "mask_feature" in kwargs: + deprecation_message = "The use of `mask_feature` is deprecated. It is no longer used in any computation and that doesn't affect the end results. It will be removed in a future version." + deprecate("mask_feature", "1.0.0", deprecation_message, standard_warn=False) + + if device is None: + device = self._execution_device + + if prompt is not None and isinstance(prompt, str): + batch_size = 1 + elif prompt is not None and isinstance(prompt, list): + batch_size = len(prompt) + else: + batch_size = prompt_embeds.shape[0] + + # See Section 3.1. of the paper. + max_length = max_sequence_length + + if prompt_embeds is None: + prompt = self._text_preprocessing(prompt, clean_caption=clean_caption) + text_inputs = self.tokenizer( + prompt, + padding="max_length", + max_length=max_length, + truncation=True, + add_special_tokens=True, + return_tensors="pt", + ) + text_input_ids = text_inputs.input_ids + untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids + + if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal( + text_input_ids, untruncated_ids + ): + removed_text = self.tokenizer.batch_decode(untruncated_ids[:, max_length - 1 : -1]) + logger.warning( + "The following part of your input was truncated because T5 can only handle sequences up to" + f" {max_length} tokens: {removed_text}" + ) + + prompt_attention_mask = text_inputs.attention_mask + prompt_attention_mask = prompt_attention_mask.to(device) + + prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=prompt_attention_mask) + prompt_embeds = prompt_embeds[0] + + if self.text_encoder is not None: + dtype = self.text_encoder.dtype + elif self.transformer is not None: + dtype = self.transformer.dtype + else: + dtype = None + + prompt_embeds = prompt_embeds.to(dtype=dtype, device=device) + + bs_embed, seq_len, _ = prompt_embeds.shape + # duplicate text embeddings and attention mask for each generation per prompt, using mps friendly method + prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1) + prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1) + prompt_attention_mask = prompt_attention_mask.view(bs_embed, -1) + prompt_attention_mask = prompt_attention_mask.repeat(num_images_per_prompt, 1) + + # get unconditional embeddings for classifier free guidance + if do_classifier_free_guidance and negative_prompt_embeds is None: + uncond_tokens = [negative_prompt] * batch_size + uncond_tokens = self._text_preprocessing(uncond_tokens, clean_caption=clean_caption) + max_length = prompt_embeds.shape[1] + uncond_input = self.tokenizer( + uncond_tokens, + padding="max_length", + max_length=max_length, + truncation=True, + return_attention_mask=True, + add_special_tokens=True, + return_tensors="pt", + ) + negative_prompt_attention_mask = uncond_input.attention_mask + negative_prompt_attention_mask = negative_prompt_attention_mask.to(device) + + negative_prompt_embeds = self.text_encoder( + uncond_input.input_ids.to(device), attention_mask=negative_prompt_attention_mask + ) + negative_prompt_embeds = negative_prompt_embeds[0] + + if do_classifier_free_guidance: + # duplicate unconditional embeddings for each generation per prompt, using mps friendly method + seq_len = negative_prompt_embeds.shape[1] + + negative_prompt_embeds = negative_prompt_embeds.to(dtype=dtype, device=device) + + negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1) + negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1) + + negative_prompt_attention_mask = negative_prompt_attention_mask.view(bs_embed, -1) + negative_prompt_attention_mask = negative_prompt_attention_mask.repeat(num_images_per_prompt, 1) + else: + negative_prompt_embeds = None + negative_prompt_attention_mask = None + + return prompt_embeds, prompt_attention_mask, negative_prompt_embeds, negative_prompt_attention_mask + + # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs + def prepare_extra_step_kwargs(self, generator, eta): + # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature + # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. + # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 + # and should be between [0, 1] + + accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) + extra_step_kwargs = {} + if accepts_eta: + extra_step_kwargs["eta"] = eta + + # check if the scheduler accepts generator + accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys()) + if accepts_generator: + extra_step_kwargs["generator"] = generator + return extra_step_kwargs + + def check_inputs( + self, + prompt, + height, + width, + negative_prompt, + callback_steps, + prompt_embeds=None, + negative_prompt_embeds=None, + prompt_attention_mask=None, + negative_prompt_attention_mask=None, + ): + if height % 8 != 0 or width % 8 != 0: + raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.") + + if (callback_steps is None) or ( + callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0) + ): + raise ValueError( + f"`callback_steps` has to be a positive integer but is {callback_steps} of type" + f" {type(callback_steps)}." + ) + + if prompt is not None and prompt_embeds is not None: + raise ValueError( + f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to" + " only forward one of the two." + ) + elif prompt is None and prompt_embeds is None: + raise ValueError( + "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined." + ) + elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)): + raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") + + if prompt is not None and negative_prompt_embeds is not None: + raise ValueError( + f"Cannot forward both `prompt`: {prompt} and `negative_prompt_embeds`:" + f" {negative_prompt_embeds}. Please make sure to only forward one of the two." + ) + + if negative_prompt is not None and negative_prompt_embeds is not None: + raise ValueError( + f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:" + f" {negative_prompt_embeds}. Please make sure to only forward one of the two." + ) + + if prompt_embeds is not None and prompt_attention_mask is None: + raise ValueError("Must provide `prompt_attention_mask` when specifying `prompt_embeds`.") + + if negative_prompt_embeds is not None and negative_prompt_attention_mask is None: + raise ValueError("Must provide `negative_prompt_attention_mask` when specifying `negative_prompt_embeds`.") + + if prompt_embeds is not None and negative_prompt_embeds is not None: + if prompt_embeds.shape != negative_prompt_embeds.shape: + raise ValueError( + "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but" + f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`" + f" {negative_prompt_embeds.shape}." + ) + if prompt_attention_mask.shape != negative_prompt_attention_mask.shape: + raise ValueError( + "`prompt_attention_mask` and `negative_prompt_attention_mask` must have the same shape when passed directly, but" + f" got: `prompt_attention_mask` {prompt_attention_mask.shape} != `negative_prompt_attention_mask`" + f" {negative_prompt_attention_mask.shape}." + ) + + # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline._text_preprocessing + def _text_preprocessing(self, text, clean_caption=False): + if clean_caption and not is_bs4_available(): + logger.warning(BACKENDS_MAPPING["bs4"][-1].format("Setting `clean_caption=True`")) + logger.warning("Setting `clean_caption` to False...") + clean_caption = False + + if clean_caption and not is_ftfy_available(): + logger.warning(BACKENDS_MAPPING["ftfy"][-1].format("Setting `clean_caption=True`")) + logger.warning("Setting `clean_caption` to False...") + clean_caption = False + + if not isinstance(text, (tuple, list)): + text = [text] + + def process(text: str): + if clean_caption: + text = self._clean_caption(text) + text = self._clean_caption(text) + else: + text = text.lower().strip() + return text + + return [process(t) for t in text] + + # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline._clean_caption + def _clean_caption(self, caption): + caption = str(caption) + caption = ul.unquote_plus(caption) + caption = caption.strip().lower() + caption = re.sub("", "person", caption) + # urls: + caption = re.sub( + r"\b((?:https?:(?:\/{1,3}|[a-zA-Z0-9%])|[a-zA-Z0-9.\-]+[.](?:com|co|ru|net|org|edu|gov|it)[\w/-]*\b\/?(?!@)))", # noqa + "", + caption, + ) # regex for urls + caption = re.sub( + r"\b((?:www:(?:\/{1,3}|[a-zA-Z0-9%])|[a-zA-Z0-9.\-]+[.](?:com|co|ru|net|org|edu|gov|it)[\w/-]*\b\/?(?!@)))", # noqa + "", + caption, + ) # regex for urls + # html: + caption = BeautifulSoup(caption, features="html.parser").text + + # @ + caption = re.sub(r"@[\w\d]+\b", "", caption) + + # 31C0—31EF CJK Strokes + # 31F0—31FF Katakana Phonetic Extensions + # 3200—32FF Enclosed CJK Letters and Months + # 3300—33FF CJK Compatibility + # 3400—4DBF CJK Unified Ideographs Extension A + # 4DC0—4DFF Yijing Hexagram Symbols + # 4E00—9FFF CJK Unified Ideographs + caption = re.sub(r"[\u31c0-\u31ef]+", "", caption) + caption = re.sub(r"[\u31f0-\u31ff]+", "", caption) + caption = re.sub(r"[\u3200-\u32ff]+", "", caption) + caption = re.sub(r"[\u3300-\u33ff]+", "", caption) + caption = re.sub(r"[\u3400-\u4dbf]+", "", caption) + caption = re.sub(r"[\u4dc0-\u4dff]+", "", caption) + caption = re.sub(r"[\u4e00-\u9fff]+", "", caption) + ####################################################### + + # все виды тире / all types of dash --> "-" + caption = re.sub( + r"[\u002D\u058A\u05BE\u1400\u1806\u2010-\u2015\u2E17\u2E1A\u2E3A\u2E3B\u2E40\u301C\u3030\u30A0\uFE31\uFE32\uFE58\uFE63\uFF0D]+", # noqa + "-", + caption, + ) + + # кавычки к одному стандарту + caption = re.sub(r"[`´«»“”¨]", '"', caption) + caption = re.sub(r"[‘’]", "'", caption) + + # " + caption = re.sub(r""?", "", caption) + # & + caption = re.sub(r"&", "", caption) + + # ip adresses: + caption = re.sub(r"\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}", " ", caption) + + # article ids: + caption = re.sub(r"\d:\d\d\s+$", "", caption) + + # \n + caption = re.sub(r"\\n", " ", caption) + + # "#123" + caption = re.sub(r"#\d{1,3}\b", "", caption) + # "#12345.." + caption = re.sub(r"#\d{5,}\b", "", caption) + # "123456.." + caption = re.sub(r"\b\d{6,}\b", "", caption) + # filenames: + caption = re.sub(r"[\S]+\.(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)", "", caption) + + # + caption = re.sub(r"[\"\']{2,}", r'"', caption) # """AUSVERKAUFT""" + caption = re.sub(r"[\.]{2,}", r" ", caption) # """AUSVERKAUFT""" + + caption = re.sub(self.bad_punct_regex, r" ", caption) # ***AUSVERKAUFT***, #AUSVERKAUFT + caption = re.sub(r"\s+\.\s+", r" ", caption) # " . " + + # this-is-my-cute-cat / this_is_my_cute_cat + regex2 = re.compile(r"(?:\-|\_)") + if len(re.findall(regex2, caption)) > 3: + caption = re.sub(regex2, " ", caption) + + caption = ftfy.fix_text(caption) + caption = html.unescape(html.unescape(caption)) + + caption = re.sub(r"\b[a-zA-Z]{1,3}\d{3,15}\b", "", caption) # jc6640 + caption = re.sub(r"\b[a-zA-Z]+\d+[a-zA-Z]+\b", "", caption) # jc6640vc + caption = re.sub(r"\b\d+[a-zA-Z]+\d+\b", "", caption) # 6640vc231 + + caption = re.sub(r"(worldwide\s+)?(free\s+)?shipping", "", caption) + caption = re.sub(r"(free\s)?download(\sfree)?", "", caption) + caption = re.sub(r"\bclick\b\s(?:for|on)\s\w+", "", caption) + caption = re.sub(r"\b(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)(\simage[s]?)?", "", caption) + caption = re.sub(r"\bpage\s+\d+\b", "", caption) + + caption = re.sub(r"\b\d*[a-zA-Z]+\d+[a-zA-Z]+\d+[a-zA-Z\d]*\b", r" ", caption) # j2d1a2a... + + caption = re.sub(r"\b\d+\.?\d*[xх×]\d+\.?\d*\b", "", caption) + + caption = re.sub(r"\b\s+\:\s+", r": ", caption) + caption = re.sub(r"(\D[,\./])\b", r"\1 ", caption) + caption = re.sub(r"\s+", " ", caption) + + caption.strip() + + caption = re.sub(r"^[\"\']([\w\W]+)[\"\']$", r"\1", caption) + caption = re.sub(r"^[\'\_,\-\:;]", r"", caption) + caption = re.sub(r"[\'\_,\-\:\-\+]$", r"", caption) + caption = re.sub(r"^\.\S+$", "", caption) + + return caption.strip() + + # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents + def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None): + shape = ( + batch_size, + num_channels_latents, + int(height) // self.vae_scale_factor, + int(width) // self.vae_scale_factor, + ) + if isinstance(generator, list) and len(generator) != batch_size: + raise ValueError( + f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" + f" size of {batch_size}. Make sure the batch size matches the length of the generators." + ) + + if latents is None: + latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype) + else: + latents = latents.to(device) + + # scale the initial noise by the standard deviation required by the scheduler + latents = latents * self.scheduler.init_noise_sigma + return latents + + @torch.no_grad() + @replace_example_docstring(EXAMPLE_DOC_STRING) + def __call__( + self, + prompt: Union[str, List[str]] = None, + negative_prompt: str = "", + num_inference_steps: int = 20, + timesteps: List[int] = None, + sigmas: List[float] = None, + guidance_scale: float = 4.5, + num_images_per_prompt: Optional[int] = 1, + height: Optional[int] = None, + width: Optional[int] = None, + eta: float = 0.0, + generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, + latents: Optional[torch.Tensor] = None, + prompt_embeds: Optional[torch.Tensor] = None, + prompt_attention_mask: Optional[torch.Tensor] = None, + negative_prompt_embeds: Optional[torch.Tensor] = None, + negative_prompt_attention_mask: Optional[torch.Tensor] = None, + output_type: Optional[str] = "pil", + return_dict: bool = True, + callback: Optional[Callable[[int, int, torch.Tensor], None]] = None, + callback_steps: int = 1, + clean_caption: bool = True, + use_resolution_binning: bool = True, + max_sequence_length: int = 120, + **kwargs, + ) -> Union[ImagePipelineOutput, Tuple]: + """ + Function invoked when calling the pipeline for generation. + + Args: + prompt (`str` or `List[str]`, *optional*): + The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`. + instead. + negative_prompt (`str` or `List[str]`, *optional*): + The prompt or prompts not to guide the image generation. If not defined, one has to pass + `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is + less than `1`). + num_inference_steps (`int`, *optional*, defaults to 100): + The number of denoising steps. More denoising steps usually lead to a higher quality image at the + expense of slower inference. + timesteps (`List[int]`, *optional*): + Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument + in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is + passed will be used. Must be in descending order. + sigmas (`List[float]`, *optional*): + Custom sigmas to use for the denoising process with schedulers which support a `sigmas` argument in + their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed + will be used. + guidance_scale (`float`, *optional*, defaults to 4.5): + Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). + `guidance_scale` is defined as `w` of equation 2. of [Imagen + Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > + 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, + usually at the expense of lower image quality. + num_images_per_prompt (`int`, *optional*, defaults to 1): + The number of images to generate per prompt. + height (`int`, *optional*, defaults to self.unet.config.sample_size): + The height in pixels of the generated image. + width (`int`, *optional*, defaults to self.unet.config.sample_size): + The width in pixels of the generated image. + eta (`float`, *optional*, defaults to 0.0): + Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to + [`schedulers.DDIMScheduler`], will be ignored for others. + generator (`torch.Generator` or `List[torch.Generator]`, *optional*): + One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html) + to make generation deterministic. + latents (`torch.Tensor`, *optional*): + Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image + generation. Can be used to tweak the same generation with different prompts. If not provided, a latents + tensor will ge generated by sampling using the supplied random `generator`. + prompt_embeds (`torch.Tensor`, *optional*): + Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not + provided, text embeddings will be generated from `prompt` input argument. + prompt_attention_mask (`torch.Tensor`, *optional*): Pre-generated attention mask for text embeddings. + negative_prompt_embeds (`torch.Tensor`, *optional*): + Pre-generated negative text embeddings. For Lumina-T2I this negative prompt should be "". If not + provided, negative_prompt_embeds will be generated from `negative_prompt` input argument. + negative_prompt_attention_mask (`torch.Tensor`, *optional*): + Pre-generated attention mask for negative text embeddings. + output_type (`str`, *optional*, defaults to `"pil"`): + The output format of the generate image. Choose between + [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. + return_dict (`bool`, *optional*, defaults to `True`): + Whether or not to return a [`~pipelines.stable_diffusion.IFPipelineOutput`] instead of a plain tuple. + callback (`Callable`, *optional*): + A function that will be called every `callback_steps` steps during inference. The function will be + called with the following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`. + callback_steps (`int`, *optional*, defaults to 1): + The frequency at which the `callback` function will be called. If not specified, the callback will be + called at every step. + clean_caption (`bool`, *optional*, defaults to `True`): + Whether or not to clean the caption before creating embeddings. Requires `beautifulsoup4` and `ftfy` to + be installed. If the dependencies are not installed, the embeddings will be created from the raw + prompt. + use_resolution_binning (`bool` defaults to `True`): + If set to `True`, the requested height and width are first mapped to the closest resolutions using + `ASPECT_RATIO_1024_BIN`. After the produced latents are decoded into images, they are resized back to + the requested resolution. Useful for generating non-square images. + max_sequence_length (`int` defaults to 120): Maximum sequence length to use with the `prompt`. + + Examples: + + Returns: + [`~pipelines.ImagePipelineOutput`] or `tuple`: + If `return_dict` is `True`, [`~pipelines.ImagePipelineOutput`] is returned, otherwise a `tuple` is + returned where the first element is a list with the generated images + """ + if "mask_feature" in kwargs: + deprecation_message = "The use of `mask_feature` is deprecated. It is no longer used in any computation and that doesn't affect the end results. It will be removed in a future version." + deprecate("mask_feature", "1.0.0", deprecation_message, standard_warn=False) + # 1. Check inputs. Raise error if not correct + height = height or self.transformer.config.sample_size * self.vae_scale_factor + width = width or self.transformer.config.sample_size * self.vae_scale_factor + if use_resolution_binning: + if self.transformer.config.sample_size == 128: + aspect_ratio_bin = ASPECT_RATIO_1024_BIN + elif self.transformer.config.sample_size == 64: + aspect_ratio_bin = ASPECT_RATIO_512_BIN + elif self.transformer.config.sample_size == 32: + aspect_ratio_bin = ASPECT_RATIO_256_BIN + else: + raise ValueError("Invalid sample size") + orig_height, orig_width = height, width + height, width = self.image_processor.classify_height_width_bin(height, width, ratios=aspect_ratio_bin) + + self.check_inputs( + prompt, + height, + width, + negative_prompt, + callback_steps, + prompt_embeds, + negative_prompt_embeds, + prompt_attention_mask, + negative_prompt_attention_mask, + ) + + # 2. Default height and width to transformer + if prompt is not None and isinstance(prompt, str): + batch_size = 1 + elif prompt is not None and isinstance(prompt, list): + batch_size = len(prompt) + else: + batch_size = prompt_embeds.shape[0] + + device = self._execution_device + + # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) + # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` + # corresponds to doing no classifier free guidance. + do_classifier_free_guidance = guidance_scale > 1.0 + + # 3. Encode input prompt + ( + prompt_embeds, + prompt_attention_mask, + negative_prompt_embeds, + negative_prompt_attention_mask, + ) = self.encode_prompt( + prompt, + do_classifier_free_guidance, + negative_prompt=negative_prompt, + num_images_per_prompt=num_images_per_prompt, + device=device, + prompt_embeds=prompt_embeds, + negative_prompt_embeds=negative_prompt_embeds, + prompt_attention_mask=prompt_attention_mask, + negative_prompt_attention_mask=negative_prompt_attention_mask, + clean_caption=clean_caption, + max_sequence_length=max_sequence_length, + ) + if do_classifier_free_guidance: + prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0) + prompt_attention_mask = torch.cat([negative_prompt_attention_mask, prompt_attention_mask], dim=0) + + # 4. Prepare timesteps + timesteps, num_inference_steps = retrieve_timesteps( + self.scheduler, num_inference_steps, device, timesteps, sigmas + ) + + # 5. Prepare latents. + latent_channels = self.transformer.config.in_channels + latents = self.prepare_latents( + batch_size * num_images_per_prompt, + latent_channels, + height, + width, + prompt_embeds.dtype, + device, + generator, + latents, + ) + + # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline + extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) + + # 6.1 Prepare micro-conditions. + added_cond_kwargs = {"resolution": None, "aspect_ratio": None} + if self.transformer.config.sample_size == 128: + resolution = torch.tensor([height, width]).repeat(batch_size * num_images_per_prompt, 1) + aspect_ratio = torch.tensor([float(height / width)]).repeat(batch_size * num_images_per_prompt, 1) + resolution = resolution.to(dtype=prompt_embeds.dtype, device=device) + aspect_ratio = aspect_ratio.to(dtype=prompt_embeds.dtype, device=device) + + if do_classifier_free_guidance: + resolution = torch.cat([resolution, resolution], dim=0) + aspect_ratio = torch.cat([aspect_ratio, aspect_ratio], dim=0) + + added_cond_kwargs = {"resolution": resolution, "aspect_ratio": aspect_ratio} + + # 7. Denoising loop + num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0) + + with self.progress_bar(total=num_inference_steps) as progress_bar: + for i, t in enumerate(timesteps): + latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents + latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) + + current_timestep = t + if not torch.is_tensor(current_timestep): + # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can + # This would be a good case for the `match` statement (Python 3.10+) + is_mps = latent_model_input.device.type == "mps" + if isinstance(current_timestep, float): + dtype = torch.float32 if is_mps else torch.float64 + else: + dtype = torch.int32 if is_mps else torch.int64 + current_timestep = torch.tensor([current_timestep], dtype=dtype, device=latent_model_input.device) + elif len(current_timestep.shape) == 0: + current_timestep = current_timestep[None].to(latent_model_input.device) + # broadcast to batch dimension in a way that's compatible with ONNX/Core ML + current_timestep = current_timestep.expand(latent_model_input.shape[0]) + + # predict noise model_output + noise_pred = self.transformer( + latent_model_input, + encoder_hidden_states=prompt_embeds, + encoder_attention_mask=prompt_attention_mask, + timestep=current_timestep, + added_cond_kwargs=added_cond_kwargs, + return_dict=False, + )[0] + + # perform guidance + if do_classifier_free_guidance: + noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) + noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) + + # learned sigma + if self.transformer.config.out_channels // 2 == latent_channels: + noise_pred = noise_pred.chunk(2, dim=1)[0] + else: + noise_pred = noise_pred + + # compute previous image: x_t -> x_t-1 + if num_inference_steps == 1: + # For DMD one step sampling: https://arxiv.org/abs/2311.18828 + latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).pred_original_sample + else: + latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0] + + # call the callback, if provided + if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0): + progress_bar.update() + if callback is not None and i % callback_steps == 0: + step_idx = i // getattr(self.scheduler, "order", 1) + callback(step_idx, t, latents) + + if not output_type == "latent": + image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0] + if use_resolution_binning: + image = self.image_processor.resize_and_crop_tensor(image, orig_width, orig_height) + else: + image = latents + + if not output_type == "latent": + image = self.image_processor.postprocess(image, output_type=output_type) + + # Offload all models + self.maybe_free_model_hooks() + + if not return_dict: + return (image,) + + return ImagePipelineOutput(images=image) From 6db8b822b5cd0392dcf080b0bc228d0fda7805de Mon Sep 17 00:00:00 2001 From: PommesPeter Date: Sun, 26 May 2024 02:17:01 +0800 Subject: [PATCH 07/92] added flag-dit and next-dit model --- src/diffusers/pipelines/lumina/__init__.py | 2 +- .../pipelines/lumina/modeling_flag_dit.py | 999 ++++++++++++++++ .../pipelines/lumina/modeling_next_dit.py | 1032 +++++++++++++++++ ...eline_lumina_t2i.py => pipeline_lumina.py} | 302 +++-- 4 files changed, 2229 insertions(+), 106 deletions(-) create mode 100644 src/diffusers/pipelines/lumina/modeling_flag_dit.py create mode 100644 src/diffusers/pipelines/lumina/modeling_next_dit.py rename src/diffusers/pipelines/lumina/{pipeline_lumina_t2i.py => pipeline_lumina.py} (81%) diff --git a/src/diffusers/pipelines/lumina/__init__.py b/src/diffusers/pipelines/lumina/__init__.py index 32503a6442c7..38ad13939515 100644 --- a/src/diffusers/pipelines/lumina/__init__.py +++ b/src/diffusers/pipelines/lumina/__init__.py @@ -1 +1 @@ -from .pipeline_lumina_t2i import LuminaText2ImgPipeline \ No newline at end of file +from .pipeline_lumina import LuminaText2ImgPipeline \ No newline at end of file diff --git a/src/diffusers/pipelines/lumina/modeling_flag_dit.py b/src/diffusers/pipelines/lumina/modeling_flag_dit.py new file mode 100644 index 000000000000..e57b6fe1d445 --- /dev/null +++ b/src/diffusers/pipelines/lumina/modeling_flag_dit.py @@ -0,0 +1,999 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. +# -------------------------------------------------------- +# References: +# GLIDE: https://github.com/openai/glide-text2im +# MAE: https://github.com/facebookresearch/mae/blob/main/models_mae.py +# -------------------------------------------------------- + +import functools +import math +from typing import Optional, Tuple, List + +from .components import RMSNorm +from flash_attn import flash_attn_varlen_func +from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa +import torch +import torch.nn as nn +import torch.nn.functional as F + +from ...configuration_utils import ConfigMixin, register_to_config +from ...models import ModelMixin +from ...models.transformers.transformer_2d import Transformer2DModelOutput +from ...utils import logging + + +def modulate(x, shift, scale): + return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1) + + +class RMSNorm(torch.nn.Module): + def __init__(self, dim: int, eps: float = 1e-6): + """ + Initialize the RMSNorm normalization layer. + + Args: + dim (int): The dimension of the input tensor. + eps (float, optional): A small value added to the denominator for numerical stability. Default is 1e-6. + + Attributes: + eps (float): A small value added to the denominator for numerical stability. + weight (nn.Parameter): Learnable scaling parameter. + + """ + super().__init__() + self.eps = eps + self.weight = nn.Parameter(torch.ones(dim)) + + def _norm(self, x): + """ + Apply the RMSNorm normalization to the input tensor. + + Args: + x (torch.Tensor): The input tensor. + + Returns: + torch.Tensor: The normalized tensor. + + """ + return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps) + + def forward(self, x): + """ + Forward pass through the RMSNorm layer. + + Args: + x (torch.Tensor): The input tensor. + + Returns: + torch.Tensor: The output tensor after applying RMSNorm. + + """ + output = self._norm(x.float()).type_as(x) + return output * self.weight + + +class TimestepEmbedder(nn.Module): + """ + Embeds scalar timesteps into vector representations. + """ + + def __init__(self, hidden_size, frequency_embedding_size=256): + super().__init__() + self.mlp = nn.Sequential( + nn.Linear( + frequency_embedding_size, + hidden_size, + bias=True, + ), + nn.SiLU(), + nn.Linear( + hidden_size, + hidden_size, + bias=True, + ), + ) + self.frequency_embedding_size = frequency_embedding_size + + @staticmethod + def timestep_embedding(t, dim, max_period=10000): + """ + Create sinusoidal timestep embeddings. + :param t: a 1-D Tensor of N indices, one per batch element. + These may be fractional. + :param dim: the dimension of the output. + :param max_period: controls the minimum frequency of the embeddings. + :return: an (N, D) Tensor of positional embeddings. + """ + # https://github.com/openai/glide-text2im/blob/main/glide_text2im/nn.py + half = dim // 2 + freqs = torch.exp( + -math.log(max_period) + * torch.arange(start=0, end=half, dtype=torch.float32) + / half + ).to(device=t.device) + args = t[:, None].float() * freqs[None] + embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) + if dim % 2: + embedding = torch.cat( + [embedding, torch.zeros_like(embedding[:, :1])], dim=-1 + ) + return embedding + + def forward(self, t): + t_freq = self.timestep_embedding(t, self.frequency_embedding_size) + t_emb = self.mlp(t_freq.to(self.mlp[0].weight.dtype)) + return t_emb + + +class LabelEmbedder(nn.Module): + r"""Embeds class labels into vector representations. Also handles label + dropout for classifier-free guidance. + """ + + def __init__(self, num_classes, hidden_size, dropout_prob): + super().__init__() + use_cfg_embedding = int(dropout_prob > 0) + self.embedding_table = nn.Embedding(num_classes + use_cfg_embedding) + self.num_classes = num_classes + self.dropout_prob = dropout_prob + + def token_drop(self, labels, force_drop_ids=None): + """ + Drops labels to enable classifier-free guidance. + """ + if force_drop_ids is None: + drop_ids = ( + torch.rand(labels.shape[0], device=labels.device) < self.dropout_prob + ) + drop_ids = drop_ids.cuda() + drop_ids = drop_ids.to(labels.device) + else: + drop_ids = force_drop_ids == 1 + labels = torch.where(drop_ids, self.num_classes, labels) + return labels + + def forward(self, labels, train, force_drop_ids=None): + use_dropout = self.dropout_prob > 0 + if (train and use_dropout) or (force_drop_ids is not None): + labels = self.token_drop(labels, force_drop_ids) + embeddings = self.embedding_table(labels) + return embeddings + + +############################################################################# +# Core DiT Model # +############################################################################# + + +class Attention(nn.Module): + """Multi-head attention module.""" + + def __init__( + self, + dim: int, + n_heads: int, + n_kv_heads: Optional[int], + qk_norm: bool, + y_dim: int, + ): + """ + Initialize the Attention module. + + Args: + dim (int): Number of input dimensions. + n_heads (int): Number of heads. + n_kv_heads (Optional[int]): Number of kv heads, if using GQA. + + Attributes: + n_kv_heads (int): Number of key and value heads. + n_heads (int): Number of local query heads. + n_kv_heads (int): Number of local key and value heads. + n_rep (int): Number of repetitions for local heads. + head_dim (int): Dimension size of each attention head. + wq (nn.Linear): Linear transformation for queries. + wk (nn.Linear): Linear transformation for keys. + wv (nn.Linear): Linear transformation for values. + wo (nn.Linear): Linear transformation for output. + cache_k (torch.Tensor): Cached keys for attention. + cache_v (torch.Tensor): Cached values for attention. + + """ + super().__init__() + self.n_kv_heads = n_heads if n_kv_heads is None else n_kv_heads + self.n_heads = n_heads + self.n_kv_heads = n_kv_heads + self.n_rep = n_heads + self.head_dim = dim // n_heads + + self.wq = nn.Linear(dim, n_heads * self.head_dim, bias=False) + self.wk = nn.Linear(dim, self.n_kv_heads * self.head_dim, bias=False) + self.wv = nn.Linear(dim, self.n_kv_heads * self.head_dim, bias=False) + if y_dim > 0: + self.wk_y = nn.Linear(y_dim, self.n_kv_heads * self.head_dim, bias=False) + self.wv_y = nn.Linear(y_dim, self.n_kv_heads * self.head_dim, bias=False) + self.gate = nn.Parameter(torch.zeros([self.n_heads])) + + self.wo = nn.Linear(n_heads * self.head_dim, dim, bias=False) + + if qk_norm: + self.q_norm = nn.LayerNorm(self.n_heads * self.head_dim) + self.k_norm = nn.LayerNorm(self.n_kv_heads * self.head_dim) + if y_dim > 0: + self.ky_norm = nn.LayerNorm(self.n_kv_heads * self.head_dim) + else: + self.ky_norm = nn.Identity() + else: + self.q_norm = self.k_norm = nn.Identity() + self.ky_norm = nn.Identity() + + # for proportional attention computation + self.base_seqlen = None + self.proportional_attn = False + + @staticmethod + def reshape_for_broadcast(freqs_cis: torch.Tensor, x: torch.Tensor): + """ + Reshape frequency tensor for broadcasting it with another tensor. + + This function reshapes the frequency tensor to have the same shape as + the target tensor 'x' for the purpose of broadcasting the frequency + tensor during element-wise operations. + + Args: + freqs_cis (torch.Tensor): Frequency tensor to be reshaped. + x (torch.Tensor): Target tensor for broadcasting compatibility. + + Returns: + torch.Tensor: Reshaped frequency tensor. + + Raises: + AssertionError: If the frequency tensor doesn't match the expected + shape. + AssertionError: If the target tensor 'x' doesn't have the expected + number of dimensions. + """ + ndim = x.ndim + assert 0 <= 1 < ndim + assert freqs_cis.shape == (x.shape[1], x.shape[-1]) + shape = [d if i == 1 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)] + return freqs_cis.view(*shape) + + @staticmethod + def apply_rotary_emb( + xq: torch.Tensor, + xk: torch.Tensor, + freqs_cis: torch.Tensor, + ) -> Tuple[torch.Tensor, torch.Tensor]: + """ + Apply rotary embeddings to input tensors using the given frequency + tensor. + + This function applies rotary embeddings to the given query 'xq' and + key 'xk' tensors using the provided frequency tensor 'freqs_cis'. The + input tensors are reshaped as complex numbers, and the frequency tensor + is reshaped for broadcasting compatibility. The resulting tensors + contain rotary embeddings and are returned as real tensors. + + Args: + xq (torch.Tensor): Query tensor to apply rotary embeddings. + xk (torch.Tensor): Key tensor to apply rotary embeddings. + freqs_cis (torch.Tensor): Precomputed frequency tensor for complex + exponentials. + + Returns: + Tuple[torch.Tensor, torch.Tensor]: Tuple of modified query tensor + and key tensor with rotary embeddings. + """ + with torch.cuda.amp.autocast(enabled=False): + xq_ = torch.view_as_complex(xq.float().reshape(*xq.shape[:-1], -1, 2)) + xk_ = torch.view_as_complex(xk.float().reshape(*xk.shape[:-1], -1, 2)) + freqs_cis = Attention.reshape_for_broadcast(freqs_cis, xq_) + xq_out = torch.view_as_real(xq_ * freqs_cis).flatten(3) + xk_out = torch.view_as_real(xk_ * freqs_cis).flatten(3) + return xq_out.type_as(xq), xk_out.type_as(xk) + + # copied from huggingface modeling_llama.py + def _upad_input( + self, query_layer, key_layer, value_layer, attention_mask, query_length + ): + + def _get_unpad_data(attention_mask): + seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32) + indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten() + max_seqlen_in_batch = seqlens_in_batch.max().item() + cu_seqlens = F.pad( + torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0) + ) + return ( + indices, + cu_seqlens, + max_seqlen_in_batch, + ) + + indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask) + batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape + + key_layer = index_first_axis( + key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), + indices_k, + ) + value_layer = index_first_axis( + value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), + indices_k, + ) + if query_length == kv_seq_len: + query_layer = index_first_axis( + query_layer.reshape(batch_size * kv_seq_len, self.n_heads, head_dim), + indices_k, + ) + cu_seqlens_q = cu_seqlens_k + max_seqlen_in_batch_q = max_seqlen_in_batch_k + indices_q = indices_k + elif query_length == 1: + max_seqlen_in_batch_q = 1 + cu_seqlens_q = torch.arange( + batch_size + 1, dtype=torch.int32, device=query_layer.device + ) # There is a memcpy here, that is very bad. + indices_q = cu_seqlens_q[:-1] + query_layer = query_layer.squeeze(1) + else: + # The -q_len: slice assumes left padding. + attention_mask = attention_mask[:, -query_length:] + query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input( + query_layer, attention_mask + ) + + return ( + query_layer, + key_layer, + value_layer, + indices_q, + (cu_seqlens_q, cu_seqlens_k), + (max_seqlen_in_batch_q, max_seqlen_in_batch_k), + ) + + def forward( + self, + x: torch.Tensor, + x_mask: torch.Tensor, + freqs_cis: torch.Tensor, + y: torch.Tensor, + y_mask: torch.Tensor, + ) -> torch.Tensor: + """ + Forward pass of the attention module. + + Args: + x (torch.Tensor): Input tensor. + freqs_cis (torch.Tensor): Precomputed frequency tensor. + + Returns: + torch.Tensor: Output tensor after attention. + + """ + bsz, seqlen, _ = x.shape + xq, xk, xv = self.wq(x), self.wk(x), self.wv(x) + dtype = xq.dtype + + xq = self.q_norm(xq) + xk = self.k_norm(xk) + + xq = xq.view(bsz, seqlen, self.n_heads, self.head_dim) + xk = xk.view(bsz, seqlen, self.n_kv_heads, self.head_dim) + xv = xv.view(bsz, seqlen, self.n_kv_heads, self.head_dim) + + xq, xk = Attention.apply_rotary_emb(xq, xk, freqs_cis=freqs_cis) + xq, xk = xq.to(dtype), xk.to(dtype) + + if dtype in [torch.float16, torch.bfloat16]: + # begin var_len flash attn + ( + query_states, + key_states, + value_states, + indices_q, + cu_seq_lens, + max_seq_lens, + ) = self._upad_input(xq, xk, xv, x_mask, seqlen) + + cu_seqlens_q, cu_seqlens_k = cu_seq_lens + max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens + + if self.proportional_attn: + softmax_scale = math.sqrt( + math.log(seqlen, self.base_seqlen) / self.head_dim + ) + else: + softmax_scale = math.sqrt(1 / self.head_dim) + attn_output_unpad = flash_attn_varlen_func( + query_states, + key_states, + value_states, + cu_seqlens_q=cu_seqlens_q, + cu_seqlens_k=cu_seqlens_k, + max_seqlen_q=max_seqlen_in_batch_q, + max_seqlen_k=max_seqlen_in_batch_k, + dropout_p=0.0, + causal=False, + softmax_scale=softmax_scale, + ) + output = pad_input(attn_output_unpad, indices_q, bsz, seqlen) + # end var_len_flash_attn + + else: + output = ( + F.scaled_dot_product_attention( + xq.permute(0, 2, 1, 3), + xk.permute(0, 2, 1, 3), + xv.permute(0, 2, 1, 3), + attn_mask=x_mask.bool() + .view(bsz, 1, 1, seqlen) + .expand(-1, self.n_heads, seqlen, -1), + ) + .permute(0, 2, 1, 3) + .to(dtype) + ) + + if hasattr(self, "wk_y"): + yk = self.ky_norm(self.wk_y(y)).view( + bsz, -1, self.n_kv_heads, self.head_dim + ) + yv = self.wv_y(y).view(bsz, -1, self.n_kv_heads, self.head_dim) + n_rep = self.n_heads // self.n_kv_heads + if n_rep >= 1: + yk = yk.unsqueeze(3).repeat(1, 1, 1, n_rep, 1).flatten(2, 3) + yv = yv.unsqueeze(3).repeat(1, 1, 1, n_rep, 1).flatten(2, 3) + output_y = F.scaled_dot_product_attention( + xq.permute(0, 2, 1, 3), + yk.permute(0, 2, 1, 3), + yv.permute(0, 2, 1, 3), + y_mask.view(bsz, 1, 1, -1).expand(bsz, self.n_heads, seqlen, -1), + ).permute(0, 2, 1, 3) + output_y = output_y * self.gate.tanh().view(1, 1, -1, 1) + output = output + output_y + + output = output.flatten(-2) + + return self.wo(output) + + +class FeedForward(nn.Module): + def __init__( + self, + dim: int, + hidden_dim: int, + multiple_of: int, + ffn_dim_multiplier: Optional[float], + ): + """ + Initialize the FeedForward module. + + Args: + dim (int): Input dimension. + hidden_dim (int): Hidden dimension of the feedforward layer. + multiple_of (int): Value to ensure hidden dimension is a multiple + of this value. + ffn_dim_multiplier (float, optional): Custom multiplier for hidden + dimension. Defaults to None. + + Attributes: + w1 (nn.Linear): Linear transformation for the first + layer. + w2 (nn.Linear): Linear transformation for the second layer. + w3 (nn.Linear): Linear transformation for the third + layer. + + """ + super().__init__() + hidden_dim = int(2 * hidden_dim / 3) + # custom dim factor multiplier + if ffn_dim_multiplier is not None: + hidden_dim = int(ffn_dim_multiplier * hidden_dim) + hidden_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of) + + self.w1 = nn.Linear(dim, hidden_dim, bias=False) + self.w2 = nn.Linear(hidden_dim, dim, bias=False) + self.w3 = nn.Linear(dim, hidden_dim, bias=False) + + # @torch.compile + def _forward_silu_gating(self, x1, x3): + return F.silu(x1) * x3 + + def forward(self, x): + return self.w2(self._forward_silu_gating(self.w1(x), self.w3(x))) + + +class TransformerBlock(nn.Module): + def __init__( + self, + layer_id: int, + dim: int, + n_heads: int, + n_kv_heads: int, + multiple_of: int, + ffn_dim_multiplier: float, + norm_eps: float, + qk_norm: bool, + y_dim: int, + ) -> None: + """ + Initialize a TransformerBlock. + + Args: + layer_id (int): Identifier for the layer. + dim (int): Embedding dimension of the input features. + n_heads (int): Number of attention heads. + n_kv_heads (Optional[int]): Number of attention heads in key and + value features (if using GQA), or set to None for the same as + query. + multiple_of (int): + ffn_dim_multiplier (float): + norm_eps (float): + + Attributes: + n_heads (int): Number of attention heads. + dim (int): Dimension size of the model. + head_dim (int): Dimension size of each attention head. + attention (Attention): Attention module. + feed_forward (FeedForward): FeedForward module. + layer_id (int): Identifier for the layer. + attention_norm (RMSNorm): Layer normalization for attention output. + ffn_norm (RMSNorm): Layer normalization for feedforward output. + + """ + super().__init__() + self.dim = dim + self.head_dim = dim // n_heads + self.attention = Attention(dim, n_heads, n_kv_heads, qk_norm, y_dim) + self.feed_forward = FeedForward( + dim=dim, + hidden_dim=4 * dim, + multiple_of=multiple_of, + ffn_dim_multiplier=ffn_dim_multiplier, + ) + self.layer_id = layer_id + self.attention_norm = RMSNorm(dim, eps=norm_eps) + self.ffn_norm = RMSNorm(dim, eps=norm_eps) + + self.adaLN_modulation = nn.Sequential( + nn.SiLU(), + nn.Linear(min(dim, 1024), 6 * dim, bias=True), + ) + + self.attention_y_norm = RMSNorm(y_dim, eps=norm_eps) + + def forward( + self, + x: torch.Tensor, + x_mask: torch.Tensor, + y: torch.Tensor, + y_mask: torch.Tensor, + freqs_cis: torch.Tensor, + adaln_input: Optional[torch.Tensor] = None, + ): + """ + Perform a forward pass through the TransformerBlock. + + Args: + x (torch.Tensor): Input tensor. + freqs_cis (torch.Tensor): Precomputed cosine and sine frequencies. + mask (torch.Tensor, optional): Masking tensor for attention. + Defaults to None. + + Returns: + torch.Tensor: Output tensor after applying attention and + feedforward layers. + + """ + if adaln_input is not None: + shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = ( + self.adaLN_modulation(adaln_input).chunk(6, dim=1) + ) + + x = x + gate_msa.unsqueeze(1) * self.attention( + modulate(self.attention_norm(x), shift_msa, scale_msa), + x_mask, + freqs_cis, + self.attention_y_norm(y), + y_mask, + ) + x = x + gate_mlp.unsqueeze(1) * self.feed_forward( + modulate(self.ffn_norm(x), shift_mlp, scale_mlp), + ) + + else: + x = x + self.attention( + self.attention_norm(x), + x_mask, + freqs_cis, + self.attention_y_norm(y), + y_mask, + ) + x = x + self.feed_forward(self.ffn_norm(x)) + + return x + + +class FinalLayer(nn.Module): + """ + The final layer of DiT. + """ + + def __init__(self, hidden_size, patch_size, out_channels): + super().__init__() + self.norm_final = nn.LayerNorm( + hidden_size, + elementwise_affine=False, + eps=1e-6, + ) + self.linear = nn.Linear(hidden_size, patch_size * patch_size * out_channels) + self.adaLN_modulation = nn.Sequential( + nn.SiLU(), + nn.Linear(min(hidden_size, 1024), 2 * hidden_size), + ) + + def forward(self, x, c): + shift, scale = self.adaLN_modulation(c).chunk(2, dim=1) + x = modulate(self.norm_final(x), shift, scale) + x = self.linear(x) + return x + + +class FlagDiffuserTransformer(ModelMixin, ConfigMixin): + """ + Diffusion model with a Transformer backbone. + """ + + def __init__( + self, + patch_size: int = 2, + in_channels: int = 4, + dim: int = 4096, + n_layers: int = 32, + n_heads: int = 32, + n_kv_heads: Optional[int] = None, + multiple_of: int = 256, + ffn_dim_multiplier: Optional[float] = None, + norm_eps: float = 1e-5, + learn_sigma: bool = True, + qk_norm: bool = False, + cap_feat_dim: int = 5120, + rope_scaling_factor: float = 1.0, + ntk_factor: float = 1.0, + ) -> None: + super().__init__() + self.learn_sigma = learn_sigma + self.in_channels = in_channels + self.out_channels = in_channels * 2 if learn_sigma else in_channels + self.patch_size = patch_size + + self.x_embedder = nn.Linear( + in_features=patch_size * patch_size * in_channels, + out_features=dim, + bias=True, + ) + nn.init.constant_(self.x_embedder.bias, 0.0) + + self.t_embedder = TimestepEmbedder(min(dim, 1024)) + self.cap_embedder = nn.Sequential( + nn.LayerNorm(cap_feat_dim), + nn.Linear(cap_feat_dim, min(dim, 1024), bias=True), + ) + + self.layers = nn.ModuleList( + [ + TransformerBlock( + layer_id, + dim, + n_heads, + n_kv_heads, + multiple_of, + ffn_dim_multiplier, + norm_eps, + qk_norm, + cap_feat_dim, + ) + for layer_id in range(n_layers) + ] + ) + self.final_layer = FinalLayer(dim, patch_size, self.out_channels) + + self.freqs_cis = DiT_Llama.precompute_freqs_cis( + dim // n_heads, + 40000, + rope_scaling_factor=rope_scaling_factor, + ntk_factor=ntk_factor, + ) + self.dim = dim + self.n_heads = n_heads + self.rope_scaling_factor = rope_scaling_factor + self.ntk_factor = ntk_factor + self.eol_token = nn.Parameter(torch.empty(dim)) + self.pad_token = nn.Parameter(torch.empty(dim)) + nn.init.normal_(self.eol_token, std=0.02) + nn.init.normal_(self.pad_token, std=0.02) + + def unpatchify( + self, x: torch.Tensor, img_size: List[Tuple[int, int]], return_tensor=False + ) -> List[torch.Tensor]: + """ + x: (N, T, patch_size**2 * C) + imgs: (N, H, W, C) + """ + pH = pW = self.patch_size + if return_tensor: + H, W = img_size[0] + B = x.size(0) + L = (H // pH) * (W // pW + 1) # one additional for eol + x = x[:, :L].view(B, H // pH, W // pW + 1, pH, pW, self.out_channels) + x = x[:, :, :-1] + x = x.permute(0, 5, 1, 3, 2, 4).flatten(4, 5).flatten(2, 3) + return x + else: + imgs = [] + for i in range(x.size(0)): + H, W = img_size[i] + L = (H // pH) * (W // pW + 1) + imgs.append( + x[i][:L] + .view(H // pH, W // pW + 1, pH, pW, self.out_channels)[ + :, :-1, :, :, : + ] + .permute(4, 0, 2, 1, 3) + .flatten(3, 4) + .flatten(1, 2) + ) + return imgs + + def patchify_and_embed( + self, x: List[torch.Tensor] | torch.Tensor + ) -> Tuple[torch.Tensor, torch.Tensor, List[Tuple[int, int]]]: + if isinstance(x, torch.Tensor): + pH = pW = self.patch_size + B, C, H, W = x.size() + x = ( + x.view(B, C, H // pH, pH, W // pW, pW) + .permute(0, 2, 4, 1, 3, 5) + .flatten(3) + ) + x = self.x_embedder(x) + x = torch.cat( + [ + x, + self.eol_token.view(1, 1, 1, -1).expand(B, H // pH, 1, -1), + ], + dim=2, + ) + x = x.flatten(1, 2) + + mask = torch.ones( + x.shape[0], x.shape[1], dtype=torch.int32, device=x.device + ) + return x, mask, [(H, W)] * B + else: + pH = pW = self.patch_size + x_embed = [] + img_size = [] + l_effective_seq_len = [] + + for img in x: + C, H, W = img.size() + img_size.append((H, W)) + img = ( + img.view(C, H // pH, pH, W // pW, pW) + .permute(1, 3, 0, 2, 4) + .flatten(2) + ) + img = self.x_embedder(img) + img = torch.cat( + [ + img, + self.eol_token.view(1, 1, -1).expand(H // pH, 1, -1), + ], + dim=1, + ) + img = img.flatten(0, 1) + l_effective_seq_len.append(len(img)) + x_embed.append(img) + + max_seq_len = max(l_effective_seq_len) + mask = torch.zeros( + len(x), max_seq_len, dtype=torch.int32, device=x[0].device + ) + padded_x_embed = [] + for i, (item_embed, item_seq_len) in enumerate( + zip(x_embed, l_effective_seq_len) + ): + item_embed = torch.cat( + [ + item_embed, + self.pad_token.view(1, -1).expand( + max_seq_len - item_seq_len, -1 + ), + ], + dim=0, + ) + padded_x_embed.append(item_embed) + mask[i][:item_seq_len] = 1 + + x_embed = torch.stack(padded_x_embed, dim=0) + return x_embed, mask, img_size + + def forward(self, x, t, cap_feats, cap_mask): + """ + Forward pass of DiT. + t: (N,) tensor of diffusion timesteps + y: (N,) tensor of class labels + """ + x_is_tensor = isinstance(x, torch.Tensor) + x, mask, img_size = self.patchify_and_embed(x) + self.freqs_cis = self.freqs_cis.to(x.device) + + t = self.t_embedder(t) # (N, D) + cap_mask_float = cap_mask.float().unsqueeze(-1) + cap_feats_pool = (cap_feats * cap_mask_float).sum(dim=1) / cap_mask_float.sum( + dim=1 + ) + cap_feats_pool = cap_feats_pool.to(cap_feats) + cap_emb = self.cap_embedder(cap_feats_pool) + adaln_input = t + cap_emb + + cap_mask = cap_mask.bool() + for layer in self.layers: + x = layer( + x, + mask, + cap_feats, + cap_mask, + self.freqs_cis[: x.size(1)], + adaln_input=adaln_input, + ) + + x = self.final_layer(x, adaln_input) + x = self.unpatchify(x, img_size, return_tensor=x_is_tensor) + if self.learn_sigma: + if x_is_tensor: + x, _ = x.chunk(2, dim=1) + else: + x = [_.chunk(2, dim=0)[0] for _ in x] + return x + + def forward_with_cfg( + self, + x, + t, + cap_feats, + cap_mask, + cfg_scale, + rope_scaling_factor=None, + ntk_factor=None, + base_seqlen: Optional[int] = None, + proportional_attn: bool = False, + ): + """ + Forward pass of DiT, but also batches the unconditional forward pass + for classifier-free guidance. + """ + # https://github.com/openai/glide-text2im/blob/main/notebooks/text2im.ipynb + + if rope_scaling_factor is not None or ntk_factor is not None: + rope_scaling_factor = ( + rope_scaling_factor + if rope_scaling_factor is not None + else self.rope_scaling_factor + ) + ntk_factor = ntk_factor if ntk_factor is not None else self.ntk_factor + if ( + rope_scaling_factor != self.rope_scaling_factor + or ntk_factor != self.ntk_factor + ): + print( + f"override freqs_cis, rope_scaling {rope_scaling_factor}, ntk {ntk_factor}", + flush=True, + ) + self.freqs_cis = DiT_Llama.precompute_freqs_cis( + self.dim // self.n_heads, + 40000, + rope_scaling_factor=rope_scaling_factor, + ntk_factor=ntk_factor, + ) + self.rope_scaling_factor = rope_scaling_factor + self.ntk_factor = ntk_factor + + if proportional_attn: + assert base_seqlen is not None + for layer in self.layers: + layer.attention.base_seqlen = base_seqlen + layer.attention.proportional_attn = proportional_attn + else: + for layer in self.layers: + layer.attention.base_seqlen = None + layer.attention.proportional_attn = proportional_attn + + half = x[: len(x) // 2] + combined = torch.cat([half, half], dim=0) + model_out = self(combined, t, cap_feats, cap_mask) + # For exact reproducibility reasons, we apply classifier-free guidance on only + # three channels by default. The standard approach to cfg applies it to all channels. + # This can be done by uncommenting the following line and commenting-out the line following that. + # eps, rest = model_out[:, :self.in_channels], model_out[:, self.in_channels:] + eps, rest = model_out[:, :3], model_out[:, 3:] + cond_eps, uncond_eps = torch.split(eps, len(eps) // 2, dim=0) + half_eps = uncond_eps + cfg_scale * (cond_eps - uncond_eps) + eps = torch.cat([half_eps, half_eps], dim=0) + return torch.cat([eps, rest], dim=1) + + @staticmethod + def precompute_freqs_cis( + dim: int, + end: int, + theta: float = 10000.0, + rope_scaling_factor: float = 1.0, + ntk_factor: float = 1.0, + ): + """ + Precompute the frequency tensor for complex exponentials (cis) with + given dimensions. + + This function calculates a frequency tensor with complex exponentials + using the given dimension 'dim' and the end index 'end'. The 'theta' + parameter scales the frequencies. The returned tensor contains complex + values in complex64 data type. + + Args: + dim (int): Dimension of the frequency tensor. + end (int): End index for precomputing frequencies. + theta (float, optional): Scaling factor for frequency computation. + Defaults to 10000.0. + + Returns: + torch.Tensor: Precomputed frequency tensor with complex + exponentials. + """ + + theta = theta * ntk_factor + + print(f"theta {theta} rope scaling {rope_scaling_factor} ntk {ntk_factor}") + freqs = 1.0 / ( + theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float().cuda() / dim) + ) + t = torch.arange(end, device=freqs.device, dtype=torch.float) # type: ignore + t = t / rope_scaling_factor + freqs = torch.outer(t, freqs).float() # type: ignore + freqs_cis = torch.polar(torch.ones_like(freqs), freqs) # complex64 + return freqs_cis + + def parameter_count(self) -> int: + tensor_module_list = ( + nn.Linear, + nn.Linear, + nn.Embedding, + ) + total_params = 0 + + def _recursive_count_params(module): + nonlocal total_params + is_tp_module = isinstance(module, tensor_module_list) + for param in module.parameters(recurse=False): + total_params += param.numel() + for submodule in module.children(): + _recursive_count_params(submodule) + + _recursive_count_params(self) + return total_params + + def get_fsdp_wrap_module_list(self) -> List[nn.Module]: + return list(self.layers) + + +############################################################################# +# DiT Configs # +############################################################################# + + +def DiT_Llama_5B_patch2(**kwargs): + return DiT_Llama(patch_size=2, dim=3072, n_layers=32, n_heads=32, **kwargs) diff --git a/src/diffusers/pipelines/lumina/modeling_next_dit.py b/src/diffusers/pipelines/lumina/modeling_next_dit.py new file mode 100644 index 000000000000..2aadfe00c0df --- /dev/null +++ b/src/diffusers/pipelines/lumina/modeling_next_dit.py @@ -0,0 +1,1032 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. + +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. +# -------------------------------------------------------- +# References: +# GLIDE: https://github.com/openai/glide-text2im +# MAE: https://github.com/facebookresearch/mae/blob/main/models_mae.py +# -------------------------------------------------------- + +import logging +import math +from typing import Optional, Tuple, List + +from flash_attn import flash_attn_varlen_func +from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa +import torch +import torch.nn as nn +import torch.nn.functional as F + +from ...configuration_utils import ConfigMixin, register_to_config +from ...models import ModelMixin +from ...models.transformers.transformer_2d import Transformer2DModelOutput +from ...utils import logging + +logger = logging.getLogger(__name__) + + +def modulate(x, shift, scale): + return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1) + + +class RMSNorm(torch.nn.Module): + def __init__(self, dim: int, eps: float = 1e-6): + """ + Initialize the RMSNorm normalization layer. + + Args: + dim (int): The dimension of the input tensor. + eps (float, optional): A small value added to the denominator for numerical stability. Default is 1e-6. + + Attributes: + eps (float): A small value added to the denominator for numerical stability. + weight (nn.Parameter): Learnable scaling parameter. + + """ + super().__init__() + self.eps = eps + self.weight = nn.Parameter(torch.ones(dim)) + + def _norm(self, x): + """ + Apply the RMSNorm normalization to the input tensor. + + Args: + x (torch.Tensor): The input tensor. + + Returns: + torch.Tensor: The normalized tensor. + + """ + return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps) + + def forward(self, x): + """ + Forward pass through the RMSNorm layer. + + Args: + x (torch.Tensor): The input tensor. + + Returns: + torch.Tensor: The output tensor after applying RMSNorm. + + """ + output = self._norm(x.float()).type_as(x) + return output * self.weight + + +############################################################################# +# Embedding Layers for Timesteps and Class Labels # +############################################################################# + + +class TimestepEmbedder(nn.Module): + """ + Embeds scalar timesteps into vector representations. + """ + + def __init__(self, hidden_size, frequency_embedding_size=256): + super().__init__() + self.mlp = nn.Sequential( + nn.Linear(frequency_embedding_size, hidden_size, bias=True), + nn.SiLU(), + nn.Linear(hidden_size, hidden_size, bias=True), + ) + self.frequency_embedding_size = frequency_embedding_size + + @staticmethod + def timestep_embedding(t, dim, max_period=10000): + """ + Create sinusoidal timestep embeddings. + :param t: a 1-D Tensor of N indices, one per batch element. + These may be fractional. + :param dim: the dimension of the output. + :param max_period: controls the minimum frequency of the embeddings. + :return: an (N, D) Tensor of positional embeddings. + """ + # https://github.com/openai/glide-text2im/blob/main/glide_text2im/nn.py + half = dim // 2 + freqs = torch.exp( + -math.log(max_period) + * torch.arange(start=0, end=half, dtype=torch.float32) + / half + ).to(device=t.device) + args = t[:, None].float() * freqs[None] + embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) + if dim % 2: + embedding = torch.cat( + [embedding, torch.zeros_like(embedding[:, :1])], dim=-1 + ) + return embedding + + def forward(self, t): + t_freq = self.timestep_embedding(t, self.frequency_embedding_size) + t_emb = self.mlp(t_freq.to(self.mlp[0].weight.dtype)) + return t_emb + + +class ParallelLabelEmbedder(nn.Module): + r"""Embeds class labels into vector representations. Also handles label + dropout for classifier-free guidance. + """ + + def __init__(self, num_classes, hidden_size, dropout_prob): + super().__init__() + use_cfg_embedding = int(dropout_prob > 0) + self.embedding_table = nn.Embedding( + num_classes + use_cfg_embedding, hidden_size + ) + self.num_classes = num_classes + self.dropout_prob = dropout_prob + + def token_drop(self, labels, force_drop_ids=None): + """ + Drops labels to enable classifier-free guidance. + """ + if force_drop_ids is None: + drop_ids = ( + torch.rand(labels.shape[0], device=labels.device) < self.dropout_prob + ) + drop_ids = drop_ids.cuda() + drop_ids = drop_ids.to(labels.device) + else: + drop_ids = force_drop_ids == 1 + labels = torch.where(drop_ids, self.num_classes, labels) + return labels + + def forward(self, labels, train, force_drop_ids=None): + use_dropout = self.dropout_prob > 0 + if (train and use_dropout) or (force_drop_ids is not None): + labels = self.token_drop(labels, force_drop_ids) + embeddings = self.embedding_table(labels) + return embeddings + + +############################################################################# +# Core NextDiT Model # +############################################################################# + + +class Attention(nn.Module): + """Multi-head attention module.""" + + def __init__( + self, + dim: int, + n_heads: int, + n_kv_heads: Optional[int], + qk_norm: bool, + y_dim: int, + ): + """ + Initialize the Attention module. + + Args: + dim (int): Number of input dimensions. + n_heads (int): Number of heads. + n_kv_heads (Optional[int]): Number of kv heads, if using GQA. + + """ + super().__init__() + self.n_kv_heads = n_heads if n_kv_heads is None else n_kv_heads + self.n_heads = n_heads + self.n_rep = self.n_heads // self.n_kv_heads + self.head_dim = dim // n_heads + + self.wq = nn.Linear(dim, n_heads * self.head_dim, bias=False) + self.wk = nn.Linear(dim, self.n_kv_heads * self.head_dim, bias=False) + self.wv = nn.Linear(dim, self.n_kv_heads * self.head_dim, bias=False) + if y_dim > 0: + self.wk_y = nn.Linear(y_dim, self.n_kv_heads * self.head_dim, bias=False) + self.wv_y = nn.Linear(y_dim, self.n_kv_heads * self.head_dim, bias=False) + self.gate = nn.Parameter(torch.zeros([self.n_heads])) + + self.wo = nn.Linear(n_heads * self.head_dim, dim, bias=False) + + if qk_norm: + self.q_norm = nn.LayerNorm(self.n_heads * self.head_dim) + self.k_norm = nn.LayerNorm(self.n_kv_heads * self.head_dim) + if y_dim > 0: + self.ky_norm = nn.LayerNorm(self.n_kv_heads * self.head_dim) + else: + self.ky_norm = nn.Identity() + else: + self.q_norm = self.k_norm = nn.Identity() + self.ky_norm = nn.Identity() + + # for proportional attention computation + self.base_seqlen = None + self.proportional_attn = False + + @staticmethod + def reshape_for_broadcast(freqs_cis: torch.Tensor, x: torch.Tensor): + """ + Reshape frequency tensor for broadcasting it with another tensor. + + This function reshapes the frequency tensor to have the same shape as + the target tensor 'x' for the purpose of broadcasting the frequency + tensor during element-wise operations. + + Args: + freqs_cis (torch.Tensor): Frequency tensor to be reshaped. + x (torch.Tensor): Target tensor for broadcasting compatibility. + + Returns: + torch.Tensor: Reshaped frequency tensor. + + Raises: + AssertionError: If the frequency tensor doesn't match the expected + shape. + AssertionError: If the target tensor 'x' doesn't have the expected + number of dimensions. + """ + ndim = x.ndim + assert 0 <= 1 < ndim + assert freqs_cis.shape == (x.shape[1], x.shape[-1]) + shape = [d if i == 1 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)] + return freqs_cis.view(*shape) + + @staticmethod + def apply_rotary_emb( + x_in: torch.Tensor, + freqs_cis: torch.Tensor, + ) -> torch.Tensor: + """ + Apply rotary embeddings to input tensors using the given frequency + tensor. + + This function applies rotary embeddings to the given query 'xq' and + key 'xk' tensors using the provided frequency tensor 'freqs_cis'. The + input tensors are reshaped as complex numbers, and the frequency tensor + is reshaped for broadcasting compatibility. The resulting tensors + contain rotary embeddings and are returned as real tensors. + + Args: + x_in (torch.Tensor): Query or Key tensor to apply rotary embeddings. + freqs_cis (torch.Tensor): Precomputed frequency tensor for complex + exponentials. + + Returns: + Tuple[torch.Tensor, torch.Tensor]: Tuple of modified query tensor + and key tensor with rotary embeddings. + """ + with torch.cuda.amp.autocast(enabled=False): + x = torch.view_as_complex(x_in.float().reshape(*x_in.shape[:-1], -1, 2)) + freqs_cis = freqs_cis.unsqueeze(2) + x_out = torch.view_as_real(x * freqs_cis).flatten(3) + return x_out.type_as(x_in) + + # copied from huggingface modeling_llama.py + def _upad_input( + self, query_layer, key_layer, value_layer, attention_mask, query_length + ): + + def _get_unpad_data(attention_mask): + seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32) + indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten() + max_seqlen_in_batch = seqlens_in_batch.max().item() + cu_seqlens = F.pad( + torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0) + ) + return ( + indices, + cu_seqlens, + max_seqlen_in_batch, + ) + + indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask) + batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape + + key_layer = index_first_axis( + key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), + indices_k, + ) + value_layer = index_first_axis( + value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), + indices_k, + ) + if query_length == kv_seq_len: + query_layer = index_first_axis( + query_layer.reshape(batch_size * kv_seq_len, self.n_heads, head_dim), + indices_k, + ) + cu_seqlens_q = cu_seqlens_k + max_seqlen_in_batch_q = max_seqlen_in_batch_k + indices_q = indices_k + elif query_length == 1: + max_seqlen_in_batch_q = 1 + cu_seqlens_q = torch.arange( + batch_size + 1, dtype=torch.int32, device=query_layer.device + ) # There is a memcpy here, that is very bad. + indices_q = cu_seqlens_q[:-1] + query_layer = query_layer.squeeze(1) + else: + # The -q_len: slice assumes left padding. + attention_mask = attention_mask[:, -query_length:] + query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input( + query_layer, attention_mask + ) + + return ( + query_layer, + key_layer, + value_layer, + indices_q, + (cu_seqlens_q, cu_seqlens_k), + (max_seqlen_in_batch_q, max_seqlen_in_batch_k), + ) + + def forward( + self, + x: torch.Tensor, + x_mask: torch.Tensor, + freqs_cis: torch.Tensor, + y: torch.Tensor, + y_mask: torch.Tensor, + ) -> torch.Tensor: + """ + + Args: + x: + x_mask: + freqs_cis: + y: + y_mask: + + Returns: + + """ + bsz, seqlen, _ = x.shape + xq, xk, xv = self.wq(x), self.wk(x), self.wv(x) + dtype = xq.dtype + + xq = self.q_norm(xq) + xk = self.k_norm(xk) + + xq = xq.view(bsz, seqlen, self.n_heads, self.head_dim) + xk = xk.view(bsz, seqlen, self.n_kv_heads, self.head_dim) + xv = xv.view(bsz, seqlen, self.n_kv_heads, self.head_dim) + + xq = Attention.apply_rotary_emb(xq, freqs_cis=freqs_cis) + xk = Attention.apply_rotary_emb(xk, freqs_cis=freqs_cis) + + xq, xk = xq.to(dtype), xk.to(dtype) + + if dtype in [torch.float16, torch.bfloat16]: + # begin var_len flash attn + ( + query_states, + key_states, + value_states, + indices_q, + cu_seq_lens, + max_seq_lens, + ) = self._upad_input(xq, xk, xv, x_mask, seqlen) + + cu_seqlens_q, cu_seqlens_k = cu_seq_lens + max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens + + if self.proportional_attn: + softmax_scale = math.sqrt( + math.log(seqlen, self.base_seqlen) / self.head_dim + ) + else: + softmax_scale = math.sqrt(1 / self.head_dim) + + attn_output_unpad = flash_attn_varlen_func( + query_states, + key_states, + value_states, + cu_seqlens_q=cu_seqlens_q, + cu_seqlens_k=cu_seqlens_k, + max_seqlen_q=max_seqlen_in_batch_q, + max_seqlen_k=max_seqlen_in_batch_k, + dropout_p=0.0, + causal=False, + softmax_scale=softmax_scale, + ) + output = pad_input(attn_output_unpad, indices_q, bsz, seqlen) + # end var_len_flash_attn + + else: + output = ( + F.scaled_dot_product_attention( + xq.permute(0, 2, 1, 3), + xk.permute(0, 2, 1, 3), + xv.permute(0, 2, 1, 3), + attn_mask=x_mask.bool() + .view(bsz, 1, 1, seqlen) + .expand(-1, self.n_heads, seqlen, -1), + ) + .permute(0, 2, 1, 3) + .to(dtype) + ) + + if hasattr(self, "wk_y"): + # todo better flash_attn support + yk = self.ky_norm(self.wk_y(y)).view( + bsz, -1, self.n_kv_heads, self.head_dim + ) + yv = self.wv_y(y).view(bsz, -1, self.n_kv_heads, self.head_dim) + n_rep = self.n_heads // self.n_kv_heads + if n_rep >= 1: + yk = yk.unsqueeze(3).repeat(1, 1, 1, n_rep, 1).flatten(2, 3) + yv = yv.unsqueeze(3).repeat(1, 1, 1, n_rep, 1).flatten(2, 3) + output_y = F.scaled_dot_product_attention( + xq.permute(0, 2, 1, 3), + yk.permute(0, 2, 1, 3), + yv.permute(0, 2, 1, 3), + y_mask.view(bsz, 1, 1, -1).expand(bsz, self.n_heads, seqlen, -1), + ).permute(0, 2, 1, 3) + output_y = output_y * self.gate.tanh().view(1, 1, -1, 1) + output = output + output_y + + output = output.flatten(-2) + + return self.wo(output) + + +class FeedForward(nn.Module): + def __init__( + self, + dim: int, + hidden_dim: int, + multiple_of: int, + ffn_dim_multiplier: Optional[float], + ): + """ + Initialize the FeedForward module. + + Args: + dim (int): Input dimension. + hidden_dim (int): Hidden dimension of the feedforward layer. + multiple_of (int): Value to ensure hidden dimension is a multiple + of this value. + ffn_dim_multiplier (float, optional): Custom multiplier for hidden + dimension. Defaults to None. + + Attributes: + w1 (nn.Linear): Linear transformation for the first + layer. + w2 (nn.Linear): Linear transformation for the second layer. + w3 (nn.Linear): Linear transformation for the third + layer. + + """ + super().__init__() + hidden_dim = int(2 * hidden_dim / 3) + # custom dim factor multiplier + if ffn_dim_multiplier is not None: + hidden_dim = int(ffn_dim_multiplier * hidden_dim) + hidden_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of) + + self.w1 = nn.Linear(dim, hidden_dim, bias=False) + self.w2 = nn.Linear(hidden_dim, dim, bias=False) + self.w3 = nn.Linear(dim, hidden_dim, bias=False) + + # @torch.compile + def _forward_silu_gating(self, x1, x3): + return F.silu(x1) * x3 + + def forward(self, x): + return self.w2(self._forward_silu_gating(self.w1(x), self.w3(x))) + + +class TransformerBlock(nn.Module): + def __init__( + self, + layer_id: int, + dim: int, + n_heads: int, + n_kv_heads: int, + multiple_of: int, + ffn_dim_multiplier: float, + norm_eps: float, + qk_norm: bool, + y_dim: int, + ) -> None: + """ + Initialize a TransformerBlock. + + Args: + layer_id (int): Identifier for the layer. + dim (int): Embedding dimension of the input features. + n_heads (int): Number of attention heads. + n_kv_heads (Optional[int]): Number of attention heads in key and + value features (if using GQA), or set to None for the same as + query. + multiple_of (int): + ffn_dim_multiplier (float): + norm_eps (float): + + Attributes: + n_heads (int): Number of attention heads. + dim (int): Dimension size of the model. + head_dim (int): Dimension size of each attention head. + attention (Attention): Attention module. + feed_forward (FeedForward): FeedForward module. + layer_id (int): Identifier for the layer. + attention_norm (RMSNorm): Layer normalization for attention output. + ffn_norm (RMSNorm): Layer normalization for feedforward output. + + """ + super().__init__() + self.dim = dim + self.head_dim = dim // n_heads + self.attention = Attention(dim, n_heads, n_kv_heads, qk_norm, y_dim) + self.feed_forward = FeedForward( + dim=dim, + hidden_dim=4 * dim, + multiple_of=multiple_of, + ffn_dim_multiplier=ffn_dim_multiplier, + ) + self.layer_id = layer_id + self.attention_norm = RMSNorm(dim, eps=norm_eps) + self.attention_norm1 = RMSNorm(dim, eps=norm_eps) + self.ffn_norm = RMSNorm(dim, eps=norm_eps) + self.ffn_norm1 = RMSNorm(dim, eps=norm_eps) + + self.adaLN_modulation = nn.Sequential( + nn.SiLU(), + nn.Linear(min(dim, 1024), 6 * dim, bias=True), + ) + + self.attention_y_norm = RMSNorm(y_dim, eps=norm_eps) + + def forward( + self, + x: torch.Tensor, + x_mask: torch.Tensor, + freqs_cis: torch.Tensor, + y: torch.Tensor, + y_mask: torch.Tensor, + adaln_input: Optional[torch.Tensor] = None, + ): + """ + Perform a forward pass through the TransformerBlock. + + Args: + x (torch.Tensor): Input tensor. + freqs_cis (torch.Tensor): Precomputed cosine and sine frequencies. + + Returns: + torch.Tensor: Output tensor after applying attention and + feedforward layers. + + """ + if adaln_input is not None: + shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = ( + self.adaLN_modulation(adaln_input).chunk(6, dim=1) + ) + + x = x + self.attention_norm1( + gate_msa.unsqueeze(1) + * self.attention( + modulate(self.attention_norm(x), shift_msa, scale_msa), + x_mask, + freqs_cis, + self.attention_y_norm(y), + y_mask, + ) + ) + d = x.shape[-1] + x = x + self.ffn_norm1( + gate_mlp.unsqueeze(1) + * self.feed_forward( + modulate(self.ffn_norm(x), shift_mlp, scale_mlp).view(-1, d), + ).view(*x.shape) + ) + + else: + x = x + self.attention_norm1( + self.attention( + self.attention_norm(x), + x_mask, + freqs_cis, + self.attention_y_norm(y), + y_mask, + ) + ) + # for compatibility with torch.compile because the sequence length changes + B, L, D = x.shape + x = x.view(B * L, D) + x = x + self.ffn_norm1(self.feed_forward(self.ffn_norm(x))) + x = x.view(B, L, D) + + return x + + +class FinalLayer(nn.Module): + """ + The final layer of NextDiT. + """ + + def __init__(self, hidden_size, patch_size, out_channels): + super().__init__() + self.norm_final = nn.LayerNorm( + hidden_size, + elementwise_affine=False, + eps=1e-6, + ) + self.linear = nn.Linear(hidden_size, patch_size * patch_size * out_channels) + self.adaLN_modulation = nn.Sequential( + nn.SiLU(), + nn.Linear(min(hidden_size, 1024), 2 * hidden_size), + ) + + def forward(self, x, c): + shift, scale = self.adaLN_modulation(c).chunk(2, dim=1) + x = modulate(self.norm_final(x), shift, scale) + x = self.linear(x) + return x + + +class NextFlagDiffuserTransformer2DModel(ModelMixin, ConfigMixin): + """ + Diffusion model with a Transformer backbone. + """ + + @register_to_config + def __init__( + self, + patch_size: int = 2, + in_channels: int = 4, + dim: int = 4096, + n_layers: int = 32, + n_heads: int = 32, + n_kv_heads: Optional[int] = None, + multiple_of: int = 256, + ffn_dim_multiplier: Optional[float] = None, + norm_eps: float = 1e-5, + learn_sigma: bool = True, + qk_norm: bool = False, + cap_feat_dim: int = 5120, + rope_scaling_factor: float = 1.0, + ntk_factor: float = 1.0, + ) -> None: + super().__init__() + self.learn_sigma = learn_sigma + self.in_channels = in_channels + self.out_channels = in_channels * 2 if learn_sigma else in_channels + self.patch_size = patch_size + + self.x_embedder = nn.Linear( + in_features=patch_size * patch_size * in_channels, + out_features=dim, + bias=True, + ) + nn.init.constant_(self.x_embedder.bias, 0.0) + + self.t_embedder = TimestepEmbedder(min(dim, 1024)) + self.cap_embedder = nn.Sequential( + nn.LayerNorm(cap_feat_dim), + nn.Linear(cap_feat_dim, min(dim, 1024), bias=True), + ) + + self.layers = nn.ModuleList( + [ + TransformerBlock( + layer_id, + dim, + n_heads, + n_kv_heads, + multiple_of, + ffn_dim_multiplier, + norm_eps, + qk_norm, + cap_feat_dim, + ) + for layer_id in range(n_layers) + ] + ) + self.final_layer = FinalLayer(dim, patch_size, self.out_channels) + + assert (dim // n_heads) % 4 == 0, "2d rope needs head dim to be divisible by 4" + self.dim = dim + self.n_heads = n_heads + self.freqs_cis = NextDiT.precompute_freqs_cis( + dim // n_heads, + 384, + rope_scaling_factor=rope_scaling_factor, + ntk_factor=ntk_factor, + ) + self.rope_scaling_factor = rope_scaling_factor + self.ntk_factor = ntk_factor + # self.eol_token = nn.Parameter(torch.empty(dim)) + self.pad_token = nn.Parameter(torch.empty(dim)) + # nn.init.normal_(self.eol_token, std=0.02) + nn.init.normal_(self.pad_token, std=0.02) + + def unpatchify( + self, x: torch.Tensor, img_size: List[Tuple[int, int]], return_tensor=False + ) -> List[torch.Tensor]: + """ + x: (N, T, patch_size**2 * C) + imgs: (N, H, W, C) + """ + pH = pW = self.patch_size + if return_tensor: + H, W = img_size[0] + B = x.size(0) + L = (H // pH) * (W // pW) + x = x[:, :L].view(B, H // pH, W // pW, pH, pW, self.out_channels) + x = x.permute(0, 5, 1, 3, 2, 4).flatten(4, 5).flatten(2, 3) + return x + else: + imgs = [] + for i in range(x.size(0)): + H, W = img_size[i] + L = (H // pH) * (W // pW) + imgs.append( + x[i][:L] + .view(H // pH, W // pW, pH, pW, self.out_channels) + .permute(4, 0, 2, 1, 3) + .flatten(3, 4) + .flatten(1, 2) + ) + return imgs + + def patchify_and_embed( + self, x: List[torch.Tensor] | torch.Tensor + ) -> Tuple[torch.Tensor, torch.Tensor, List[Tuple[int, int]], torch.Tensor]: + self.freqs_cis = self.freqs_cis.to(x[0].device) + if isinstance(x, torch.Tensor): + pH = pW = self.patch_size + B, C, H, W = x.size() + x = ( + x.view(B, C, H // pH, pH, W // pW, pW) + .permute(0, 2, 4, 1, 3, 5) + .flatten(3) + ) + x = self.x_embedder(x) + x = x.flatten(1, 2) + + mask = torch.ones( + x.shape[0], x.shape[1], dtype=torch.int32, device=x.device + ) + # leave the first line for text + return ( + x, + mask, + [(H, W)] * B, + self.freqs_cis[: H // pH, : W // pW].flatten(0, 1).unsqueeze(0), + ) + else: + pH = pW = self.patch_size + x_embed = [] + freqs_cis = [] + img_size = [] + l_effective_seq_len = [] + + for img in x: + C, H, W = img.size() + item_freqs_cis = self.freqs_cis[: H // pH, : W // pW] + freqs_cis.append(item_freqs_cis.flatten(0, 1)) + img_size.append((H, W)) + img = ( + img.view(C, H // pH, pH, W // pW, pW) + .permute(1, 3, 0, 2, 4) + .flatten(2) + ) + img = self.x_embedder(img) + img = img.flatten(0, 1) + l_effective_seq_len.append(len(img)) + x_embed.append(img) + + max_seq_len = max(l_effective_seq_len) + mask = torch.zeros( + len(x), max_seq_len, dtype=torch.int32, device=x[0].device + ) + padded_x_embed = [] + padded_freqs_cis = [] + for i, (item_embed, item_freqs_cis, item_seq_len) in enumerate( + zip(x_embed, freqs_cis, l_effective_seq_len) + ): + item_embed = torch.cat( + [ + item_embed, + self.pad_token.view(1, -1).expand( + max_seq_len - item_seq_len, -1 + ), + ], + dim=0, + ) + item_freqs_cis = torch.cat( + [ + item_freqs_cis, + item_freqs_cis[-1:].expand(max_seq_len - item_seq_len, -1), + ], + dim=0, + ) + padded_x_embed.append(item_embed) + padded_freqs_cis.append(item_freqs_cis) + mask[i][:item_seq_len] = 1 + + x_embed = torch.stack(padded_x_embed, dim=0) + freqs_cis = torch.stack(padded_freqs_cis, dim=0) + return x_embed, mask, img_size, freqs_cis + + def forward( + self, + hidden_states, + timestep=None, + encoder_hidden_states=None, + encoder_attn_mask=None, + return_dict: bool = True, + hidden_states_is_tensor: bool = False, + unpatchify: bool = True, + ): + """ + Forward pass of NextDiT. + t: (N,) tensor of diffusion timesteps + y: (N,) tensor of class labels + """ + + # 0. Check inputs + if not unpatchify and return_dict: + raise ValueError( + f"Cannot both define `unpatchify`: {unpatchify} and `return_dict`: {return_dict} since when" + f" `unpatchify` is {unpatchify} the returned output is of shape (batch_size, seq_len, hidden_dim)" + " rather than (batch_size, num_channels, height, width)." + ) + + # 1. Patchify and Get embedding + hidden_states, mask, img_size, freqs_cis = self.patchify_and_embed(hidden_states) + freqs_cis = freqs_cis.to(hidden_states.device) + + # 2. Get timestep embedding + timestep = self.t_embedder(timestep) # (N, D) + + # 3. Get encoder hidden_state + encoder_attn_mask_float = encoder_attn_mask.float().unsqueeze(-1) + encoder_hidden_states_pool = (encoder_hidden_states * encoder_attn_mask_float).sum(dim=1) / encoder_attn_mask_float.sum( + dim=1 + ) + encoder_hidden_states_pool = encoder_hidden_states_pool.to(encoder_hidden_states) + cap_emb = self.cap_embedder(encoder_hidden_states_pool) + encoder_attn_mask = encoder_attn_mask.bool() + + adaln_input = timestep + cap_emb + # 4. Get hidden_state from transformer + for layer in self.layers: + hidden_states = layer(hidden_states, mask, freqs_cis, encoder_hidden_states, encoder_attn_mask, adaln_input=adaln_input) + + hidden_states = self.final_layer(hidden_states, adaln_input) + + if unpatchify: + hidden_states = self.unpatchify(hidden_states, img_size, return_tensor=hidden_states_is_tensor) + else: + output = hidden_states + + if self.learn_sigma: + if hidden_states_is_tensor: + hidden_states, _ = hidden_states.chunk(2, dim=1) + else: + hidden_states = [_.chunk(2, dim=0)[0] for _ in hidden_states] + + if not return_dict: + return (output,) + + return Transformer2DModelOutput(sample=output) + + def forward_with_cfg( + self, + x, + t, + cap_feats, + cap_mask, + cfg_scale, + rope_scaling_factor=None, + ntk_factor=None, + base_seqlen: Optional[int] = None, + proportional_attn: bool = False, + ): + # """ + # Forward pass of NextDiT, but also batches the unconNextditional forward pass + # for classifier-free guidance. + # """ + # # https://github.com/openai/glide-text2im/blob/main/notebooks/text2im.ipynb + # print(ntk_factor, rope_scaling_factor, self.ntk_factor, self.rope_scaling_factor) + if rope_scaling_factor is not None or ntk_factor is not None: + rope_scaling_factor = ( + rope_scaling_factor + if rope_scaling_factor is not None + else self.rope_scaling_factor + ) + ntk_factor = ntk_factor if ntk_factor is not None else self.ntk_factor + if ( + rope_scaling_factor != self.rope_scaling_factor + or ntk_factor != self.ntk_factor + ): + print( + f"override freqs_cis, rope_scaling {rope_scaling_factor}, ntk {ntk_factor}", + flush=True, + ) + self.freqs_cis = NextDiT.precompute_freqs_cis( + self.dim // self.n_heads, + 384, + rope_scaling_factor=rope_scaling_factor, + ntk_factor=ntk_factor, + ) + self.rope_scaling_factor = rope_scaling_factor + self.ntk_factor = ntk_factor + + if proportional_attn: + assert base_seqlen is not None + for layer in self.layers: + layer.attention.base_seqlen = base_seqlen + layer.attention.proportional_attn = proportional_attn + else: + for layer in self.layers: + layer.attention.base_seqlen = None + layer.attention.proportional_attn = proportional_attn + + half = x[: len(x) // 2] + combined = torch.cat([half, half], dim=0) + model_out = self.forward(combined, t, cap_feats, cap_mask) + # For exact reproducibility reasons, we apply classifier-free guidance on only + # three channels by default. The standard approach to cfg applies it to all channels. + # This can be done by uncommenting the following line and commenting-out the line following that. + # eps, rest = model_out[:, :self.in_channels], model_out[:, self.in_channels:] + eps, rest = model_out[:, :3], model_out[:, 3:] + cond_eps, uncond_eps = torch.split(eps, len(eps) // 2, dim=0) + half_eps = uncond_eps + cfg_scale * (cond_eps - uncond_eps) + eps = torch.cat([half_eps, half_eps], dim=0) + return torch.cat([eps, rest], dim=1) + + @staticmethod + def precompute_freqs_cis( + dim: int, + end: int, + theta: float = 10000.0, + rope_scaling_factor: float = 1.0, + ntk_factor: float = 1.0, + ): + """ + Precompute the frequency tensor for complex exponentials (cis) with + given dimensions. + + This function calculates a frequency tensor with complex exponentials + using the given dimension 'dim' and the end index 'end'. The 'theta' + parameter scales the frequencies. The returned tensor contains complex + values in complex64 data type. + + Args: + dim (int): Dimension of the frequency tensor. + end (int): End index for precomputing frequencies. + theta (float, optional): Scaling factor for frequency computation. + Defaults to 10000.0. + + Returns: + torch.Tensor: Precomputed frequency tensor with complex + exponentials. + """ + + theta = theta * ntk_factor + + logger.info( + f"theta {theta} rope scaling {rope_scaling_factor} ntk {ntk_factor}" + ) + freqs = 1.0 / ( + theta ** (torch.arange(0, dim, 4)[: (dim // 4)].float().cuda() / dim) + ) + t = torch.arange(end, device=freqs.device, dtype=torch.float) # type: ignore + t = t / rope_scaling_factor + freqs = torch.outer(t, freqs).float() # type: ignore + freqs_cis = torch.polar(torch.ones_like(freqs), freqs) # complex64 + + freqs_cis_h = freqs_cis.view(end, 1, dim // 4, 1).repeat(1, end, 1, 1) + freqs_cis_w = freqs_cis.view(1, end, dim // 4, 1).repeat(end, 1, 1, 1) + freqs_cis = torch.cat([freqs_cis_h, freqs_cis_w], dim=-1).flatten(2) + return freqs_cis + + def parameter_count(self) -> int: + tensor_parallel_module_list = ( + nn.Linear, + nn.Linear, + nn.Embedding, + ) + total_params = 0 + + def _recursive_count_params(module): + nonlocal total_params + is_tp_module = isinstance(module, tensor_parallel_module_list) + for param in module.parameters(recurse=False): + total_params += param.numel() + for submodule in module.children(): + _recursive_count_params(submodule) + + _recursive_count_params(self) + return total_params + + def get_fsdp_wrap_module_list(self) -> List[nn.Module]: + return list(self.layers) + + +############################################################################# +# NextDiT Configs # +############################################################################# +def NextDiT_2B_patch2(**kwargs): + return NextDiT(patch_size=2, dim=2304, n_layers=24, n_heads=32, **kwargs) diff --git a/src/diffusers/pipelines/lumina/pipeline_lumina_t2i.py b/src/diffusers/pipelines/lumina/pipeline_lumina.py similarity index 81% rename from src/diffusers/pipelines/lumina/pipeline_lumina_t2i.py rename to src/diffusers/pipelines/lumina/pipeline_lumina.py index 3b6a5636bd58..9b67aeab0173 100644 --- a/src/diffusers/pipelines/lumina/pipeline_lumina_t2i.py +++ b/src/diffusers/pipelines/lumina/pipeline_lumina.py @@ -19,10 +19,10 @@ from typing import Callable, List, Optional, Tuple, Union import torch -from transformers import T5EncoderModel, T5Tokenizer +from transformers import AutoModel, AutoTokenizer -from ...image_processor import LuminaImageProcessor -from ...models import AutoencoderKL, Transformer2DModel +from ...image_processor import VaeImageProcessor +from ...models import AutoencoderKL from ...schedulers import DPMSolverMultistepScheduler from ...utils import ( BACKENDS_MAPPING, @@ -34,6 +34,7 @@ ) from ...utils.torch_utils import randn_tensor from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput +from .modeling_next_dit import NextFlagDiffuserTransformer2DModel logger = logging.get_logger(__name__) # pylint: disable=invalid-name @@ -202,9 +203,13 @@ def retrieve_timesteps( second element is the number of inference steps. """ if timesteps is not None and sigmas is not None: - raise ValueError("Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values") + raise ValueError( + "Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values" + ) if timesteps is not None: - accepts_timesteps = "timesteps" in set(inspect.signature(scheduler.set_timesteps).parameters.keys()) + accepts_timesteps = "timesteps" in set( + inspect.signature(scheduler.set_timesteps).parameters.keys() + ) if not accepts_timesteps: raise ValueError( f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom" @@ -214,7 +219,9 @@ def retrieve_timesteps( timesteps = scheduler.timesteps num_inference_steps = len(timesteps) elif sigmas is not None: - accept_sigmas = "sigmas" in set(inspect.signature(scheduler.set_timesteps).parameters.keys()) + accept_sigmas = "sigmas" in set( + inspect.signature(scheduler.set_timesteps).parameters.keys() + ) if not accept_sigmas: raise ValueError( f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom" @@ -239,13 +246,13 @@ class LuminaText2ImgPipeline(DiffusionPipeline): Args: vae ([`AutoencoderKL`]): Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. - text_encoder ([`T5EncoderModel`]): + text_encoder ([`AutoModel`]): Frozen text-encoder. Lumina-T2I uses - [T5](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5EncoderModel), specifically the + [T5](https://huggingface.co/docs/transformers/model_doc/t5#transformers.AutoModel), specifically the [t5-v1_1-xxl](https://huggingface.co/Alpha-VLLM/tree/main/t5-v1_1-xxl) variant. - tokenizer (`T5Tokenizer`): + tokenizer (`AutoModel`): Tokenizer of class - [T5Tokenizer](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5Tokenizer). + [AutoModel](https://huggingface.co/docs/transformers/model_doc/t5#transformers.AutoModel). transformer ([`Transformer2DModel`]): A text conditioned `Transformer2DModel` to denoise the encoded image latents. scheduler ([`SchedulerMixin`]): @@ -273,20 +280,24 @@ class LuminaText2ImgPipeline(DiffusionPipeline): def __init__( self, - tokenizer: T5Tokenizer, - text_encoder: T5EncoderModel, + tokenizer: AutoTokenizer, + text_encoder: AutoModel, vae: AutoencoderKL, - transformer: Transformer2DModel, + transformer: NextFlagDiffuserTransformer2DModel, scheduler: DPMSolverMultistepScheduler, ): super().__init__() self.register_modules( - tokenizer=tokenizer, text_encoder=text_encoder, vae=vae, transformer=transformer, scheduler=scheduler + tokenizer=tokenizer, + text_encoder=text_encoder, + vae=vae, + transformer=transformer, + scheduler=scheduler, ) - self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) - self.image_processor = LuminaImageProcessor(vae_scale_factor=self.vae_scale_factor) + self.vae_scale_factor = 0.18215 + self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor) # Adapted from diffusers.pipelines.deepfloyd_if.pipeline_if.encode_prompt def encode_prompt( @@ -301,7 +312,7 @@ def encode_prompt( prompt_attention_mask: Optional[torch.Tensor] = None, negative_prompt_attention_mask: Optional[torch.Tensor] = None, clean_caption: bool = False, - max_sequence_length: int = 120, + max_sequence_length: int = 256, **kwargs, ): r""" @@ -345,26 +356,29 @@ def encode_prompt( else: batch_size = prompt_embeds.shape[0] - # See Section 3.1. of the paper. max_length = max_sequence_length - if prompt_embeds is None: prompt = self._text_preprocessing(prompt, clean_caption=clean_caption) text_inputs = self.tokenizer( prompt, - padding="max_length", + padding=True, + pad_to_multiple_of=8, max_length=max_length, truncation=True, - add_special_tokens=True, return_tensors="pt", ) - text_input_ids = text_inputs.input_ids - untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids - if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal( - text_input_ids, untruncated_ids - ): - removed_text = self.tokenizer.batch_decode(untruncated_ids[:, max_length - 1 : -1]) + text_input_ids = text_inputs.input_ids + untruncated_ids = self.tokenizer( + prompt, padding="longest", return_tensors="pt" + ).input_ids + + if untruncated_ids.shape[-1] >= text_input_ids.shape[ + -1 + ] and not torch.equal(text_input_ids, untruncated_ids): + removed_text = self.tokenizer.batch_decode( + untruncated_ids[:, max_length - 1 : -1] + ) logger.warning( "The following part of your input was truncated because T5 can only handle sequences up to" f" {max_length} tokens: {removed_text}" @@ -373,8 +387,10 @@ def encode_prompt( prompt_attention_mask = text_inputs.attention_mask prompt_attention_mask = prompt_attention_mask.to(device) - prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=prompt_attention_mask) - prompt_embeds = prompt_embeds[0] + prompt_embeds = self.text_encoder( + text_input_ids.to(device), attention_mask=prompt_attention_mask + ) + prompt_embeds = prompt_embeds.hidden_states[-2] if self.text_encoder is not None: dtype = self.text_encoder.dtype @@ -388,48 +404,51 @@ def encode_prompt( bs_embed, seq_len, _ = prompt_embeds.shape # duplicate text embeddings and attention mask for each generation per prompt, using mps friendly method prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1) - prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1) + prompt_embeds = prompt_embeds.view( + bs_embed * num_images_per_prompt, seq_len, -1 + ) prompt_attention_mask = prompt_attention_mask.view(bs_embed, -1) prompt_attention_mask = prompt_attention_mask.repeat(num_images_per_prompt, 1) # get unconditional embeddings for classifier free guidance - if do_classifier_free_guidance and negative_prompt_embeds is None: - uncond_tokens = [negative_prompt] * batch_size - uncond_tokens = self._text_preprocessing(uncond_tokens, clean_caption=clean_caption) - max_length = prompt_embeds.shape[1] - uncond_input = self.tokenizer( - uncond_tokens, - padding="max_length", - max_length=max_length, - truncation=True, - return_attention_mask=True, - add_special_tokens=True, - return_tensors="pt", - ) - negative_prompt_attention_mask = uncond_input.attention_mask - negative_prompt_attention_mask = negative_prompt_attention_mask.to(device) - - negative_prompt_embeds = self.text_encoder( - uncond_input.input_ids.to(device), attention_mask=negative_prompt_attention_mask - ) - negative_prompt_embeds = negative_prompt_embeds[0] - - if do_classifier_free_guidance: - # duplicate unconditional embeddings for each generation per prompt, using mps friendly method - seq_len = negative_prompt_embeds.shape[1] - - negative_prompt_embeds = negative_prompt_embeds.to(dtype=dtype, device=device) - - negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1) - negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1) - - negative_prompt_attention_mask = negative_prompt_attention_mask.view(bs_embed, -1) - negative_prompt_attention_mask = negative_prompt_attention_mask.repeat(num_images_per_prompt, 1) - else: - negative_prompt_embeds = None - negative_prompt_attention_mask = None - - return prompt_embeds, prompt_attention_mask, negative_prompt_embeds, negative_prompt_attention_mask + # if do_classifier_free_guidance and negative_prompt_embeds is None: + # uncond_tokens = [negative_prompt] * batch_size + # uncond_tokens = self._text_preprocessing(uncond_tokens, clean_caption=clean_caption) + # max_length = prompt_embeds.shape[1] + # uncond_input = self.tokenizer( + # uncond_tokens, + # padding="max_length", + # max_length=max_length, + # truncation=True, + # return_attention_mask=True, + # add_special_tokens=True, + # return_tensors="pt", + # ) + # negative_prompt_attention_mask = uncond_input.attention_mask + # negative_prompt_attention_mask = negative_prompt_attention_mask.to(device) + + # negative_prompt_embeds = self.text_encoder( + # uncond_input.input_ids.to(device), attention_mask=negative_prompt_attention_mask + # ) + # negative_prompt_embeds = negative_prompt_embeds[0] + + # if do_classifier_free_guidance: + # # duplicate unconditional embeddings for each generation per prompt, using mps friendly method + # seq_len = negative_prompt_embeds.shape[1] + + # negative_prompt_embeds = negative_prompt_embeds.to(dtype=dtype, device=device) + + # negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1) + # negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1) + + # negative_prompt_attention_mask = negative_prompt_attention_mask.view(bs_embed, -1) + # negative_prompt_attention_mask = negative_prompt_attention_mask.repeat(num_images_per_prompt, 1) + # else: + # negative_prompt_embeds = None + # negative_prompt_attention_mask = None + + # return prompt_embeds, prompt_attention_mask, negative_prompt_embeds, negative_prompt_attention_mask + return prompt_embeds, prompt_attention_mask # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs def prepare_extra_step_kwargs(self, generator, eta): @@ -438,13 +457,17 @@ def prepare_extra_step_kwargs(self, generator, eta): # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 # and should be between [0, 1] - accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) + accepts_eta = "eta" in set( + inspect.signature(self.scheduler.step).parameters.keys() + ) extra_step_kwargs = {} if accepts_eta: extra_step_kwargs["eta"] = eta # check if the scheduler accepts generator - accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys()) + accepts_generator = "generator" in set( + inspect.signature(self.scheduler.step).parameters.keys() + ) if accepts_generator: extra_step_kwargs["generator"] = generator return extra_step_kwargs @@ -462,10 +485,13 @@ def check_inputs( negative_prompt_attention_mask=None, ): if height % 8 != 0 or width % 8 != 0: - raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.") + raise ValueError( + f"`height` and `width` have to be divisible by 8 but are {height} and {width}." + ) if (callback_steps is None) or ( - callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0) + callback_steps is not None + and (not isinstance(callback_steps, int) or callback_steps <= 0) ): raise ValueError( f"`callback_steps` has to be a positive integer but is {callback_steps} of type" @@ -481,8 +507,12 @@ def check_inputs( raise ValueError( "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined." ) - elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)): - raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") + elif prompt is not None and ( + not isinstance(prompt, str) and not isinstance(prompt, list) + ): + raise ValueError( + f"`prompt` has to be of type `str` or `list` but is {type(prompt)}" + ) if prompt is not None and negative_prompt_embeds is not None: raise ValueError( @@ -497,10 +527,17 @@ def check_inputs( ) if prompt_embeds is not None and prompt_attention_mask is None: - raise ValueError("Must provide `prompt_attention_mask` when specifying `prompt_embeds`.") + raise ValueError( + "Must provide `prompt_attention_mask` when specifying `prompt_embeds`." + ) - if negative_prompt_embeds is not None and negative_prompt_attention_mask is None: - raise ValueError("Must provide `negative_prompt_attention_mask` when specifying `negative_prompt_embeds`.") + if ( + negative_prompt_embeds is not None + and negative_prompt_attention_mask is None + ): + raise ValueError( + "Must provide `negative_prompt_attention_mask` when specifying `negative_prompt_embeds`." + ) if prompt_embeds is not None and negative_prompt_embeds is not None: if prompt_embeds.shape != negative_prompt_embeds.shape: @@ -519,12 +556,16 @@ def check_inputs( # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline._text_preprocessing def _text_preprocessing(self, text, clean_caption=False): if clean_caption and not is_bs4_available(): - logger.warning(BACKENDS_MAPPING["bs4"][-1].format("Setting `clean_caption=True`")) + logger.warning( + BACKENDS_MAPPING["bs4"][-1].format("Setting `clean_caption=True`") + ) logger.warning("Setting `clean_caption` to False...") clean_caption = False if clean_caption and not is_ftfy_available(): - logger.warning(BACKENDS_MAPPING["ftfy"][-1].format("Setting `clean_caption=True`")) + logger.warning( + BACKENDS_MAPPING["ftfy"][-1].format("Setting `clean_caption=True`") + ) logger.warning("Setting `clean_caption` to False...") clean_caption = False @@ -612,13 +653,17 @@ def _clean_caption(self, caption): # "123456.." caption = re.sub(r"\b\d{6,}\b", "", caption) # filenames: - caption = re.sub(r"[\S]+\.(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)", "", caption) + caption = re.sub( + r"[\S]+\.(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)", "", caption + ) # caption = re.sub(r"[\"\']{2,}", r'"', caption) # """AUSVERKAUFT""" caption = re.sub(r"[\.]{2,}", r" ", caption) # """AUSVERKAUFT""" - caption = re.sub(self.bad_punct_regex, r" ", caption) # ***AUSVERKAUFT***, #AUSVERKAUFT + caption = re.sub( + self.bad_punct_regex, r" ", caption + ) # ***AUSVERKAUFT***, #AUSVERKAUFT caption = re.sub(r"\s+\.\s+", r" ", caption) # " . " # this-is-my-cute-cat / this_is_my_cute_cat @@ -636,10 +681,14 @@ def _clean_caption(self, caption): caption = re.sub(r"(worldwide\s+)?(free\s+)?shipping", "", caption) caption = re.sub(r"(free\s)?download(\sfree)?", "", caption) caption = re.sub(r"\bclick\b\s(?:for|on)\s\w+", "", caption) - caption = re.sub(r"\b(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)(\simage[s]?)?", "", caption) + caption = re.sub( + r"\b(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)(\simage[s]?)?", "", caption + ) caption = re.sub(r"\bpage\s+\d+\b", "", caption) - caption = re.sub(r"\b\d*[a-zA-Z]+\d+[a-zA-Z]+\d+[a-zA-Z\d]*\b", r" ", caption) # j2d1a2a... + caption = re.sub( + r"\b\d*[a-zA-Z]+\d+[a-zA-Z]+\d+[a-zA-Z\d]*\b", r" ", caption + ) # j2d1a2a... caption = re.sub(r"\b\d+\.?\d*[xх×]\d+\.?\d*\b", "", caption) @@ -657,7 +706,17 @@ def _clean_caption(self, caption): return caption.strip() # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents - def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None): + def prepare_latents( + self, + batch_size, + num_channels_latents, + height, + width, + dtype, + device, + generator, + latents=None, + ): shape = ( batch_size, num_channels_latents, @@ -671,7 +730,9 @@ def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype ) if latents is None: - latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype) + latents = randn_tensor( + shape, generator=generator, device=device, dtype=dtype + ) else: latents = latents.to(device) @@ -805,7 +866,9 @@ def __call__( else: raise ValueError("Invalid sample size") orig_height, orig_width = height, width - height, width = self.image_processor.classify_height_width_bin(height, width, ratios=aspect_ratio_bin) + height, width = self.image_processor.classify_height_width_bin( + height, width, ratios=aspect_ratio_bin + ) self.check_inputs( prompt, @@ -838,8 +901,8 @@ def __call__( ( prompt_embeds, prompt_attention_mask, - negative_prompt_embeds, - negative_prompt_attention_mask, + # negative_prompt_embeds, + # negative_prompt_attention_mask, ) = self.encode_prompt( prompt, do_classifier_free_guidance, @@ -853,9 +916,9 @@ def __call__( clean_caption=clean_caption, max_sequence_length=max_sequence_length, ) - if do_classifier_free_guidance: - prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0) - prompt_attention_mask = torch.cat([negative_prompt_attention_mask, prompt_attention_mask], dim=0) + # if do_classifier_free_guidance: + # prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0) + # prompt_attention_mask = torch.cat([negative_prompt_attention_mask, prompt_attention_mask], dim=0) # 4. Prepare timesteps timesteps, num_inference_steps = retrieve_timesteps( @@ -875,14 +938,18 @@ def __call__( latents, ) - # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline + # 6. Prepare extra step kwargs. TODO: Logigic should ideally just be moved out of the pipeline extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) # 6.1 Prepare micro-conditions. added_cond_kwargs = {"resolution": None, "aspect_ratio": None} if self.transformer.config.sample_size == 128: - resolution = torch.tensor([height, width]).repeat(batch_size * num_images_per_prompt, 1) - aspect_ratio = torch.tensor([float(height / width)]).repeat(batch_size * num_images_per_prompt, 1) + resolution = torch.tensor([height, width]).repeat( + batch_size * num_images_per_prompt, 1 + ) + aspect_ratio = torch.tensor([float(height / width)]).repeat( + batch_size * num_images_per_prompt, 1 + ) resolution = resolution.to(dtype=prompt_embeds.dtype, device=device) aspect_ratio = aspect_ratio.to(dtype=prompt_embeds.dtype, device=device) @@ -893,12 +960,18 @@ def __call__( added_cond_kwargs = {"resolution": resolution, "aspect_ratio": aspect_ratio} # 7. Denoising loop - num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0) + num_warmup_steps = max( + len(timesteps) - num_inference_steps * self.scheduler.order, 0 + ) with self.progress_bar(total=num_inference_steps) as progress_bar: for i, t in enumerate(timesteps): - latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents - latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) + latent_model_input = ( + torch.cat([latents] * 2) if do_classifier_free_guidance else latents + ) + latent_model_input = self.scheduler.scale_model_input( + latent_model_input, t + ) current_timestep = t if not torch.is_tensor(current_timestep): @@ -909,9 +982,15 @@ def __call__( dtype = torch.float32 if is_mps else torch.float64 else: dtype = torch.int32 if is_mps else torch.int64 - current_timestep = torch.tensor([current_timestep], dtype=dtype, device=latent_model_input.device) + current_timestep = torch.tensor( + [current_timestep], + dtype=dtype, + device=latent_model_input.device, + ) elif len(current_timestep.shape) == 0: - current_timestep = current_timestep[None].to(latent_model_input.device) + current_timestep = current_timestep[None].to( + latent_model_input.device + ) # broadcast to batch dimension in a way that's compatible with ONNX/Core ML current_timestep = current_timestep.expand(latent_model_input.shape[0]) @@ -928,7 +1007,9 @@ def __call__( # perform guidance if do_classifier_free_guidance: noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) - noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) + noise_pred = noise_pred_uncond + guidance_scale * ( + noise_pred_text - noise_pred_uncond + ) # learned sigma if self.transformer.config.out_channels // 2 == latent_channels: @@ -939,21 +1020,32 @@ def __call__( # compute previous image: x_t -> x_t-1 if num_inference_steps == 1: # For DMD one step sampling: https://arxiv.org/abs/2311.18828 - latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).pred_original_sample + latents = self.scheduler.step( + noise_pred, t, latents, **extra_step_kwargs + ).pred_original_sample else: - latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0] + latents = self.scheduler.step( + noise_pred, t, latents, **extra_step_kwargs, return_dict=False + )[0] # call the callback, if provided - if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0): + if i == len(timesteps) - 1 or ( + (i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0 + ): progress_bar.update() if callback is not None and i % callback_steps == 0: step_idx = i // getattr(self.scheduler, "order", 1) callback(step_idx, t, latents) if not output_type == "latent": - image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0] - if use_resolution_binning: - image = self.image_processor.resize_and_crop_tensor(image, orig_width, orig_height) + image = self.vae.decode( + latents / self.vae.config.scaling_factor, return_dict=False + ).sample + image = (image + 1.0) / 2.0 + image.clamp_(0.0, 1.0) + # real_image = to_pil_image(image[0].float()) + # if use_resolution_binning: + # image = self.image_processor.resize_and_crop_tensor(image, orig_width, orig_height) else: image = latents From 4b598ad6ca0fe1372a066d8589cb5bb6eeba1640 Mon Sep 17 00:00:00 2001 From: PommesPeter Date: Sun, 26 May 2024 22:36:49 +0800 Subject: [PATCH 08/92] fixed typo and added test code --- src/diffusers/__init__.py | 1 + .../pipelines/lumina/modeling_next_dit.py | 92 ++-- .../dummy_torch_and_transformers_objects.py | 15 + tests/pipelines/lumina/__init__.py | 0 tests/pipelines/lumina/test_lumina.py | 442 ++++++++++++++++++ 5 files changed, 505 insertions(+), 45 deletions(-) create mode 100644 tests/pipelines/lumina/__init__.py create mode 100644 tests/pipelines/lumina/test_lumina.py diff --git a/src/diffusers/__init__.py b/src/diffusers/__init__.py index cad0ca544026..f5aa34025732 100644 --- a/src/diffusers/__init__.py +++ b/src/diffusers/__init__.py @@ -264,6 +264,7 @@ "KandinskyV22PriorPipeline", "LatentConsistencyModelImg2ImgPipeline", "LatentConsistencyModelPipeline", + "LuminaText2ImgPipeline", "LDMTextToImagePipeline", "LEditsPPPipelineStableDiffusion", "LEditsPPPipelineStableDiffusionXL", diff --git a/src/diffusers/pipelines/lumina/modeling_next_dit.py b/src/diffusers/pipelines/lumina/modeling_next_dit.py index 2aadfe00c0df..0a9914b84338 100644 --- a/src/diffusers/pipelines/lumina/modeling_next_dit.py +++ b/src/diffusers/pipelines/lumina/modeling_next_dit.py @@ -24,7 +24,7 @@ from ...models.transformers.transformer_2d import Transformer2DModelOutput from ...utils import logging -logger = logging.getLogger(__name__) +logger = logging.get_logger(__name__) def modulate(x, shift, scale): @@ -127,7 +127,7 @@ def forward(self, t): return t_emb -class ParallelLabelEmbedder(nn.Module): +class LabelEmbedder(nn.Module): r"""Embeds class labels into vector representations. Also handles label dropout for classifier-free guidance. """ @@ -165,7 +165,7 @@ def forward(self, labels, train, force_drop_ids=None): ############################################################################# -# Core NextDiT Model # +# Core NextFlagDiffuserTransformer2DModel Model # ############################################################################# @@ -191,8 +191,8 @@ def __init__( """ super().__init__() self.n_kv_heads = n_heads if n_kv_heads is None else n_kv_heads - self.n_heads = n_heads - self.n_rep = self.n_heads // self.n_kv_heads + self.num_attention_heads = n_heads + self.n_rep = self.num_attention_heads // self.n_kv_heads self.head_dim = dim // n_heads self.wq = nn.Linear(dim, n_heads * self.head_dim, bias=False) @@ -201,12 +201,12 @@ def __init__( if y_dim > 0: self.wk_y = nn.Linear(y_dim, self.n_kv_heads * self.head_dim, bias=False) self.wv_y = nn.Linear(y_dim, self.n_kv_heads * self.head_dim, bias=False) - self.gate = nn.Parameter(torch.zeros([self.n_heads])) + self.gate = nn.Parameter(torch.zeros([self.num_attention_heads])) self.wo = nn.Linear(n_heads * self.head_dim, dim, bias=False) if qk_norm: - self.q_norm = nn.LayerNorm(self.n_heads * self.head_dim) + self.q_norm = nn.LayerNorm(self.num_attention_heads * self.head_dim) self.k_norm = nn.LayerNorm(self.n_kv_heads * self.head_dim) if y_dim > 0: self.ky_norm = nn.LayerNorm(self.n_kv_heads * self.head_dim) @@ -309,7 +309,7 @@ def _get_unpad_data(attention_mask): ) if query_length == kv_seq_len: query_layer = index_first_axis( - query_layer.reshape(batch_size * kv_seq_len, self.n_heads, head_dim), + query_layer.reshape(batch_size * kv_seq_len, self.num_attention_heads, head_dim), indices_k, ) cu_seqlens_q = cu_seqlens_k @@ -365,7 +365,7 @@ def forward( xq = self.q_norm(xq) xk = self.k_norm(xk) - xq = xq.view(bsz, seqlen, self.n_heads, self.head_dim) + xq = xq.view(bsz, seqlen, self.num_attention_heads, self.head_dim) xk = xk.view(bsz, seqlen, self.n_kv_heads, self.head_dim) xv = xv.view(bsz, seqlen, self.n_kv_heads, self.head_dim) @@ -418,7 +418,7 @@ def forward( xv.permute(0, 2, 1, 3), attn_mask=x_mask.bool() .view(bsz, 1, 1, seqlen) - .expand(-1, self.n_heads, seqlen, -1), + .expand(-1, self.num_attention_heads, seqlen, -1), ) .permute(0, 2, 1, 3) .to(dtype) @@ -430,7 +430,7 @@ def forward( bsz, -1, self.n_kv_heads, self.head_dim ) yv = self.wv_y(y).view(bsz, -1, self.n_kv_heads, self.head_dim) - n_rep = self.n_heads // self.n_kv_heads + n_rep = self.num_attention_heads // self.n_kv_heads if n_rep >= 1: yk = yk.unsqueeze(3).repeat(1, 1, 1, n_rep, 1).flatten(2, 3) yv = yv.unsqueeze(3).repeat(1, 1, 1, n_rep, 1).flatten(2, 3) @@ -438,7 +438,7 @@ def forward( xq.permute(0, 2, 1, 3), yk.permute(0, 2, 1, 3), yv.permute(0, 2, 1, 3), - y_mask.view(bsz, 1, 1, -1).expand(bsz, self.n_heads, seqlen, -1), + y_mask.view(bsz, 1, 1, -1).expand(bsz, self.num_attention_heads, seqlen, -1), ).permute(0, 2, 1, 3) output_y = output_y * self.gate.tanh().view(1, 1, -1, 1) output = output + output_y @@ -620,7 +620,7 @@ def forward( class FinalLayer(nn.Module): """ - The final layer of NextDiT. + The final layer of NextFlagDiffuserTransformer2DModel. """ def __init__(self, hidden_size, patch_size, out_channels): @@ -651,18 +651,20 @@ class NextFlagDiffuserTransformer2DModel(ModelMixin, ConfigMixin): @register_to_config def __init__( self, - patch_size: int = 2, - in_channels: int = 4, - dim: int = 4096, - n_layers: int = 32, - n_heads: int = 32, - n_kv_heads: Optional[int] = None, + patch_size: Optional[int] = 2, + in_channels: Optional[int] = None, + out_channels: Optional[int] = None, + hidden_size: int = 4096, + num_layers: int = 32, + num_attention_heads: int = 32, + num_kv_heads: Optional[int] = None, multiple_of: int = 256, ffn_dim_multiplier: Optional[float] = None, + norm_elementwise_affine: bool = True, norm_eps: float = 1e-5, learn_sigma: bool = True, qk_norm: bool = False, - cap_feat_dim: int = 5120, + text_dim: int = 5120, rope_scaling_factor: float = 1.0, ntk_factor: float = 1.0, ) -> None: @@ -671,51 +673,51 @@ def __init__( self.in_channels = in_channels self.out_channels = in_channels * 2 if learn_sigma else in_channels self.patch_size = patch_size + self.num_attention_heads = num_attention_heads + self.rope_scaling_factor = rope_scaling_factor + self.ntk_factor = ntk_factor self.x_embedder = nn.Linear( in_features=patch_size * patch_size * in_channels, - out_features=dim, + out_features=hidden_size, bias=True, ) nn.init.constant_(self.x_embedder.bias, 0.0) - self.t_embedder = TimestepEmbedder(min(dim, 1024)) + self.t_embedder = TimestepEmbedder(min(hidden_size, 1024)) self.cap_embedder = nn.Sequential( - nn.LayerNorm(cap_feat_dim), - nn.Linear(cap_feat_dim, min(dim, 1024), bias=True), + nn.LayerNorm(text_dim), + nn.Linear(text_dim, min(hidden_size, 1024), bias=True), ) self.layers = nn.ModuleList( [ TransformerBlock( layer_id, - dim, - n_heads, - n_kv_heads, + hidden_size, + num_attention_heads, + num_kv_heads, multiple_of, ffn_dim_multiplier, norm_eps, qk_norm, - cap_feat_dim, + text_dim, ) - for layer_id in range(n_layers) + for layer_id in range(num_layers) ] ) - self.final_layer = FinalLayer(dim, patch_size, self.out_channels) + self.final_layer = FinalLayer(hidden_size, patch_size, self.out_channels) - assert (dim // n_heads) % 4 == 0, "2d rope needs head dim to be divisible by 4" - self.dim = dim - self.n_heads = n_heads - self.freqs_cis = NextDiT.precompute_freqs_cis( - dim // n_heads, + assert (hidden_size // num_attention_heads) % 4 == 0, "2d rope needs head dim to be divisible by 4" + self.hidden_size = hidden_size + self.freqs_cis = NextFlagDiffuserTransformer2DModel.precompute_freqs_cis( + hidden_size // num_attention_heads, 384, rope_scaling_factor=rope_scaling_factor, ntk_factor=ntk_factor, ) - self.rope_scaling_factor = rope_scaling_factor - self.ntk_factor = ntk_factor # self.eol_token = nn.Parameter(torch.empty(dim)) - self.pad_token = nn.Parameter(torch.empty(dim)) + self.pad_token = nn.Parameter(torch.empty(hidden_size)) # nn.init.normal_(self.eol_token, std=0.02) nn.init.normal_(self.pad_token, std=0.02) @@ -839,7 +841,7 @@ def forward( unpatchify: bool = True, ): """ - Forward pass of NextDiT. + Forward pass of NextFlagDiffuserTransformer2DModel. t: (N,) tensor of diffusion timesteps y: (N,) tensor of class labels """ @@ -904,7 +906,7 @@ def forward_with_cfg( proportional_attn: bool = False, ): # """ - # Forward pass of NextDiT, but also batches the unconNextditional forward pass + # Forward pass of NextFlagDiffuserTransformer2DModel, but also batches the unconNextFlagDiffuserTransformer2DModelional forward pass # for classifier-free guidance. # """ # # https://github.com/openai/glide-text2im/blob/main/notebooks/text2im.ipynb @@ -924,8 +926,8 @@ def forward_with_cfg( f"override freqs_cis, rope_scaling {rope_scaling_factor}, ntk {ntk_factor}", flush=True, ) - self.freqs_cis = NextDiT.precompute_freqs_cis( - self.dim // self.n_heads, + self.freqs_cis = NextFlagDiffuserTransformer2DModel.precompute_freqs_cis( + self.hidden_size // self.num_attention_heads, 384, rope_scaling_factor=rope_scaling_factor, ntk_factor=ntk_factor, @@ -1026,7 +1028,7 @@ def get_fsdp_wrap_module_list(self) -> List[nn.Module]: ############################################################################# -# NextDiT Configs # +# NextFlagDiffuserTransformer2DModel Configs # ############################################################################# -def NextDiT_2B_patch2(**kwargs): - return NextDiT(patch_size=2, dim=2304, n_layers=24, n_heads=32, **kwargs) +def NextFlagDiffuserTransformer2DModel_2B_patch2(**kwargs): + return NextFlagDiffuserTransformer2DModel(patch_size=2, dim=2304, num_layers=24, n_heads=32, **kwargs) diff --git a/src/diffusers/utils/dummy_torch_and_transformers_objects.py b/src/diffusers/utils/dummy_torch_and_transformers_objects.py index b5d17f3fce65..550967623ea7 100644 --- a/src/diffusers/utils/dummy_torch_and_transformers_objects.py +++ b/src/diffusers/utils/dummy_torch_and_transformers_objects.py @@ -737,6 +737,21 @@ def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers"]) +class LuminaText2ImgPipeline(metaclass=DummyObject): + _backends = ["torch", "transformers"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch", "transformers"]) + + @classmethod + def from_config(cls, *args, **kwargs): + requires_backends(cls, ["torch", "transformers"]) + + @classmethod + def from_pretrained(cls, *args, **kwargs): + requires_backends(cls, ["torch", "transformers"]) + + class MusicLDMPipeline(metaclass=DummyObject): _backends = ["torch", "transformers"] diff --git a/tests/pipelines/lumina/__init__.py b/tests/pipelines/lumina/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/tests/pipelines/lumina/test_lumina.py b/tests/pipelines/lumina/test_lumina.py new file mode 100644 index 000000000000..59ddfe2916e4 --- /dev/null +++ b/tests/pipelines/lumina/test_lumina.py @@ -0,0 +1,442 @@ +# coding=utf-8 +# Copyright 2024 HuggingFace Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import gc +import tempfile +import unittest + +import numpy as np +import torch +from transformers import AutoTokenizer, AutoModel + +from diffusers import ( + AutoencoderKL, + DDIMScheduler, + LuminaText2ImgPipeline, + Transformer2DModel, +) +from diffusers.utils.testing_utils import ( + enable_full_determinism, + numpy_cosine_similarity_distance, + require_torch_gpu, + slow, + torch_device, +) + +from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS +from ..test_pipelines_common import PipelineTesterMixin, to_np + + +enable_full_determinism() + + +class LuminaText2ImgPipelineFastTests(PipelineTesterMixin, unittest.TestCase): + pipeline_class = LuminaText2ImgPipeline + params = TEXT_TO_IMAGE_PARAMS - {"cross_attention_kwargs"} + batch_params = TEXT_TO_IMAGE_BATCH_PARAMS + image_params = TEXT_TO_IMAGE_IMAGE_PARAMS + image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS + + required_optional_params = PipelineTesterMixin.required_optional_params + + def get_dummy_components(self): + torch.manual_seed(0) + transformer = Transformer2DModel( + sample_size=8, + num_layers=2, + patch_size=2, + attention_head_dim=8, + num_attention_heads=3, + caption_channels=32, + in_channels=4, + cross_attention_dim=24, + out_channels=8, + attention_bias=True, + activation_fn="gelu-approximate", + num_embeds_ada_norm=1000, + norm_type="ada_norm_single", + norm_elementwise_affine=False, + norm_eps=1e-6, + ) + torch.manual_seed(0) + vae = AutoencoderKL() + + scheduler = DDIMScheduler() + text_encoder = AutoModel.from_pretrained("google/gemma-2b") + + tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b") + + components = { + "transformer": transformer.eval(), + "vae": vae.eval(), + "scheduler": scheduler, + "text_encoder": text_encoder, + "tokenizer": tokenizer, + } + return components + + def get_dummy_inputs(self, device, seed=0): + if str(device).startswith("mps"): + generator = torch.manual_seed(seed) + else: + generator = torch.Generator(device=device).manual_seed(seed) + inputs = { + "prompt": "A painting of a squirrel eating a burger", + "generator": generator, + "num_inference_steps": 2, + "guidance_scale": 5.0, + "use_resolution_binning": False, + "output_type": "np", + } + return inputs + + def test_sequential_cpu_offload_forward_pass(self): + # TODO(PVP, Sayak) need to fix later + return + + def test_save_load_optional_components(self): + components = self.get_dummy_components() + pipe = self.pipeline_class(**components) + pipe.to(torch_device) + pipe.set_progress_bar_config(disable=None) + + inputs = self.get_dummy_inputs(torch_device) + + prompt = inputs["prompt"] + generator = inputs["generator"] + num_inference_steps = inputs["num_inference_steps"] + output_type = inputs["output_type"] + + ( + prompt_embeds, + prompt_attention_mask, + negative_prompt_embeds, + negative_prompt_attention_mask, + ) = pipe.encode_prompt(prompt) + + # inputs with prompt converted to embeddings + inputs = { + "prompt_embeds": prompt_embeds, + "prompt_attention_mask": prompt_attention_mask, + "negative_prompt": None, + "negative_prompt_embeds": negative_prompt_embeds, + "negative_prompt_attention_mask": negative_prompt_attention_mask, + "generator": generator, + "num_inference_steps": num_inference_steps, + "output_type": output_type, + "use_resolution_binning": False, + } + + # set all optional components to None + for optional_component in pipe._optional_components: + setattr(pipe, optional_component, None) + + output = pipe(**inputs)[0] + + with tempfile.TemporaryDirectory() as tmpdir: + pipe.save_pretrained(tmpdir) + pipe_loaded = self.pipeline_class.from_pretrained(tmpdir) + pipe_loaded.to(torch_device) + pipe_loaded.set_progress_bar_config(disable=None) + + for optional_component in pipe._optional_components: + self.assertTrue( + getattr(pipe_loaded, optional_component) is None, + f"`{optional_component}` did not stay set to None after loading.", + ) + + inputs = self.get_dummy_inputs(torch_device) + + generator = inputs["generator"] + num_inference_steps = inputs["num_inference_steps"] + output_type = inputs["output_type"] + + # inputs with prompt converted to embeddings + inputs = { + "prompt_embeds": prompt_embeds, + "prompt_attention_mask": prompt_attention_mask, + "negative_prompt": None, + "negative_prompt_embeds": negative_prompt_embeds, + "negative_prompt_attention_mask": negative_prompt_attention_mask, + "generator": generator, + "num_inference_steps": num_inference_steps, + "output_type": output_type, + "use_resolution_binning": False, + } + + output_loaded = pipe_loaded(**inputs)[0] + + max_diff = np.abs(to_np(output) - to_np(output_loaded)).max() + self.assertLess(max_diff, 1e-4) + + def test_inference(self): + device = "cpu" + + components = self.get_dummy_components() + pipe = self.pipeline_class(**components) + pipe.to(device) + pipe.set_progress_bar_config(disable=None) + + inputs = self.get_dummy_inputs(device) + image = pipe(**inputs).images + image_slice = image[0, -3:, -3:, -1] + + self.assertEqual(image.shape, (1, 8, 8, 3)) + expected_slice = np.array([0.6319, 0.3526, 0.3806, 0.6327, 0.4639, 0.483, 0.2583, 0.5331, 0.4852]) + max_diff = np.abs(image_slice.flatten() - expected_slice).max() + self.assertLessEqual(max_diff, 1e-3) + + def test_inference_non_square_images(self): + device = "cpu" + + components = self.get_dummy_components() + pipe = self.pipeline_class(**components) + pipe.to(device) + pipe.set_progress_bar_config(disable=None) + + inputs = self.get_dummy_inputs(device) + image = pipe(**inputs, height=32, width=48).images + image_slice = image[0, -3:, -3:, -1] + self.assertEqual(image.shape, (1, 32, 48, 3)) + + expected_slice = np.array([0.6493, 0.537, 0.4081, 0.4762, 0.3695, 0.4711, 0.3026, 0.5218, 0.5263]) + max_diff = np.abs(image_slice.flatten() - expected_slice).max() + self.assertLessEqual(max_diff, 1e-3) + + def test_inference_with_embeddings_and_multiple_images(self): + components = self.get_dummy_components() + pipe = self.pipeline_class(**components) + pipe.to(torch_device) + pipe.set_progress_bar_config(disable=None) + + inputs = self.get_dummy_inputs(torch_device) + + prompt = inputs["prompt"] + generator = inputs["generator"] + num_inference_steps = inputs["num_inference_steps"] + output_type = inputs["output_type"] + + prompt_embeds, prompt_attn_mask, negative_prompt_embeds, neg_prompt_attn_mask = pipe.encode_prompt(prompt) + + # inputs with prompt converted to embeddings + inputs = { + "prompt_embeds": prompt_embeds, + "prompt_attention_mask": prompt_attn_mask, + "negative_prompt": None, + "negative_prompt_embeds": negative_prompt_embeds, + "negative_prompt_attention_mask": neg_prompt_attn_mask, + "generator": generator, + "num_inference_steps": num_inference_steps, + "output_type": output_type, + "num_images_per_prompt": 2, + "use_resolution_binning": False, + } + + # set all optional components to None + for optional_component in pipe._optional_components: + setattr(pipe, optional_component, None) + + output = pipe(**inputs)[0] + + with tempfile.TemporaryDirectory() as tmpdir: + pipe.save_pretrained(tmpdir) + pipe_loaded = self.pipeline_class.from_pretrained(tmpdir) + pipe_loaded.to(torch_device) + pipe_loaded.set_progress_bar_config(disable=None) + + for optional_component in pipe._optional_components: + self.assertTrue( + getattr(pipe_loaded, optional_component) is None, + f"`{optional_component}` did not stay set to None after loading.", + ) + + inputs = self.get_dummy_inputs(torch_device) + + generator = inputs["generator"] + num_inference_steps = inputs["num_inference_steps"] + output_type = inputs["output_type"] + + # inputs with prompt converted to embeddings + inputs = { + "prompt_embeds": prompt_embeds, + "prompt_attention_mask": prompt_attn_mask, + "negative_prompt": None, + "negative_prompt_embeds": negative_prompt_embeds, + "negative_prompt_attention_mask": neg_prompt_attn_mask, + "generator": generator, + "num_inference_steps": num_inference_steps, + "output_type": output_type, + "num_images_per_prompt": 2, + "use_resolution_binning": False, + } + + output_loaded = pipe_loaded(**inputs)[0] + + max_diff = np.abs(to_np(output) - to_np(output_loaded)).max() + self.assertLess(max_diff, 1e-4) + + def test_inference_with_multiple_images_per_prompt(self): + device = "cpu" + + components = self.get_dummy_components() + pipe = self.pipeline_class(**components) + pipe.to(device) + pipe.set_progress_bar_config(disable=None) + + inputs = self.get_dummy_inputs(device) + inputs["num_images_per_prompt"] = 2 + image = pipe(**inputs).images + image_slice = image[0, -3:, -3:, -1] + + self.assertEqual(image.shape, (2, 8, 8, 3)) + expected_slice = np.array([0.6319, 0.3526, 0.3806, 0.6327, 0.4639, 0.483, 0.2583, 0.5331, 0.4852]) + max_diff = np.abs(image_slice.flatten() - expected_slice).max() + self.assertLessEqual(max_diff, 1e-3) + + def test_raises_warning_for_mask_feature(self): + device = "cpu" + + components = self.get_dummy_components() + pipe = self.pipeline_class(**components) + pipe.to(device) + pipe.set_progress_bar_config(disable=None) + + inputs = self.get_dummy_inputs(device) + inputs.update({"mask_feature": True}) + + with self.assertWarns(FutureWarning) as warning_ctx: + _ = pipe(**inputs).images + + assert "mask_feature" in str(warning_ctx.warning) + + def test_inference_batch_single_identical(self): + self._test_inference_batch_single_identical(expected_max_diff=1e-3) + + +@slow +@require_torch_gpu +class LuminaText2ImgPipelineIntegrationTests(unittest.TestCase): + ckpt_id_1024 = "PixArt-alpha/PixArt-XL-2-1024-MS" + ckpt_id_512 = "PixArt-alpha/PixArt-XL-2-512x512" + prompt = "A small cactus with a happy face in the Sahara desert." + + def setUp(self): + super().setUp() + gc.collect() + torch.cuda.empty_cache() + + def tearDown(self): + super().tearDown() + gc.collect() + torch.cuda.empty_cache() + + def test_pixart_1024(self): + generator = torch.Generator("cpu").manual_seed(0) + + pipe = LuminaText2ImgPipeline.from_pretrained(self.ckpt_id_1024, torch_dtype=torch.float16) + pipe.enable_model_cpu_offload() + prompt = self.prompt + + image = pipe(prompt, generator=generator, num_inference_steps=2, output_type="np").images + + image_slice = image[0, -3:, -3:, -1] + expected_slice = np.array([0.0742, 0.0835, 0.2114, 0.0295, 0.0784, 0.2361, 0.1738, 0.2251, 0.3589]) + + max_diff = numpy_cosine_similarity_distance(image_slice.flatten(), expected_slice) + self.assertLessEqual(max_diff, 1e-4) + + def test_pixart_512(self): + generator = torch.Generator("cpu").manual_seed(0) + + pipe = LuminaText2ImgPipeline.from_pretrained(self.ckpt_id_512, torch_dtype=torch.float16) + pipe.enable_model_cpu_offload() + + prompt = self.prompt + + image = pipe(prompt, generator=generator, num_inference_steps=2, output_type="np").images + + image_slice = image[0, -3:, -3:, -1] + expected_slice = np.array([0.3477, 0.3882, 0.4541, 0.3413, 0.3821, 0.4463, 0.4001, 0.4409, 0.4958]) + + max_diff = numpy_cosine_similarity_distance(image_slice.flatten(), expected_slice) + self.assertLessEqual(max_diff, 1e-4) + + def test_pixart_1024_without_resolution_binning(self): + generator = torch.manual_seed(0) + + pipe = LuminaText2ImgPipeline.from_pretrained(self.ckpt_id_1024, torch_dtype=torch.float16) + pipe.enable_model_cpu_offload() + + prompt = self.prompt + height, width = 1024, 768 + num_inference_steps = 2 + + image = pipe( + prompt, + height=height, + width=width, + generator=generator, + num_inference_steps=num_inference_steps, + output_type="np", + ).images + image_slice = image[0, -3:, -3:, -1] + + generator = torch.manual_seed(0) + no_res_bin_image = pipe( + prompt, + height=height, + width=width, + generator=generator, + num_inference_steps=num_inference_steps, + output_type="np", + use_resolution_binning=False, + ).images + no_res_bin_image_slice = no_res_bin_image[0, -3:, -3:, -1] + + assert not np.allclose(image_slice, no_res_bin_image_slice, atol=1e-4, rtol=1e-4) + + def test_pixart_512_without_resolution_binning(self): + generator = torch.manual_seed(0) + + pipe = LuminaText2ImgPipeline.from_pretrained(self.ckpt_id_512, torch_dtype=torch.float16) + pipe.enable_model_cpu_offload() + + prompt = self.prompt + height, width = 512, 768 + num_inference_steps = 2 + + image = pipe( + prompt, + height=height, + width=width, + generator=generator, + num_inference_steps=num_inference_steps, + output_type="np", + ).images + image_slice = image[0, -3:, -3:, -1] + + generator = torch.manual_seed(0) + no_res_bin_image = pipe( + prompt, + height=height, + width=width, + generator=generator, + num_inference_steps=num_inference_steps, + output_type="np", + use_resolution_binning=False, + ).images + no_res_bin_image_slice = no_res_bin_image[0, -3:, -3:, -1] + + assert not np.allclose(image_slice, no_res_bin_image_slice, atol=1e-4, rtol=1e-4) From 609f3db0632bec35c5f289e842ff4531d44e9ffd Mon Sep 17 00:00:00 2001 From: PommesPeter Date: Tue, 18 Jun 2024 16:49:42 +0800 Subject: [PATCH 09/92] reformated demo and models --- .../pipelines/lumina/modeling_next_dit.py | 55 +++++++++++++------ 1 file changed, 39 insertions(+), 16 deletions(-) diff --git a/src/diffusers/pipelines/lumina/modeling_next_dit.py b/src/diffusers/pipelines/lumina/modeling_next_dit.py index 0a9914b84338..d19258441a31 100644 --- a/src/diffusers/pipelines/lumina/modeling_next_dit.py +++ b/src/diffusers/pipelines/lumina/modeling_next_dit.py @@ -309,7 +309,9 @@ def _get_unpad_data(attention_mask): ) if query_length == kv_seq_len: query_layer = index_first_axis( - query_layer.reshape(batch_size * kv_seq_len, self.num_attention_heads, head_dim), + query_layer.reshape( + batch_size * kv_seq_len, self.num_attention_heads, head_dim + ), indices_k, ) cu_seqlens_q = cu_seqlens_k @@ -438,7 +440,9 @@ def forward( xq.permute(0, 2, 1, 3), yk.permute(0, 2, 1, 3), yv.permute(0, 2, 1, 3), - y_mask.view(bsz, 1, 1, -1).expand(bsz, self.num_attention_heads, seqlen, -1), + y_mask.view(bsz, 1, 1, -1).expand( + bsz, self.num_attention_heads, seqlen, -1 + ), ).permute(0, 2, 1, 3) output_y = output_y * self.gate.tanh().view(1, 1, -1, 1) output = output + output_y @@ -708,7 +712,9 @@ def __init__( ) self.final_layer = FinalLayer(hidden_size, patch_size, self.out_channels) - assert (hidden_size // num_attention_heads) % 4 == 0, "2d rope needs head dim to be divisible by 4" + assert ( + hidden_size // num_attention_heads + ) % 4 == 0, "2d rope needs head dim to be divisible by 4" self.hidden_size = hidden_size self.freqs_cis = NextFlagDiffuserTransformer2DModel.precompute_freqs_cis( hidden_size // num_attention_heads, @@ -855,7 +861,9 @@ def forward( ) # 1. Patchify and Get embedding - hidden_states, mask, img_size, freqs_cis = self.patchify_and_embed(hidden_states) + hidden_states, mask, img_size, freqs_cis = self.patchify_and_embed( + hidden_states + ) freqs_cis = freqs_cis.to(hidden_states.device) # 2. Get timestep embedding @@ -863,22 +871,33 @@ def forward( # 3. Get encoder hidden_state encoder_attn_mask_float = encoder_attn_mask.float().unsqueeze(-1) - encoder_hidden_states_pool = (encoder_hidden_states * encoder_attn_mask_float).sum(dim=1) / encoder_attn_mask_float.sum( - dim=1 + encoder_hidden_states_pool = ( + encoder_hidden_states * encoder_attn_mask_float + ).sum(dim=1) / encoder_attn_mask_float.sum(dim=1) + encoder_hidden_states_pool = encoder_hidden_states_pool.to( + encoder_hidden_states ) - encoder_hidden_states_pool = encoder_hidden_states_pool.to(encoder_hidden_states) cap_emb = self.cap_embedder(encoder_hidden_states_pool) encoder_attn_mask = encoder_attn_mask.bool() - + adaln_input = timestep + cap_emb # 4. Get hidden_state from transformer for layer in self.layers: - hidden_states = layer(hidden_states, mask, freqs_cis, encoder_hidden_states, encoder_attn_mask, adaln_input=adaln_input) + hidden_states = layer( + hidden_states, + mask, + freqs_cis, + encoder_hidden_states, + encoder_attn_mask, + adaln_input=adaln_input, + ) hidden_states = self.final_layer(hidden_states, adaln_input) if unpatchify: - hidden_states = self.unpatchify(hidden_states, img_size, return_tensor=hidden_states_is_tensor) + hidden_states = self.unpatchify( + hidden_states, img_size, return_tensor=hidden_states_is_tensor + ) else: output = hidden_states @@ -926,11 +945,13 @@ def forward_with_cfg( f"override freqs_cis, rope_scaling {rope_scaling_factor}, ntk {ntk_factor}", flush=True, ) - self.freqs_cis = NextFlagDiffuserTransformer2DModel.precompute_freqs_cis( - self.hidden_size // self.num_attention_heads, - 384, - rope_scaling_factor=rope_scaling_factor, - ntk_factor=ntk_factor, + self.freqs_cis = ( + NextFlagDiffuserTransformer2DModel.precompute_freqs_cis( + self.hidden_size // self.num_attention_heads, + 384, + rope_scaling_factor=rope_scaling_factor, + ntk_factor=ntk_factor, + ) ) self.rope_scaling_factor = rope_scaling_factor self.ntk_factor = ntk_factor @@ -1031,4 +1052,6 @@ def get_fsdp_wrap_module_list(self) -> List[nn.Module]: # NextFlagDiffuserTransformer2DModel Configs # ############################################################################# def NextFlagDiffuserTransformer2DModel_2B_patch2(**kwargs): - return NextFlagDiffuserTransformer2DModel(patch_size=2, dim=2304, num_layers=24, n_heads=32, **kwargs) + return NextFlagDiffuserTransformer2DModel( + patch_size=2, dim=2304, num_layers=24, n_heads=32, **kwargs + ) From 08fcefb2918770e56871db655628d05aedc2b07d Mon Sep 17 00:00:00 2001 From: zhuole1025 Date: Thu, 20 Jun 2024 08:54:17 +0000 Subject: [PATCH 10/92] Add heun sampler for flow matching models --- .../scheduling_flow_match_heun_discrete.py | 322 ++++++++++++++++++ 1 file changed, 322 insertions(+) create mode 100644 src/diffusers/schedulers/scheduling_flow_match_heun_discrete.py diff --git a/src/diffusers/schedulers/scheduling_flow_match_heun_discrete.py b/src/diffusers/schedulers/scheduling_flow_match_heun_discrete.py new file mode 100644 index 000000000000..cebf6106744e --- /dev/null +++ b/src/diffusers/schedulers/scheduling_flow_match_heun_discrete.py @@ -0,0 +1,322 @@ +# Copyright 2024 Stability AI, Katherine Crowson and The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from dataclasses import dataclass +from typing import Optional, Tuple, Union + +import numpy as np +import torch + +from ..configuration_utils import ConfigMixin, register_to_config +from ..utils import BaseOutput, logging +from ..utils.torch_utils import randn_tensor +from .scheduling_utils import SchedulerMixin + + +logger = logging.get_logger(__name__) # pylint: disable=invalid-name + + +@dataclass +class FlowMatchHeunDiscreteSchedulerOutput(BaseOutput): + """ + Output class for the scheduler's `step` function output. + + Args: + prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images): + Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be used as next model input in the + denoising loop. + """ + + prev_sample: torch.FloatTensor + + +class FlowMatchHeunDiscreteScheduler(SchedulerMixin, ConfigMixin): + """ + Heun scheduler. + + This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic + methods the library implements for all schedulers such as loading and saving. + + Args: + num_train_timesteps (`int`, defaults to 1000): + The number of diffusion steps to train the model. + timestep_spacing (`str`, defaults to `"linspace"`): + The way the timesteps should be scaled. Refer to Table 2 of the [Common Diffusion Noise Schedules and + Sample Steps are Flawed](https://huggingface.co/papers/2305.08891) for more information. + shift (`float`, defaults to 1.0): + The shift value for the timestep schedule. + """ + + _compatibles = [] + order = 1 + + @register_to_config + def __init__( + self, + num_train_timesteps: int = 1000, + shift: float = 1.0, + ): + timesteps = np.linspace(1, num_train_timesteps, num_train_timesteps, dtype=np.float32)[::-1].copy() + timesteps = torch.from_numpy(timesteps).to(dtype=torch.float32) + + sigmas = timesteps / num_train_timesteps + sigmas = shift * sigmas / (1 + (shift - 1) * sigmas) + + self.timesteps = sigmas * num_train_timesteps + + self._step_index = None + self._begin_index = None + + self.sigmas = sigmas.to("cpu") # to avoid too much CPU/GPU communication + self.sigma_min = self.sigmas[-1].item() + self.sigma_max = self.sigmas[0].item() + + @property + def step_index(self): + """ + The index counter for current timestep. It will increase 1 after each scheduler step. + """ + return self._step_index + + @property + def begin_index(self): + """ + The index for the first timestep. It should be set from pipeline with `set_begin_index` method. + """ + return self._begin_index + + # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.set_begin_index + def set_begin_index(self, begin_index: int = 0): + """ + Sets the begin index for the scheduler. This function should be run from pipeline before the inference. + + Args: + begin_index (`int`): + The begin index for the scheduler. + """ + self._begin_index = begin_index + + def scale_noise( + self, + sample: torch.FloatTensor, + timestep: Union[float, torch.FloatTensor], + noise: Optional[torch.FloatTensor] = None, + ) -> torch.FloatTensor: + """ + Forward process in flow-matching + + Args: + sample (`torch.FloatTensor`): + The input sample. + timestep (`int`, *optional*): + The current timestep in the diffusion chain. + + Returns: + `torch.FloatTensor`: + A scaled input sample. + """ + if self.step_index is None: + self._init_step_index(timestep) + + sigma = self.sigmas[self.step_index] + sample = sigma * noise + (1.0 - sigma) * sample + + return sample + + def _sigma_to_t(self, sigma): + return sigma * self.config.num_train_timesteps + + def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.device] = None): + """ + Sets the discrete timesteps used for the diffusion chain (to be run before inference). + + Args: + num_inference_steps (`int`): + The number of diffusion steps used when generating samples with a pre-trained model. + device (`str` or `torch.device`, *optional*): + The device to which the timesteps should be moved to. If `None`, the timesteps are not moved. + """ + self.num_inference_steps = num_inference_steps + + timesteps = np.linspace( + self._sigma_to_t(self.sigma_max), self._sigma_to_t(self.sigma_min), num_inference_steps + ) + + sigmas = timesteps / self.config.num_train_timesteps + sigmas = self.config.shift * sigmas / (1 + (self.config.shift - 1) * sigmas) + sigmas = torch.from_numpy(sigmas).to(dtype=torch.float32, device=device) + + timesteps = sigmas * self.config.num_train_timesteps + timesteps = torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2)]) + self.timesteps = timesteps.to(device=device) + + sigmas = torch.cat([sigmas, torch.zeros(1, device=sigmas.device)]) + self.sigmas = torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2), sigmas[-1:]]) + + # empty dt and derivative + self.prev_derivative = None + self.dt = None + + self._step_index = None + self._begin_index = None + + def index_for_timestep(self, timestep, schedule_timesteps=None): + if schedule_timesteps is None: + schedule_timesteps = self.timesteps + + indices = (schedule_timesteps == timestep).nonzero() + + # The sigma index that is taken for the **very** first `step` + # is always the second index (or the last index if there is only 1) + # This way we can ensure we don't accidentally skip a sigma in + # case we start in the middle of the denoising schedule (e.g. for image-to-image) + pos = 1 if len(indices) > 1 else 0 + + return indices[pos].item() + + def _init_step_index(self, timestep): + if self.begin_index is None: + if isinstance(timestep, torch.Tensor): + timestep = timestep.to(self.timesteps.device) + self._step_index = self.index_for_timestep(timestep) + else: + self._step_index = self._begin_index + + @property + def state_in_first_order(self): + return self.dt is None + + def step( + self, + model_output: torch.FloatTensor, + timestep: Union[float, torch.FloatTensor], + sample: torch.FloatTensor, + s_churn: float = 0.0, + s_tmin: float = 0.0, + s_tmax: float = float("inf"), + s_noise: float = 1.0, + generator: Optional[torch.Generator] = None, + return_dict: bool = True, + ) -> Union[FlowMatchHeunDiscreteSchedulerOutput, Tuple]: + """ + Predict the sample from the previous timestep by reversing the SDE. This function propagates the diffusion + process from the learned model outputs (most often the predicted noise). + + Args: + model_output (`torch.FloatTensor`): + The direct output from learned diffusion model. + timestep (`float`): + The current discrete timestep in the diffusion chain. + sample (`torch.FloatTensor`): + A current instance of a sample created by the diffusion process. + s_churn (`float`): + s_tmin (`float`): + s_tmax (`float`): + s_noise (`float`, defaults to 1.0): + Scaling factor for noise added to the sample. + generator (`torch.Generator`, *optional*): + A random number generator. + return_dict (`bool`): + Whether or not to return a [`~schedulers.scheduling_Heun_discrete.HeunDiscreteSchedulerOutput`] or + tuple. + + Returns: + [`~schedulers.scheduling_Heun_discrete.HeunDiscreteSchedulerOutput`] or `tuple`: + If return_dict is `True`, [`~schedulers.scheduling_Heun_discrete.HeunDiscreteSchedulerOutput`] is + returned, otherwise a tuple is returned where the first element is the sample tensor. + """ + + if ( + isinstance(timestep, int) + or isinstance(timestep, torch.IntTensor) + or isinstance(timestep, torch.LongTensor) + ): + raise ValueError( + ( + "Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to" + " `HeunDiscreteScheduler.step()` is not supported. Make sure to pass" + " one of the `scheduler.timesteps` as a timestep." + ), + ) + + if self.step_index is None: + self._init_step_index(timestep) + + # Upcast to avoid precision issues when computing prev_sample + sample = sample.to(torch.float32) + + if self.state_in_first_order: + sigma = self.sigmas[self.step_index] + sigma_next = self.sigmas[self.step_index + 1] + else: + # 2nd order / Heun's method + sigma = self.sigmas[self.step_index - 1] + sigma_next = self.sigmas[self.step_index] + + gamma = min(s_churn / (len(self.sigmas) - 1), 2**0.5 - 1) if s_tmin <= sigma <= s_tmax else 0.0 + + noise = randn_tensor( + model_output.shape, dtype=model_output.dtype, device=model_output.device, generator=generator + ) + + eps = noise * s_noise + sigma_hat = sigma * (gamma + 1) + + if gamma > 0: + sample = sample + eps * (sigma_hat**2 - sigma**2) ** 0.5 + + # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise + # NOTE: "original_sample" should not be an expected prediction_type but is left in for + # backwards compatibility + denoised = sample - model_output * sigma + + if self.state_in_first_order: + # 2. Convert to an ODE derivative for 1st order + derivative = (sample - denoised) / sigma_hat + # 3. Delta timestep + dt = self.sigmas_next - sigma_hat + + # store for 2nd order step + self.prev_derivative = derivative + self.dt = dt + self.sample = sample + else: + # 2. 2nd order / Heun's method + derivative = (sample - denoised) / sigma_next + derivative = 0.5 * (self.prev_derivative + derivative) + + # 3. take prev timestep & sample + dt = self.dt + sample = self.sample + + # free dt and derivative + # Note, this puts the scheduler in "first order mode" + self.prev_derivative = None + self.dt = None + self.sample = None + + prev_sample = sample + derivative * dt + # Cast sample back to model compatible dtype + prev_sample = prev_sample.to(model_output.dtype) + + # upon completion increase step index by one + self._step_index += 1 + + if not return_dict: + return (prev_sample,) + + return FlowMatchHeunDiscreteSchedulerOutput(prev_sample=prev_sample) + + def __len__(self): + return self.config.num_train_timesteps \ No newline at end of file From 576171c8442c1ce43a6c9aefd46f48cd7322e13d Mon Sep 17 00:00:00 2001 From: PommesPeter Date: Thu, 20 Jun 2024 23:06:15 +0800 Subject: [PATCH 11/92] Added Lumina-Next-SFT model to diffusers --- scripts/convert_lumina_to_diffusers.py | 139 +++ src/diffusers/__init__.py | 5 +- src/diffusers/models/__init__.py | 2 + src/diffusers/models/attention.py | 54 + src/diffusers/models/embeddings.py | 41 + src/diffusers/models/transformers/__init__.py | 1 + .../models/transformers/lumina_nextdit_2d.py | 919 ++++++++++++++ src/diffusers/pipelines/__init__.py | 2 + src/diffusers/pipelines/lumina/__init__.py | 49 +- .../pipelines/lumina/modeling_flag_dit.py | 999 ---------------- .../pipelines/lumina/modeling_next_dit.py | 1057 ----------------- .../pipelines/lumina/pipeline_lumina.py | 438 ++++--- 12 files changed, 1417 insertions(+), 2289 deletions(-) create mode 100644 scripts/convert_lumina_to_diffusers.py create mode 100644 src/diffusers/models/transformers/lumina_nextdit_2d.py delete mode 100644 src/diffusers/pipelines/lumina/modeling_flag_dit.py delete mode 100644 src/diffusers/pipelines/lumina/modeling_next_dit.py diff --git a/scripts/convert_lumina_to_diffusers.py b/scripts/convert_lumina_to_diffusers.py new file mode 100644 index 000000000000..e0090f652e16 --- /dev/null +++ b/scripts/convert_lumina_to_diffusers.py @@ -0,0 +1,139 @@ +import argparse +import os + +import torch +from transformers import AutoModel, AutoTokenizer + +from diffusers import LuminaText2ImgPipeline, LuminaNextDiT2DModel, AutoencoderKL, FlowMatchEulerDiscreteScheduler +from safetensors.torch import load_file + + +def main(args): + # checkpoint from https://huggingface.co/Alpha-VLLM/Lumina-Next-SFT or https://huggingface.co/Alpha-VLLM/Lumina-Next-T2I + all_sd = load_file(args.origin_ckpt_path, device="cpu") + converted_state_dict = {} + + # Pad token + converted_state_dict["pad_token"] = all_sd["pad_token"] + + # Patch Embed + converted_state_dict["patch_embedder.weight"] = all_sd["x_embedder.weight"] + converted_state_dict["patch_embedder.bias"] = all_sd["x_embedder.bias"] + + # Timestep and Caption Embed + converted_state_dict["time_caption_embed.timestep_embedder.linear_1.weight"] = all_sd["t_embedder.mlp.0.weight"] + converted_state_dict["time_caption_embed.timestep_embedder.linear_1.bias"] = all_sd["t_embedder.mlp.0.bias"] + converted_state_dict["time_caption_embed.timestep_embedder.linear_2.weight"] = all_sd["t_embedder.mlp.2.weight"] + converted_state_dict["time_caption_embed.timestep_embedder.linear_2.bias"] = all_sd["t_embedder.mlp.2.bias"] + converted_state_dict["time_caption_embed.caption_embedder.0.weight"] = all_sd["cap_embedder.0.weight"] + converted_state_dict["time_caption_embed.caption_embedder.0.bias"] = all_sd["cap_embedder.0.bias"] + converted_state_dict["time_caption_embed.caption_embedder.1.weight"] = all_sd["cap_embedder.1.weight"] + converted_state_dict["time_caption_embed.caption_embedder.1.bias"] = all_sd["cap_embedder.1.bias"] + + for i in range(24): + # adaLN + converted_state_dict[f"layers.{i}.attention.gate"] = all_sd[f"layers.{i}.attention.gate"] + converted_state_dict[f"layers.{i}.adaLN_modulation.1.weight"] = all_sd[f"layers.{i}.adaLN_modulation.1.weight"] + converted_state_dict[f"layers.{i}.adaLN_modulation.1.bias"] = all_sd[f"layers.{i}.adaLN_modulation.1.bias"] + + # QKV + converted_state_dict[f"layers.{i}.attention.wq.weight"] = all_sd[f"layers.{i}.attention.wq.weight"] + converted_state_dict[f"layers.{i}.attention.wk.weight"] = all_sd[f"layers.{i}.attention.wk.weight"] + converted_state_dict[f"layers.{i}.attention.wv.weight"] = all_sd[f"layers.{i}.attention.wv.weight"] + + # Caption KV + converted_state_dict[f"layers.{i}.attention.wk_cap.weight"] = all_sd[f"layers.{i}.attention.wk_y.weight"] + converted_state_dict[f"layers.{i}.attention.wv_cap.weight"] = all_sd[f"layers.{i}.attention.wv_y.weight"] + + # Output Layer + converted_state_dict[f"layers.{i}.attention.wo.weight"] = all_sd[f"layers.{i}.attention.wo.weight"] + + # Attention + # QK Norm + converted_state_dict[f"layers.{i}.attention.q_norm.weight"] = all_sd[f"layers.{i}.attention.q_norm.weight"] + converted_state_dict[f"layers.{i}.attention.q_norm.bias"] = all_sd[f"layers.{i}.attention.q_norm.bias"] + + converted_state_dict[f"layers.{i}.attention.k_norm.weight"] = all_sd[f"layers.{i}.attention.k_norm.weight"] + converted_state_dict[f"layers.{i}.attention.k_norm.bias"] = all_sd[f"layers.{i}.attention.k_norm.bias"] + + # Caption K Norm + converted_state_dict[f"layers.{i}.attention.k_cap_norm.weight"] = all_sd[f"layers.{i}.attention.ky_norm.weight"] + converted_state_dict[f"layers.{i}.attention.k_cap_norm.bias"] = all_sd[f"layers.{i}.attention.ky_norm.bias"] + + # Attention Norm + converted_state_dict[f"layers.{i}.attention_norm1.weight"] = all_sd[f"layers.{i}.attention_norm1.weight"] + converted_state_dict[f"layers.{i}.attention_norm2.weight"] = all_sd[f"layers.{i}.attention_norm2.weight"] + converted_state_dict[f"layers.{i}.attention_caption_norm.weight"] = all_sd[f"layers.{i}.attention_y_norm.weight"] + + # Feed Forward layer + converted_state_dict[f"layers.{i}.feed_forward.w1.weight"] = all_sd[f"layers.{i}.feed_forward.w1.weight"] + converted_state_dict[f"layers.{i}.feed_forward.w2.weight"] = all_sd[f"layers.{i}.feed_forward.w2.weight"] + converted_state_dict[f"layers.{i}.feed_forward.w3.weight"] = all_sd[f"layers.{i}.feed_forward.w3.weight"] + + # Feed Forward Norm + converted_state_dict[f"layers.{i}.ffn_norm1.weight"] = all_sd[f"layers.{i}.ffn_norm1.weight"] + converted_state_dict[f"layers.{i}.ffn_norm2.weight"] = all_sd[f"layers.{i}.ffn_norm2.weight"] + + # Final Layer + converted_state_dict["final_layer.linear.weight"] = all_sd["final_layer.linear.weight"] + converted_state_dict["final_layer.linear.bias"] = all_sd["final_layer.linear.bias"] + + converted_state_dict["final_layer.adaLN_modulation.1.weight"] = all_sd["final_layer.adaLN_modulation.1.weight"] + converted_state_dict["final_layer.adaLN_modulation.1.bias"] = all_sd["final_layer.adaLN_modulation.1.bias"] + + # Lumina-Next-SFT 2B + transformer = LuminaNextDiT2DModel( + patch_size=2, + in_channels=4, + hidden_size=2304, + num_layers=24, + num_attention_heads=32, + num_kv_heads=8, + multiple_of=256, + ffn_dim_multiplier=None, + norm_eps=1e-5, + learn_sigma=True, + qk_norm=True, + caption_dim=2048, + scale_factor=1.0, + ) + transformer.load_state_dict(converted_state_dict, strict=True) + + num_model_params = sum(p.numel() for p in transformer.parameters()) + print(f"Total number of transformer parameters: {num_model_params}") + + if args.only_transformer: + transformer.save_pretrained(os.path.join(args.dump_path, "transformer")) + else: + scheduler = FlowMatchEulerDiscreteScheduler() + + vae = AutoencoderKL.from_pretrained("stabilityai/sdxl-vae", torch_dtype=torch.float32) + + tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b") + text_encoder = AutoModel.from_pretrained("google/gemma-2b") + + pipeline = LuminaText2ImgPipeline( + tokenizer=tokenizer, text_encoder=text_encoder, transformer=transformer, vae=vae, scheduler=scheduler + ) + pipeline.save_pretrained(args.dump_path) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + + parser.add_argument( + "--origin_ckpt_path", default=None, type=str, required=False, help="Path to the checkpoint to convert." + ) + parser.add_argument( + "--image_size", + default=1024, + type=int, + choices=[256, 512, 1024], + required=False, + help="Image size of pretrained model, either 512 or 1024.", + ) + parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output pipeline.") + parser.add_argument("--only_transformer", default=True, type=bool, required=True) + + args = parser.parse_args() + main(args) diff --git a/src/diffusers/__init__.py b/src/diffusers/__init__.py index f5aa34025732..f21fdb9dc66c 100644 --- a/src/diffusers/__init__.py +++ b/src/diffusers/__init__.py @@ -86,6 +86,7 @@ "HunyuanDiT2DModel", "I2VGenXLUNet", "Kandinsky3UNet", + "LuminaNextDiT2DModel", "ModelMixin", "MotionAdapter", "MultiAdapter", @@ -264,10 +265,10 @@ "KandinskyV22PriorPipeline", "LatentConsistencyModelImg2ImgPipeline", "LatentConsistencyModelPipeline", - "LuminaText2ImgPipeline", "LDMTextToImagePipeline", "LEditsPPPipelineStableDiffusion", "LEditsPPPipelineStableDiffusionXL", + "LuminaText2ImgPipeline", "MarigoldDepthPipeline", "MarigoldNormalsPipeline", "MusicLDMPipeline", @@ -500,6 +501,7 @@ HunyuanDiT2DModel, I2VGenXLUNet, Kandinsky3UNet, + LuminaNextDiT2DModel, ModelMixin, MotionAdapter, MultiAdapter, @@ -659,6 +661,7 @@ LDMTextToImagePipeline, LEditsPPPipelineStableDiffusion, LEditsPPPipelineStableDiffusionXL, + LuminaText2ImgPipeline, MarigoldDepthPipeline, MarigoldNormalsPipeline, MusicLDMPipeline, diff --git a/src/diffusers/models/__init__.py b/src/diffusers/models/__init__.py index e863c8010a3d..7ea74466c083 100644 --- a/src/diffusers/models/__init__.py +++ b/src/diffusers/models/__init__.py @@ -40,6 +40,7 @@ _import_structure["transformers.dit_transformer_2d"] = ["DiTTransformer2DModel"] _import_structure["transformers.dual_transformer_2d"] = ["DualTransformer2DModel"] _import_structure["transformers.hunyuan_transformer_2d"] = ["HunyuanDiT2DModel"] + _import_structure["transformers.lumina_nextdit_2d"] = ["LuminaNextDiT2DModel"] _import_structure["transformers.pixart_transformer_2d"] = ["PixArtTransformer2DModel"] _import_structure["transformers.prior_transformer"] = ["PriorTransformer"] _import_structure["transformers.t5_film_transformer"] = ["T5FilmDecoder"] @@ -83,6 +84,7 @@ DiTTransformer2DModel, DualTransformer2DModel, HunyuanDiT2DModel, + LuminaNextDiT2DModel, PixArtTransformer2DModel, PriorTransformer, SD3Transformer2DModel, diff --git a/src/diffusers/models/attention.py b/src/diffusers/models/attention.py index e19b087431a2..9eb653da810f 100644 --- a/src/diffusers/models/attention.py +++ b/src/diffusers/models/attention.py @@ -527,6 +527,60 @@ def forward( return hidden_states +class LuminaFeedForward(nn.Module): + r""" + A feed-forward layer. + + Parameters: + hidden_size (`int`): The dimensionality of the hidden layers in the model. This parameter determines the width of the model's hidden representations. + intermediate_size (`int`): The intermediate dimension of the feedforward layer. + multiple_of (`int`, *optional*): Value to ensure hidden dimension is a multiple + of this value. + ffn_dim_multiplier (float, *optional*): Custom multiplier for hidden + dimension. Defaults to None. + """ + + def __init__( + self, + hidden_size: int, + intermediate_size: int, + multiple_of: Optional[int] = 256, + ffn_dim_multiplier: Optional[float] = None, + ): + super().__init__() + intermediate_size = int(2 * intermediate_size / 3) + # custom hidden_size factor multiplier + if ffn_dim_multiplier is not None: + intermediate_size = int(ffn_dim_multiplier * intermediate_size) + intermediate_size = multiple_of * ((intermediate_size + multiple_of - 1) // multiple_of) + + self.w1 = nn.Linear( + hidden_size, + intermediate_size, + bias=False, + ) + self.w2 = nn.Linear( + intermediate_size, + hidden_size, + bias=False, + ) + self.w3 = nn.Linear( + hidden_size, + intermediate_size, + bias=False, + ) + nn.init.xavier_uniform_(self.w1.weight) + nn.init.xavier_uniform_(self.w2.weight) + nn.init.xavier_uniform_(self.w3.weight) + + # @torch.compile + def _forward_silu_gating(self, x1, x3): + return F.silu(x1) * x3 + + def forward(self, x): + return self.w2(self._forward_silu_gating(self.w1(x), self.w3(x))) + + @maybe_allow_in_graph class TemporalBasicTransformerBlock(nn.Module): r""" diff --git a/src/diffusers/models/embeddings.py b/src/diffusers/models/embeddings.py index a95102169879..16019193eb66 100644 --- a/src/diffusers/models/embeddings.py +++ b/src/diffusers/models/embeddings.py @@ -758,6 +758,47 @@ def forward(self, timestep, encoder_hidden_states, image_meta_size, style, hidde return conditioning +class LuminaCombinedTimestepCaptionEmbedding(nn.Module): + def __init__(self, hidden_size=4096, caption_dim=5120, frequency_embedding_size=256): + super().__init__() + self.time_proj = Timesteps(num_channels=frequency_embedding_size, flip_sin_to_cos=False, downscale_freq_shift=0.0) + + self.timestep_embedder = TimestepEmbedding( + in_channels=frequency_embedding_size, + time_embed_dim=hidden_size) + + nn.init.normal_(self.timestep_embedder.linear_1.weight, std=0.02) + nn.init.zeros_(self.timestep_embedder.linear_1.bias) + nn.init.normal_(self.timestep_embedder.linear_2.weight, std=0.02) + nn.init.zeros_(self.timestep_embedder.linear_2.bias) + + self.caption_embedder = nn.Sequential( + nn.LayerNorm(caption_dim), + nn.Linear( + caption_dim, + hidden_size, + bias=True, + ), + ) + nn.init.zeros_(self.caption_embedder[1].weight) + nn.init.zeros_(self.caption_embedder[1].bias) + + def forward(self, timestep, caption_feat, caption_mask): + # timestep embedding: + time_freq = self.time_proj(timestep) + time_embed = self.timestep_embedder(time_freq.to(dtype=self.timestep_embedder.linear_1.dtype)) + + # caption condition embedding: + caption_mask_float = caption_mask.float().unsqueeze(-1) + caption_feats_pool = (caption_feat * caption_mask_float).sum(dim=1) / caption_mask_float.sum(dim=1) + caption_feats_pool = caption_feats_pool.to(caption_feat) + caption_embed = self.caption_embedder(caption_feats_pool) + + conditioning = time_embed + caption_embed + + return conditioning + + class TextTimeEmbedding(nn.Module): def __init__(self, encoder_dim: int, time_embed_dim: int, num_heads: int = 64): super().__init__() diff --git a/src/diffusers/models/transformers/__init__.py b/src/diffusers/models/transformers/__init__.py index 04bd21b70737..f4e41239b88c 100644 --- a/src/diffusers/models/transformers/__init__.py +++ b/src/diffusers/models/transformers/__init__.py @@ -5,6 +5,7 @@ from .dit_transformer_2d import DiTTransformer2DModel from .dual_transformer_2d import DualTransformer2DModel from .hunyuan_transformer_2d import HunyuanDiT2DModel + from .lumina_nextdit_2d import LuminaNextDiT2DModel from .pixart_transformer_2d import PixArtTransformer2DModel from .prior_transformer import PriorTransformer from .t5_film_transformer import T5FilmDecoder diff --git a/src/diffusers/models/transformers/lumina_nextdit_2d.py b/src/diffusers/models/transformers/lumina_nextdit_2d.py new file mode 100644 index 000000000000..4b84cb48b1fe --- /dev/null +++ b/src/diffusers/models/transformers/lumina_nextdit_2d.py @@ -0,0 +1,919 @@ +# Copyright 2024 Alpha-VLLM Authors and The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import List, Optional, Tuple +import math + +from flash_attn import flash_attn_varlen_func +from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa +import torch +import torch.nn as nn +import torch.nn.functional as F + +from ...configuration_utils import ConfigMixin, register_to_config +from ...utils import logging +from ..attention import LuminaFeedForward +from ..embeddings import ( + LuminaCombinedTimestepCaptionEmbedding, +) +from ..modeling_outputs import Transformer2DModelOutput +from ..modeling_utils import ModelMixin +from ..normalization import RMSNorm + + +logger = logging.get_logger(__name__) # pylint: disable=invalid-name + + +def modulate(x, scale): + return x * (1 + scale.unsqueeze(1)) + + +class Attention(nn.Module): + """ + Initialize the Multi-head attention module. + + Args: + hidden_size (int): Number of input dimensions. + num_attention_heads (int): Number of heads. + num_kv_heads (Optional[int]): Number of kv heads, if using GQA. + """ + def __init__( + self, + hidden_size: int, + num_attention_heads: int, + num_kv_heads: Optional[int], + qk_norm: bool, + caption_dim: int, + ): + super().__init__() + self.num_kv_heads = num_attention_heads if num_kv_heads is None else num_kv_heads + self.num_attention_heads = num_attention_heads + self.num_kv_heads = self.num_kv_heads + self.n_rep = self.num_attention_heads // self.num_kv_heads + self.head_dim = hidden_size // num_attention_heads + + self.wq = nn.Linear( + hidden_size, + num_attention_heads * self.head_dim, + bias=False, + ) + nn.init.xavier_uniform_(self.wq.weight) + self.wk = nn.Linear( + hidden_size, + self.num_kv_heads * self.head_dim, + bias=False, + ) + nn.init.xavier_uniform_(self.wk.weight) + self.wv = nn.Linear( + hidden_size, + self.num_kv_heads * self.head_dim, + bias=False, + ) + nn.init.xavier_uniform_(self.wv.weight) + if caption_dim > 0: + self.wk_cap = nn.Linear( + caption_dim, + self.num_kv_heads * self.head_dim, + bias=False, + ) + nn.init.xavier_uniform_(self.wk_cap.weight) + self.wv_cap = nn.Linear( + caption_dim, + self.num_kv_heads * self.head_dim, + bias=False, + ) + nn.init.xavier_uniform_(self.wv_cap.weight) + self.gate = nn.Parameter(torch.zeros([self.num_attention_heads])) + + self.wo = nn.Linear( + num_attention_heads * self.head_dim, + hidden_size, + bias=False, + ) + nn.init.xavier_uniform_(self.wo.weight) + + if qk_norm: + self.q_norm = nn.LayerNorm(self.num_attention_heads * self.head_dim) + self.k_norm = nn.LayerNorm(self.num_kv_heads * self.head_dim) + if caption_dim > 0: + self.k_cap_norm = nn.LayerNorm(self.num_kv_heads * self.head_dim) + else: + self.k_cap_norm = nn.Identity() + else: + self.q_norm = self.k_norm = nn.Identity() + self.k_cap_norm = nn.Identity() + + # for proportional attention computation + self.base_seqlen = None + self.proportional_attn = False + self.use_flash_attn = True + + @staticmethod + def reshape_for_broadcast(freqs_cis: torch.Tensor, x: torch.Tensor): + """ + Reshape frequency tensor for broadcasting it with another tensor. + + This function reshapes the frequency tensor to have the same shape as + the target tensor 'x' for the purpose of broadcasting the frequency + tensor during element-wise operations. + + Args: + freqs_cis (torch.Tensor): Frequency tensor to be reshaped. + x (torch.Tensor): Target tensor for broadcasting compatibility. + + Returns: + torch.Tensor: Reshaped frequency tensor. + + Raises: + AssertionError: If the frequency tensor doesn't match the expected + shape. + AssertionError: If the target tensor 'x' doesn't have the expected + number of dimensions. + """ + ndim = x.ndim + assert 0 <= 1 < ndim + assert freqs_cis.shape == (x.shape[1], x.shape[-1]) + shape = [d if i == 1 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)] + return freqs_cis.view(*shape) + + @staticmethod + def apply_rotary_emb( + x_in: torch.Tensor, + freqs_cis: torch.Tensor, + ) -> torch.Tensor: + """ + Apply rotary embeddings to input tensors using the given frequency + tensor. + + This function applies rotary embeddings to the given query 'xq' and + key 'xk' tensors using the provided frequency tensor 'freqs_cis'. The + input tensors are reshaped as complex numbers, and the frequency tensor + is reshaped for broadcasting compatibility. The resulting tensors + contain rotary embeddings and are returned as real tensors. + + Args: + x_in (torch.Tensor): Query or Key tensor to apply rotary embeddings. + freqs_cis (torch.Tensor): Precomputed frequency tensor for complex + exponentials. + + Returns: + Tuple[torch.Tensor, torch.Tensor]: Tuple of modified query tensor + and key tensor with rotary embeddings. + """ + with torch.cuda.amp.autocast(enabled=False): + x = torch.view_as_complex(x_in.float().reshape(*x_in.shape[:-1], -1, 2)) + freqs_cis = freqs_cis.unsqueeze(2) + x_out = torch.view_as_real(x * freqs_cis).flatten(3) + return x_out.type_as(x_in) + + # copied from huggingface modeling_llama.py + def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length): + def _get_unpad_data(attention_mask): + seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32) + indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten() + max_seqlen_in_batch = seqlens_in_batch.max().item() + cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0)) + return ( + indices, + cu_seqlens, + max_seqlen_in_batch, + ) + + indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask) + batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape + + key_layer = index_first_axis( + key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), + indices_k, + ) + value_layer = index_first_axis( + value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), + indices_k, + ) + if query_length == kv_seq_len: + query_layer = index_first_axis( + query_layer.reshape(batch_size * kv_seq_len, self.num_attention_heads, head_dim), + indices_k, + ) + cu_seqlens_q = cu_seqlens_k + max_seqlen_in_batch_q = max_seqlen_in_batch_k + indices_q = indices_k + elif query_length == 1: + max_seqlen_in_batch_q = 1 + cu_seqlens_q = torch.arange( + batch_size + 1, dtype=torch.int32, device=query_layer.device + ) # There is a memcpy here, that is very bad. + indices_q = cu_seqlens_q[:-1] + query_layer = query_layer.squeeze(1) + else: + # The -q_len: slice assumes left padding. + attention_mask = attention_mask[:, -query_length:] + query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask) + + return ( + query_layer, + key_layer, + value_layer, + indices_q, + (cu_seqlens_q, cu_seqlens_k), + (max_seqlen_in_batch_q, max_seqlen_in_batch_k), + ) + + def forward( + self, + x: torch.Tensor, + x_mask: torch.Tensor, + freqs_cis: torch.Tensor, + caption_feat: torch.Tensor, + caption_mask: torch.Tensor, + ) -> torch.Tensor: + """ + Apply the attention mechanism to the input tensors. + + Args: + x (torch.Tensor): Input tensor. + x_mask (torch.Tensor): Mask for the input tensor. + freqs_cis (torch.Tensor): Frequency tensor for complex exponentials. + caption_feat (torch.Tensor): Additional input tensor. + caption_mask (torch.Tensor): Mask for the additional input tensor. + + Returns: + torch.Tensor: Output tensor after applying the attention mechanism. + """ + bsz, seqlen, _ = x.shape + xq, xk, xv = self.wq(x), self.wk(x), self.wv(x) + dtype = xq.dtype + + xq = self.q_norm(xq) + xk = self.k_norm(xk) + + xq = xq.view(bsz, seqlen, self.num_attention_heads, self.head_dim) + xk = xk.view(bsz, seqlen, self.num_kv_heads, self.head_dim) + xv = xv.view(bsz, seqlen, self.num_kv_heads, self.head_dim) + + xq = Attention.apply_rotary_emb(xq, freqs_cis=freqs_cis) + xk = Attention.apply_rotary_emb(xk, freqs_cis=freqs_cis) + + xq, xk = xq.to(dtype), xk.to(dtype) + + if self.proportional_attn: + softmax_scale = math.sqrt(math.log(seqlen, self.base_seqlen) / self.head_dim) + else: + softmax_scale = math.sqrt(1 / self.head_dim) + + if self.use_flash_attn and dtype in [torch.float16, torch.bfloat16]: + ( + query_states, + key_states, + value_states, + indices_q, + cu_seq_lens, + max_seq_lens, + ) = self._upad_input(xq, xk, xv, x_mask, seqlen) + + cu_seqlens_q, cu_seqlens_k = cu_seq_lens + max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens + + attn_output_unpad = flash_attn_varlen_func( + query_states, + key_states, + value_states, + cu_seqlens_q=cu_seqlens_q, + cu_seqlens_k=cu_seqlens_k, + max_seqlen_q=max_seqlen_in_batch_q, + max_seqlen_k=max_seqlen_in_batch_k, + dropout_p=0.0, + causal=False, + softmax_scale=softmax_scale, + ) + output = pad_input(attn_output_unpad, indices_q, bsz, seqlen) + + else: + n_rep = self.num_attention_heads // self.num_kv_heads + if n_rep >= 1: + xk = xk.unsqueeze(3).repeat(1, 1, 1, n_rep, 1).flatten(2, 3) + xv = xv.unsqueeze(3).repeat(1, 1, 1, n_rep, 1).flatten(2, 3) + output = ( + F.scaled_dot_product_attention( + xq.permute(0, 2, 1, 3), + xk.permute(0, 2, 1, 3), + xv.permute(0, 2, 1, 3), + attn_mask=x_mask.bool().view(bsz, 1, 1, seqlen).expand(-1, self.num_attention_heads, seqlen, -1), + scale=softmax_scale, + ) + .permute(0, 2, 1, 3) + .to(dtype) + ) + + if hasattr(self, "wk_cap"): + yk = self.k_cap_norm(self.wk_cap(caption_feat)).view(bsz, -1, self.num_kv_heads, self.head_dim) + yv = self.wv_cap(caption_feat).view(bsz, -1, self.num_kv_heads, self.head_dim) + + n_rep = self.num_attention_heads // self.num_kv_heads + + if n_rep >= 1: + yk = yk.unsqueeze(3).repeat(1, 1, 1, n_rep, 1).flatten(2, 3) + yv = yv.unsqueeze(3).repeat(1, 1, 1, n_rep, 1).flatten(2, 3) + + output_caption = F.scaled_dot_product_attention( + xq.permute(0, 2, 1, 3), + yk.permute(0, 2, 1, 3), + yv.permute(0, 2, 1, 3), + caption_mask.view(bsz, 1, 1, -1).expand(bsz, self.num_attention_heads, seqlen, -1), + ).permuste(0, 2, 1, 3) + + output_caption = output_caption * self.gate.tanh().view(1, 1, -1, 1) + output = output + output_caption + + output = output.flatten(-2) + + return self.wo(output) + + +class FinalLayer(nn.Module): + """ + The final layer of LuminaNextDiT. + + Parameters: + hidden_size (`int`): The dimensionality of the hidden layers in the model. This parameter determines the width of the model's hidden representations. + patch_size (`int`): The patch size of noise. + out_channels (`int`): The number of output channels. + """ + + def __init__(self, hidden_size, patch_size, out_channels): + super().__init__() + self.norm_final = nn.LayerNorm( + hidden_size, + elementwise_affine=False, + eps=1e-6, + ) + self.linear = nn.Linear( + hidden_size, + patch_size * patch_size * out_channels, + bias=True, + ) + nn.init.zeros_(self.linear.weight) + nn.init.zeros_(self.linear.bias) + + self.adaLN_modulation = nn.Sequential( + nn.SiLU(), + nn.Linear( + min(hidden_size, 1024), + hidden_size, + bias=True, + ), + ) + nn.init.zeros_(self.adaLN_modulation[1].weight) + nn.init.zeros_(self.adaLN_modulation[1].bias) + + def forward(self, x, c): + """ + Forward pass of the FinalLayer. + + Args: + x (torch.Tensor): The input tensor. + c (torch.Tensor): The conditioning tensor. + Returns: + torch.Tensor: The output tensor. + """ + scale = self.adaLN_modulation(c) + + x = modulate(self.norm_final(x), scale) + x = self.linear(x) + + return x + + +class LuminaNextDiTBlock(nn.Module): + """ + Initialize a LuminaNextDiTBlock. + + Args: + layer_id (int): Identifier for the layer. + hidden_size (int): Embedding dimension of the input features. + num_attention_heads (int): Number of attention heads. + num_kv_heads (Optional[int]): Number of attention heads in key and + value features (if using GQA), or set to None for the same as + query. + multiple_of (int): + ffn_dim_multiplier (float): + norm_eps (float): + + Attributes: + num_attention_heads (int): Number of attention heads. + hidden_size (int): Dimension size of the model. + head_dim (int): Dimension size of each attention head. + attention (Attention): Attention module. + feed_forward (FeedForward): FeedForward module. + layer_id (int): Identifier for the layer. + attention_norm (RMSNorm): Layer normalization for attention output. + ffn_norm (RMSNorm): Layer normalization for feedforward output. + """ + def __init__( + self, + layer_id: int, + hidden_size: int, + num_attention_heads: int, + num_kv_heads: int, + multiple_of: int, + ffn_dim_multiplier: float, + norm_eps: float, + qk_norm: bool, + caption_dim: int, + elementwise_affine: bool = True, + ) -> None: + super().__init__() + self.hidden_size = hidden_size + self.head_dim = hidden_size // num_attention_heads + self.attention = Attention(hidden_size, num_attention_heads, num_kv_heads, qk_norm, caption_dim) + self.feed_forward = LuminaFeedForward( + hidden_size=hidden_size, + intermediate_size=4 * hidden_size, + multiple_of=multiple_of, + ffn_dim_multiplier=ffn_dim_multiplier, + ) + self.layer_id = layer_id + self.attention_norm1 = RMSNorm(hidden_size, eps=norm_eps, elementwise_affine=elementwise_affine) + self.ffn_norm1 = RMSNorm(hidden_size, eps=norm_eps, elementwise_affine=elementwise_affine) + + self.attention_norm2 = RMSNorm(hidden_size, eps=norm_eps, elementwise_affine=elementwise_affine) + self.ffn_norm2 = RMSNorm(hidden_size, eps=norm_eps, elementwise_affine=elementwise_affine) + + self.adaLN_modulation = nn.Sequential( + nn.SiLU(), + nn.Linear( + min(hidden_size, 1024), + 4 * hidden_size, + bias=True, + ), + ) + nn.init.zeros_(self.adaLN_modulation[1].weight) + nn.init.zeros_(self.adaLN_modulation[1].bias) + + self.attention_caption_norm = RMSNorm(caption_dim, eps=norm_eps, elementwise_affine=elementwise_affine) + + def forward( + self, + x: torch.Tensor, + x_mask: torch.Tensor, + freqs_cis: torch.Tensor, + caption_feat: torch.Tensor, + caption_mask: torch.Tensor, + adaln_input: Optional[torch.Tensor] = None, + ): + """ + Perform a forward pass through the LuminaNextDiTBlock. + + Args: + x (torch.Tensor): Input tensor. + freqs_cis (torch.Tensor): Precomputed cosine and sine frequencies. + Returns: + torch.Tensor: Output tensor after applying attention and + feedforward layers. + """ + if adaln_input is not None: + scale_msa, gate_msa, scale_mlp, gate_mlp = self.adaLN_modulation(adaln_input).chunk(4, dim=1) + + x = x + gate_msa.unsqueeze(1).tanh() * self.attention_norm2( + self.attention( + modulate(self.attention_norm1(x), scale_msa), + x_mask, + freqs_cis, + self.attention_caption_norm(caption_feat), + caption_mask, + ) + ) + x = x + gate_mlp.unsqueeze(1).tanh() * self.ffn_norm2( + self.feed_forward( + modulate(self.ffn_norm1(x), scale_mlp), + ) + ) + else: + x = x + self.attention_norm2( + self.attention( + self.attention_norm1(x), + x_mask, + freqs_cis, + self.attention_caption_norm(caption_feat), + caption_mask, + ) + ) + x = x + self.ffn_norm2(self.feed_forward(self.ffn_norm1(x))) + + return x + + +class LuminaNextDiT2DModel(ModelMixin, ConfigMixin): + """ + LuminaNextDiT: Diffusion model with a Transformer backbone. + + Inherit ModelMixin and ConfigMixin to be compatible with the sampler StableDiffusionPipeline of diffusers. + + Parameters: + patch_size (`int`, *optional*, (`int`, *optional*, defaults to 2): + The size of each patch in the image. This parameter defines the resolution of patches fed into the model. + in_channels (`int`, *optional*, defaults to 4): + The number of input channels for the model. Typically, this matches the number of channels in the input images. + hidden_size (`int`, *optional*, defaults to 4096): + The dimensionality of the hidden layers in the model. This parameter determines the width of the model's hidden representations. + num_layers (`int`, *optional*, default to 32): + The number of layers in the model. This defines the depth of the neural network. + num_attention_heads (`int`, *optional*, defaults to 32): + The number of attention heads in each attention layer. This parameter specifies how many separate attention mechanisms are used. + num_kv_heads (`int`, *optional*, defaults to 8): + The number of key-value heads in the attention mechanism, if different from the number of attention heads. If None, it defaults to num_attention_heads. + multiple_of (`int`, *optional*, defaults to 256): + A factor that the hidden size should be a multiple of. This can help optimize certain hardware configurations. + ffn_dim_multiplier (`float`, *optional*): + A multiplier for the dimensionality of the feed-forward network. If None, it uses a default value based on the model configuration. + norm_eps float = (`float`, *optional*, defaults to 1e-5): + A small value added to the denominator for numerical stability in normalization layers. + learn_sigma bool = (`bool`, *optional*, defaults to True): + Whether the model should learn the sigma parameter, which might be related to uncertainty or variance in predictions. + qk_norm (`bool`, *optional*, defaults to True): + Indicates if the queries and keys in the attention mechanism should be normalized. + caption_dim (`int`, *optional*, defaults to 2048): + The dimensionality of the text embeddings. This parameter defines the size of the text representations used in the model. + scale_factor (`float`, *optional*, defaults to 1.0): + A scaling factor applied to certain parameters or layers in the model. This can be used for adjusting the overall scale of the model's operations. + """ + + @register_to_config + def __init__( + self, + patch_size: Optional[int] = 2, + in_channels: Optional[int] = 4, + hidden_size: Optional[int] = 4096, + num_layers: Optional[int] = 32, + num_attention_heads: Optional[int] = 32, + num_kv_heads: Optional[int] = None, + multiple_of: Optional[int] = 256, + ffn_dim_multiplier: Optional[float] = None, + norm_eps: Optional[float] = 1e-5, + learn_sigma: Optional[bool] = True, + qk_norm: Optional[bool] = False, + caption_dim: Optional[int] = 2048, + scale_factor: Optional[float] = 1.0, + ) -> None: + super().__init__() + self.patch_size = patch_size + self.in_channels = in_channels + self.out_channels = in_channels * 2 if learn_sigma else in_channels + self.hidden_size = hidden_size + self.num_attention_heads = num_attention_heads + self.scale_factor = scale_factor + self.learn_sigma = learn_sigma + + self.patch_embedder = nn.Linear( + in_features=patch_size * patch_size * in_channels, + out_features=hidden_size, + bias=True, + ) + nn.init.xavier_uniform_(self.patch_embedder.weight) + nn.init.constant_(self.patch_embedder.bias, 0.0) + + self.pad_token = nn.Parameter(torch.empty(hidden_size)) + nn.init.normal_(self.pad_token, std=0.02) + + self.time_caption_embed = LuminaCombinedTimestepCaptionEmbedding(hidden_size=min(hidden_size, 1024), caption_dim=caption_dim) + + self.layers = nn.ModuleList( + [ + LuminaNextDiTBlock( + layer_id, + hidden_size, + num_attention_heads, + num_kv_heads, + multiple_of, + ffn_dim_multiplier, + norm_eps, + qk_norm, + caption_dim, + ) + for layer_id in range(num_layers) + ] + ) + self.final_layer = FinalLayer(hidden_size, patch_size, self.out_channels) + + assert (hidden_size // num_attention_heads) % 4 == 0, "2d rope needs head dim to be divisible by 4" + self.freqs_cis = LuminaNextDiT2DModel.precompute_freqs_cis( + hidden_size // num_attention_heads, + 384, + scale_factor=scale_factor, + ) + + def unpatchify(self, x: torch.Tensor, img_size: List[Tuple[int, int]], return_tensor=False) -> List[torch.Tensor]: + """ + Reconstructs the original images from the patchified tensor. + + Args: + x (torch.Tensor): The patchified tensor of shape (N, T, patch_size**2 * C). + img_size (List[Tuple[int, int]]): The list of image sizes for each image in the batch. + return_tensor (bool, optional): Whether to return the reconstructed images as a tensor. + If False, the reconstructed images will be returned as a list of tensors. + Defaults to False. + + Returns: + List[torch.Tensor] or torch.Tensor: The reconstructed images. + If return_tensor is True, the reconstructed images will be returned as a tensor of shape (N, C, H, W). + If return_tensor is False, the reconstructed images will be returned as a list of tensors, + where each tensor has shape (H, W, C). + """ + pH = pW = self.patch_size + if return_tensor: + H, W = img_size[0] + B = x.size(0) + L = (H // pH) * (W // pW) + x = x[:, :L].view(B, H // pH, W // pW, pH, pW, self.out_channels) + x = x.permute(0, 5, 1, 3, 2, 4).flatten(4, 5).flatten(2, 3) + return x + else: + imgs = [] + for i in range(x.size(0)): + H, W = img_size[i] + L = (H // pH) * (W // pW) + imgs.append( + x[i][:L] + .view(H // pH, W // pW, pH, pW, self.out_channels) + .permute(4, 0, 2, 1, 3) + .flatten(3, 4) + .flatten(1, 2) + ) + return imgs + + def patchify_and_embed( + self, x: List[torch.Tensor] | torch.Tensor + ) -> Tuple[torch.Tensor, torch.Tensor, List[Tuple[int, int]], torch.Tensor]: + """ + Patchifies and embeds the input tensor(s). + + Args: + x (List[torch.Tensor] | torch.Tensor): The input tensor(s) to be patchified and embedded. + + Returns: + Tuple[torch.Tensor, torch.Tensor, List[Tuple[int, int]], torch.Tensor]: A tuple containing the patchified and embedded tensor(s), + the mask indicating the valid patches, the original image size(s), and the frequency tensor(s). + """ + self.freqs_cis = self.freqs_cis.to(x[0].device) + if isinstance(x, torch.Tensor): + pH = pW = self.patch_size + B, C, H, W = x.size() + x = x.view(B, C, H // pH, pH, W // pW, pW).permute(0, 2, 4, 1, 3, 5).flatten(3) + x = self.patch_embedder(x) + x = x.flatten(1, 2) + + mask = torch.ones(x.shape[0], x.shape[1], dtype=torch.int32, device=x.device) + + return ( + x, + mask, + [(H, W)] * B, + self.freqs_cis[: H // pH, : W // pW].flatten(0, 1).unsqueeze(0), + ) + else: + pH = pW = self.patch_size + x_embed = [] + freqs_cis = [] + img_size = [] + l_effective_seq_len = [] + + for img in x: + C, H, W = img.size() + item_freqs_cis = self.freqs_cis[: H // pH, : W // pW] + freqs_cis.append(item_freqs_cis.flatten(0, 1)) + img_size.append((H, W)) + img = img.view(C, H // pH, pH, W // pW, pW).permute(1, 3, 0, 2, 4).flatten(2) + img = self.x_embedder(img) + img = img.flatten(0, 1) + l_effective_seq_len.append(len(img)) + x_embed.append(img) + + max_seq_len = max(l_effective_seq_len) + mask = torch.zeros(len(x), max_seq_len, dtype=torch.int32, device=x[0].device) + padded_x_embed = [] + padded_freqs_cis = [] + for i, (item_embed, item_freqs_cis, item_seq_len) in enumerate( + zip(x_embed, freqs_cis, l_effective_seq_len) + ): + item_embed = torch.cat( + [ + item_embed, + self.pad_token.view(1, -1).expand(max_seq_len - item_seq_len, -1), + ], + dim=0, + ) + item_freqs_cis = torch.cat( + [ + item_freqs_cis, + item_freqs_cis[-1:].expand(max_seq_len - item_seq_len, -1), + ], + dim=0, + ) + padded_x_embed.append(item_embed) + padded_freqs_cis.append(item_freqs_cis) + mask[i][:item_seq_len] = 1 + + x_embed = torch.stack(padded_x_embed, dim=0) + freqs_cis = torch.stack(padded_freqs_cis, dim=0) + + return x_embed, mask, img_size, freqs_cis + + def forward(self, + x: torch.Tensor, + timestep: torch.Tensor, + caption_feat: torch.Tensor, + caption_mask: torch.Tensor, + return_dict=True) -> torch.Tensor: + """ + Forward pass of LuminaNextDiT. + + Args: + x (torch.Tensor): Input tensor of shape (N, C, H, W). + timestep (torch.Tensor): Tensor of diffusion timesteps of shape (N,). + caption_feat (torch.Tensor): Tensor of caption features of shape (N, D). + caption_mask (torch.Tensor): Tensor of caption masks of shape (N, L). + + Returns: + torch.Tensor: Output tensor of shape (N, C, H, W). + """ + x_is_tensor = isinstance(x, torch.Tensor) + x, mask, img_size, freqs_cis = self.patchify_and_embed(x) + freqs_cis = freqs_cis.to(x.device) + + adaln_input = self.time_caption_embed(timestep, caption_feat, caption_mask) + + caption_mask = caption_mask.bool() + for layer in self.layers: + x = layer(x, mask, freqs_cis, caption_feat, caption_mask, adaln_input=adaln_input) + + x = self.final_layer(x, adaln_input) + x = self.unpatchify(x, img_size, return_tensor=x_is_tensor) + + if self.learn_sigma: + if x_is_tensor: + output, _ = x.chunk(2, dim=1) + else: + output = [_.chunk(2, dim=0)[0] for _ in x] + + if not return_dict: + return (output,) + + return Transformer2DModelOutput(sample=output) + + def forward_with_cfg( + self, + x, + timestep, + caption_feat, + caption_mask, + cfg_scale, + scale_factor=1.0, + scale_watershed=1.0, + base_seqlen: Optional[int] = None, + proportional_attn: bool = False, + return_dict: bool = True, + ): + """ + Forward pass of LuminaNextDiT, but also batches the unconditional forward pass + for classifier-free guidance. + + Args: + x: + The input tensor, typically representing image data. + timestep: + A tensor representing the time steps or sequence positions. + caption_feat: + The caption features, which are used for conditioning the model. + caption_mask: + A mask for the caption features, indicating which elements are valid. + cfg_scale: + The classifier-free guidance scale, used to adjust the influence of the conditioning information. + scale_factor (float, optional, defaults to 1.0): + A scaling factor applied to certain operations or parameters within the forward pass. + scale_watershed (float, optional, defaults to 1.0): + A specific scaling factor used in conjunction with the main scale factor, potentially for different stages of processing. + base_seqlen (Optional[int], optional): + The base sequence length, which can be used to set the length of sequences processed by the model. If None, a default value is used. + proportional_attn (bool, optional, defaults to False): + Whether to use proportional attention mechanisms within the model, adjusting attention weights proportionally. + """ + # https://github.com/openai/glide-text2im/blob/main/notebooks/text2im.ipynb + self.freqs_cis = LuminaNextDiT2DModel.precompute_freqs_cis( + self.hidden_size // self.num_attention_heads, + 384, + scale_factor=scale_factor, + scale_watershed=scale_watershed, + timestep=timestep[0].item(), + ) + + if proportional_attn: + assert base_seqlen is not None + for layer in self.layers: + layer.attention.base_seqlen = base_seqlen + layer.attention.proportional_attn = proportional_attn + else: + for layer in self.layers: + layer.attention.base_seqlen = None + layer.attention.proportional_attn = proportional_attn + + half = x[: len(x) // 2] + combined = torch.cat([half, half], dim=0) + model_out = self(combined, timestep, caption_feat, caption_mask) + # For exact reproducibility reasons, we apply classifier-free guidance on only + # three channels by default. The standard approach to cfg applies it to all channels. + # This can be done by uncommenting the following line and commenting-out the line following that. + # eps, rest = model_out[:, :self.in_channels], model_out[:, self.in_channels:] + eps, rest = model_out[:, :3], model_out[:, 3:] + cond_eps, uncond_eps = torch.split(eps, len(eps) // 2, dim=0) + half_eps = uncond_eps + cfg_scale * (cond_eps - uncond_eps) + eps = torch.cat([half_eps, half_eps], dim=0) + output = torch.cat([eps, rest], dim=1) + + if not return_dict: + return (output,) + + return Transformer2DModelOutput(sample=output) + + @classmethod + def precompute_freqs_cis( + cls, + dim: int, + end: int, + theta: float = 10000.0, + scale_factor: float = 1.0, + scale_watershed: float = 1.0, + timestep: float = 1.0, + ): + """ + Precompute the frequency tensor for complex exponentials (cis) with + given dimensions. + + This function calculates a frequency tensor with complex exponentials + using the given dimension 'dim' and the end index 'end'. The 'theta' + parameter scales the frequencies. The returned tensor contains complex + values in complex64 data type. + + Args: + dim (int): Dimension of the frequency tensor. + end (int): End index for precomputing frequencies. + theta (float, optional): Scaling factor for frequency computation. + Defaults to 10000.0. + + Returns: + torch.Tensor: Precomputed frequency tensor with complex + exponentials. + """ + + if timestep < scale_watershed: + linear_factor = scale_factor + ntk_factor = 1.0 + else: + linear_factor = 1.0 + ntk_factor = scale_factor + + theta = theta * ntk_factor + freqs = 1.0 / (theta ** (torch.arange(0, dim, 4)[: (dim // 4)].float().cuda() / dim)) / linear_factor + + timestep = torch.arange(end, device=freqs.device, dtype=torch.float) # type: ignore + + freqs = torch.outer(timestep, freqs).float() # type: ignore + freqs_cis = torch.polar(torch.ones_like(freqs), freqs) # complex64 + + freqs_cis_h = freqs_cis.view(end, 1, dim // 4, 1).repeat(1, end, 1, 1) + freqs_cis_w = freqs_cis.view(1, end, dim // 4, 1).repeat(end, 1, 1, 1) + freqs_cis = torch.cat([freqs_cis_h, freqs_cis_w], dim=-1).flatten(2) + + return freqs_cis + + def parameter_count(self) -> int: + total_params = 0 + + def _recursive_count_params(module): + nonlocal total_params + for param in module.parameters(recurse=False): + total_params += param.numel() + for submodule in module.children(): + _recursive_count_params(submodule) + + _recursive_count_params(self) + return total_params + + +# todo: delete it after creating hf format model +def LuminaNextDiT2DModel_2B_patch2(**kwargs): + return LuminaNextDiT2DModel(patch_size=2, dim=2304, n_layers=24, num_attention_heads=32, **kwargs) + + +def LuminaNextDiT2DModel_2B_GQA_patch2(**kwargs): + return LuminaNextDiT2DModel(patch_size=2, dim=2304, n_layers=24, num_attention_heads=32, num_kv_heads=8, **kwargs) \ No newline at end of file diff --git a/src/diffusers/pipelines/__init__.py b/src/diffusers/pipelines/__init__.py index a2041d6ea07b..246f38ee6203 100644 --- a/src/diffusers/pipelines/__init__.py +++ b/src/diffusers/pipelines/__init__.py @@ -193,6 +193,7 @@ "LEditsPPPipelineStableDiffusionXL", ] ) + _import_structure["lumina"] = ["LuminaText2ImgPipeline"] _import_structure["marigold"].extend( [ "MarigoldDepthPipeline", @@ -467,6 +468,7 @@ LEditsPPPipelineStableDiffusion, LEditsPPPipelineStableDiffusionXL, ) + from .lumina import LuminaText2ImgPipeline from .marigold import ( MarigoldDepthPipeline, MarigoldNormalsPipeline, diff --git a/src/diffusers/pipelines/lumina/__init__.py b/src/diffusers/pipelines/lumina/__init__.py index 38ad13939515..ca1396359721 100644 --- a/src/diffusers/pipelines/lumina/__init__.py +++ b/src/diffusers/pipelines/lumina/__init__.py @@ -1 +1,48 @@ -from .pipeline_lumina import LuminaText2ImgPipeline \ No newline at end of file +from typing import TYPE_CHECKING + +from ...utils import ( + DIFFUSERS_SLOW_IMPORT, + OptionalDependencyNotAvailable, + _LazyModule, + get_objects_from_module, + is_torch_available, + is_transformers_available, +) + + +_dummy_objects = {} +_import_structure = {} + + +try: + if not (is_transformers_available() and is_torch_available()): + raise OptionalDependencyNotAvailable() +except OptionalDependencyNotAvailable: + from ...utils import dummy_torch_and_transformers_objects # noqa F403 + + _dummy_objects.update(get_objects_from_module(dummy_torch_and_transformers_objects)) +else: + _import_structure["pipeline_lumina"] = ["LuminaText2ImgPipeline"] + +if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT: + try: + if not (is_transformers_available() and is_torch_available()): + raise OptionalDependencyNotAvailable() + + except OptionalDependencyNotAvailable: + from ...utils.dummy_torch_and_transformers_objects import * + else: + from .pipeline_lumina import LuminaText2ImgPipeline + +else: + import sys + + sys.modules[__name__] = _LazyModule( + __name__, + globals()["__file__"], + _import_structure, + module_spec=__spec__, + ) + + for name, value in _dummy_objects.items(): + setattr(sys.modules[__name__], name, value) diff --git a/src/diffusers/pipelines/lumina/modeling_flag_dit.py b/src/diffusers/pipelines/lumina/modeling_flag_dit.py deleted file mode 100644 index e57b6fe1d445..000000000000 --- a/src/diffusers/pipelines/lumina/modeling_flag_dit.py +++ /dev/null @@ -1,999 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. - -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. -# -------------------------------------------------------- -# References: -# GLIDE: https://github.com/openai/glide-text2im -# MAE: https://github.com/facebookresearch/mae/blob/main/models_mae.py -# -------------------------------------------------------- - -import functools -import math -from typing import Optional, Tuple, List - -from .components import RMSNorm -from flash_attn import flash_attn_varlen_func -from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa -import torch -import torch.nn as nn -import torch.nn.functional as F - -from ...configuration_utils import ConfigMixin, register_to_config -from ...models import ModelMixin -from ...models.transformers.transformer_2d import Transformer2DModelOutput -from ...utils import logging - - -def modulate(x, shift, scale): - return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1) - - -class RMSNorm(torch.nn.Module): - def __init__(self, dim: int, eps: float = 1e-6): - """ - Initialize the RMSNorm normalization layer. - - Args: - dim (int): The dimension of the input tensor. - eps (float, optional): A small value added to the denominator for numerical stability. Default is 1e-6. - - Attributes: - eps (float): A small value added to the denominator for numerical stability. - weight (nn.Parameter): Learnable scaling parameter. - - """ - super().__init__() - self.eps = eps - self.weight = nn.Parameter(torch.ones(dim)) - - def _norm(self, x): - """ - Apply the RMSNorm normalization to the input tensor. - - Args: - x (torch.Tensor): The input tensor. - - Returns: - torch.Tensor: The normalized tensor. - - """ - return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps) - - def forward(self, x): - """ - Forward pass through the RMSNorm layer. - - Args: - x (torch.Tensor): The input tensor. - - Returns: - torch.Tensor: The output tensor after applying RMSNorm. - - """ - output = self._norm(x.float()).type_as(x) - return output * self.weight - - -class TimestepEmbedder(nn.Module): - """ - Embeds scalar timesteps into vector representations. - """ - - def __init__(self, hidden_size, frequency_embedding_size=256): - super().__init__() - self.mlp = nn.Sequential( - nn.Linear( - frequency_embedding_size, - hidden_size, - bias=True, - ), - nn.SiLU(), - nn.Linear( - hidden_size, - hidden_size, - bias=True, - ), - ) - self.frequency_embedding_size = frequency_embedding_size - - @staticmethod - def timestep_embedding(t, dim, max_period=10000): - """ - Create sinusoidal timestep embeddings. - :param t: a 1-D Tensor of N indices, one per batch element. - These may be fractional. - :param dim: the dimension of the output. - :param max_period: controls the minimum frequency of the embeddings. - :return: an (N, D) Tensor of positional embeddings. - """ - # https://github.com/openai/glide-text2im/blob/main/glide_text2im/nn.py - half = dim // 2 - freqs = torch.exp( - -math.log(max_period) - * torch.arange(start=0, end=half, dtype=torch.float32) - / half - ).to(device=t.device) - args = t[:, None].float() * freqs[None] - embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) - if dim % 2: - embedding = torch.cat( - [embedding, torch.zeros_like(embedding[:, :1])], dim=-1 - ) - return embedding - - def forward(self, t): - t_freq = self.timestep_embedding(t, self.frequency_embedding_size) - t_emb = self.mlp(t_freq.to(self.mlp[0].weight.dtype)) - return t_emb - - -class LabelEmbedder(nn.Module): - r"""Embeds class labels into vector representations. Also handles label - dropout for classifier-free guidance. - """ - - def __init__(self, num_classes, hidden_size, dropout_prob): - super().__init__() - use_cfg_embedding = int(dropout_prob > 0) - self.embedding_table = nn.Embedding(num_classes + use_cfg_embedding) - self.num_classes = num_classes - self.dropout_prob = dropout_prob - - def token_drop(self, labels, force_drop_ids=None): - """ - Drops labels to enable classifier-free guidance. - """ - if force_drop_ids is None: - drop_ids = ( - torch.rand(labels.shape[0], device=labels.device) < self.dropout_prob - ) - drop_ids = drop_ids.cuda() - drop_ids = drop_ids.to(labels.device) - else: - drop_ids = force_drop_ids == 1 - labels = torch.where(drop_ids, self.num_classes, labels) - return labels - - def forward(self, labels, train, force_drop_ids=None): - use_dropout = self.dropout_prob > 0 - if (train and use_dropout) or (force_drop_ids is not None): - labels = self.token_drop(labels, force_drop_ids) - embeddings = self.embedding_table(labels) - return embeddings - - -############################################################################# -# Core DiT Model # -############################################################################# - - -class Attention(nn.Module): - """Multi-head attention module.""" - - def __init__( - self, - dim: int, - n_heads: int, - n_kv_heads: Optional[int], - qk_norm: bool, - y_dim: int, - ): - """ - Initialize the Attention module. - - Args: - dim (int): Number of input dimensions. - n_heads (int): Number of heads. - n_kv_heads (Optional[int]): Number of kv heads, if using GQA. - - Attributes: - n_kv_heads (int): Number of key and value heads. - n_heads (int): Number of local query heads. - n_kv_heads (int): Number of local key and value heads. - n_rep (int): Number of repetitions for local heads. - head_dim (int): Dimension size of each attention head. - wq (nn.Linear): Linear transformation for queries. - wk (nn.Linear): Linear transformation for keys. - wv (nn.Linear): Linear transformation for values. - wo (nn.Linear): Linear transformation for output. - cache_k (torch.Tensor): Cached keys for attention. - cache_v (torch.Tensor): Cached values for attention. - - """ - super().__init__() - self.n_kv_heads = n_heads if n_kv_heads is None else n_kv_heads - self.n_heads = n_heads - self.n_kv_heads = n_kv_heads - self.n_rep = n_heads - self.head_dim = dim // n_heads - - self.wq = nn.Linear(dim, n_heads * self.head_dim, bias=False) - self.wk = nn.Linear(dim, self.n_kv_heads * self.head_dim, bias=False) - self.wv = nn.Linear(dim, self.n_kv_heads * self.head_dim, bias=False) - if y_dim > 0: - self.wk_y = nn.Linear(y_dim, self.n_kv_heads * self.head_dim, bias=False) - self.wv_y = nn.Linear(y_dim, self.n_kv_heads * self.head_dim, bias=False) - self.gate = nn.Parameter(torch.zeros([self.n_heads])) - - self.wo = nn.Linear(n_heads * self.head_dim, dim, bias=False) - - if qk_norm: - self.q_norm = nn.LayerNorm(self.n_heads * self.head_dim) - self.k_norm = nn.LayerNorm(self.n_kv_heads * self.head_dim) - if y_dim > 0: - self.ky_norm = nn.LayerNorm(self.n_kv_heads * self.head_dim) - else: - self.ky_norm = nn.Identity() - else: - self.q_norm = self.k_norm = nn.Identity() - self.ky_norm = nn.Identity() - - # for proportional attention computation - self.base_seqlen = None - self.proportional_attn = False - - @staticmethod - def reshape_for_broadcast(freqs_cis: torch.Tensor, x: torch.Tensor): - """ - Reshape frequency tensor for broadcasting it with another tensor. - - This function reshapes the frequency tensor to have the same shape as - the target tensor 'x' for the purpose of broadcasting the frequency - tensor during element-wise operations. - - Args: - freqs_cis (torch.Tensor): Frequency tensor to be reshaped. - x (torch.Tensor): Target tensor for broadcasting compatibility. - - Returns: - torch.Tensor: Reshaped frequency tensor. - - Raises: - AssertionError: If the frequency tensor doesn't match the expected - shape. - AssertionError: If the target tensor 'x' doesn't have the expected - number of dimensions. - """ - ndim = x.ndim - assert 0 <= 1 < ndim - assert freqs_cis.shape == (x.shape[1], x.shape[-1]) - shape = [d if i == 1 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)] - return freqs_cis.view(*shape) - - @staticmethod - def apply_rotary_emb( - xq: torch.Tensor, - xk: torch.Tensor, - freqs_cis: torch.Tensor, - ) -> Tuple[torch.Tensor, torch.Tensor]: - """ - Apply rotary embeddings to input tensors using the given frequency - tensor. - - This function applies rotary embeddings to the given query 'xq' and - key 'xk' tensors using the provided frequency tensor 'freqs_cis'. The - input tensors are reshaped as complex numbers, and the frequency tensor - is reshaped for broadcasting compatibility. The resulting tensors - contain rotary embeddings and are returned as real tensors. - - Args: - xq (torch.Tensor): Query tensor to apply rotary embeddings. - xk (torch.Tensor): Key tensor to apply rotary embeddings. - freqs_cis (torch.Tensor): Precomputed frequency tensor for complex - exponentials. - - Returns: - Tuple[torch.Tensor, torch.Tensor]: Tuple of modified query tensor - and key tensor with rotary embeddings. - """ - with torch.cuda.amp.autocast(enabled=False): - xq_ = torch.view_as_complex(xq.float().reshape(*xq.shape[:-1], -1, 2)) - xk_ = torch.view_as_complex(xk.float().reshape(*xk.shape[:-1], -1, 2)) - freqs_cis = Attention.reshape_for_broadcast(freqs_cis, xq_) - xq_out = torch.view_as_real(xq_ * freqs_cis).flatten(3) - xk_out = torch.view_as_real(xk_ * freqs_cis).flatten(3) - return xq_out.type_as(xq), xk_out.type_as(xk) - - # copied from huggingface modeling_llama.py - def _upad_input( - self, query_layer, key_layer, value_layer, attention_mask, query_length - ): - - def _get_unpad_data(attention_mask): - seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32) - indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten() - max_seqlen_in_batch = seqlens_in_batch.max().item() - cu_seqlens = F.pad( - torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0) - ) - return ( - indices, - cu_seqlens, - max_seqlen_in_batch, - ) - - indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask) - batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape - - key_layer = index_first_axis( - key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), - indices_k, - ) - value_layer = index_first_axis( - value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), - indices_k, - ) - if query_length == kv_seq_len: - query_layer = index_first_axis( - query_layer.reshape(batch_size * kv_seq_len, self.n_heads, head_dim), - indices_k, - ) - cu_seqlens_q = cu_seqlens_k - max_seqlen_in_batch_q = max_seqlen_in_batch_k - indices_q = indices_k - elif query_length == 1: - max_seqlen_in_batch_q = 1 - cu_seqlens_q = torch.arange( - batch_size + 1, dtype=torch.int32, device=query_layer.device - ) # There is a memcpy here, that is very bad. - indices_q = cu_seqlens_q[:-1] - query_layer = query_layer.squeeze(1) - else: - # The -q_len: slice assumes left padding. - attention_mask = attention_mask[:, -query_length:] - query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input( - query_layer, attention_mask - ) - - return ( - query_layer, - key_layer, - value_layer, - indices_q, - (cu_seqlens_q, cu_seqlens_k), - (max_seqlen_in_batch_q, max_seqlen_in_batch_k), - ) - - def forward( - self, - x: torch.Tensor, - x_mask: torch.Tensor, - freqs_cis: torch.Tensor, - y: torch.Tensor, - y_mask: torch.Tensor, - ) -> torch.Tensor: - """ - Forward pass of the attention module. - - Args: - x (torch.Tensor): Input tensor. - freqs_cis (torch.Tensor): Precomputed frequency tensor. - - Returns: - torch.Tensor: Output tensor after attention. - - """ - bsz, seqlen, _ = x.shape - xq, xk, xv = self.wq(x), self.wk(x), self.wv(x) - dtype = xq.dtype - - xq = self.q_norm(xq) - xk = self.k_norm(xk) - - xq = xq.view(bsz, seqlen, self.n_heads, self.head_dim) - xk = xk.view(bsz, seqlen, self.n_kv_heads, self.head_dim) - xv = xv.view(bsz, seqlen, self.n_kv_heads, self.head_dim) - - xq, xk = Attention.apply_rotary_emb(xq, xk, freqs_cis=freqs_cis) - xq, xk = xq.to(dtype), xk.to(dtype) - - if dtype in [torch.float16, torch.bfloat16]: - # begin var_len flash attn - ( - query_states, - key_states, - value_states, - indices_q, - cu_seq_lens, - max_seq_lens, - ) = self._upad_input(xq, xk, xv, x_mask, seqlen) - - cu_seqlens_q, cu_seqlens_k = cu_seq_lens - max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens - - if self.proportional_attn: - softmax_scale = math.sqrt( - math.log(seqlen, self.base_seqlen) / self.head_dim - ) - else: - softmax_scale = math.sqrt(1 / self.head_dim) - attn_output_unpad = flash_attn_varlen_func( - query_states, - key_states, - value_states, - cu_seqlens_q=cu_seqlens_q, - cu_seqlens_k=cu_seqlens_k, - max_seqlen_q=max_seqlen_in_batch_q, - max_seqlen_k=max_seqlen_in_batch_k, - dropout_p=0.0, - causal=False, - softmax_scale=softmax_scale, - ) - output = pad_input(attn_output_unpad, indices_q, bsz, seqlen) - # end var_len_flash_attn - - else: - output = ( - F.scaled_dot_product_attention( - xq.permute(0, 2, 1, 3), - xk.permute(0, 2, 1, 3), - xv.permute(0, 2, 1, 3), - attn_mask=x_mask.bool() - .view(bsz, 1, 1, seqlen) - .expand(-1, self.n_heads, seqlen, -1), - ) - .permute(0, 2, 1, 3) - .to(dtype) - ) - - if hasattr(self, "wk_y"): - yk = self.ky_norm(self.wk_y(y)).view( - bsz, -1, self.n_kv_heads, self.head_dim - ) - yv = self.wv_y(y).view(bsz, -1, self.n_kv_heads, self.head_dim) - n_rep = self.n_heads // self.n_kv_heads - if n_rep >= 1: - yk = yk.unsqueeze(3).repeat(1, 1, 1, n_rep, 1).flatten(2, 3) - yv = yv.unsqueeze(3).repeat(1, 1, 1, n_rep, 1).flatten(2, 3) - output_y = F.scaled_dot_product_attention( - xq.permute(0, 2, 1, 3), - yk.permute(0, 2, 1, 3), - yv.permute(0, 2, 1, 3), - y_mask.view(bsz, 1, 1, -1).expand(bsz, self.n_heads, seqlen, -1), - ).permute(0, 2, 1, 3) - output_y = output_y * self.gate.tanh().view(1, 1, -1, 1) - output = output + output_y - - output = output.flatten(-2) - - return self.wo(output) - - -class FeedForward(nn.Module): - def __init__( - self, - dim: int, - hidden_dim: int, - multiple_of: int, - ffn_dim_multiplier: Optional[float], - ): - """ - Initialize the FeedForward module. - - Args: - dim (int): Input dimension. - hidden_dim (int): Hidden dimension of the feedforward layer. - multiple_of (int): Value to ensure hidden dimension is a multiple - of this value. - ffn_dim_multiplier (float, optional): Custom multiplier for hidden - dimension. Defaults to None. - - Attributes: - w1 (nn.Linear): Linear transformation for the first - layer. - w2 (nn.Linear): Linear transformation for the second layer. - w3 (nn.Linear): Linear transformation for the third - layer. - - """ - super().__init__() - hidden_dim = int(2 * hidden_dim / 3) - # custom dim factor multiplier - if ffn_dim_multiplier is not None: - hidden_dim = int(ffn_dim_multiplier * hidden_dim) - hidden_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of) - - self.w1 = nn.Linear(dim, hidden_dim, bias=False) - self.w2 = nn.Linear(hidden_dim, dim, bias=False) - self.w3 = nn.Linear(dim, hidden_dim, bias=False) - - # @torch.compile - def _forward_silu_gating(self, x1, x3): - return F.silu(x1) * x3 - - def forward(self, x): - return self.w2(self._forward_silu_gating(self.w1(x), self.w3(x))) - - -class TransformerBlock(nn.Module): - def __init__( - self, - layer_id: int, - dim: int, - n_heads: int, - n_kv_heads: int, - multiple_of: int, - ffn_dim_multiplier: float, - norm_eps: float, - qk_norm: bool, - y_dim: int, - ) -> None: - """ - Initialize a TransformerBlock. - - Args: - layer_id (int): Identifier for the layer. - dim (int): Embedding dimension of the input features. - n_heads (int): Number of attention heads. - n_kv_heads (Optional[int]): Number of attention heads in key and - value features (if using GQA), or set to None for the same as - query. - multiple_of (int): - ffn_dim_multiplier (float): - norm_eps (float): - - Attributes: - n_heads (int): Number of attention heads. - dim (int): Dimension size of the model. - head_dim (int): Dimension size of each attention head. - attention (Attention): Attention module. - feed_forward (FeedForward): FeedForward module. - layer_id (int): Identifier for the layer. - attention_norm (RMSNorm): Layer normalization for attention output. - ffn_norm (RMSNorm): Layer normalization for feedforward output. - - """ - super().__init__() - self.dim = dim - self.head_dim = dim // n_heads - self.attention = Attention(dim, n_heads, n_kv_heads, qk_norm, y_dim) - self.feed_forward = FeedForward( - dim=dim, - hidden_dim=4 * dim, - multiple_of=multiple_of, - ffn_dim_multiplier=ffn_dim_multiplier, - ) - self.layer_id = layer_id - self.attention_norm = RMSNorm(dim, eps=norm_eps) - self.ffn_norm = RMSNorm(dim, eps=norm_eps) - - self.adaLN_modulation = nn.Sequential( - nn.SiLU(), - nn.Linear(min(dim, 1024), 6 * dim, bias=True), - ) - - self.attention_y_norm = RMSNorm(y_dim, eps=norm_eps) - - def forward( - self, - x: torch.Tensor, - x_mask: torch.Tensor, - y: torch.Tensor, - y_mask: torch.Tensor, - freqs_cis: torch.Tensor, - adaln_input: Optional[torch.Tensor] = None, - ): - """ - Perform a forward pass through the TransformerBlock. - - Args: - x (torch.Tensor): Input tensor. - freqs_cis (torch.Tensor): Precomputed cosine and sine frequencies. - mask (torch.Tensor, optional): Masking tensor for attention. - Defaults to None. - - Returns: - torch.Tensor: Output tensor after applying attention and - feedforward layers. - - """ - if adaln_input is not None: - shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = ( - self.adaLN_modulation(adaln_input).chunk(6, dim=1) - ) - - x = x + gate_msa.unsqueeze(1) * self.attention( - modulate(self.attention_norm(x), shift_msa, scale_msa), - x_mask, - freqs_cis, - self.attention_y_norm(y), - y_mask, - ) - x = x + gate_mlp.unsqueeze(1) * self.feed_forward( - modulate(self.ffn_norm(x), shift_mlp, scale_mlp), - ) - - else: - x = x + self.attention( - self.attention_norm(x), - x_mask, - freqs_cis, - self.attention_y_norm(y), - y_mask, - ) - x = x + self.feed_forward(self.ffn_norm(x)) - - return x - - -class FinalLayer(nn.Module): - """ - The final layer of DiT. - """ - - def __init__(self, hidden_size, patch_size, out_channels): - super().__init__() - self.norm_final = nn.LayerNorm( - hidden_size, - elementwise_affine=False, - eps=1e-6, - ) - self.linear = nn.Linear(hidden_size, patch_size * patch_size * out_channels) - self.adaLN_modulation = nn.Sequential( - nn.SiLU(), - nn.Linear(min(hidden_size, 1024), 2 * hidden_size), - ) - - def forward(self, x, c): - shift, scale = self.adaLN_modulation(c).chunk(2, dim=1) - x = modulate(self.norm_final(x), shift, scale) - x = self.linear(x) - return x - - -class FlagDiffuserTransformer(ModelMixin, ConfigMixin): - """ - Diffusion model with a Transformer backbone. - """ - - def __init__( - self, - patch_size: int = 2, - in_channels: int = 4, - dim: int = 4096, - n_layers: int = 32, - n_heads: int = 32, - n_kv_heads: Optional[int] = None, - multiple_of: int = 256, - ffn_dim_multiplier: Optional[float] = None, - norm_eps: float = 1e-5, - learn_sigma: bool = True, - qk_norm: bool = False, - cap_feat_dim: int = 5120, - rope_scaling_factor: float = 1.0, - ntk_factor: float = 1.0, - ) -> None: - super().__init__() - self.learn_sigma = learn_sigma - self.in_channels = in_channels - self.out_channels = in_channels * 2 if learn_sigma else in_channels - self.patch_size = patch_size - - self.x_embedder = nn.Linear( - in_features=patch_size * patch_size * in_channels, - out_features=dim, - bias=True, - ) - nn.init.constant_(self.x_embedder.bias, 0.0) - - self.t_embedder = TimestepEmbedder(min(dim, 1024)) - self.cap_embedder = nn.Sequential( - nn.LayerNorm(cap_feat_dim), - nn.Linear(cap_feat_dim, min(dim, 1024), bias=True), - ) - - self.layers = nn.ModuleList( - [ - TransformerBlock( - layer_id, - dim, - n_heads, - n_kv_heads, - multiple_of, - ffn_dim_multiplier, - norm_eps, - qk_norm, - cap_feat_dim, - ) - for layer_id in range(n_layers) - ] - ) - self.final_layer = FinalLayer(dim, patch_size, self.out_channels) - - self.freqs_cis = DiT_Llama.precompute_freqs_cis( - dim // n_heads, - 40000, - rope_scaling_factor=rope_scaling_factor, - ntk_factor=ntk_factor, - ) - self.dim = dim - self.n_heads = n_heads - self.rope_scaling_factor = rope_scaling_factor - self.ntk_factor = ntk_factor - self.eol_token = nn.Parameter(torch.empty(dim)) - self.pad_token = nn.Parameter(torch.empty(dim)) - nn.init.normal_(self.eol_token, std=0.02) - nn.init.normal_(self.pad_token, std=0.02) - - def unpatchify( - self, x: torch.Tensor, img_size: List[Tuple[int, int]], return_tensor=False - ) -> List[torch.Tensor]: - """ - x: (N, T, patch_size**2 * C) - imgs: (N, H, W, C) - """ - pH = pW = self.patch_size - if return_tensor: - H, W = img_size[0] - B = x.size(0) - L = (H // pH) * (W // pW + 1) # one additional for eol - x = x[:, :L].view(B, H // pH, W // pW + 1, pH, pW, self.out_channels) - x = x[:, :, :-1] - x = x.permute(0, 5, 1, 3, 2, 4).flatten(4, 5).flatten(2, 3) - return x - else: - imgs = [] - for i in range(x.size(0)): - H, W = img_size[i] - L = (H // pH) * (W // pW + 1) - imgs.append( - x[i][:L] - .view(H // pH, W // pW + 1, pH, pW, self.out_channels)[ - :, :-1, :, :, : - ] - .permute(4, 0, 2, 1, 3) - .flatten(3, 4) - .flatten(1, 2) - ) - return imgs - - def patchify_and_embed( - self, x: List[torch.Tensor] | torch.Tensor - ) -> Tuple[torch.Tensor, torch.Tensor, List[Tuple[int, int]]]: - if isinstance(x, torch.Tensor): - pH = pW = self.patch_size - B, C, H, W = x.size() - x = ( - x.view(B, C, H // pH, pH, W // pW, pW) - .permute(0, 2, 4, 1, 3, 5) - .flatten(3) - ) - x = self.x_embedder(x) - x = torch.cat( - [ - x, - self.eol_token.view(1, 1, 1, -1).expand(B, H // pH, 1, -1), - ], - dim=2, - ) - x = x.flatten(1, 2) - - mask = torch.ones( - x.shape[0], x.shape[1], dtype=torch.int32, device=x.device - ) - return x, mask, [(H, W)] * B - else: - pH = pW = self.patch_size - x_embed = [] - img_size = [] - l_effective_seq_len = [] - - for img in x: - C, H, W = img.size() - img_size.append((H, W)) - img = ( - img.view(C, H // pH, pH, W // pW, pW) - .permute(1, 3, 0, 2, 4) - .flatten(2) - ) - img = self.x_embedder(img) - img = torch.cat( - [ - img, - self.eol_token.view(1, 1, -1).expand(H // pH, 1, -1), - ], - dim=1, - ) - img = img.flatten(0, 1) - l_effective_seq_len.append(len(img)) - x_embed.append(img) - - max_seq_len = max(l_effective_seq_len) - mask = torch.zeros( - len(x), max_seq_len, dtype=torch.int32, device=x[0].device - ) - padded_x_embed = [] - for i, (item_embed, item_seq_len) in enumerate( - zip(x_embed, l_effective_seq_len) - ): - item_embed = torch.cat( - [ - item_embed, - self.pad_token.view(1, -1).expand( - max_seq_len - item_seq_len, -1 - ), - ], - dim=0, - ) - padded_x_embed.append(item_embed) - mask[i][:item_seq_len] = 1 - - x_embed = torch.stack(padded_x_embed, dim=0) - return x_embed, mask, img_size - - def forward(self, x, t, cap_feats, cap_mask): - """ - Forward pass of DiT. - t: (N,) tensor of diffusion timesteps - y: (N,) tensor of class labels - """ - x_is_tensor = isinstance(x, torch.Tensor) - x, mask, img_size = self.patchify_and_embed(x) - self.freqs_cis = self.freqs_cis.to(x.device) - - t = self.t_embedder(t) # (N, D) - cap_mask_float = cap_mask.float().unsqueeze(-1) - cap_feats_pool = (cap_feats * cap_mask_float).sum(dim=1) / cap_mask_float.sum( - dim=1 - ) - cap_feats_pool = cap_feats_pool.to(cap_feats) - cap_emb = self.cap_embedder(cap_feats_pool) - adaln_input = t + cap_emb - - cap_mask = cap_mask.bool() - for layer in self.layers: - x = layer( - x, - mask, - cap_feats, - cap_mask, - self.freqs_cis[: x.size(1)], - adaln_input=adaln_input, - ) - - x = self.final_layer(x, adaln_input) - x = self.unpatchify(x, img_size, return_tensor=x_is_tensor) - if self.learn_sigma: - if x_is_tensor: - x, _ = x.chunk(2, dim=1) - else: - x = [_.chunk(2, dim=0)[0] for _ in x] - return x - - def forward_with_cfg( - self, - x, - t, - cap_feats, - cap_mask, - cfg_scale, - rope_scaling_factor=None, - ntk_factor=None, - base_seqlen: Optional[int] = None, - proportional_attn: bool = False, - ): - """ - Forward pass of DiT, but also batches the unconditional forward pass - for classifier-free guidance. - """ - # https://github.com/openai/glide-text2im/blob/main/notebooks/text2im.ipynb - - if rope_scaling_factor is not None or ntk_factor is not None: - rope_scaling_factor = ( - rope_scaling_factor - if rope_scaling_factor is not None - else self.rope_scaling_factor - ) - ntk_factor = ntk_factor if ntk_factor is not None else self.ntk_factor - if ( - rope_scaling_factor != self.rope_scaling_factor - or ntk_factor != self.ntk_factor - ): - print( - f"override freqs_cis, rope_scaling {rope_scaling_factor}, ntk {ntk_factor}", - flush=True, - ) - self.freqs_cis = DiT_Llama.precompute_freqs_cis( - self.dim // self.n_heads, - 40000, - rope_scaling_factor=rope_scaling_factor, - ntk_factor=ntk_factor, - ) - self.rope_scaling_factor = rope_scaling_factor - self.ntk_factor = ntk_factor - - if proportional_attn: - assert base_seqlen is not None - for layer in self.layers: - layer.attention.base_seqlen = base_seqlen - layer.attention.proportional_attn = proportional_attn - else: - for layer in self.layers: - layer.attention.base_seqlen = None - layer.attention.proportional_attn = proportional_attn - - half = x[: len(x) // 2] - combined = torch.cat([half, half], dim=0) - model_out = self(combined, t, cap_feats, cap_mask) - # For exact reproducibility reasons, we apply classifier-free guidance on only - # three channels by default. The standard approach to cfg applies it to all channels. - # This can be done by uncommenting the following line and commenting-out the line following that. - # eps, rest = model_out[:, :self.in_channels], model_out[:, self.in_channels:] - eps, rest = model_out[:, :3], model_out[:, 3:] - cond_eps, uncond_eps = torch.split(eps, len(eps) // 2, dim=0) - half_eps = uncond_eps + cfg_scale * (cond_eps - uncond_eps) - eps = torch.cat([half_eps, half_eps], dim=0) - return torch.cat([eps, rest], dim=1) - - @staticmethod - def precompute_freqs_cis( - dim: int, - end: int, - theta: float = 10000.0, - rope_scaling_factor: float = 1.0, - ntk_factor: float = 1.0, - ): - """ - Precompute the frequency tensor for complex exponentials (cis) with - given dimensions. - - This function calculates a frequency tensor with complex exponentials - using the given dimension 'dim' and the end index 'end'. The 'theta' - parameter scales the frequencies. The returned tensor contains complex - values in complex64 data type. - - Args: - dim (int): Dimension of the frequency tensor. - end (int): End index for precomputing frequencies. - theta (float, optional): Scaling factor for frequency computation. - Defaults to 10000.0. - - Returns: - torch.Tensor: Precomputed frequency tensor with complex - exponentials. - """ - - theta = theta * ntk_factor - - print(f"theta {theta} rope scaling {rope_scaling_factor} ntk {ntk_factor}") - freqs = 1.0 / ( - theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float().cuda() / dim) - ) - t = torch.arange(end, device=freqs.device, dtype=torch.float) # type: ignore - t = t / rope_scaling_factor - freqs = torch.outer(t, freqs).float() # type: ignore - freqs_cis = torch.polar(torch.ones_like(freqs), freqs) # complex64 - return freqs_cis - - def parameter_count(self) -> int: - tensor_module_list = ( - nn.Linear, - nn.Linear, - nn.Embedding, - ) - total_params = 0 - - def _recursive_count_params(module): - nonlocal total_params - is_tp_module = isinstance(module, tensor_module_list) - for param in module.parameters(recurse=False): - total_params += param.numel() - for submodule in module.children(): - _recursive_count_params(submodule) - - _recursive_count_params(self) - return total_params - - def get_fsdp_wrap_module_list(self) -> List[nn.Module]: - return list(self.layers) - - -############################################################################# -# DiT Configs # -############################################################################# - - -def DiT_Llama_5B_patch2(**kwargs): - return DiT_Llama(patch_size=2, dim=3072, n_layers=32, n_heads=32, **kwargs) diff --git a/src/diffusers/pipelines/lumina/modeling_next_dit.py b/src/diffusers/pipelines/lumina/modeling_next_dit.py deleted file mode 100644 index d19258441a31..000000000000 --- a/src/diffusers/pipelines/lumina/modeling_next_dit.py +++ /dev/null @@ -1,1057 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. - -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. -# -------------------------------------------------------- -# References: -# GLIDE: https://github.com/openai/glide-text2im -# MAE: https://github.com/facebookresearch/mae/blob/main/models_mae.py -# -------------------------------------------------------- - -import logging -import math -from typing import Optional, Tuple, List - -from flash_attn import flash_attn_varlen_func -from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa -import torch -import torch.nn as nn -import torch.nn.functional as F - -from ...configuration_utils import ConfigMixin, register_to_config -from ...models import ModelMixin -from ...models.transformers.transformer_2d import Transformer2DModelOutput -from ...utils import logging - -logger = logging.get_logger(__name__) - - -def modulate(x, shift, scale): - return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1) - - -class RMSNorm(torch.nn.Module): - def __init__(self, dim: int, eps: float = 1e-6): - """ - Initialize the RMSNorm normalization layer. - - Args: - dim (int): The dimension of the input tensor. - eps (float, optional): A small value added to the denominator for numerical stability. Default is 1e-6. - - Attributes: - eps (float): A small value added to the denominator for numerical stability. - weight (nn.Parameter): Learnable scaling parameter. - - """ - super().__init__() - self.eps = eps - self.weight = nn.Parameter(torch.ones(dim)) - - def _norm(self, x): - """ - Apply the RMSNorm normalization to the input tensor. - - Args: - x (torch.Tensor): The input tensor. - - Returns: - torch.Tensor: The normalized tensor. - - """ - return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps) - - def forward(self, x): - """ - Forward pass through the RMSNorm layer. - - Args: - x (torch.Tensor): The input tensor. - - Returns: - torch.Tensor: The output tensor after applying RMSNorm. - - """ - output = self._norm(x.float()).type_as(x) - return output * self.weight - - -############################################################################# -# Embedding Layers for Timesteps and Class Labels # -############################################################################# - - -class TimestepEmbedder(nn.Module): - """ - Embeds scalar timesteps into vector representations. - """ - - def __init__(self, hidden_size, frequency_embedding_size=256): - super().__init__() - self.mlp = nn.Sequential( - nn.Linear(frequency_embedding_size, hidden_size, bias=True), - nn.SiLU(), - nn.Linear(hidden_size, hidden_size, bias=True), - ) - self.frequency_embedding_size = frequency_embedding_size - - @staticmethod - def timestep_embedding(t, dim, max_period=10000): - """ - Create sinusoidal timestep embeddings. - :param t: a 1-D Tensor of N indices, one per batch element. - These may be fractional. - :param dim: the dimension of the output. - :param max_period: controls the minimum frequency of the embeddings. - :return: an (N, D) Tensor of positional embeddings. - """ - # https://github.com/openai/glide-text2im/blob/main/glide_text2im/nn.py - half = dim // 2 - freqs = torch.exp( - -math.log(max_period) - * torch.arange(start=0, end=half, dtype=torch.float32) - / half - ).to(device=t.device) - args = t[:, None].float() * freqs[None] - embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) - if dim % 2: - embedding = torch.cat( - [embedding, torch.zeros_like(embedding[:, :1])], dim=-1 - ) - return embedding - - def forward(self, t): - t_freq = self.timestep_embedding(t, self.frequency_embedding_size) - t_emb = self.mlp(t_freq.to(self.mlp[0].weight.dtype)) - return t_emb - - -class LabelEmbedder(nn.Module): - r"""Embeds class labels into vector representations. Also handles label - dropout for classifier-free guidance. - """ - - def __init__(self, num_classes, hidden_size, dropout_prob): - super().__init__() - use_cfg_embedding = int(dropout_prob > 0) - self.embedding_table = nn.Embedding( - num_classes + use_cfg_embedding, hidden_size - ) - self.num_classes = num_classes - self.dropout_prob = dropout_prob - - def token_drop(self, labels, force_drop_ids=None): - """ - Drops labels to enable classifier-free guidance. - """ - if force_drop_ids is None: - drop_ids = ( - torch.rand(labels.shape[0], device=labels.device) < self.dropout_prob - ) - drop_ids = drop_ids.cuda() - drop_ids = drop_ids.to(labels.device) - else: - drop_ids = force_drop_ids == 1 - labels = torch.where(drop_ids, self.num_classes, labels) - return labels - - def forward(self, labels, train, force_drop_ids=None): - use_dropout = self.dropout_prob > 0 - if (train and use_dropout) or (force_drop_ids is not None): - labels = self.token_drop(labels, force_drop_ids) - embeddings = self.embedding_table(labels) - return embeddings - - -############################################################################# -# Core NextFlagDiffuserTransformer2DModel Model # -############################################################################# - - -class Attention(nn.Module): - """Multi-head attention module.""" - - def __init__( - self, - dim: int, - n_heads: int, - n_kv_heads: Optional[int], - qk_norm: bool, - y_dim: int, - ): - """ - Initialize the Attention module. - - Args: - dim (int): Number of input dimensions. - n_heads (int): Number of heads. - n_kv_heads (Optional[int]): Number of kv heads, if using GQA. - - """ - super().__init__() - self.n_kv_heads = n_heads if n_kv_heads is None else n_kv_heads - self.num_attention_heads = n_heads - self.n_rep = self.num_attention_heads // self.n_kv_heads - self.head_dim = dim // n_heads - - self.wq = nn.Linear(dim, n_heads * self.head_dim, bias=False) - self.wk = nn.Linear(dim, self.n_kv_heads * self.head_dim, bias=False) - self.wv = nn.Linear(dim, self.n_kv_heads * self.head_dim, bias=False) - if y_dim > 0: - self.wk_y = nn.Linear(y_dim, self.n_kv_heads * self.head_dim, bias=False) - self.wv_y = nn.Linear(y_dim, self.n_kv_heads * self.head_dim, bias=False) - self.gate = nn.Parameter(torch.zeros([self.num_attention_heads])) - - self.wo = nn.Linear(n_heads * self.head_dim, dim, bias=False) - - if qk_norm: - self.q_norm = nn.LayerNorm(self.num_attention_heads * self.head_dim) - self.k_norm = nn.LayerNorm(self.n_kv_heads * self.head_dim) - if y_dim > 0: - self.ky_norm = nn.LayerNorm(self.n_kv_heads * self.head_dim) - else: - self.ky_norm = nn.Identity() - else: - self.q_norm = self.k_norm = nn.Identity() - self.ky_norm = nn.Identity() - - # for proportional attention computation - self.base_seqlen = None - self.proportional_attn = False - - @staticmethod - def reshape_for_broadcast(freqs_cis: torch.Tensor, x: torch.Tensor): - """ - Reshape frequency tensor for broadcasting it with another tensor. - - This function reshapes the frequency tensor to have the same shape as - the target tensor 'x' for the purpose of broadcasting the frequency - tensor during element-wise operations. - - Args: - freqs_cis (torch.Tensor): Frequency tensor to be reshaped. - x (torch.Tensor): Target tensor for broadcasting compatibility. - - Returns: - torch.Tensor: Reshaped frequency tensor. - - Raises: - AssertionError: If the frequency tensor doesn't match the expected - shape. - AssertionError: If the target tensor 'x' doesn't have the expected - number of dimensions. - """ - ndim = x.ndim - assert 0 <= 1 < ndim - assert freqs_cis.shape == (x.shape[1], x.shape[-1]) - shape = [d if i == 1 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)] - return freqs_cis.view(*shape) - - @staticmethod - def apply_rotary_emb( - x_in: torch.Tensor, - freqs_cis: torch.Tensor, - ) -> torch.Tensor: - """ - Apply rotary embeddings to input tensors using the given frequency - tensor. - - This function applies rotary embeddings to the given query 'xq' and - key 'xk' tensors using the provided frequency tensor 'freqs_cis'. The - input tensors are reshaped as complex numbers, and the frequency tensor - is reshaped for broadcasting compatibility. The resulting tensors - contain rotary embeddings and are returned as real tensors. - - Args: - x_in (torch.Tensor): Query or Key tensor to apply rotary embeddings. - freqs_cis (torch.Tensor): Precomputed frequency tensor for complex - exponentials. - - Returns: - Tuple[torch.Tensor, torch.Tensor]: Tuple of modified query tensor - and key tensor with rotary embeddings. - """ - with torch.cuda.amp.autocast(enabled=False): - x = torch.view_as_complex(x_in.float().reshape(*x_in.shape[:-1], -1, 2)) - freqs_cis = freqs_cis.unsqueeze(2) - x_out = torch.view_as_real(x * freqs_cis).flatten(3) - return x_out.type_as(x_in) - - # copied from huggingface modeling_llama.py - def _upad_input( - self, query_layer, key_layer, value_layer, attention_mask, query_length - ): - - def _get_unpad_data(attention_mask): - seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32) - indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten() - max_seqlen_in_batch = seqlens_in_batch.max().item() - cu_seqlens = F.pad( - torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0) - ) - return ( - indices, - cu_seqlens, - max_seqlen_in_batch, - ) - - indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask) - batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape - - key_layer = index_first_axis( - key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), - indices_k, - ) - value_layer = index_first_axis( - value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), - indices_k, - ) - if query_length == kv_seq_len: - query_layer = index_first_axis( - query_layer.reshape( - batch_size * kv_seq_len, self.num_attention_heads, head_dim - ), - indices_k, - ) - cu_seqlens_q = cu_seqlens_k - max_seqlen_in_batch_q = max_seqlen_in_batch_k - indices_q = indices_k - elif query_length == 1: - max_seqlen_in_batch_q = 1 - cu_seqlens_q = torch.arange( - batch_size + 1, dtype=torch.int32, device=query_layer.device - ) # There is a memcpy here, that is very bad. - indices_q = cu_seqlens_q[:-1] - query_layer = query_layer.squeeze(1) - else: - # The -q_len: slice assumes left padding. - attention_mask = attention_mask[:, -query_length:] - query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input( - query_layer, attention_mask - ) - - return ( - query_layer, - key_layer, - value_layer, - indices_q, - (cu_seqlens_q, cu_seqlens_k), - (max_seqlen_in_batch_q, max_seqlen_in_batch_k), - ) - - def forward( - self, - x: torch.Tensor, - x_mask: torch.Tensor, - freqs_cis: torch.Tensor, - y: torch.Tensor, - y_mask: torch.Tensor, - ) -> torch.Tensor: - """ - - Args: - x: - x_mask: - freqs_cis: - y: - y_mask: - - Returns: - - """ - bsz, seqlen, _ = x.shape - xq, xk, xv = self.wq(x), self.wk(x), self.wv(x) - dtype = xq.dtype - - xq = self.q_norm(xq) - xk = self.k_norm(xk) - - xq = xq.view(bsz, seqlen, self.num_attention_heads, self.head_dim) - xk = xk.view(bsz, seqlen, self.n_kv_heads, self.head_dim) - xv = xv.view(bsz, seqlen, self.n_kv_heads, self.head_dim) - - xq = Attention.apply_rotary_emb(xq, freqs_cis=freqs_cis) - xk = Attention.apply_rotary_emb(xk, freqs_cis=freqs_cis) - - xq, xk = xq.to(dtype), xk.to(dtype) - - if dtype in [torch.float16, torch.bfloat16]: - # begin var_len flash attn - ( - query_states, - key_states, - value_states, - indices_q, - cu_seq_lens, - max_seq_lens, - ) = self._upad_input(xq, xk, xv, x_mask, seqlen) - - cu_seqlens_q, cu_seqlens_k = cu_seq_lens - max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens - - if self.proportional_attn: - softmax_scale = math.sqrt( - math.log(seqlen, self.base_seqlen) / self.head_dim - ) - else: - softmax_scale = math.sqrt(1 / self.head_dim) - - attn_output_unpad = flash_attn_varlen_func( - query_states, - key_states, - value_states, - cu_seqlens_q=cu_seqlens_q, - cu_seqlens_k=cu_seqlens_k, - max_seqlen_q=max_seqlen_in_batch_q, - max_seqlen_k=max_seqlen_in_batch_k, - dropout_p=0.0, - causal=False, - softmax_scale=softmax_scale, - ) - output = pad_input(attn_output_unpad, indices_q, bsz, seqlen) - # end var_len_flash_attn - - else: - output = ( - F.scaled_dot_product_attention( - xq.permute(0, 2, 1, 3), - xk.permute(0, 2, 1, 3), - xv.permute(0, 2, 1, 3), - attn_mask=x_mask.bool() - .view(bsz, 1, 1, seqlen) - .expand(-1, self.num_attention_heads, seqlen, -1), - ) - .permute(0, 2, 1, 3) - .to(dtype) - ) - - if hasattr(self, "wk_y"): - # todo better flash_attn support - yk = self.ky_norm(self.wk_y(y)).view( - bsz, -1, self.n_kv_heads, self.head_dim - ) - yv = self.wv_y(y).view(bsz, -1, self.n_kv_heads, self.head_dim) - n_rep = self.num_attention_heads // self.n_kv_heads - if n_rep >= 1: - yk = yk.unsqueeze(3).repeat(1, 1, 1, n_rep, 1).flatten(2, 3) - yv = yv.unsqueeze(3).repeat(1, 1, 1, n_rep, 1).flatten(2, 3) - output_y = F.scaled_dot_product_attention( - xq.permute(0, 2, 1, 3), - yk.permute(0, 2, 1, 3), - yv.permute(0, 2, 1, 3), - y_mask.view(bsz, 1, 1, -1).expand( - bsz, self.num_attention_heads, seqlen, -1 - ), - ).permute(0, 2, 1, 3) - output_y = output_y * self.gate.tanh().view(1, 1, -1, 1) - output = output + output_y - - output = output.flatten(-2) - - return self.wo(output) - - -class FeedForward(nn.Module): - def __init__( - self, - dim: int, - hidden_dim: int, - multiple_of: int, - ffn_dim_multiplier: Optional[float], - ): - """ - Initialize the FeedForward module. - - Args: - dim (int): Input dimension. - hidden_dim (int): Hidden dimension of the feedforward layer. - multiple_of (int): Value to ensure hidden dimension is a multiple - of this value. - ffn_dim_multiplier (float, optional): Custom multiplier for hidden - dimension. Defaults to None. - - Attributes: - w1 (nn.Linear): Linear transformation for the first - layer. - w2 (nn.Linear): Linear transformation for the second layer. - w3 (nn.Linear): Linear transformation for the third - layer. - - """ - super().__init__() - hidden_dim = int(2 * hidden_dim / 3) - # custom dim factor multiplier - if ffn_dim_multiplier is not None: - hidden_dim = int(ffn_dim_multiplier * hidden_dim) - hidden_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of) - - self.w1 = nn.Linear(dim, hidden_dim, bias=False) - self.w2 = nn.Linear(hidden_dim, dim, bias=False) - self.w3 = nn.Linear(dim, hidden_dim, bias=False) - - # @torch.compile - def _forward_silu_gating(self, x1, x3): - return F.silu(x1) * x3 - - def forward(self, x): - return self.w2(self._forward_silu_gating(self.w1(x), self.w3(x))) - - -class TransformerBlock(nn.Module): - def __init__( - self, - layer_id: int, - dim: int, - n_heads: int, - n_kv_heads: int, - multiple_of: int, - ffn_dim_multiplier: float, - norm_eps: float, - qk_norm: bool, - y_dim: int, - ) -> None: - """ - Initialize a TransformerBlock. - - Args: - layer_id (int): Identifier for the layer. - dim (int): Embedding dimension of the input features. - n_heads (int): Number of attention heads. - n_kv_heads (Optional[int]): Number of attention heads in key and - value features (if using GQA), or set to None for the same as - query. - multiple_of (int): - ffn_dim_multiplier (float): - norm_eps (float): - - Attributes: - n_heads (int): Number of attention heads. - dim (int): Dimension size of the model. - head_dim (int): Dimension size of each attention head. - attention (Attention): Attention module. - feed_forward (FeedForward): FeedForward module. - layer_id (int): Identifier for the layer. - attention_norm (RMSNorm): Layer normalization for attention output. - ffn_norm (RMSNorm): Layer normalization for feedforward output. - - """ - super().__init__() - self.dim = dim - self.head_dim = dim // n_heads - self.attention = Attention(dim, n_heads, n_kv_heads, qk_norm, y_dim) - self.feed_forward = FeedForward( - dim=dim, - hidden_dim=4 * dim, - multiple_of=multiple_of, - ffn_dim_multiplier=ffn_dim_multiplier, - ) - self.layer_id = layer_id - self.attention_norm = RMSNorm(dim, eps=norm_eps) - self.attention_norm1 = RMSNorm(dim, eps=norm_eps) - self.ffn_norm = RMSNorm(dim, eps=norm_eps) - self.ffn_norm1 = RMSNorm(dim, eps=norm_eps) - - self.adaLN_modulation = nn.Sequential( - nn.SiLU(), - nn.Linear(min(dim, 1024), 6 * dim, bias=True), - ) - - self.attention_y_norm = RMSNorm(y_dim, eps=norm_eps) - - def forward( - self, - x: torch.Tensor, - x_mask: torch.Tensor, - freqs_cis: torch.Tensor, - y: torch.Tensor, - y_mask: torch.Tensor, - adaln_input: Optional[torch.Tensor] = None, - ): - """ - Perform a forward pass through the TransformerBlock. - - Args: - x (torch.Tensor): Input tensor. - freqs_cis (torch.Tensor): Precomputed cosine and sine frequencies. - - Returns: - torch.Tensor: Output tensor after applying attention and - feedforward layers. - - """ - if adaln_input is not None: - shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = ( - self.adaLN_modulation(adaln_input).chunk(6, dim=1) - ) - - x = x + self.attention_norm1( - gate_msa.unsqueeze(1) - * self.attention( - modulate(self.attention_norm(x), shift_msa, scale_msa), - x_mask, - freqs_cis, - self.attention_y_norm(y), - y_mask, - ) - ) - d = x.shape[-1] - x = x + self.ffn_norm1( - gate_mlp.unsqueeze(1) - * self.feed_forward( - modulate(self.ffn_norm(x), shift_mlp, scale_mlp).view(-1, d), - ).view(*x.shape) - ) - - else: - x = x + self.attention_norm1( - self.attention( - self.attention_norm(x), - x_mask, - freqs_cis, - self.attention_y_norm(y), - y_mask, - ) - ) - # for compatibility with torch.compile because the sequence length changes - B, L, D = x.shape - x = x.view(B * L, D) - x = x + self.ffn_norm1(self.feed_forward(self.ffn_norm(x))) - x = x.view(B, L, D) - - return x - - -class FinalLayer(nn.Module): - """ - The final layer of NextFlagDiffuserTransformer2DModel. - """ - - def __init__(self, hidden_size, patch_size, out_channels): - super().__init__() - self.norm_final = nn.LayerNorm( - hidden_size, - elementwise_affine=False, - eps=1e-6, - ) - self.linear = nn.Linear(hidden_size, patch_size * patch_size * out_channels) - self.adaLN_modulation = nn.Sequential( - nn.SiLU(), - nn.Linear(min(hidden_size, 1024), 2 * hidden_size), - ) - - def forward(self, x, c): - shift, scale = self.adaLN_modulation(c).chunk(2, dim=1) - x = modulate(self.norm_final(x), shift, scale) - x = self.linear(x) - return x - - -class NextFlagDiffuserTransformer2DModel(ModelMixin, ConfigMixin): - """ - Diffusion model with a Transformer backbone. - """ - - @register_to_config - def __init__( - self, - patch_size: Optional[int] = 2, - in_channels: Optional[int] = None, - out_channels: Optional[int] = None, - hidden_size: int = 4096, - num_layers: int = 32, - num_attention_heads: int = 32, - num_kv_heads: Optional[int] = None, - multiple_of: int = 256, - ffn_dim_multiplier: Optional[float] = None, - norm_elementwise_affine: bool = True, - norm_eps: float = 1e-5, - learn_sigma: bool = True, - qk_norm: bool = False, - text_dim: int = 5120, - rope_scaling_factor: float = 1.0, - ntk_factor: float = 1.0, - ) -> None: - super().__init__() - self.learn_sigma = learn_sigma - self.in_channels = in_channels - self.out_channels = in_channels * 2 if learn_sigma else in_channels - self.patch_size = patch_size - self.num_attention_heads = num_attention_heads - self.rope_scaling_factor = rope_scaling_factor - self.ntk_factor = ntk_factor - - self.x_embedder = nn.Linear( - in_features=patch_size * patch_size * in_channels, - out_features=hidden_size, - bias=True, - ) - nn.init.constant_(self.x_embedder.bias, 0.0) - - self.t_embedder = TimestepEmbedder(min(hidden_size, 1024)) - self.cap_embedder = nn.Sequential( - nn.LayerNorm(text_dim), - nn.Linear(text_dim, min(hidden_size, 1024), bias=True), - ) - - self.layers = nn.ModuleList( - [ - TransformerBlock( - layer_id, - hidden_size, - num_attention_heads, - num_kv_heads, - multiple_of, - ffn_dim_multiplier, - norm_eps, - qk_norm, - text_dim, - ) - for layer_id in range(num_layers) - ] - ) - self.final_layer = FinalLayer(hidden_size, patch_size, self.out_channels) - - assert ( - hidden_size // num_attention_heads - ) % 4 == 0, "2d rope needs head dim to be divisible by 4" - self.hidden_size = hidden_size - self.freqs_cis = NextFlagDiffuserTransformer2DModel.precompute_freqs_cis( - hidden_size // num_attention_heads, - 384, - rope_scaling_factor=rope_scaling_factor, - ntk_factor=ntk_factor, - ) - # self.eol_token = nn.Parameter(torch.empty(dim)) - self.pad_token = nn.Parameter(torch.empty(hidden_size)) - # nn.init.normal_(self.eol_token, std=0.02) - nn.init.normal_(self.pad_token, std=0.02) - - def unpatchify( - self, x: torch.Tensor, img_size: List[Tuple[int, int]], return_tensor=False - ) -> List[torch.Tensor]: - """ - x: (N, T, patch_size**2 * C) - imgs: (N, H, W, C) - """ - pH = pW = self.patch_size - if return_tensor: - H, W = img_size[0] - B = x.size(0) - L = (H // pH) * (W // pW) - x = x[:, :L].view(B, H // pH, W // pW, pH, pW, self.out_channels) - x = x.permute(0, 5, 1, 3, 2, 4).flatten(4, 5).flatten(2, 3) - return x - else: - imgs = [] - for i in range(x.size(0)): - H, W = img_size[i] - L = (H // pH) * (W // pW) - imgs.append( - x[i][:L] - .view(H // pH, W // pW, pH, pW, self.out_channels) - .permute(4, 0, 2, 1, 3) - .flatten(3, 4) - .flatten(1, 2) - ) - return imgs - - def patchify_and_embed( - self, x: List[torch.Tensor] | torch.Tensor - ) -> Tuple[torch.Tensor, torch.Tensor, List[Tuple[int, int]], torch.Tensor]: - self.freqs_cis = self.freqs_cis.to(x[0].device) - if isinstance(x, torch.Tensor): - pH = pW = self.patch_size - B, C, H, W = x.size() - x = ( - x.view(B, C, H // pH, pH, W // pW, pW) - .permute(0, 2, 4, 1, 3, 5) - .flatten(3) - ) - x = self.x_embedder(x) - x = x.flatten(1, 2) - - mask = torch.ones( - x.shape[0], x.shape[1], dtype=torch.int32, device=x.device - ) - # leave the first line for text - return ( - x, - mask, - [(H, W)] * B, - self.freqs_cis[: H // pH, : W // pW].flatten(0, 1).unsqueeze(0), - ) - else: - pH = pW = self.patch_size - x_embed = [] - freqs_cis = [] - img_size = [] - l_effective_seq_len = [] - - for img in x: - C, H, W = img.size() - item_freqs_cis = self.freqs_cis[: H // pH, : W // pW] - freqs_cis.append(item_freqs_cis.flatten(0, 1)) - img_size.append((H, W)) - img = ( - img.view(C, H // pH, pH, W // pW, pW) - .permute(1, 3, 0, 2, 4) - .flatten(2) - ) - img = self.x_embedder(img) - img = img.flatten(0, 1) - l_effective_seq_len.append(len(img)) - x_embed.append(img) - - max_seq_len = max(l_effective_seq_len) - mask = torch.zeros( - len(x), max_seq_len, dtype=torch.int32, device=x[0].device - ) - padded_x_embed = [] - padded_freqs_cis = [] - for i, (item_embed, item_freqs_cis, item_seq_len) in enumerate( - zip(x_embed, freqs_cis, l_effective_seq_len) - ): - item_embed = torch.cat( - [ - item_embed, - self.pad_token.view(1, -1).expand( - max_seq_len - item_seq_len, -1 - ), - ], - dim=0, - ) - item_freqs_cis = torch.cat( - [ - item_freqs_cis, - item_freqs_cis[-1:].expand(max_seq_len - item_seq_len, -1), - ], - dim=0, - ) - padded_x_embed.append(item_embed) - padded_freqs_cis.append(item_freqs_cis) - mask[i][:item_seq_len] = 1 - - x_embed = torch.stack(padded_x_embed, dim=0) - freqs_cis = torch.stack(padded_freqs_cis, dim=0) - return x_embed, mask, img_size, freqs_cis - - def forward( - self, - hidden_states, - timestep=None, - encoder_hidden_states=None, - encoder_attn_mask=None, - return_dict: bool = True, - hidden_states_is_tensor: bool = False, - unpatchify: bool = True, - ): - """ - Forward pass of NextFlagDiffuserTransformer2DModel. - t: (N,) tensor of diffusion timesteps - y: (N,) tensor of class labels - """ - - # 0. Check inputs - if not unpatchify and return_dict: - raise ValueError( - f"Cannot both define `unpatchify`: {unpatchify} and `return_dict`: {return_dict} since when" - f" `unpatchify` is {unpatchify} the returned output is of shape (batch_size, seq_len, hidden_dim)" - " rather than (batch_size, num_channels, height, width)." - ) - - # 1. Patchify and Get embedding - hidden_states, mask, img_size, freqs_cis = self.patchify_and_embed( - hidden_states - ) - freqs_cis = freqs_cis.to(hidden_states.device) - - # 2. Get timestep embedding - timestep = self.t_embedder(timestep) # (N, D) - - # 3. Get encoder hidden_state - encoder_attn_mask_float = encoder_attn_mask.float().unsqueeze(-1) - encoder_hidden_states_pool = ( - encoder_hidden_states * encoder_attn_mask_float - ).sum(dim=1) / encoder_attn_mask_float.sum(dim=1) - encoder_hidden_states_pool = encoder_hidden_states_pool.to( - encoder_hidden_states - ) - cap_emb = self.cap_embedder(encoder_hidden_states_pool) - encoder_attn_mask = encoder_attn_mask.bool() - - adaln_input = timestep + cap_emb - # 4. Get hidden_state from transformer - for layer in self.layers: - hidden_states = layer( - hidden_states, - mask, - freqs_cis, - encoder_hidden_states, - encoder_attn_mask, - adaln_input=adaln_input, - ) - - hidden_states = self.final_layer(hidden_states, adaln_input) - - if unpatchify: - hidden_states = self.unpatchify( - hidden_states, img_size, return_tensor=hidden_states_is_tensor - ) - else: - output = hidden_states - - if self.learn_sigma: - if hidden_states_is_tensor: - hidden_states, _ = hidden_states.chunk(2, dim=1) - else: - hidden_states = [_.chunk(2, dim=0)[0] for _ in hidden_states] - - if not return_dict: - return (output,) - - return Transformer2DModelOutput(sample=output) - - def forward_with_cfg( - self, - x, - t, - cap_feats, - cap_mask, - cfg_scale, - rope_scaling_factor=None, - ntk_factor=None, - base_seqlen: Optional[int] = None, - proportional_attn: bool = False, - ): - # """ - # Forward pass of NextFlagDiffuserTransformer2DModel, but also batches the unconNextFlagDiffuserTransformer2DModelional forward pass - # for classifier-free guidance. - # """ - # # https://github.com/openai/glide-text2im/blob/main/notebooks/text2im.ipynb - # print(ntk_factor, rope_scaling_factor, self.ntk_factor, self.rope_scaling_factor) - if rope_scaling_factor is not None or ntk_factor is not None: - rope_scaling_factor = ( - rope_scaling_factor - if rope_scaling_factor is not None - else self.rope_scaling_factor - ) - ntk_factor = ntk_factor if ntk_factor is not None else self.ntk_factor - if ( - rope_scaling_factor != self.rope_scaling_factor - or ntk_factor != self.ntk_factor - ): - print( - f"override freqs_cis, rope_scaling {rope_scaling_factor}, ntk {ntk_factor}", - flush=True, - ) - self.freqs_cis = ( - NextFlagDiffuserTransformer2DModel.precompute_freqs_cis( - self.hidden_size // self.num_attention_heads, - 384, - rope_scaling_factor=rope_scaling_factor, - ntk_factor=ntk_factor, - ) - ) - self.rope_scaling_factor = rope_scaling_factor - self.ntk_factor = ntk_factor - - if proportional_attn: - assert base_seqlen is not None - for layer in self.layers: - layer.attention.base_seqlen = base_seqlen - layer.attention.proportional_attn = proportional_attn - else: - for layer in self.layers: - layer.attention.base_seqlen = None - layer.attention.proportional_attn = proportional_attn - - half = x[: len(x) // 2] - combined = torch.cat([half, half], dim=0) - model_out = self.forward(combined, t, cap_feats, cap_mask) - # For exact reproducibility reasons, we apply classifier-free guidance on only - # three channels by default. The standard approach to cfg applies it to all channels. - # This can be done by uncommenting the following line and commenting-out the line following that. - # eps, rest = model_out[:, :self.in_channels], model_out[:, self.in_channels:] - eps, rest = model_out[:, :3], model_out[:, 3:] - cond_eps, uncond_eps = torch.split(eps, len(eps) // 2, dim=0) - half_eps = uncond_eps + cfg_scale * (cond_eps - uncond_eps) - eps = torch.cat([half_eps, half_eps], dim=0) - return torch.cat([eps, rest], dim=1) - - @staticmethod - def precompute_freqs_cis( - dim: int, - end: int, - theta: float = 10000.0, - rope_scaling_factor: float = 1.0, - ntk_factor: float = 1.0, - ): - """ - Precompute the frequency tensor for complex exponentials (cis) with - given dimensions. - - This function calculates a frequency tensor with complex exponentials - using the given dimension 'dim' and the end index 'end'. The 'theta' - parameter scales the frequencies. The returned tensor contains complex - values in complex64 data type. - - Args: - dim (int): Dimension of the frequency tensor. - end (int): End index for precomputing frequencies. - theta (float, optional): Scaling factor for frequency computation. - Defaults to 10000.0. - - Returns: - torch.Tensor: Precomputed frequency tensor with complex - exponentials. - """ - - theta = theta * ntk_factor - - logger.info( - f"theta {theta} rope scaling {rope_scaling_factor} ntk {ntk_factor}" - ) - freqs = 1.0 / ( - theta ** (torch.arange(0, dim, 4)[: (dim // 4)].float().cuda() / dim) - ) - t = torch.arange(end, device=freqs.device, dtype=torch.float) # type: ignore - t = t / rope_scaling_factor - freqs = torch.outer(t, freqs).float() # type: ignore - freqs_cis = torch.polar(torch.ones_like(freqs), freqs) # complex64 - - freqs_cis_h = freqs_cis.view(end, 1, dim // 4, 1).repeat(1, end, 1, 1) - freqs_cis_w = freqs_cis.view(1, end, dim // 4, 1).repeat(end, 1, 1, 1) - freqs_cis = torch.cat([freqs_cis_h, freqs_cis_w], dim=-1).flatten(2) - return freqs_cis - - def parameter_count(self) -> int: - tensor_parallel_module_list = ( - nn.Linear, - nn.Linear, - nn.Embedding, - ) - total_params = 0 - - def _recursive_count_params(module): - nonlocal total_params - is_tp_module = isinstance(module, tensor_parallel_module_list) - for param in module.parameters(recurse=False): - total_params += param.numel() - for submodule in module.children(): - _recursive_count_params(submodule) - - _recursive_count_params(self) - return total_params - - def get_fsdp_wrap_module_list(self) -> List[nn.Module]: - return list(self.layers) - - -############################################################################# -# NextFlagDiffuserTransformer2DModel Configs # -############################################################################# -def NextFlagDiffuserTransformer2DModel_2B_patch2(**kwargs): - return NextFlagDiffuserTransformer2DModel( - patch_size=2, dim=2304, num_layers=24, n_heads=32, **kwargs - ) diff --git a/src/diffusers/pipelines/lumina/pipeline_lumina.py b/src/diffusers/pipelines/lumina/pipeline_lumina.py index 9b67aeab0173..6435cddcd880 100644 --- a/src/diffusers/pipelines/lumina/pipeline_lumina.py +++ b/src/diffusers/pipelines/lumina/pipeline_lumina.py @@ -1,4 +1,4 @@ -# Copyright 2024 Lumina-T2X Authors and The HuggingFace Team. All rights reserved. +# Copyright 2024 Alpha-VLLM and The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -15,15 +15,16 @@ import html import inspect import re +import math import urllib.parse as ul -from typing import Callable, List, Optional, Tuple, Union +from typing import Callable, List, Optional, Tuple, Union, Dict import torch from transformers import AutoModel, AutoTokenizer from ...image_processor import VaeImageProcessor from ...models import AutoencoderKL -from ...schedulers import DPMSolverMultistepScheduler +from ...schedulers import FlowMatchEulerDiscreteScheduler from ...utils import ( BACKENDS_MAPPING, deprecate, @@ -34,7 +35,7 @@ ) from ...utils.torch_utils import randn_tensor from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput -from .modeling_next_dit import NextFlagDiffuserTransformer2DModel +from ...models.transformers.lumina_nextdit_2d import LuminaNextDiT2DModel logger = logging.get_logger(__name__) # pylint: disable=invalid-name @@ -52,7 +53,7 @@ >>> from diffusers import LuminaText2ImgPipeline >>> # You can replace the checkpoint id with "Alpha-VLLM/Lumina-Next-T2I" too. - >>> pipe = LuminaText2ImgPipeline.from_pretrained("Alpha-VLLM/Lumina-T2I", torch_dtype=torch.float16) + >>> pipe = LuminaText2ImgPipeline.from_pretrained("Alpha-VLLM/Lumina-T2I", torch_dtype=torch.bfloat16) >>> # Enable memory optimizations. >>> pipe.enable_model_cpu_offload() @@ -280,24 +281,91 @@ class LuminaText2ImgPipeline(DiffusionPipeline): def __init__( self, - tokenizer: AutoTokenizer, - text_encoder: AutoModel, + transformer: LuminaNextDiT2DModel, + scheduler: FlowMatchEulerDiscreteScheduler, vae: AutoencoderKL, - transformer: NextFlagDiffuserTransformer2DModel, - scheduler: DPMSolverMultistepScheduler, + text_encoder: AutoModel, + tokenizer: AutoTokenizer, ): super().__init__() self.register_modules( - tokenizer=tokenizer, - text_encoder=text_encoder, vae=vae, + text_encoder=text_encoder, + tokenizer=tokenizer, transformer=transformer, scheduler=scheduler, ) - self.vae_scale_factor = 0.18215 + self.vae_scale_factor = ( + 2 ** (len(self.vae.config.block_out_channels) - 1) if hasattr(self, "vae") and self.vae is not None else 8 + ) self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor) + self.tokenizer_max_length = ( + self.tokenizer.model_max_length if hasattr(self, "tokenizer") and self.tokenizer is not None else 256 + ) + + def _get_gemma_prompt_embeds( + self, + prompt: Union[str, List[str]], + num_images_per_prompt: int = 1, + device: Optional[torch.device] = None, + clip_skip: Optional[int] = None, + clip_model_index: int = 0, + clean_caption: Optional[bool] = False, + ): + device = device or self._execution_device + prompt = [prompt] if isinstance(prompt, str) else prompt + batch_size = len(prompt) + + prompt = self._text_preprocessing(prompt, clean_caption=clean_caption) + text_inputs = self.tokenizer( + prompt, + padding=True, + pad_to_multiple_of=8, + max_length=self.tokenizer_max_length, + truncation=True, + return_tensors="pt", + ) + text_input_ids = text_inputs.input_ids + untruncated_ids = self.tokenizer( + prompt, padding="longest", return_tensors="pt" + ).input_ids + + if untruncated_ids.shape[-1] >= text_input_ids.shape[ + -1 + ] and not torch.equal(text_input_ids, untruncated_ids): + removed_text = self.tokenizer.batch_decode( + untruncated_ids[:, self.tokenizer_max_length - 1 : -1] + ) + logger.warning( + "The following part of your input was truncated because T5 can only handle sequences up to" + f" {self.tokenizer_max_length} tokens: {removed_text}" + ) + + prompt_attention_mask = text_inputs.attention_mask + prompt_attention_mask = prompt_attention_mask.to(device) + + prompt_embeds = self.text_encoder( + text_input_ids.to(device), attention_mask=prompt_attention_mask, output_hidden_states=True + ) + prompt_embeds = prompt_embeds.hidden_states[-2] + + if self.text_encoder is not None: + dtype = self.text_encoder.dtype + elif self.transformer is not None: + dtype = self.transformer.dtype + else: + dtype = None + + prompt_embeds = prompt_embeds.to(dtype=dtype, device=device) + + _, seq_len, _ = prompt_embeds.shape + # duplicate text embeddings and attention mask for each generation per prompt, using mps friendly method + prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1) + prompt_embeds = prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1) + + return prompt_embeds, prompt_attention_mask # Adapted from diffusers.pipelines.deepfloyd_if.pipeline_if.encode_prompt def encode_prompt( @@ -341,114 +409,49 @@ def encode_prompt( If `True`, the function will preprocess and clean the provided caption before encoding. max_sequence_length (`int`, defaults to 120): Maximum sequence length to use for the prompt. """ - - if "mask_feature" in kwargs: - deprecation_message = "The use of `mask_feature` is deprecated. It is no longer used in any computation and that doesn't affect the end results. It will be removed in a future version." - deprecate("mask_feature", "1.0.0", deprecation_message, standard_warn=False) - if device is None: device = self._execution_device - if prompt is not None and isinstance(prompt, str): - batch_size = 1 - elif prompt is not None and isinstance(prompt, list): + prompt = [prompt] if isinstance(prompt, str) else prompt + if prompt is not None: batch_size = len(prompt) else: batch_size = prompt_embeds.shape[0] - max_length = max_sequence_length if prompt_embeds is None: - prompt = self._text_preprocessing(prompt, clean_caption=clean_caption) - text_inputs = self.tokenizer( - prompt, - padding=True, - pad_to_multiple_of=8, - max_length=max_length, - truncation=True, - return_tensors="pt", + prompt_embeds, prompt_attention_mask = self._get_gemma_prompt_embeds( + prompt=prompt, + num_images_per_prompt=num_images_per_prompt, + device=device, + clean_caption=clean_caption, ) - - text_input_ids = text_inputs.input_ids - untruncated_ids = self.tokenizer( - prompt, padding="longest", return_tensors="pt" - ).input_ids - - if untruncated_ids.shape[-1] >= text_input_ids.shape[ - -1 - ] and not torch.equal(text_input_ids, untruncated_ids): - removed_text = self.tokenizer.batch_decode( - untruncated_ids[:, max_length - 1 : -1] + + # get negative embeddings for classifier free guidance + if do_classifier_free_guidance and negative_prompt_embeds is None: + negative_prompt = negative_prompt if negative_prompt is not None else "" + + # normalize str to list + negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt + + if prompt is not None and type(prompt) is not type(negative_prompt): + raise TypeError( + f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=" + f" {type(prompt)}." ) - logger.warning( - "The following part of your input was truncated because T5 can only handle sequences up to" - f" {max_length} tokens: {removed_text}" + elif batch_size != len(negative_prompt): + raise ValueError( + f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:" + f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches" + " the batch size of `prompt`." ) - prompt_attention_mask = text_inputs.attention_mask - prompt_attention_mask = prompt_attention_mask.to(device) - - prompt_embeds = self.text_encoder( - text_input_ids.to(device), attention_mask=prompt_attention_mask + negative_prompt_embeds, negative_prompt_attention_mask = self._get_gemma_prompt_embeds( + prompt=prompt, + num_images_per_prompt=num_images_per_prompt, + device=device ) - prompt_embeds = prompt_embeds.hidden_states[-2] - - if self.text_encoder is not None: - dtype = self.text_encoder.dtype - elif self.transformer is not None: - dtype = self.transformer.dtype - else: - dtype = None - - prompt_embeds = prompt_embeds.to(dtype=dtype, device=device) - - bs_embed, seq_len, _ = prompt_embeds.shape - # duplicate text embeddings and attention mask for each generation per prompt, using mps friendly method - prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1) - prompt_embeds = prompt_embeds.view( - bs_embed * num_images_per_prompt, seq_len, -1 - ) - prompt_attention_mask = prompt_attention_mask.view(bs_embed, -1) - prompt_attention_mask = prompt_attention_mask.repeat(num_images_per_prompt, 1) - - # get unconditional embeddings for classifier free guidance - # if do_classifier_free_guidance and negative_prompt_embeds is None: - # uncond_tokens = [negative_prompt] * batch_size - # uncond_tokens = self._text_preprocessing(uncond_tokens, clean_caption=clean_caption) - # max_length = prompt_embeds.shape[1] - # uncond_input = self.tokenizer( - # uncond_tokens, - # padding="max_length", - # max_length=max_length, - # truncation=True, - # return_attention_mask=True, - # add_special_tokens=True, - # return_tensors="pt", - # ) - # negative_prompt_attention_mask = uncond_input.attention_mask - # negative_prompt_attention_mask = negative_prompt_attention_mask.to(device) - - # negative_prompt_embeds = self.text_encoder( - # uncond_input.input_ids.to(device), attention_mask=negative_prompt_attention_mask - # ) - # negative_prompt_embeds = negative_prompt_embeds[0] - - # if do_classifier_free_guidance: - # # duplicate unconditional embeddings for each generation per prompt, using mps friendly method - # seq_len = negative_prompt_embeds.shape[1] - - # negative_prompt_embeds = negative_prompt_embeds.to(dtype=dtype, device=device) - - # negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1) - # negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1) - - # negative_prompt_attention_mask = negative_prompt_attention_mask.view(bs_embed, -1) - # negative_prompt_attention_mask = negative_prompt_attention_mask.repeat(num_images_per_prompt, 1) - # else: - # negative_prompt_embeds = None - # negative_prompt_attention_mask = None - - # return prompt_embeds, prompt_attention_mask, negative_prompt_embeds, negative_prompt_attention_mask - return prompt_embeds, prompt_attention_mask + + return prompt_embeds, prompt_attention_mask, negative_prompt_embeds, negative_prompt_attention_mask # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs def prepare_extra_step_kwargs(self, generator, eta): @@ -478,7 +481,6 @@ def check_inputs( height, width, negative_prompt, - callback_steps, prompt_embeds=None, negative_prompt_embeds=None, prompt_attention_mask=None, @@ -489,15 +491,6 @@ def check_inputs( f"`height` and `width` have to be divisible by 8 but are {height} and {width}." ) - if (callback_steps is None) or ( - callback_steps is not None - and (not isinstance(callback_steps, int) or callback_steps <= 0) - ): - raise ValueError( - f"`callback_steps` has to be a positive integer but is {callback_steps} of type" - f" {type(callback_steps)}." - ) - if prompt is not None and prompt_embeds is not None: raise ValueError( f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to" @@ -736,38 +729,49 @@ def prepare_latents( else: latents = latents.to(device) - # scale the initial noise by the standard deviation required by the scheduler - latents = latents * self.scheduler.init_noise_sigma return latents + @property + def guidance_scale(self): + return self._guidance_scale + + # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) + # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` + # corresponds to doing no classifier free guidance. + @property + def do_classifier_free_guidance(self): + return self._guidance_scale > 1 + + @property + def num_timesteps(self): + return self._num_timesteps + @torch.no_grad() @replace_example_docstring(EXAMPLE_DOC_STRING) def __call__( self, prompt: Union[str, List[str]] = None, - negative_prompt: str = "", + width: Optional[int] = None, + height: Optional[int] = None, num_inference_steps: int = 20, timesteps: List[int] = None, - sigmas: List[float] = None, guidance_scale: float = 4.5, + negative_prompt: str = "", + sigmas: List[float] = None, num_images_per_prompt: Optional[int] = 1, - height: Optional[int] = None, - width: Optional[int] = None, - eta: float = 0.0, generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, latents: Optional[torch.Tensor] = None, prompt_embeds: Optional[torch.Tensor] = None, - prompt_attention_mask: Optional[torch.Tensor] = None, negative_prompt_embeds: Optional[torch.Tensor] = None, + prompt_attention_mask: Optional[torch.Tensor] = None, negative_prompt_attention_mask: Optional[torch.Tensor] = None, output_type: Optional[str] = "pil", return_dict: bool = True, - callback: Optional[Callable[[int, int, torch.Tensor], None]] = None, - callback_steps: int = 1, clean_caption: bool = True, - use_resolution_binning: bool = True, max_sequence_length: int = 120, - **kwargs, + scaling_watershed: Optional[float] = 0.3, + callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None, + callback_on_step_end_tensor_inputs: List[str] = ["latents"], ) -> Union[ImagePipelineOutput, Tuple]: """ Function invoked when calling the pipeline for generation. @@ -827,21 +831,21 @@ def __call__( [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~pipelines.stable_diffusion.IFPipelineOutput`] instead of a plain tuple. - callback (`Callable`, *optional*): - A function that will be called every `callback_steps` steps during inference. The function will be - called with the following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`. - callback_steps (`int`, *optional*, defaults to 1): - The frequency at which the `callback` function will be called. If not specified, the callback will be - called at every step. clean_caption (`bool`, *optional*, defaults to `True`): Whether or not to clean the caption before creating embeddings. Requires `beautifulsoup4` and `ftfy` to be installed. If the dependencies are not installed, the embeddings will be created from the raw prompt. - use_resolution_binning (`bool` defaults to `True`): - If set to `True`, the requested height and width are first mapped to the closest resolutions using - `ASPECT_RATIO_1024_BIN`. After the produced latents are decoded into images, they are resized back to - the requested resolution. Useful for generating non-square images. - max_sequence_length (`int` defaults to 120): Maximum sequence length to use with the `prompt`. + max_sequence_length (`int` defaults to 120): + Maximum sequence length to use with the `prompt`. + callback_on_step_end (`Callable`, *optional*): + A function that calls at the end of each denoising steps during the inference. The function is called + with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int, + callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by + `callback_on_step_end_tensor_inputs`. + callback_on_step_end_tensor_inputs (`List`, *optional*): + The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list + will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the + `._callback_tensor_inputs` attribute of your pipeline class. Examples: @@ -850,39 +854,22 @@ def __call__( If `return_dict` is `True`, [`~pipelines.ImagePipelineOutput`] is returned, otherwise a `tuple` is returned where the first element is a list with the generated images """ - if "mask_feature" in kwargs: - deprecation_message = "The use of `mask_feature` is deprecated. It is no longer used in any computation and that doesn't affect the end results. It will be removed in a future version." - deprecate("mask_feature", "1.0.0", deprecation_message, standard_warn=False) + height = height or self.default_sample_size * self.vae_scale_factor + width = width or self.default_sample_size * self.vae_scale_factor + # 1. Check inputs. Raise error if not correct - height = height or self.transformer.config.sample_size * self.vae_scale_factor - width = width or self.transformer.config.sample_size * self.vae_scale_factor - if use_resolution_binning: - if self.transformer.config.sample_size == 128: - aspect_ratio_bin = ASPECT_RATIO_1024_BIN - elif self.transformer.config.sample_size == 64: - aspect_ratio_bin = ASPECT_RATIO_512_BIN - elif self.transformer.config.sample_size == 32: - aspect_ratio_bin = ASPECT_RATIO_256_BIN - else: - raise ValueError("Invalid sample size") - orig_height, orig_width = height, width - height, width = self.image_processor.classify_height_width_bin( - height, width, ratios=aspect_ratio_bin - ) - self.check_inputs( prompt, height, width, negative_prompt, - callback_steps, - prompt_embeds, - negative_prompt_embeds, - prompt_attention_mask, - negative_prompt_attention_mask, + prompt_embeds=prompt_embeds, + negative_prompt_embeds=negative_prompt_embeds, + prompt_attention_mask=prompt_attention_mask, + negative_prompt_attention_mask=negative_prompt_attention_mask, ) - # 2. Default height and width to transformer + # 2. Define call parameters if prompt is not None and isinstance(prompt, str): batch_size = 1 elif prompt is not None and isinstance(prompt, list): @@ -901,8 +888,8 @@ def __call__( ( prompt_embeds, prompt_attention_mask, - # negative_prompt_embeds, - # negative_prompt_attention_mask, + negative_prompt_embeds, + negative_prompt_attention_mask, ) = self.encode_prompt( prompt, do_classifier_free_guidance, @@ -938,41 +925,49 @@ def __call__( latents, ) - # 6. Prepare extra step kwargs. TODO: Logigic should ideally just be moved out of the pipeline - extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) - - # 6.1 Prepare micro-conditions. - added_cond_kwargs = {"resolution": None, "aspect_ratio": None} - if self.transformer.config.sample_size == 128: - resolution = torch.tensor([height, width]).repeat( - batch_size * num_images_per_prompt, 1 - ) - aspect_ratio = torch.tensor([float(height / width)]).repeat( - batch_size * num_images_per_prompt, 1 - ) - resolution = resolution.to(dtype=prompt_embeds.dtype, device=device) - aspect_ratio = aspect_ratio.to(dtype=prompt_embeds.dtype, device=device) - - if do_classifier_free_guidance: - resolution = torch.cat([resolution, resolution], dim=0) - aspect_ratio = torch.cat([aspect_ratio, aspect_ratio], dim=0) - - added_cond_kwargs = {"resolution": resolution, "aspect_ratio": aspect_ratio} - - # 7. Denoising loop - num_warmup_steps = max( - len(timesteps) - num_inference_steps * self.scheduler.order, 0 - ) - + # 6. Denoising loop with self.progress_bar(total=num_inference_steps) as progress_bar: for i, t in enumerate(timesteps): + # expand the latents if we are doing classifier free guidance latent_model_input = ( torch.cat([latents] * 2) if do_classifier_free_guidance else latents ) - latent_model_input = self.scheduler.scale_model_input( - latent_model_input, t + # broadcast to batch dimension in a way that's compatible with ONNX/Core ML + timestep = t.expand(latent_model_input.shape[0]) + + hidden_size = self.transformer.config.hidden_size + num_attention_heads = self.transformer.config.num_attention_heads + attention_head_dim = hidden_size // num_attention_heads + + scaling_factor = math.sqrt(width * height / 1024 ** 2) + + self.transformer.freqs_cis = self.transformer.precompute_freqs_cis( + attention_head_dim, + 384, + scale_factor=scaling_factor, + scaling_watershed=scaling_watershed, + timestep=timesteps[0] ) + noise_pred = self.transformer( + x=latent_model_input, + timestep=timestep, + encoder_hidden_states=prompt_embeds, + joint_attention_kwargs=self.joint_attention_kwargs, + return_dict=False, + )[0] + + # perform guidance + # NOTE: For exact reproducibility reasons, we apply classifier-free guidance on only + # three channels by default. The standard approach to cfg applies it to all channels. + # This can be done by uncommenting the following line and commenting-out the line following that. + # eps, rest = model_out[:, :self.in_channels], model_out[:, self.in_channels:] + if do_classifier_free_guidance: + noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) + noise_pred = noise_pred_uncond + guidance_scale * ( + noise_pred_text - noise_pred_uncond + ) + current_timestep = t if not torch.is_tensor(current_timestep): # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can @@ -996,20 +991,13 @@ def __call__( # predict noise model_output noise_pred = self.transformer( - latent_model_input, - encoder_hidden_states=prompt_embeds, - encoder_attention_mask=prompt_attention_mask, + x=latent_model_input, timestep=current_timestep, - added_cond_kwargs=added_cond_kwargs, + caption_feat=prompt_embeds, + caption_mask=prompt_attention_mask, return_dict=False, )[0] - - # perform guidance - if do_classifier_free_guidance: - noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) - noise_pred = noise_pred_uncond + guidance_scale * ( - noise_pred_text - noise_pred_uncond - ) + # learned sigma if self.transformer.config.out_channels // 2 == latent_channels: @@ -1017,35 +1005,23 @@ def __call__( else: noise_pred = noise_pred - # compute previous image: x_t -> x_t-1 - if num_inference_steps == 1: - # For DMD one step sampling: https://arxiv.org/abs/2311.18828 - latents = self.scheduler.step( - noise_pred, t, latents, **extra_step_kwargs - ).pred_original_sample - else: - latents = self.scheduler.step( - noise_pred, t, latents, **extra_step_kwargs, return_dict=False - )[0] - - # call the callback, if provided - if i == len(timesteps) - 1 or ( - (i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0 - ): - progress_bar.update() - if callback is not None and i % callback_steps == 0: - step_idx = i // getattr(self.scheduler, "order", 1) - callback(step_idx, t, latents) + # # compute previous image: x_t -> x_t-1 + # if num_inference_steps == 1: + # # For DMD one step sampling: https://arxiv.org/abs/2311.18828 + # latents = self.scheduler.step( + # noise_pred, t, latents, **extra_step_kwargs + # ).pred_original_sample + # else: + # latents = self.scheduler.step( + # noise_pred, t, latents, **extra_step_kwargs, return_dict=False + # )[0] + if not output_type == "latent": - image = self.vae.decode( - latents / self.vae.config.scaling_factor, return_dict=False - ).sample + latents = latents / self.vae.config.scaling_factor + image = self.vae.decode(latents, return_dict=False)[0] image = (image + 1.0) / 2.0 image.clamp_(0.0, 1.0) - # real_image = to_pil_image(image[0].float()) - # if use_resolution_binning: - # image = self.image_processor.resize_and_crop_tensor(image, orig_width, orig_height) else: image = latents From f93b90334d4be4c4b0f5907fa23cf59494064b9b Mon Sep 17 00:00:00 2001 From: PommesPeter Date: Thu, 20 Jun 2024 23:24:06 +0800 Subject: [PATCH 12/92] Format code style and fixed merge unused code --- scripts/convert_lumina_to_diffusers.py | 42 +- src/diffusers/__init__.py | 2 +- src/diffusers/models/attention.py | 4 +- src/diffusers/models/embeddings.py | 22 +- .../models/transformers/lumina_nextdit_2d.py | 147 +-- .../pipelines/lumina/modeling_flag_dit.py | 999 ---------------- .../pipelines/lumina/modeling_next_dit.py | 1034 ----------------- .../pipelines/lumina/pipeline_lumina.py | 139 +-- .../scheduling_flow_match_heun_discrete.py | 22 +- ...{test_lumina.py => test_lumina_nextdit.py} | 11 +- 10 files changed, 178 insertions(+), 2244 deletions(-) delete mode 100644 src/diffusers/pipelines/lumina/modeling_flag_dit.py delete mode 100644 src/diffusers/pipelines/lumina/modeling_next_dit.py rename tests/pipelines/lumina/{test_lumina.py => test_lumina_nextdit.py} (98%) diff --git a/scripts/convert_lumina_to_diffusers.py b/scripts/convert_lumina_to_diffusers.py index e0090f652e16..2d95df7d0e0e 100644 --- a/scripts/convert_lumina_to_diffusers.py +++ b/scripts/convert_lumina_to_diffusers.py @@ -2,10 +2,10 @@ import os import torch +from safetensors.torch import load_file from transformers import AutoModel, AutoTokenizer -from diffusers import LuminaText2ImgPipeline, LuminaNextDiT2DModel, AutoencoderKL, FlowMatchEulerDiscreteScheduler -from safetensors.torch import load_file +from diffusers import AutoencoderKL, FlowMatchEulerDiscreteScheduler, LuminaNextDiT2DModel, LuminaText2ImgPipeline def main(args): @@ -13,7 +13,7 @@ def main(args): all_sd = load_file(args.origin_ckpt_path, device="cpu") converted_state_dict = {} - # Pad token + # Pad token converted_state_dict["pad_token"] = all_sd["pad_token"] # Patch Embed @@ -35,45 +35,49 @@ def main(args): converted_state_dict[f"layers.{i}.attention.gate"] = all_sd[f"layers.{i}.attention.gate"] converted_state_dict[f"layers.{i}.adaLN_modulation.1.weight"] = all_sd[f"layers.{i}.adaLN_modulation.1.weight"] converted_state_dict[f"layers.{i}.adaLN_modulation.1.bias"] = all_sd[f"layers.{i}.adaLN_modulation.1.bias"] - + # QKV converted_state_dict[f"layers.{i}.attention.wq.weight"] = all_sd[f"layers.{i}.attention.wq.weight"] converted_state_dict[f"layers.{i}.attention.wk.weight"] = all_sd[f"layers.{i}.attention.wk.weight"] converted_state_dict[f"layers.{i}.attention.wv.weight"] = all_sd[f"layers.{i}.attention.wv.weight"] - + # Caption KV converted_state_dict[f"layers.{i}.attention.wk_cap.weight"] = all_sd[f"layers.{i}.attention.wk_y.weight"] converted_state_dict[f"layers.{i}.attention.wv_cap.weight"] = all_sd[f"layers.{i}.attention.wv_y.weight"] - + # Output Layer converted_state_dict[f"layers.{i}.attention.wo.weight"] = all_sd[f"layers.{i}.attention.wo.weight"] - + # Attention # QK Norm converted_state_dict[f"layers.{i}.attention.q_norm.weight"] = all_sd[f"layers.{i}.attention.q_norm.weight"] converted_state_dict[f"layers.{i}.attention.q_norm.bias"] = all_sd[f"layers.{i}.attention.q_norm.bias"] - + converted_state_dict[f"layers.{i}.attention.k_norm.weight"] = all_sd[f"layers.{i}.attention.k_norm.weight"] - converted_state_dict[f"layers.{i}.attention.k_norm.bias"] = all_sd[f"layers.{i}.attention.k_norm.bias"] - + converted_state_dict[f"layers.{i}.attention.k_norm.bias"] = all_sd[f"layers.{i}.attention.k_norm.bias"] + # Caption K Norm - converted_state_dict[f"layers.{i}.attention.k_cap_norm.weight"] = all_sd[f"layers.{i}.attention.ky_norm.weight"] + converted_state_dict[f"layers.{i}.attention.k_cap_norm.weight"] = all_sd[ + f"layers.{i}.attention.ky_norm.weight" + ] converted_state_dict[f"layers.{i}.attention.k_cap_norm.bias"] = all_sd[f"layers.{i}.attention.ky_norm.bias"] - + # Attention Norm converted_state_dict[f"layers.{i}.attention_norm1.weight"] = all_sd[f"layers.{i}.attention_norm1.weight"] converted_state_dict[f"layers.{i}.attention_norm2.weight"] = all_sd[f"layers.{i}.attention_norm2.weight"] - converted_state_dict[f"layers.{i}.attention_caption_norm.weight"] = all_sd[f"layers.{i}.attention_y_norm.weight"] - + converted_state_dict[f"layers.{i}.attention_caption_norm.weight"] = all_sd[ + f"layers.{i}.attention_y_norm.weight" + ] + # Feed Forward layer converted_state_dict[f"layers.{i}.feed_forward.w1.weight"] = all_sd[f"layers.{i}.feed_forward.w1.weight"] converted_state_dict[f"layers.{i}.feed_forward.w2.weight"] = all_sd[f"layers.{i}.feed_forward.w2.weight"] converted_state_dict[f"layers.{i}.feed_forward.w3.weight"] = all_sd[f"layers.{i}.feed_forward.w3.weight"] - + # Feed Forward Norm converted_state_dict[f"layers.{i}.ffn_norm1.weight"] = all_sd[f"layers.{i}.ffn_norm1.weight"] converted_state_dict[f"layers.{i}.ffn_norm2.weight"] = all_sd[f"layers.{i}.ffn_norm2.weight"] - + # Final Layer converted_state_dict["final_layer.linear.weight"] = all_sd["final_layer.linear.weight"] converted_state_dict["final_layer.linear.bias"] = all_sd["final_layer.linear.bias"] @@ -83,7 +87,7 @@ def main(args): # Lumina-Next-SFT 2B transformer = LuminaNextDiT2DModel( - patch_size=2, + patch_size=2, in_channels=4, hidden_size=2304, num_layers=24, @@ -106,9 +110,9 @@ def main(args): transformer.save_pretrained(os.path.join(args.dump_path, "transformer")) else: scheduler = FlowMatchEulerDiscreteScheduler() - + vae = AutoencoderKL.from_pretrained("stabilityai/sdxl-vae", torch_dtype=torch.float32) - + tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b") text_encoder = AutoModel.from_pretrained("google/gemma-2b") diff --git a/src/diffusers/__init__.py b/src/diffusers/__init__.py index b87e3b33f26e..78cc90293e43 100644 --- a/src/diffusers/__init__.py +++ b/src/diffusers/__init__.py @@ -265,11 +265,11 @@ "KandinskyV22PriorPipeline", "LatentConsistencyModelImg2ImgPipeline", "LatentConsistencyModelPipeline", - "LuminaText2ImgPipeline", "LDMTextToImagePipeline", "LEditsPPPipelineStableDiffusion", "LEditsPPPipelineStableDiffusionXL", "LuminaText2ImgPipeline", + "LuminaText2ImgPipeline", "MarigoldDepthPipeline", "MarigoldNormalsPipeline", "MusicLDMPipeline", diff --git a/src/diffusers/models/attention.py b/src/diffusers/models/attention.py index 9eb653da810f..5aeadab580c7 100644 --- a/src/diffusers/models/attention.py +++ b/src/diffusers/models/attention.py @@ -532,7 +532,9 @@ class LuminaFeedForward(nn.Module): A feed-forward layer. Parameters: - hidden_size (`int`): The dimensionality of the hidden layers in the model. This parameter determines the width of the model's hidden representations. + hidden_size (`int`): + The dimensionality of the hidden layers in the model. This parameter determines the width of the model's + hidden representations. intermediate_size (`int`): The intermediate dimension of the feedforward layer. multiple_of (`int`, *optional*): Value to ensure hidden dimension is a multiple of this value. diff --git a/src/diffusers/models/embeddings.py b/src/diffusers/models/embeddings.py index 16019193eb66..09fa4ed90d64 100644 --- a/src/diffusers/models/embeddings.py +++ b/src/diffusers/models/embeddings.py @@ -761,17 +761,17 @@ def forward(self, timestep, encoder_hidden_states, image_meta_size, style, hidde class LuminaCombinedTimestepCaptionEmbedding(nn.Module): def __init__(self, hidden_size=4096, caption_dim=5120, frequency_embedding_size=256): super().__init__() - self.time_proj = Timesteps(num_channels=frequency_embedding_size, flip_sin_to_cos=False, downscale_freq_shift=0.0) - - self.timestep_embedder = TimestepEmbedding( - in_channels=frequency_embedding_size, - time_embed_dim=hidden_size) - + self.time_proj = Timesteps( + num_channels=frequency_embedding_size, flip_sin_to_cos=False, downscale_freq_shift=0.0 + ) + + self.timestep_embedder = TimestepEmbedding(in_channels=frequency_embedding_size, time_embed_dim=hidden_size) + nn.init.normal_(self.timestep_embedder.linear_1.weight, std=0.02) nn.init.zeros_(self.timestep_embedder.linear_1.bias) nn.init.normal_(self.timestep_embedder.linear_2.weight, std=0.02) nn.init.zeros_(self.timestep_embedder.linear_2.bias) - + self.caption_embedder = nn.Sequential( nn.LayerNorm(caption_dim), nn.Linear( @@ -782,20 +782,20 @@ def __init__(self, hidden_size=4096, caption_dim=5120, frequency_embedding_size= ) nn.init.zeros_(self.caption_embedder[1].weight) nn.init.zeros_(self.caption_embedder[1].bias) - + def forward(self, timestep, caption_feat, caption_mask): # timestep embedding: time_freq = self.time_proj(timestep) time_embed = self.timestep_embedder(time_freq.to(dtype=self.timestep_embedder.linear_1.dtype)) - + # caption condition embedding: caption_mask_float = caption_mask.float().unsqueeze(-1) caption_feats_pool = (caption_feat * caption_mask_float).sum(dim=1) / caption_mask_float.sum(dim=1) caption_feats_pool = caption_feats_pool.to(caption_feat) caption_embed = self.caption_embedder(caption_feats_pool) - + conditioning = time_embed + caption_embed - + return conditioning diff --git a/src/diffusers/models/transformers/lumina_nextdit_2d.py b/src/diffusers/models/transformers/lumina_nextdit_2d.py index 4b84cb48b1fe..e6e4224f2441 100644 --- a/src/diffusers/models/transformers/lumina_nextdit_2d.py +++ b/src/diffusers/models/transformers/lumina_nextdit_2d.py @@ -12,14 +12,14 @@ # See the License for the specific language governing permissions and # limitations under the License. -from typing import List, Optional, Tuple import math +from typing import List, Optional, Tuple -from flash_attn import flash_attn_varlen_func -from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa import torch import torch.nn as nn import torch.nn.functional as F +from flash_attn import flash_attn_varlen_func +from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa from ...configuration_utils import ConfigMixin, register_to_config from ...utils import logging @@ -48,6 +48,7 @@ class Attention(nn.Module): num_attention_heads (int): Number of heads. num_kv_heads (Optional[int]): Number of kv heads, if using GQA. """ + def __init__( self, hidden_size: int, @@ -124,9 +125,8 @@ def reshape_for_broadcast(freqs_cis: torch.Tensor, x: torch.Tensor): """ Reshape frequency tensor for broadcasting it with another tensor. - This function reshapes the frequency tensor to have the same shape as - the target tensor 'x' for the purpose of broadcasting the frequency - tensor during element-wise operations. + This function reshapes the frequency tensor to have the same shape as the target tensor 'x' for the purpose of + broadcasting the frequency tensor during element-wise operations. Args: freqs_cis (torch.Tensor): Frequency tensor to be reshaped. @@ -153,14 +153,12 @@ def apply_rotary_emb( freqs_cis: torch.Tensor, ) -> torch.Tensor: """ - Apply rotary embeddings to input tensors using the given frequency - tensor. + Apply rotary embeddings to input tensors using the given frequency tensor. - This function applies rotary embeddings to the given query 'xq' and - key 'xk' tensors using the provided frequency tensor 'freqs_cis'. The - input tensors are reshaped as complex numbers, and the frequency tensor - is reshaped for broadcasting compatibility. The resulting tensors - contain rotary embeddings and are returned as real tensors. + This function applies rotary embeddings to the given query 'xq' and key 'xk' tensors using the provided + frequency tensor 'freqs_cis'. The input tensors are reshaped as complex numbers, and the frequency tensor is + reshaped for broadcasting compatibility. The resulting tensors contain rotary embeddings and are returned as + real tensors. Args: x_in (torch.Tensor): Query or Key tensor to apply rotary embeddings. @@ -319,20 +317,20 @@ def forward( if hasattr(self, "wk_cap"): yk = self.k_cap_norm(self.wk_cap(caption_feat)).view(bsz, -1, self.num_kv_heads, self.head_dim) yv = self.wv_cap(caption_feat).view(bsz, -1, self.num_kv_heads, self.head_dim) - + n_rep = self.num_attention_heads // self.num_kv_heads - + if n_rep >= 1: yk = yk.unsqueeze(3).repeat(1, 1, 1, n_rep, 1).flatten(2, 3) yv = yv.unsqueeze(3).repeat(1, 1, 1, n_rep, 1).flatten(2, 3) - + output_caption = F.scaled_dot_product_attention( xq.permute(0, 2, 1, 3), yk.permute(0, 2, 1, 3), yv.permute(0, 2, 1, 3), caption_mask.view(bsz, 1, 1, -1).expand(bsz, self.num_attention_heads, seqlen, -1), ).permuste(0, 2, 1, 3) - + output_caption = output_caption * self.gate.tanh().view(1, 1, -1, 1) output = output + output_caption @@ -346,7 +344,9 @@ class FinalLayer(nn.Module): The final layer of LuminaNextDiT. Parameters: - hidden_size (`int`): The dimensionality of the hidden layers in the model. This parameter determines the width of the model's hidden representations. + hidden_size (`int`): + The dimensionality of the hidden layers in the model. This parameter determines the width of the model's + hidden representations. patch_size (`int`): The patch size of noise. out_channels (`int`): The number of output channels. """ @@ -391,7 +391,7 @@ def forward(self, x, c): x = modulate(self.norm_final(x), scale) x = self.linear(x) - + return x @@ -404,8 +404,7 @@ class LuminaNextDiTBlock(nn.Module): hidden_size (int): Embedding dimension of the input features. num_attention_heads (int): Number of attention heads. num_kv_heads (Optional[int]): Number of attention heads in key and - value features (if using GQA), or set to None for the same as - query. + value features (if using GQA), or set to None for the same as query. multiple_of (int): ffn_dim_multiplier (float): norm_eps (float): @@ -420,6 +419,7 @@ class LuminaNextDiTBlock(nn.Module): attention_norm (RMSNorm): Layer normalization for attention output. ffn_norm (RMSNorm): Layer normalization for feedforward output. """ + def __init__( self, layer_id: int, @@ -524,29 +524,38 @@ class LuminaNextDiT2DModel(ModelMixin, ConfigMixin): patch_size (`int`, *optional*, (`int`, *optional*, defaults to 2): The size of each patch in the image. This parameter defines the resolution of patches fed into the model. in_channels (`int`, *optional*, defaults to 4): - The number of input channels for the model. Typically, this matches the number of channels in the input images. + The number of input channels for the model. Typically, this matches the number of channels in the input + images. hidden_size (`int`, *optional*, defaults to 4096): - The dimensionality of the hidden layers in the model. This parameter determines the width of the model's hidden representations. + The dimensionality of the hidden layers in the model. This parameter determines the width of the model's + hidden representations. num_layers (`int`, *optional*, default to 32): The number of layers in the model. This defines the depth of the neural network. num_attention_heads (`int`, *optional*, defaults to 32): - The number of attention heads in each attention layer. This parameter specifies how many separate attention mechanisms are used. + The number of attention heads in each attention layer. This parameter specifies how many separate attention + mechanisms are used. num_kv_heads (`int`, *optional*, defaults to 8): - The number of key-value heads in the attention mechanism, if different from the number of attention heads. If None, it defaults to num_attention_heads. + The number of key-value heads in the attention mechanism, if different from the number of attention heads. + If None, it defaults to num_attention_heads. multiple_of (`int`, *optional*, defaults to 256): - A factor that the hidden size should be a multiple of. This can help optimize certain hardware configurations. + A factor that the hidden size should be a multiple of. This can help optimize certain hardware + configurations. ffn_dim_multiplier (`float`, *optional*): - A multiplier for the dimensionality of the feed-forward network. If None, it uses a default value based on the model configuration. + A multiplier for the dimensionality of the feed-forward network. If None, it uses a default value based on + the model configuration. norm_eps float = (`float`, *optional*, defaults to 1e-5): A small value added to the denominator for numerical stability in normalization layers. learn_sigma bool = (`bool`, *optional*, defaults to True): - Whether the model should learn the sigma parameter, which might be related to uncertainty or variance in predictions. + Whether the model should learn the sigma parameter, which might be related to uncertainty or variance in + predictions. qk_norm (`bool`, *optional*, defaults to True): Indicates if the queries and keys in the attention mechanism should be normalized. caption_dim (`int`, *optional*, defaults to 2048): - The dimensionality of the text embeddings. This parameter defines the size of the text representations used in the model. + The dimensionality of the text embeddings. This parameter defines the size of the text representations used + in the model. scale_factor (`float`, *optional*, defaults to 1.0): - A scaling factor applied to certain parameters or layers in the model. This can be used for adjusting the overall scale of the model's operations. + A scaling factor applied to certain parameters or layers in the model. This can be used for adjusting the + overall scale of the model's operations. """ @register_to_config @@ -586,7 +595,9 @@ def __init__( self.pad_token = nn.Parameter(torch.empty(hidden_size)) nn.init.normal_(self.pad_token, std=0.02) - self.time_caption_embed = LuminaCombinedTimestepCaptionEmbedding(hidden_size=min(hidden_size, 1024), caption_dim=caption_dim) + self.time_caption_embed = LuminaCombinedTimestepCaptionEmbedding( + hidden_size=min(hidden_size, 1024), caption_dim=caption_dim + ) self.layers = nn.ModuleList( [ @@ -620,15 +631,14 @@ def unpatchify(self, x: torch.Tensor, img_size: List[Tuple[int, int]], return_te Args: x (torch.Tensor): The patchified tensor of shape (N, T, patch_size**2 * C). img_size (List[Tuple[int, int]]): The list of image sizes for each image in the batch. - return_tensor (bool, optional): Whether to return the reconstructed images as a tensor. - If False, the reconstructed images will be returned as a list of tensors. - Defaults to False. + return_tensor (bool, optional): Whether to return the reconstructed images as a tensor. + If False, the reconstructed images will be returned as a list of tensors. Defaults to False. Returns: - List[torch.Tensor] or torch.Tensor: The reconstructed images. + List[torch.Tensor] or torch.Tensor: The reconstructed images. If return_tensor is True, the reconstructed images will be returned as a tensor of shape (N, C, H, W). - If return_tensor is False, the reconstructed images will be returned as a list of tensors, - where each tensor has shape (H, W, C). + If return_tensor is False, the reconstructed images will be returned as a list of tensors, where each + tensor has shape (H, W, C). """ pH = pW = self.patch_size if return_tensor: @@ -662,8 +672,9 @@ def patchify_and_embed( x (List[torch.Tensor] | torch.Tensor): The input tensor(s) to be patchified and embedded. Returns: - Tuple[torch.Tensor, torch.Tensor, List[Tuple[int, int]], torch.Tensor]: A tuple containing the patchified and embedded tensor(s), - the mask indicating the valid patches, the original image size(s), and the frequency tensor(s). + Tuple[torch.Tensor, torch.Tensor, List[Tuple[int, int]], torch.Tensor]: A tuple containing the patchified + and embedded tensor(s), the mask indicating the valid patches, the original image size(s), and the + frequency tensor(s). """ self.freqs_cis = self.freqs_cis.to(x[0].device) if isinstance(x, torch.Tensor): @@ -726,15 +737,17 @@ def patchify_and_embed( x_embed = torch.stack(padded_x_embed, dim=0) freqs_cis = torch.stack(padded_freqs_cis, dim=0) - + return x_embed, mask, img_size, freqs_cis - def forward(self, - x: torch.Tensor, - timestep: torch.Tensor, - caption_feat: torch.Tensor, - caption_mask: torch.Tensor, - return_dict=True) -> torch.Tensor: + def forward( + self, + x: torch.Tensor, + timestep: torch.Tensor, + caption_feat: torch.Tensor, + caption_mask: torch.Tensor, + return_dict=True, + ) -> torch.Tensor: """ Forward pass of LuminaNextDiT. @@ -785,28 +798,30 @@ def forward_with_cfg( return_dict: bool = True, ): """ - Forward pass of LuminaNextDiT, but also batches the unconditional forward pass - for classifier-free guidance. + Forward pass of LuminaNextDiT, but also batches the unconditional forward pass for classifier-free guidance. Args: - x: + x: The input tensor, typically representing image data. - timestep: + timestep: A tensor representing the time steps or sequence positions. - caption_feat: + caption_feat: The caption features, which are used for conditioning the model. - caption_mask: + caption_mask: A mask for the caption features, indicating which elements are valid. - cfg_scale: + cfg_scale: The classifier-free guidance scale, used to adjust the influence of the conditioning information. - scale_factor (float, optional, defaults to 1.0): + scale_factor (float, optional, defaults to 1.0): A scaling factor applied to certain operations or parameters within the forward pass. - scale_watershed (float, optional, defaults to 1.0): - A specific scaling factor used in conjunction with the main scale factor, potentially for different stages of processing. - base_seqlen (Optional[int], optional): - The base sequence length, which can be used to set the length of sequences processed by the model. If None, a default value is used. - proportional_attn (bool, optional, defaults to False): - Whether to use proportional attention mechanisms within the model, adjusting attention weights proportionally. + scale_watershed (float, optional, defaults to 1.0): + A specific scaling factor used in conjunction with the main scale factor, potentially for different + stages of processing. + base_seqlen (Optional[int], optional): + The base sequence length, which can be used to set the length of sequences processed by the model. If + None, a default value is used. + proportional_attn (bool, optional, defaults to False): + Whether to use proportional attention mechanisms within the model, adjusting attention weights + proportionally. """ # https://github.com/openai/glide-text2im/blob/main/notebooks/text2im.ipynb self.freqs_cis = LuminaNextDiT2DModel.precompute_freqs_cis( @@ -856,13 +871,11 @@ def precompute_freqs_cis( timestep: float = 1.0, ): """ - Precompute the frequency tensor for complex exponentials (cis) with - given dimensions. + Precompute the frequency tensor for complex exponentials (cis) with given dimensions. - This function calculates a frequency tensor with complex exponentials - using the given dimension 'dim' and the end index 'end'. The 'theta' - parameter scales the frequencies. The returned tensor contains complex - values in complex64 data type. + This function calculates a frequency tensor with complex exponentials using the given dimension 'dim' and the + end index 'end'. The 'theta' parameter scales the frequencies. The returned tensor contains complex values in + complex64 data type. Args: dim (int): Dimension of the frequency tensor. @@ -916,4 +929,4 @@ def LuminaNextDiT2DModel_2B_patch2(**kwargs): def LuminaNextDiT2DModel_2B_GQA_patch2(**kwargs): - return LuminaNextDiT2DModel(patch_size=2, dim=2304, n_layers=24, num_attention_heads=32, num_kv_heads=8, **kwargs) \ No newline at end of file + return LuminaNextDiT2DModel(patch_size=2, dim=2304, n_layers=24, num_attention_heads=32, num_kv_heads=8, **kwargs) diff --git a/src/diffusers/pipelines/lumina/modeling_flag_dit.py b/src/diffusers/pipelines/lumina/modeling_flag_dit.py deleted file mode 100644 index e57b6fe1d445..000000000000 --- a/src/diffusers/pipelines/lumina/modeling_flag_dit.py +++ /dev/null @@ -1,999 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. - -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. -# -------------------------------------------------------- -# References: -# GLIDE: https://github.com/openai/glide-text2im -# MAE: https://github.com/facebookresearch/mae/blob/main/models_mae.py -# -------------------------------------------------------- - -import functools -import math -from typing import Optional, Tuple, List - -from .components import RMSNorm -from flash_attn import flash_attn_varlen_func -from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa -import torch -import torch.nn as nn -import torch.nn.functional as F - -from ...configuration_utils import ConfigMixin, register_to_config -from ...models import ModelMixin -from ...models.transformers.transformer_2d import Transformer2DModelOutput -from ...utils import logging - - -def modulate(x, shift, scale): - return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1) - - -class RMSNorm(torch.nn.Module): - def __init__(self, dim: int, eps: float = 1e-6): - """ - Initialize the RMSNorm normalization layer. - - Args: - dim (int): The dimension of the input tensor. - eps (float, optional): A small value added to the denominator for numerical stability. Default is 1e-6. - - Attributes: - eps (float): A small value added to the denominator for numerical stability. - weight (nn.Parameter): Learnable scaling parameter. - - """ - super().__init__() - self.eps = eps - self.weight = nn.Parameter(torch.ones(dim)) - - def _norm(self, x): - """ - Apply the RMSNorm normalization to the input tensor. - - Args: - x (torch.Tensor): The input tensor. - - Returns: - torch.Tensor: The normalized tensor. - - """ - return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps) - - def forward(self, x): - """ - Forward pass through the RMSNorm layer. - - Args: - x (torch.Tensor): The input tensor. - - Returns: - torch.Tensor: The output tensor after applying RMSNorm. - - """ - output = self._norm(x.float()).type_as(x) - return output * self.weight - - -class TimestepEmbedder(nn.Module): - """ - Embeds scalar timesteps into vector representations. - """ - - def __init__(self, hidden_size, frequency_embedding_size=256): - super().__init__() - self.mlp = nn.Sequential( - nn.Linear( - frequency_embedding_size, - hidden_size, - bias=True, - ), - nn.SiLU(), - nn.Linear( - hidden_size, - hidden_size, - bias=True, - ), - ) - self.frequency_embedding_size = frequency_embedding_size - - @staticmethod - def timestep_embedding(t, dim, max_period=10000): - """ - Create sinusoidal timestep embeddings. - :param t: a 1-D Tensor of N indices, one per batch element. - These may be fractional. - :param dim: the dimension of the output. - :param max_period: controls the minimum frequency of the embeddings. - :return: an (N, D) Tensor of positional embeddings. - """ - # https://github.com/openai/glide-text2im/blob/main/glide_text2im/nn.py - half = dim // 2 - freqs = torch.exp( - -math.log(max_period) - * torch.arange(start=0, end=half, dtype=torch.float32) - / half - ).to(device=t.device) - args = t[:, None].float() * freqs[None] - embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) - if dim % 2: - embedding = torch.cat( - [embedding, torch.zeros_like(embedding[:, :1])], dim=-1 - ) - return embedding - - def forward(self, t): - t_freq = self.timestep_embedding(t, self.frequency_embedding_size) - t_emb = self.mlp(t_freq.to(self.mlp[0].weight.dtype)) - return t_emb - - -class LabelEmbedder(nn.Module): - r"""Embeds class labels into vector representations. Also handles label - dropout for classifier-free guidance. - """ - - def __init__(self, num_classes, hidden_size, dropout_prob): - super().__init__() - use_cfg_embedding = int(dropout_prob > 0) - self.embedding_table = nn.Embedding(num_classes + use_cfg_embedding) - self.num_classes = num_classes - self.dropout_prob = dropout_prob - - def token_drop(self, labels, force_drop_ids=None): - """ - Drops labels to enable classifier-free guidance. - """ - if force_drop_ids is None: - drop_ids = ( - torch.rand(labels.shape[0], device=labels.device) < self.dropout_prob - ) - drop_ids = drop_ids.cuda() - drop_ids = drop_ids.to(labels.device) - else: - drop_ids = force_drop_ids == 1 - labels = torch.where(drop_ids, self.num_classes, labels) - return labels - - def forward(self, labels, train, force_drop_ids=None): - use_dropout = self.dropout_prob > 0 - if (train and use_dropout) or (force_drop_ids is not None): - labels = self.token_drop(labels, force_drop_ids) - embeddings = self.embedding_table(labels) - return embeddings - - -############################################################################# -# Core DiT Model # -############################################################################# - - -class Attention(nn.Module): - """Multi-head attention module.""" - - def __init__( - self, - dim: int, - n_heads: int, - n_kv_heads: Optional[int], - qk_norm: bool, - y_dim: int, - ): - """ - Initialize the Attention module. - - Args: - dim (int): Number of input dimensions. - n_heads (int): Number of heads. - n_kv_heads (Optional[int]): Number of kv heads, if using GQA. - - Attributes: - n_kv_heads (int): Number of key and value heads. - n_heads (int): Number of local query heads. - n_kv_heads (int): Number of local key and value heads. - n_rep (int): Number of repetitions for local heads. - head_dim (int): Dimension size of each attention head. - wq (nn.Linear): Linear transformation for queries. - wk (nn.Linear): Linear transformation for keys. - wv (nn.Linear): Linear transformation for values. - wo (nn.Linear): Linear transformation for output. - cache_k (torch.Tensor): Cached keys for attention. - cache_v (torch.Tensor): Cached values for attention. - - """ - super().__init__() - self.n_kv_heads = n_heads if n_kv_heads is None else n_kv_heads - self.n_heads = n_heads - self.n_kv_heads = n_kv_heads - self.n_rep = n_heads - self.head_dim = dim // n_heads - - self.wq = nn.Linear(dim, n_heads * self.head_dim, bias=False) - self.wk = nn.Linear(dim, self.n_kv_heads * self.head_dim, bias=False) - self.wv = nn.Linear(dim, self.n_kv_heads * self.head_dim, bias=False) - if y_dim > 0: - self.wk_y = nn.Linear(y_dim, self.n_kv_heads * self.head_dim, bias=False) - self.wv_y = nn.Linear(y_dim, self.n_kv_heads * self.head_dim, bias=False) - self.gate = nn.Parameter(torch.zeros([self.n_heads])) - - self.wo = nn.Linear(n_heads * self.head_dim, dim, bias=False) - - if qk_norm: - self.q_norm = nn.LayerNorm(self.n_heads * self.head_dim) - self.k_norm = nn.LayerNorm(self.n_kv_heads * self.head_dim) - if y_dim > 0: - self.ky_norm = nn.LayerNorm(self.n_kv_heads * self.head_dim) - else: - self.ky_norm = nn.Identity() - else: - self.q_norm = self.k_norm = nn.Identity() - self.ky_norm = nn.Identity() - - # for proportional attention computation - self.base_seqlen = None - self.proportional_attn = False - - @staticmethod - def reshape_for_broadcast(freqs_cis: torch.Tensor, x: torch.Tensor): - """ - Reshape frequency tensor for broadcasting it with another tensor. - - This function reshapes the frequency tensor to have the same shape as - the target tensor 'x' for the purpose of broadcasting the frequency - tensor during element-wise operations. - - Args: - freqs_cis (torch.Tensor): Frequency tensor to be reshaped. - x (torch.Tensor): Target tensor for broadcasting compatibility. - - Returns: - torch.Tensor: Reshaped frequency tensor. - - Raises: - AssertionError: If the frequency tensor doesn't match the expected - shape. - AssertionError: If the target tensor 'x' doesn't have the expected - number of dimensions. - """ - ndim = x.ndim - assert 0 <= 1 < ndim - assert freqs_cis.shape == (x.shape[1], x.shape[-1]) - shape = [d if i == 1 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)] - return freqs_cis.view(*shape) - - @staticmethod - def apply_rotary_emb( - xq: torch.Tensor, - xk: torch.Tensor, - freqs_cis: torch.Tensor, - ) -> Tuple[torch.Tensor, torch.Tensor]: - """ - Apply rotary embeddings to input tensors using the given frequency - tensor. - - This function applies rotary embeddings to the given query 'xq' and - key 'xk' tensors using the provided frequency tensor 'freqs_cis'. The - input tensors are reshaped as complex numbers, and the frequency tensor - is reshaped for broadcasting compatibility. The resulting tensors - contain rotary embeddings and are returned as real tensors. - - Args: - xq (torch.Tensor): Query tensor to apply rotary embeddings. - xk (torch.Tensor): Key tensor to apply rotary embeddings. - freqs_cis (torch.Tensor): Precomputed frequency tensor for complex - exponentials. - - Returns: - Tuple[torch.Tensor, torch.Tensor]: Tuple of modified query tensor - and key tensor with rotary embeddings. - """ - with torch.cuda.amp.autocast(enabled=False): - xq_ = torch.view_as_complex(xq.float().reshape(*xq.shape[:-1], -1, 2)) - xk_ = torch.view_as_complex(xk.float().reshape(*xk.shape[:-1], -1, 2)) - freqs_cis = Attention.reshape_for_broadcast(freqs_cis, xq_) - xq_out = torch.view_as_real(xq_ * freqs_cis).flatten(3) - xk_out = torch.view_as_real(xk_ * freqs_cis).flatten(3) - return xq_out.type_as(xq), xk_out.type_as(xk) - - # copied from huggingface modeling_llama.py - def _upad_input( - self, query_layer, key_layer, value_layer, attention_mask, query_length - ): - - def _get_unpad_data(attention_mask): - seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32) - indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten() - max_seqlen_in_batch = seqlens_in_batch.max().item() - cu_seqlens = F.pad( - torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0) - ) - return ( - indices, - cu_seqlens, - max_seqlen_in_batch, - ) - - indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask) - batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape - - key_layer = index_first_axis( - key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), - indices_k, - ) - value_layer = index_first_axis( - value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), - indices_k, - ) - if query_length == kv_seq_len: - query_layer = index_first_axis( - query_layer.reshape(batch_size * kv_seq_len, self.n_heads, head_dim), - indices_k, - ) - cu_seqlens_q = cu_seqlens_k - max_seqlen_in_batch_q = max_seqlen_in_batch_k - indices_q = indices_k - elif query_length == 1: - max_seqlen_in_batch_q = 1 - cu_seqlens_q = torch.arange( - batch_size + 1, dtype=torch.int32, device=query_layer.device - ) # There is a memcpy here, that is very bad. - indices_q = cu_seqlens_q[:-1] - query_layer = query_layer.squeeze(1) - else: - # The -q_len: slice assumes left padding. - attention_mask = attention_mask[:, -query_length:] - query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input( - query_layer, attention_mask - ) - - return ( - query_layer, - key_layer, - value_layer, - indices_q, - (cu_seqlens_q, cu_seqlens_k), - (max_seqlen_in_batch_q, max_seqlen_in_batch_k), - ) - - def forward( - self, - x: torch.Tensor, - x_mask: torch.Tensor, - freqs_cis: torch.Tensor, - y: torch.Tensor, - y_mask: torch.Tensor, - ) -> torch.Tensor: - """ - Forward pass of the attention module. - - Args: - x (torch.Tensor): Input tensor. - freqs_cis (torch.Tensor): Precomputed frequency tensor. - - Returns: - torch.Tensor: Output tensor after attention. - - """ - bsz, seqlen, _ = x.shape - xq, xk, xv = self.wq(x), self.wk(x), self.wv(x) - dtype = xq.dtype - - xq = self.q_norm(xq) - xk = self.k_norm(xk) - - xq = xq.view(bsz, seqlen, self.n_heads, self.head_dim) - xk = xk.view(bsz, seqlen, self.n_kv_heads, self.head_dim) - xv = xv.view(bsz, seqlen, self.n_kv_heads, self.head_dim) - - xq, xk = Attention.apply_rotary_emb(xq, xk, freqs_cis=freqs_cis) - xq, xk = xq.to(dtype), xk.to(dtype) - - if dtype in [torch.float16, torch.bfloat16]: - # begin var_len flash attn - ( - query_states, - key_states, - value_states, - indices_q, - cu_seq_lens, - max_seq_lens, - ) = self._upad_input(xq, xk, xv, x_mask, seqlen) - - cu_seqlens_q, cu_seqlens_k = cu_seq_lens - max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens - - if self.proportional_attn: - softmax_scale = math.sqrt( - math.log(seqlen, self.base_seqlen) / self.head_dim - ) - else: - softmax_scale = math.sqrt(1 / self.head_dim) - attn_output_unpad = flash_attn_varlen_func( - query_states, - key_states, - value_states, - cu_seqlens_q=cu_seqlens_q, - cu_seqlens_k=cu_seqlens_k, - max_seqlen_q=max_seqlen_in_batch_q, - max_seqlen_k=max_seqlen_in_batch_k, - dropout_p=0.0, - causal=False, - softmax_scale=softmax_scale, - ) - output = pad_input(attn_output_unpad, indices_q, bsz, seqlen) - # end var_len_flash_attn - - else: - output = ( - F.scaled_dot_product_attention( - xq.permute(0, 2, 1, 3), - xk.permute(0, 2, 1, 3), - xv.permute(0, 2, 1, 3), - attn_mask=x_mask.bool() - .view(bsz, 1, 1, seqlen) - .expand(-1, self.n_heads, seqlen, -1), - ) - .permute(0, 2, 1, 3) - .to(dtype) - ) - - if hasattr(self, "wk_y"): - yk = self.ky_norm(self.wk_y(y)).view( - bsz, -1, self.n_kv_heads, self.head_dim - ) - yv = self.wv_y(y).view(bsz, -1, self.n_kv_heads, self.head_dim) - n_rep = self.n_heads // self.n_kv_heads - if n_rep >= 1: - yk = yk.unsqueeze(3).repeat(1, 1, 1, n_rep, 1).flatten(2, 3) - yv = yv.unsqueeze(3).repeat(1, 1, 1, n_rep, 1).flatten(2, 3) - output_y = F.scaled_dot_product_attention( - xq.permute(0, 2, 1, 3), - yk.permute(0, 2, 1, 3), - yv.permute(0, 2, 1, 3), - y_mask.view(bsz, 1, 1, -1).expand(bsz, self.n_heads, seqlen, -1), - ).permute(0, 2, 1, 3) - output_y = output_y * self.gate.tanh().view(1, 1, -1, 1) - output = output + output_y - - output = output.flatten(-2) - - return self.wo(output) - - -class FeedForward(nn.Module): - def __init__( - self, - dim: int, - hidden_dim: int, - multiple_of: int, - ffn_dim_multiplier: Optional[float], - ): - """ - Initialize the FeedForward module. - - Args: - dim (int): Input dimension. - hidden_dim (int): Hidden dimension of the feedforward layer. - multiple_of (int): Value to ensure hidden dimension is a multiple - of this value. - ffn_dim_multiplier (float, optional): Custom multiplier for hidden - dimension. Defaults to None. - - Attributes: - w1 (nn.Linear): Linear transformation for the first - layer. - w2 (nn.Linear): Linear transformation for the second layer. - w3 (nn.Linear): Linear transformation for the third - layer. - - """ - super().__init__() - hidden_dim = int(2 * hidden_dim / 3) - # custom dim factor multiplier - if ffn_dim_multiplier is not None: - hidden_dim = int(ffn_dim_multiplier * hidden_dim) - hidden_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of) - - self.w1 = nn.Linear(dim, hidden_dim, bias=False) - self.w2 = nn.Linear(hidden_dim, dim, bias=False) - self.w3 = nn.Linear(dim, hidden_dim, bias=False) - - # @torch.compile - def _forward_silu_gating(self, x1, x3): - return F.silu(x1) * x3 - - def forward(self, x): - return self.w2(self._forward_silu_gating(self.w1(x), self.w3(x))) - - -class TransformerBlock(nn.Module): - def __init__( - self, - layer_id: int, - dim: int, - n_heads: int, - n_kv_heads: int, - multiple_of: int, - ffn_dim_multiplier: float, - norm_eps: float, - qk_norm: bool, - y_dim: int, - ) -> None: - """ - Initialize a TransformerBlock. - - Args: - layer_id (int): Identifier for the layer. - dim (int): Embedding dimension of the input features. - n_heads (int): Number of attention heads. - n_kv_heads (Optional[int]): Number of attention heads in key and - value features (if using GQA), or set to None for the same as - query. - multiple_of (int): - ffn_dim_multiplier (float): - norm_eps (float): - - Attributes: - n_heads (int): Number of attention heads. - dim (int): Dimension size of the model. - head_dim (int): Dimension size of each attention head. - attention (Attention): Attention module. - feed_forward (FeedForward): FeedForward module. - layer_id (int): Identifier for the layer. - attention_norm (RMSNorm): Layer normalization for attention output. - ffn_norm (RMSNorm): Layer normalization for feedforward output. - - """ - super().__init__() - self.dim = dim - self.head_dim = dim // n_heads - self.attention = Attention(dim, n_heads, n_kv_heads, qk_norm, y_dim) - self.feed_forward = FeedForward( - dim=dim, - hidden_dim=4 * dim, - multiple_of=multiple_of, - ffn_dim_multiplier=ffn_dim_multiplier, - ) - self.layer_id = layer_id - self.attention_norm = RMSNorm(dim, eps=norm_eps) - self.ffn_norm = RMSNorm(dim, eps=norm_eps) - - self.adaLN_modulation = nn.Sequential( - nn.SiLU(), - nn.Linear(min(dim, 1024), 6 * dim, bias=True), - ) - - self.attention_y_norm = RMSNorm(y_dim, eps=norm_eps) - - def forward( - self, - x: torch.Tensor, - x_mask: torch.Tensor, - y: torch.Tensor, - y_mask: torch.Tensor, - freqs_cis: torch.Tensor, - adaln_input: Optional[torch.Tensor] = None, - ): - """ - Perform a forward pass through the TransformerBlock. - - Args: - x (torch.Tensor): Input tensor. - freqs_cis (torch.Tensor): Precomputed cosine and sine frequencies. - mask (torch.Tensor, optional): Masking tensor for attention. - Defaults to None. - - Returns: - torch.Tensor: Output tensor after applying attention and - feedforward layers. - - """ - if adaln_input is not None: - shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = ( - self.adaLN_modulation(adaln_input).chunk(6, dim=1) - ) - - x = x + gate_msa.unsqueeze(1) * self.attention( - modulate(self.attention_norm(x), shift_msa, scale_msa), - x_mask, - freqs_cis, - self.attention_y_norm(y), - y_mask, - ) - x = x + gate_mlp.unsqueeze(1) * self.feed_forward( - modulate(self.ffn_norm(x), shift_mlp, scale_mlp), - ) - - else: - x = x + self.attention( - self.attention_norm(x), - x_mask, - freqs_cis, - self.attention_y_norm(y), - y_mask, - ) - x = x + self.feed_forward(self.ffn_norm(x)) - - return x - - -class FinalLayer(nn.Module): - """ - The final layer of DiT. - """ - - def __init__(self, hidden_size, patch_size, out_channels): - super().__init__() - self.norm_final = nn.LayerNorm( - hidden_size, - elementwise_affine=False, - eps=1e-6, - ) - self.linear = nn.Linear(hidden_size, patch_size * patch_size * out_channels) - self.adaLN_modulation = nn.Sequential( - nn.SiLU(), - nn.Linear(min(hidden_size, 1024), 2 * hidden_size), - ) - - def forward(self, x, c): - shift, scale = self.adaLN_modulation(c).chunk(2, dim=1) - x = modulate(self.norm_final(x), shift, scale) - x = self.linear(x) - return x - - -class FlagDiffuserTransformer(ModelMixin, ConfigMixin): - """ - Diffusion model with a Transformer backbone. - """ - - def __init__( - self, - patch_size: int = 2, - in_channels: int = 4, - dim: int = 4096, - n_layers: int = 32, - n_heads: int = 32, - n_kv_heads: Optional[int] = None, - multiple_of: int = 256, - ffn_dim_multiplier: Optional[float] = None, - norm_eps: float = 1e-5, - learn_sigma: bool = True, - qk_norm: bool = False, - cap_feat_dim: int = 5120, - rope_scaling_factor: float = 1.0, - ntk_factor: float = 1.0, - ) -> None: - super().__init__() - self.learn_sigma = learn_sigma - self.in_channels = in_channels - self.out_channels = in_channels * 2 if learn_sigma else in_channels - self.patch_size = patch_size - - self.x_embedder = nn.Linear( - in_features=patch_size * patch_size * in_channels, - out_features=dim, - bias=True, - ) - nn.init.constant_(self.x_embedder.bias, 0.0) - - self.t_embedder = TimestepEmbedder(min(dim, 1024)) - self.cap_embedder = nn.Sequential( - nn.LayerNorm(cap_feat_dim), - nn.Linear(cap_feat_dim, min(dim, 1024), bias=True), - ) - - self.layers = nn.ModuleList( - [ - TransformerBlock( - layer_id, - dim, - n_heads, - n_kv_heads, - multiple_of, - ffn_dim_multiplier, - norm_eps, - qk_norm, - cap_feat_dim, - ) - for layer_id in range(n_layers) - ] - ) - self.final_layer = FinalLayer(dim, patch_size, self.out_channels) - - self.freqs_cis = DiT_Llama.precompute_freqs_cis( - dim // n_heads, - 40000, - rope_scaling_factor=rope_scaling_factor, - ntk_factor=ntk_factor, - ) - self.dim = dim - self.n_heads = n_heads - self.rope_scaling_factor = rope_scaling_factor - self.ntk_factor = ntk_factor - self.eol_token = nn.Parameter(torch.empty(dim)) - self.pad_token = nn.Parameter(torch.empty(dim)) - nn.init.normal_(self.eol_token, std=0.02) - nn.init.normal_(self.pad_token, std=0.02) - - def unpatchify( - self, x: torch.Tensor, img_size: List[Tuple[int, int]], return_tensor=False - ) -> List[torch.Tensor]: - """ - x: (N, T, patch_size**2 * C) - imgs: (N, H, W, C) - """ - pH = pW = self.patch_size - if return_tensor: - H, W = img_size[0] - B = x.size(0) - L = (H // pH) * (W // pW + 1) # one additional for eol - x = x[:, :L].view(B, H // pH, W // pW + 1, pH, pW, self.out_channels) - x = x[:, :, :-1] - x = x.permute(0, 5, 1, 3, 2, 4).flatten(4, 5).flatten(2, 3) - return x - else: - imgs = [] - for i in range(x.size(0)): - H, W = img_size[i] - L = (H // pH) * (W // pW + 1) - imgs.append( - x[i][:L] - .view(H // pH, W // pW + 1, pH, pW, self.out_channels)[ - :, :-1, :, :, : - ] - .permute(4, 0, 2, 1, 3) - .flatten(3, 4) - .flatten(1, 2) - ) - return imgs - - def patchify_and_embed( - self, x: List[torch.Tensor] | torch.Tensor - ) -> Tuple[torch.Tensor, torch.Tensor, List[Tuple[int, int]]]: - if isinstance(x, torch.Tensor): - pH = pW = self.patch_size - B, C, H, W = x.size() - x = ( - x.view(B, C, H // pH, pH, W // pW, pW) - .permute(0, 2, 4, 1, 3, 5) - .flatten(3) - ) - x = self.x_embedder(x) - x = torch.cat( - [ - x, - self.eol_token.view(1, 1, 1, -1).expand(B, H // pH, 1, -1), - ], - dim=2, - ) - x = x.flatten(1, 2) - - mask = torch.ones( - x.shape[0], x.shape[1], dtype=torch.int32, device=x.device - ) - return x, mask, [(H, W)] * B - else: - pH = pW = self.patch_size - x_embed = [] - img_size = [] - l_effective_seq_len = [] - - for img in x: - C, H, W = img.size() - img_size.append((H, W)) - img = ( - img.view(C, H // pH, pH, W // pW, pW) - .permute(1, 3, 0, 2, 4) - .flatten(2) - ) - img = self.x_embedder(img) - img = torch.cat( - [ - img, - self.eol_token.view(1, 1, -1).expand(H // pH, 1, -1), - ], - dim=1, - ) - img = img.flatten(0, 1) - l_effective_seq_len.append(len(img)) - x_embed.append(img) - - max_seq_len = max(l_effective_seq_len) - mask = torch.zeros( - len(x), max_seq_len, dtype=torch.int32, device=x[0].device - ) - padded_x_embed = [] - for i, (item_embed, item_seq_len) in enumerate( - zip(x_embed, l_effective_seq_len) - ): - item_embed = torch.cat( - [ - item_embed, - self.pad_token.view(1, -1).expand( - max_seq_len - item_seq_len, -1 - ), - ], - dim=0, - ) - padded_x_embed.append(item_embed) - mask[i][:item_seq_len] = 1 - - x_embed = torch.stack(padded_x_embed, dim=0) - return x_embed, mask, img_size - - def forward(self, x, t, cap_feats, cap_mask): - """ - Forward pass of DiT. - t: (N,) tensor of diffusion timesteps - y: (N,) tensor of class labels - """ - x_is_tensor = isinstance(x, torch.Tensor) - x, mask, img_size = self.patchify_and_embed(x) - self.freqs_cis = self.freqs_cis.to(x.device) - - t = self.t_embedder(t) # (N, D) - cap_mask_float = cap_mask.float().unsqueeze(-1) - cap_feats_pool = (cap_feats * cap_mask_float).sum(dim=1) / cap_mask_float.sum( - dim=1 - ) - cap_feats_pool = cap_feats_pool.to(cap_feats) - cap_emb = self.cap_embedder(cap_feats_pool) - adaln_input = t + cap_emb - - cap_mask = cap_mask.bool() - for layer in self.layers: - x = layer( - x, - mask, - cap_feats, - cap_mask, - self.freqs_cis[: x.size(1)], - adaln_input=adaln_input, - ) - - x = self.final_layer(x, adaln_input) - x = self.unpatchify(x, img_size, return_tensor=x_is_tensor) - if self.learn_sigma: - if x_is_tensor: - x, _ = x.chunk(2, dim=1) - else: - x = [_.chunk(2, dim=0)[0] for _ in x] - return x - - def forward_with_cfg( - self, - x, - t, - cap_feats, - cap_mask, - cfg_scale, - rope_scaling_factor=None, - ntk_factor=None, - base_seqlen: Optional[int] = None, - proportional_attn: bool = False, - ): - """ - Forward pass of DiT, but also batches the unconditional forward pass - for classifier-free guidance. - """ - # https://github.com/openai/glide-text2im/blob/main/notebooks/text2im.ipynb - - if rope_scaling_factor is not None or ntk_factor is not None: - rope_scaling_factor = ( - rope_scaling_factor - if rope_scaling_factor is not None - else self.rope_scaling_factor - ) - ntk_factor = ntk_factor if ntk_factor is not None else self.ntk_factor - if ( - rope_scaling_factor != self.rope_scaling_factor - or ntk_factor != self.ntk_factor - ): - print( - f"override freqs_cis, rope_scaling {rope_scaling_factor}, ntk {ntk_factor}", - flush=True, - ) - self.freqs_cis = DiT_Llama.precompute_freqs_cis( - self.dim // self.n_heads, - 40000, - rope_scaling_factor=rope_scaling_factor, - ntk_factor=ntk_factor, - ) - self.rope_scaling_factor = rope_scaling_factor - self.ntk_factor = ntk_factor - - if proportional_attn: - assert base_seqlen is not None - for layer in self.layers: - layer.attention.base_seqlen = base_seqlen - layer.attention.proportional_attn = proportional_attn - else: - for layer in self.layers: - layer.attention.base_seqlen = None - layer.attention.proportional_attn = proportional_attn - - half = x[: len(x) // 2] - combined = torch.cat([half, half], dim=0) - model_out = self(combined, t, cap_feats, cap_mask) - # For exact reproducibility reasons, we apply classifier-free guidance on only - # three channels by default. The standard approach to cfg applies it to all channels. - # This can be done by uncommenting the following line and commenting-out the line following that. - # eps, rest = model_out[:, :self.in_channels], model_out[:, self.in_channels:] - eps, rest = model_out[:, :3], model_out[:, 3:] - cond_eps, uncond_eps = torch.split(eps, len(eps) // 2, dim=0) - half_eps = uncond_eps + cfg_scale * (cond_eps - uncond_eps) - eps = torch.cat([half_eps, half_eps], dim=0) - return torch.cat([eps, rest], dim=1) - - @staticmethod - def precompute_freqs_cis( - dim: int, - end: int, - theta: float = 10000.0, - rope_scaling_factor: float = 1.0, - ntk_factor: float = 1.0, - ): - """ - Precompute the frequency tensor for complex exponentials (cis) with - given dimensions. - - This function calculates a frequency tensor with complex exponentials - using the given dimension 'dim' and the end index 'end'. The 'theta' - parameter scales the frequencies. The returned tensor contains complex - values in complex64 data type. - - Args: - dim (int): Dimension of the frequency tensor. - end (int): End index for precomputing frequencies. - theta (float, optional): Scaling factor for frequency computation. - Defaults to 10000.0. - - Returns: - torch.Tensor: Precomputed frequency tensor with complex - exponentials. - """ - - theta = theta * ntk_factor - - print(f"theta {theta} rope scaling {rope_scaling_factor} ntk {ntk_factor}") - freqs = 1.0 / ( - theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float().cuda() / dim) - ) - t = torch.arange(end, device=freqs.device, dtype=torch.float) # type: ignore - t = t / rope_scaling_factor - freqs = torch.outer(t, freqs).float() # type: ignore - freqs_cis = torch.polar(torch.ones_like(freqs), freqs) # complex64 - return freqs_cis - - def parameter_count(self) -> int: - tensor_module_list = ( - nn.Linear, - nn.Linear, - nn.Embedding, - ) - total_params = 0 - - def _recursive_count_params(module): - nonlocal total_params - is_tp_module = isinstance(module, tensor_module_list) - for param in module.parameters(recurse=False): - total_params += param.numel() - for submodule in module.children(): - _recursive_count_params(submodule) - - _recursive_count_params(self) - return total_params - - def get_fsdp_wrap_module_list(self) -> List[nn.Module]: - return list(self.layers) - - -############################################################################# -# DiT Configs # -############################################################################# - - -def DiT_Llama_5B_patch2(**kwargs): - return DiT_Llama(patch_size=2, dim=3072, n_layers=32, n_heads=32, **kwargs) diff --git a/src/diffusers/pipelines/lumina/modeling_next_dit.py b/src/diffusers/pipelines/lumina/modeling_next_dit.py deleted file mode 100644 index 0a9914b84338..000000000000 --- a/src/diffusers/pipelines/lumina/modeling_next_dit.py +++ /dev/null @@ -1,1034 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. - -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. -# -------------------------------------------------------- -# References: -# GLIDE: https://github.com/openai/glide-text2im -# MAE: https://github.com/facebookresearch/mae/blob/main/models_mae.py -# -------------------------------------------------------- - -import logging -import math -from typing import Optional, Tuple, List - -from flash_attn import flash_attn_varlen_func -from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa -import torch -import torch.nn as nn -import torch.nn.functional as F - -from ...configuration_utils import ConfigMixin, register_to_config -from ...models import ModelMixin -from ...models.transformers.transformer_2d import Transformer2DModelOutput -from ...utils import logging - -logger = logging.get_logger(__name__) - - -def modulate(x, shift, scale): - return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1) - - -class RMSNorm(torch.nn.Module): - def __init__(self, dim: int, eps: float = 1e-6): - """ - Initialize the RMSNorm normalization layer. - - Args: - dim (int): The dimension of the input tensor. - eps (float, optional): A small value added to the denominator for numerical stability. Default is 1e-6. - - Attributes: - eps (float): A small value added to the denominator for numerical stability. - weight (nn.Parameter): Learnable scaling parameter. - - """ - super().__init__() - self.eps = eps - self.weight = nn.Parameter(torch.ones(dim)) - - def _norm(self, x): - """ - Apply the RMSNorm normalization to the input tensor. - - Args: - x (torch.Tensor): The input tensor. - - Returns: - torch.Tensor: The normalized tensor. - - """ - return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps) - - def forward(self, x): - """ - Forward pass through the RMSNorm layer. - - Args: - x (torch.Tensor): The input tensor. - - Returns: - torch.Tensor: The output tensor after applying RMSNorm. - - """ - output = self._norm(x.float()).type_as(x) - return output * self.weight - - -############################################################################# -# Embedding Layers for Timesteps and Class Labels # -############################################################################# - - -class TimestepEmbedder(nn.Module): - """ - Embeds scalar timesteps into vector representations. - """ - - def __init__(self, hidden_size, frequency_embedding_size=256): - super().__init__() - self.mlp = nn.Sequential( - nn.Linear(frequency_embedding_size, hidden_size, bias=True), - nn.SiLU(), - nn.Linear(hidden_size, hidden_size, bias=True), - ) - self.frequency_embedding_size = frequency_embedding_size - - @staticmethod - def timestep_embedding(t, dim, max_period=10000): - """ - Create sinusoidal timestep embeddings. - :param t: a 1-D Tensor of N indices, one per batch element. - These may be fractional. - :param dim: the dimension of the output. - :param max_period: controls the minimum frequency of the embeddings. - :return: an (N, D) Tensor of positional embeddings. - """ - # https://github.com/openai/glide-text2im/blob/main/glide_text2im/nn.py - half = dim // 2 - freqs = torch.exp( - -math.log(max_period) - * torch.arange(start=0, end=half, dtype=torch.float32) - / half - ).to(device=t.device) - args = t[:, None].float() * freqs[None] - embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) - if dim % 2: - embedding = torch.cat( - [embedding, torch.zeros_like(embedding[:, :1])], dim=-1 - ) - return embedding - - def forward(self, t): - t_freq = self.timestep_embedding(t, self.frequency_embedding_size) - t_emb = self.mlp(t_freq.to(self.mlp[0].weight.dtype)) - return t_emb - - -class LabelEmbedder(nn.Module): - r"""Embeds class labels into vector representations. Also handles label - dropout for classifier-free guidance. - """ - - def __init__(self, num_classes, hidden_size, dropout_prob): - super().__init__() - use_cfg_embedding = int(dropout_prob > 0) - self.embedding_table = nn.Embedding( - num_classes + use_cfg_embedding, hidden_size - ) - self.num_classes = num_classes - self.dropout_prob = dropout_prob - - def token_drop(self, labels, force_drop_ids=None): - """ - Drops labels to enable classifier-free guidance. - """ - if force_drop_ids is None: - drop_ids = ( - torch.rand(labels.shape[0], device=labels.device) < self.dropout_prob - ) - drop_ids = drop_ids.cuda() - drop_ids = drop_ids.to(labels.device) - else: - drop_ids = force_drop_ids == 1 - labels = torch.where(drop_ids, self.num_classes, labels) - return labels - - def forward(self, labels, train, force_drop_ids=None): - use_dropout = self.dropout_prob > 0 - if (train and use_dropout) or (force_drop_ids is not None): - labels = self.token_drop(labels, force_drop_ids) - embeddings = self.embedding_table(labels) - return embeddings - - -############################################################################# -# Core NextFlagDiffuserTransformer2DModel Model # -############################################################################# - - -class Attention(nn.Module): - """Multi-head attention module.""" - - def __init__( - self, - dim: int, - n_heads: int, - n_kv_heads: Optional[int], - qk_norm: bool, - y_dim: int, - ): - """ - Initialize the Attention module. - - Args: - dim (int): Number of input dimensions. - n_heads (int): Number of heads. - n_kv_heads (Optional[int]): Number of kv heads, if using GQA. - - """ - super().__init__() - self.n_kv_heads = n_heads if n_kv_heads is None else n_kv_heads - self.num_attention_heads = n_heads - self.n_rep = self.num_attention_heads // self.n_kv_heads - self.head_dim = dim // n_heads - - self.wq = nn.Linear(dim, n_heads * self.head_dim, bias=False) - self.wk = nn.Linear(dim, self.n_kv_heads * self.head_dim, bias=False) - self.wv = nn.Linear(dim, self.n_kv_heads * self.head_dim, bias=False) - if y_dim > 0: - self.wk_y = nn.Linear(y_dim, self.n_kv_heads * self.head_dim, bias=False) - self.wv_y = nn.Linear(y_dim, self.n_kv_heads * self.head_dim, bias=False) - self.gate = nn.Parameter(torch.zeros([self.num_attention_heads])) - - self.wo = nn.Linear(n_heads * self.head_dim, dim, bias=False) - - if qk_norm: - self.q_norm = nn.LayerNorm(self.num_attention_heads * self.head_dim) - self.k_norm = nn.LayerNorm(self.n_kv_heads * self.head_dim) - if y_dim > 0: - self.ky_norm = nn.LayerNorm(self.n_kv_heads * self.head_dim) - else: - self.ky_norm = nn.Identity() - else: - self.q_norm = self.k_norm = nn.Identity() - self.ky_norm = nn.Identity() - - # for proportional attention computation - self.base_seqlen = None - self.proportional_attn = False - - @staticmethod - def reshape_for_broadcast(freqs_cis: torch.Tensor, x: torch.Tensor): - """ - Reshape frequency tensor for broadcasting it with another tensor. - - This function reshapes the frequency tensor to have the same shape as - the target tensor 'x' for the purpose of broadcasting the frequency - tensor during element-wise operations. - - Args: - freqs_cis (torch.Tensor): Frequency tensor to be reshaped. - x (torch.Tensor): Target tensor for broadcasting compatibility. - - Returns: - torch.Tensor: Reshaped frequency tensor. - - Raises: - AssertionError: If the frequency tensor doesn't match the expected - shape. - AssertionError: If the target tensor 'x' doesn't have the expected - number of dimensions. - """ - ndim = x.ndim - assert 0 <= 1 < ndim - assert freqs_cis.shape == (x.shape[1], x.shape[-1]) - shape = [d if i == 1 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)] - return freqs_cis.view(*shape) - - @staticmethod - def apply_rotary_emb( - x_in: torch.Tensor, - freqs_cis: torch.Tensor, - ) -> torch.Tensor: - """ - Apply rotary embeddings to input tensors using the given frequency - tensor. - - This function applies rotary embeddings to the given query 'xq' and - key 'xk' tensors using the provided frequency tensor 'freqs_cis'. The - input tensors are reshaped as complex numbers, and the frequency tensor - is reshaped for broadcasting compatibility. The resulting tensors - contain rotary embeddings and are returned as real tensors. - - Args: - x_in (torch.Tensor): Query or Key tensor to apply rotary embeddings. - freqs_cis (torch.Tensor): Precomputed frequency tensor for complex - exponentials. - - Returns: - Tuple[torch.Tensor, torch.Tensor]: Tuple of modified query tensor - and key tensor with rotary embeddings. - """ - with torch.cuda.amp.autocast(enabled=False): - x = torch.view_as_complex(x_in.float().reshape(*x_in.shape[:-1], -1, 2)) - freqs_cis = freqs_cis.unsqueeze(2) - x_out = torch.view_as_real(x * freqs_cis).flatten(3) - return x_out.type_as(x_in) - - # copied from huggingface modeling_llama.py - def _upad_input( - self, query_layer, key_layer, value_layer, attention_mask, query_length - ): - - def _get_unpad_data(attention_mask): - seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32) - indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten() - max_seqlen_in_batch = seqlens_in_batch.max().item() - cu_seqlens = F.pad( - torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0) - ) - return ( - indices, - cu_seqlens, - max_seqlen_in_batch, - ) - - indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask) - batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape - - key_layer = index_first_axis( - key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), - indices_k, - ) - value_layer = index_first_axis( - value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), - indices_k, - ) - if query_length == kv_seq_len: - query_layer = index_first_axis( - query_layer.reshape(batch_size * kv_seq_len, self.num_attention_heads, head_dim), - indices_k, - ) - cu_seqlens_q = cu_seqlens_k - max_seqlen_in_batch_q = max_seqlen_in_batch_k - indices_q = indices_k - elif query_length == 1: - max_seqlen_in_batch_q = 1 - cu_seqlens_q = torch.arange( - batch_size + 1, dtype=torch.int32, device=query_layer.device - ) # There is a memcpy here, that is very bad. - indices_q = cu_seqlens_q[:-1] - query_layer = query_layer.squeeze(1) - else: - # The -q_len: slice assumes left padding. - attention_mask = attention_mask[:, -query_length:] - query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input( - query_layer, attention_mask - ) - - return ( - query_layer, - key_layer, - value_layer, - indices_q, - (cu_seqlens_q, cu_seqlens_k), - (max_seqlen_in_batch_q, max_seqlen_in_batch_k), - ) - - def forward( - self, - x: torch.Tensor, - x_mask: torch.Tensor, - freqs_cis: torch.Tensor, - y: torch.Tensor, - y_mask: torch.Tensor, - ) -> torch.Tensor: - """ - - Args: - x: - x_mask: - freqs_cis: - y: - y_mask: - - Returns: - - """ - bsz, seqlen, _ = x.shape - xq, xk, xv = self.wq(x), self.wk(x), self.wv(x) - dtype = xq.dtype - - xq = self.q_norm(xq) - xk = self.k_norm(xk) - - xq = xq.view(bsz, seqlen, self.num_attention_heads, self.head_dim) - xk = xk.view(bsz, seqlen, self.n_kv_heads, self.head_dim) - xv = xv.view(bsz, seqlen, self.n_kv_heads, self.head_dim) - - xq = Attention.apply_rotary_emb(xq, freqs_cis=freqs_cis) - xk = Attention.apply_rotary_emb(xk, freqs_cis=freqs_cis) - - xq, xk = xq.to(dtype), xk.to(dtype) - - if dtype in [torch.float16, torch.bfloat16]: - # begin var_len flash attn - ( - query_states, - key_states, - value_states, - indices_q, - cu_seq_lens, - max_seq_lens, - ) = self._upad_input(xq, xk, xv, x_mask, seqlen) - - cu_seqlens_q, cu_seqlens_k = cu_seq_lens - max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens - - if self.proportional_attn: - softmax_scale = math.sqrt( - math.log(seqlen, self.base_seqlen) / self.head_dim - ) - else: - softmax_scale = math.sqrt(1 / self.head_dim) - - attn_output_unpad = flash_attn_varlen_func( - query_states, - key_states, - value_states, - cu_seqlens_q=cu_seqlens_q, - cu_seqlens_k=cu_seqlens_k, - max_seqlen_q=max_seqlen_in_batch_q, - max_seqlen_k=max_seqlen_in_batch_k, - dropout_p=0.0, - causal=False, - softmax_scale=softmax_scale, - ) - output = pad_input(attn_output_unpad, indices_q, bsz, seqlen) - # end var_len_flash_attn - - else: - output = ( - F.scaled_dot_product_attention( - xq.permute(0, 2, 1, 3), - xk.permute(0, 2, 1, 3), - xv.permute(0, 2, 1, 3), - attn_mask=x_mask.bool() - .view(bsz, 1, 1, seqlen) - .expand(-1, self.num_attention_heads, seqlen, -1), - ) - .permute(0, 2, 1, 3) - .to(dtype) - ) - - if hasattr(self, "wk_y"): - # todo better flash_attn support - yk = self.ky_norm(self.wk_y(y)).view( - bsz, -1, self.n_kv_heads, self.head_dim - ) - yv = self.wv_y(y).view(bsz, -1, self.n_kv_heads, self.head_dim) - n_rep = self.num_attention_heads // self.n_kv_heads - if n_rep >= 1: - yk = yk.unsqueeze(3).repeat(1, 1, 1, n_rep, 1).flatten(2, 3) - yv = yv.unsqueeze(3).repeat(1, 1, 1, n_rep, 1).flatten(2, 3) - output_y = F.scaled_dot_product_attention( - xq.permute(0, 2, 1, 3), - yk.permute(0, 2, 1, 3), - yv.permute(0, 2, 1, 3), - y_mask.view(bsz, 1, 1, -1).expand(bsz, self.num_attention_heads, seqlen, -1), - ).permute(0, 2, 1, 3) - output_y = output_y * self.gate.tanh().view(1, 1, -1, 1) - output = output + output_y - - output = output.flatten(-2) - - return self.wo(output) - - -class FeedForward(nn.Module): - def __init__( - self, - dim: int, - hidden_dim: int, - multiple_of: int, - ffn_dim_multiplier: Optional[float], - ): - """ - Initialize the FeedForward module. - - Args: - dim (int): Input dimension. - hidden_dim (int): Hidden dimension of the feedforward layer. - multiple_of (int): Value to ensure hidden dimension is a multiple - of this value. - ffn_dim_multiplier (float, optional): Custom multiplier for hidden - dimension. Defaults to None. - - Attributes: - w1 (nn.Linear): Linear transformation for the first - layer. - w2 (nn.Linear): Linear transformation for the second layer. - w3 (nn.Linear): Linear transformation for the third - layer. - - """ - super().__init__() - hidden_dim = int(2 * hidden_dim / 3) - # custom dim factor multiplier - if ffn_dim_multiplier is not None: - hidden_dim = int(ffn_dim_multiplier * hidden_dim) - hidden_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of) - - self.w1 = nn.Linear(dim, hidden_dim, bias=False) - self.w2 = nn.Linear(hidden_dim, dim, bias=False) - self.w3 = nn.Linear(dim, hidden_dim, bias=False) - - # @torch.compile - def _forward_silu_gating(self, x1, x3): - return F.silu(x1) * x3 - - def forward(self, x): - return self.w2(self._forward_silu_gating(self.w1(x), self.w3(x))) - - -class TransformerBlock(nn.Module): - def __init__( - self, - layer_id: int, - dim: int, - n_heads: int, - n_kv_heads: int, - multiple_of: int, - ffn_dim_multiplier: float, - norm_eps: float, - qk_norm: bool, - y_dim: int, - ) -> None: - """ - Initialize a TransformerBlock. - - Args: - layer_id (int): Identifier for the layer. - dim (int): Embedding dimension of the input features. - n_heads (int): Number of attention heads. - n_kv_heads (Optional[int]): Number of attention heads in key and - value features (if using GQA), or set to None for the same as - query. - multiple_of (int): - ffn_dim_multiplier (float): - norm_eps (float): - - Attributes: - n_heads (int): Number of attention heads. - dim (int): Dimension size of the model. - head_dim (int): Dimension size of each attention head. - attention (Attention): Attention module. - feed_forward (FeedForward): FeedForward module. - layer_id (int): Identifier for the layer. - attention_norm (RMSNorm): Layer normalization for attention output. - ffn_norm (RMSNorm): Layer normalization for feedforward output. - - """ - super().__init__() - self.dim = dim - self.head_dim = dim // n_heads - self.attention = Attention(dim, n_heads, n_kv_heads, qk_norm, y_dim) - self.feed_forward = FeedForward( - dim=dim, - hidden_dim=4 * dim, - multiple_of=multiple_of, - ffn_dim_multiplier=ffn_dim_multiplier, - ) - self.layer_id = layer_id - self.attention_norm = RMSNorm(dim, eps=norm_eps) - self.attention_norm1 = RMSNorm(dim, eps=norm_eps) - self.ffn_norm = RMSNorm(dim, eps=norm_eps) - self.ffn_norm1 = RMSNorm(dim, eps=norm_eps) - - self.adaLN_modulation = nn.Sequential( - nn.SiLU(), - nn.Linear(min(dim, 1024), 6 * dim, bias=True), - ) - - self.attention_y_norm = RMSNorm(y_dim, eps=norm_eps) - - def forward( - self, - x: torch.Tensor, - x_mask: torch.Tensor, - freqs_cis: torch.Tensor, - y: torch.Tensor, - y_mask: torch.Tensor, - adaln_input: Optional[torch.Tensor] = None, - ): - """ - Perform a forward pass through the TransformerBlock. - - Args: - x (torch.Tensor): Input tensor. - freqs_cis (torch.Tensor): Precomputed cosine and sine frequencies. - - Returns: - torch.Tensor: Output tensor after applying attention and - feedforward layers. - - """ - if adaln_input is not None: - shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = ( - self.adaLN_modulation(adaln_input).chunk(6, dim=1) - ) - - x = x + self.attention_norm1( - gate_msa.unsqueeze(1) - * self.attention( - modulate(self.attention_norm(x), shift_msa, scale_msa), - x_mask, - freqs_cis, - self.attention_y_norm(y), - y_mask, - ) - ) - d = x.shape[-1] - x = x + self.ffn_norm1( - gate_mlp.unsqueeze(1) - * self.feed_forward( - modulate(self.ffn_norm(x), shift_mlp, scale_mlp).view(-1, d), - ).view(*x.shape) - ) - - else: - x = x + self.attention_norm1( - self.attention( - self.attention_norm(x), - x_mask, - freqs_cis, - self.attention_y_norm(y), - y_mask, - ) - ) - # for compatibility with torch.compile because the sequence length changes - B, L, D = x.shape - x = x.view(B * L, D) - x = x + self.ffn_norm1(self.feed_forward(self.ffn_norm(x))) - x = x.view(B, L, D) - - return x - - -class FinalLayer(nn.Module): - """ - The final layer of NextFlagDiffuserTransformer2DModel. - """ - - def __init__(self, hidden_size, patch_size, out_channels): - super().__init__() - self.norm_final = nn.LayerNorm( - hidden_size, - elementwise_affine=False, - eps=1e-6, - ) - self.linear = nn.Linear(hidden_size, patch_size * patch_size * out_channels) - self.adaLN_modulation = nn.Sequential( - nn.SiLU(), - nn.Linear(min(hidden_size, 1024), 2 * hidden_size), - ) - - def forward(self, x, c): - shift, scale = self.adaLN_modulation(c).chunk(2, dim=1) - x = modulate(self.norm_final(x), shift, scale) - x = self.linear(x) - return x - - -class NextFlagDiffuserTransformer2DModel(ModelMixin, ConfigMixin): - """ - Diffusion model with a Transformer backbone. - """ - - @register_to_config - def __init__( - self, - patch_size: Optional[int] = 2, - in_channels: Optional[int] = None, - out_channels: Optional[int] = None, - hidden_size: int = 4096, - num_layers: int = 32, - num_attention_heads: int = 32, - num_kv_heads: Optional[int] = None, - multiple_of: int = 256, - ffn_dim_multiplier: Optional[float] = None, - norm_elementwise_affine: bool = True, - norm_eps: float = 1e-5, - learn_sigma: bool = True, - qk_norm: bool = False, - text_dim: int = 5120, - rope_scaling_factor: float = 1.0, - ntk_factor: float = 1.0, - ) -> None: - super().__init__() - self.learn_sigma = learn_sigma - self.in_channels = in_channels - self.out_channels = in_channels * 2 if learn_sigma else in_channels - self.patch_size = patch_size - self.num_attention_heads = num_attention_heads - self.rope_scaling_factor = rope_scaling_factor - self.ntk_factor = ntk_factor - - self.x_embedder = nn.Linear( - in_features=patch_size * patch_size * in_channels, - out_features=hidden_size, - bias=True, - ) - nn.init.constant_(self.x_embedder.bias, 0.0) - - self.t_embedder = TimestepEmbedder(min(hidden_size, 1024)) - self.cap_embedder = nn.Sequential( - nn.LayerNorm(text_dim), - nn.Linear(text_dim, min(hidden_size, 1024), bias=True), - ) - - self.layers = nn.ModuleList( - [ - TransformerBlock( - layer_id, - hidden_size, - num_attention_heads, - num_kv_heads, - multiple_of, - ffn_dim_multiplier, - norm_eps, - qk_norm, - text_dim, - ) - for layer_id in range(num_layers) - ] - ) - self.final_layer = FinalLayer(hidden_size, patch_size, self.out_channels) - - assert (hidden_size // num_attention_heads) % 4 == 0, "2d rope needs head dim to be divisible by 4" - self.hidden_size = hidden_size - self.freqs_cis = NextFlagDiffuserTransformer2DModel.precompute_freqs_cis( - hidden_size // num_attention_heads, - 384, - rope_scaling_factor=rope_scaling_factor, - ntk_factor=ntk_factor, - ) - # self.eol_token = nn.Parameter(torch.empty(dim)) - self.pad_token = nn.Parameter(torch.empty(hidden_size)) - # nn.init.normal_(self.eol_token, std=0.02) - nn.init.normal_(self.pad_token, std=0.02) - - def unpatchify( - self, x: torch.Tensor, img_size: List[Tuple[int, int]], return_tensor=False - ) -> List[torch.Tensor]: - """ - x: (N, T, patch_size**2 * C) - imgs: (N, H, W, C) - """ - pH = pW = self.patch_size - if return_tensor: - H, W = img_size[0] - B = x.size(0) - L = (H // pH) * (W // pW) - x = x[:, :L].view(B, H // pH, W // pW, pH, pW, self.out_channels) - x = x.permute(0, 5, 1, 3, 2, 4).flatten(4, 5).flatten(2, 3) - return x - else: - imgs = [] - for i in range(x.size(0)): - H, W = img_size[i] - L = (H // pH) * (W // pW) - imgs.append( - x[i][:L] - .view(H // pH, W // pW, pH, pW, self.out_channels) - .permute(4, 0, 2, 1, 3) - .flatten(3, 4) - .flatten(1, 2) - ) - return imgs - - def patchify_and_embed( - self, x: List[torch.Tensor] | torch.Tensor - ) -> Tuple[torch.Tensor, torch.Tensor, List[Tuple[int, int]], torch.Tensor]: - self.freqs_cis = self.freqs_cis.to(x[0].device) - if isinstance(x, torch.Tensor): - pH = pW = self.patch_size - B, C, H, W = x.size() - x = ( - x.view(B, C, H // pH, pH, W // pW, pW) - .permute(0, 2, 4, 1, 3, 5) - .flatten(3) - ) - x = self.x_embedder(x) - x = x.flatten(1, 2) - - mask = torch.ones( - x.shape[0], x.shape[1], dtype=torch.int32, device=x.device - ) - # leave the first line for text - return ( - x, - mask, - [(H, W)] * B, - self.freqs_cis[: H // pH, : W // pW].flatten(0, 1).unsqueeze(0), - ) - else: - pH = pW = self.patch_size - x_embed = [] - freqs_cis = [] - img_size = [] - l_effective_seq_len = [] - - for img in x: - C, H, W = img.size() - item_freqs_cis = self.freqs_cis[: H // pH, : W // pW] - freqs_cis.append(item_freqs_cis.flatten(0, 1)) - img_size.append((H, W)) - img = ( - img.view(C, H // pH, pH, W // pW, pW) - .permute(1, 3, 0, 2, 4) - .flatten(2) - ) - img = self.x_embedder(img) - img = img.flatten(0, 1) - l_effective_seq_len.append(len(img)) - x_embed.append(img) - - max_seq_len = max(l_effective_seq_len) - mask = torch.zeros( - len(x), max_seq_len, dtype=torch.int32, device=x[0].device - ) - padded_x_embed = [] - padded_freqs_cis = [] - for i, (item_embed, item_freqs_cis, item_seq_len) in enumerate( - zip(x_embed, freqs_cis, l_effective_seq_len) - ): - item_embed = torch.cat( - [ - item_embed, - self.pad_token.view(1, -1).expand( - max_seq_len - item_seq_len, -1 - ), - ], - dim=0, - ) - item_freqs_cis = torch.cat( - [ - item_freqs_cis, - item_freqs_cis[-1:].expand(max_seq_len - item_seq_len, -1), - ], - dim=0, - ) - padded_x_embed.append(item_embed) - padded_freqs_cis.append(item_freqs_cis) - mask[i][:item_seq_len] = 1 - - x_embed = torch.stack(padded_x_embed, dim=0) - freqs_cis = torch.stack(padded_freqs_cis, dim=0) - return x_embed, mask, img_size, freqs_cis - - def forward( - self, - hidden_states, - timestep=None, - encoder_hidden_states=None, - encoder_attn_mask=None, - return_dict: bool = True, - hidden_states_is_tensor: bool = False, - unpatchify: bool = True, - ): - """ - Forward pass of NextFlagDiffuserTransformer2DModel. - t: (N,) tensor of diffusion timesteps - y: (N,) tensor of class labels - """ - - # 0. Check inputs - if not unpatchify and return_dict: - raise ValueError( - f"Cannot both define `unpatchify`: {unpatchify} and `return_dict`: {return_dict} since when" - f" `unpatchify` is {unpatchify} the returned output is of shape (batch_size, seq_len, hidden_dim)" - " rather than (batch_size, num_channels, height, width)." - ) - - # 1. Patchify and Get embedding - hidden_states, mask, img_size, freqs_cis = self.patchify_and_embed(hidden_states) - freqs_cis = freqs_cis.to(hidden_states.device) - - # 2. Get timestep embedding - timestep = self.t_embedder(timestep) # (N, D) - - # 3. Get encoder hidden_state - encoder_attn_mask_float = encoder_attn_mask.float().unsqueeze(-1) - encoder_hidden_states_pool = (encoder_hidden_states * encoder_attn_mask_float).sum(dim=1) / encoder_attn_mask_float.sum( - dim=1 - ) - encoder_hidden_states_pool = encoder_hidden_states_pool.to(encoder_hidden_states) - cap_emb = self.cap_embedder(encoder_hidden_states_pool) - encoder_attn_mask = encoder_attn_mask.bool() - - adaln_input = timestep + cap_emb - # 4. Get hidden_state from transformer - for layer in self.layers: - hidden_states = layer(hidden_states, mask, freqs_cis, encoder_hidden_states, encoder_attn_mask, adaln_input=adaln_input) - - hidden_states = self.final_layer(hidden_states, adaln_input) - - if unpatchify: - hidden_states = self.unpatchify(hidden_states, img_size, return_tensor=hidden_states_is_tensor) - else: - output = hidden_states - - if self.learn_sigma: - if hidden_states_is_tensor: - hidden_states, _ = hidden_states.chunk(2, dim=1) - else: - hidden_states = [_.chunk(2, dim=0)[0] for _ in hidden_states] - - if not return_dict: - return (output,) - - return Transformer2DModelOutput(sample=output) - - def forward_with_cfg( - self, - x, - t, - cap_feats, - cap_mask, - cfg_scale, - rope_scaling_factor=None, - ntk_factor=None, - base_seqlen: Optional[int] = None, - proportional_attn: bool = False, - ): - # """ - # Forward pass of NextFlagDiffuserTransformer2DModel, but also batches the unconNextFlagDiffuserTransformer2DModelional forward pass - # for classifier-free guidance. - # """ - # # https://github.com/openai/glide-text2im/blob/main/notebooks/text2im.ipynb - # print(ntk_factor, rope_scaling_factor, self.ntk_factor, self.rope_scaling_factor) - if rope_scaling_factor is not None or ntk_factor is not None: - rope_scaling_factor = ( - rope_scaling_factor - if rope_scaling_factor is not None - else self.rope_scaling_factor - ) - ntk_factor = ntk_factor if ntk_factor is not None else self.ntk_factor - if ( - rope_scaling_factor != self.rope_scaling_factor - or ntk_factor != self.ntk_factor - ): - print( - f"override freqs_cis, rope_scaling {rope_scaling_factor}, ntk {ntk_factor}", - flush=True, - ) - self.freqs_cis = NextFlagDiffuserTransformer2DModel.precompute_freqs_cis( - self.hidden_size // self.num_attention_heads, - 384, - rope_scaling_factor=rope_scaling_factor, - ntk_factor=ntk_factor, - ) - self.rope_scaling_factor = rope_scaling_factor - self.ntk_factor = ntk_factor - - if proportional_attn: - assert base_seqlen is not None - for layer in self.layers: - layer.attention.base_seqlen = base_seqlen - layer.attention.proportional_attn = proportional_attn - else: - for layer in self.layers: - layer.attention.base_seqlen = None - layer.attention.proportional_attn = proportional_attn - - half = x[: len(x) // 2] - combined = torch.cat([half, half], dim=0) - model_out = self.forward(combined, t, cap_feats, cap_mask) - # For exact reproducibility reasons, we apply classifier-free guidance on only - # three channels by default. The standard approach to cfg applies it to all channels. - # This can be done by uncommenting the following line and commenting-out the line following that. - # eps, rest = model_out[:, :self.in_channels], model_out[:, self.in_channels:] - eps, rest = model_out[:, :3], model_out[:, 3:] - cond_eps, uncond_eps = torch.split(eps, len(eps) // 2, dim=0) - half_eps = uncond_eps + cfg_scale * (cond_eps - uncond_eps) - eps = torch.cat([half_eps, half_eps], dim=0) - return torch.cat([eps, rest], dim=1) - - @staticmethod - def precompute_freqs_cis( - dim: int, - end: int, - theta: float = 10000.0, - rope_scaling_factor: float = 1.0, - ntk_factor: float = 1.0, - ): - """ - Precompute the frequency tensor for complex exponentials (cis) with - given dimensions. - - This function calculates a frequency tensor with complex exponentials - using the given dimension 'dim' and the end index 'end'. The 'theta' - parameter scales the frequencies. The returned tensor contains complex - values in complex64 data type. - - Args: - dim (int): Dimension of the frequency tensor. - end (int): End index for precomputing frequencies. - theta (float, optional): Scaling factor for frequency computation. - Defaults to 10000.0. - - Returns: - torch.Tensor: Precomputed frequency tensor with complex - exponentials. - """ - - theta = theta * ntk_factor - - logger.info( - f"theta {theta} rope scaling {rope_scaling_factor} ntk {ntk_factor}" - ) - freqs = 1.0 / ( - theta ** (torch.arange(0, dim, 4)[: (dim // 4)].float().cuda() / dim) - ) - t = torch.arange(end, device=freqs.device, dtype=torch.float) # type: ignore - t = t / rope_scaling_factor - freqs = torch.outer(t, freqs).float() # type: ignore - freqs_cis = torch.polar(torch.ones_like(freqs), freqs) # complex64 - - freqs_cis_h = freqs_cis.view(end, 1, dim // 4, 1).repeat(1, end, 1, 1) - freqs_cis_w = freqs_cis.view(1, end, dim // 4, 1).repeat(end, 1, 1, 1) - freqs_cis = torch.cat([freqs_cis_h, freqs_cis_w], dim=-1).flatten(2) - return freqs_cis - - def parameter_count(self) -> int: - tensor_parallel_module_list = ( - nn.Linear, - nn.Linear, - nn.Embedding, - ) - total_params = 0 - - def _recursive_count_params(module): - nonlocal total_params - is_tp_module = isinstance(module, tensor_parallel_module_list) - for param in module.parameters(recurse=False): - total_params += param.numel() - for submodule in module.children(): - _recursive_count_params(submodule) - - _recursive_count_params(self) - return total_params - - def get_fsdp_wrap_module_list(self) -> List[nn.Module]: - return list(self.layers) - - -############################################################################# -# NextFlagDiffuserTransformer2DModel Configs # -############################################################################# -def NextFlagDiffuserTransformer2DModel_2B_patch2(**kwargs): - return NextFlagDiffuserTransformer2DModel(patch_size=2, dim=2304, num_layers=24, n_heads=32, **kwargs) diff --git a/src/diffusers/pipelines/lumina/pipeline_lumina.py b/src/diffusers/pipelines/lumina/pipeline_lumina.py index 6435cddcd880..eaac27fd1a2e 100644 --- a/src/diffusers/pipelines/lumina/pipeline_lumina.py +++ b/src/diffusers/pipelines/lumina/pipeline_lumina.py @@ -14,20 +14,20 @@ import html import inspect -import re import math +import re import urllib.parse as ul -from typing import Callable, List, Optional, Tuple, Union, Dict +from typing import Callable, Dict, List, Optional, Tuple, Union import torch from transformers import AutoModel, AutoTokenizer from ...image_processor import VaeImageProcessor from ...models import AutoencoderKL +from ...models.transformers.lumina_nextdit_2d import LuminaNextDiT2DModel from ...schedulers import FlowMatchEulerDiscreteScheduler from ...utils import ( BACKENDS_MAPPING, - deprecate, is_bs4_available, is_ftfy_available, logging, @@ -35,7 +35,6 @@ ) from ...utils.torch_utils import randn_tensor from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput -from ...models.transformers.lumina_nextdit_2d import LuminaNextDiT2DModel logger = logging.get_logger(__name__) # pylint: disable=invalid-name @@ -204,13 +203,9 @@ def retrieve_timesteps( second element is the number of inference steps. """ if timesteps is not None and sigmas is not None: - raise ValueError( - "Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values" - ) + raise ValueError("Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values") if timesteps is not None: - accepts_timesteps = "timesteps" in set( - inspect.signature(scheduler.set_timesteps).parameters.keys() - ) + accepts_timesteps = "timesteps" in set(inspect.signature(scheduler.set_timesteps).parameters.keys()) if not accepts_timesteps: raise ValueError( f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom" @@ -220,9 +215,7 @@ def retrieve_timesteps( timesteps = scheduler.timesteps num_inference_steps = len(timesteps) elif sigmas is not None: - accept_sigmas = "sigmas" in set( - inspect.signature(scheduler.set_timesteps).parameters.keys() - ) + accept_sigmas = "sigmas" in set(inspect.signature(scheduler.set_timesteps).parameters.keys()) if not accept_sigmas: raise ValueError( f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom" @@ -304,7 +297,7 @@ def __init__( self.tokenizer_max_length = ( self.tokenizer.model_max_length if hasattr(self, "tokenizer") and self.tokenizer is not None else 256 ) - + def _get_gemma_prompt_embeds( self, prompt: Union[str, List[str]], @@ -328,16 +321,10 @@ def _get_gemma_prompt_embeds( return_tensors="pt", ) text_input_ids = text_inputs.input_ids - untruncated_ids = self.tokenizer( - prompt, padding="longest", return_tensors="pt" - ).input_ids - - if untruncated_ids.shape[-1] >= text_input_ids.shape[ - -1 - ] and not torch.equal(text_input_ids, untruncated_ids): - removed_text = self.tokenizer.batch_decode( - untruncated_ids[:, self.tokenizer_max_length - 1 : -1] - ) + untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids + + if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids): + removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer_max_length - 1 : -1]) logger.warning( "The following part of your input was truncated because T5 can only handle sequences up to" f" {self.tokenizer_max_length} tokens: {removed_text}" @@ -364,7 +351,7 @@ def _get_gemma_prompt_embeds( # duplicate text embeddings and attention mask for each generation per prompt, using mps friendly method prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1) prompt_embeds = prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1) - + return prompt_embeds, prompt_attention_mask # Adapted from diffusers.pipelines.deepfloyd_if.pipeline_if.encode_prompt @@ -403,8 +390,7 @@ def encode_prompt( Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument. negative_prompt_embeds (`torch.Tensor`, *optional*): - Pre-generated negative text embeddings. For Lumina-T2I, it's should be the embeddings of the "" - string. + Pre-generated negative text embeddings. For Lumina-T2I, it's should be the embeddings of the "" string. clean_caption (`bool`, defaults to `False`): If `True`, the function will preprocess and clean the provided caption before encoding. max_sequence_length (`int`, defaults to 120): Maximum sequence length to use for the prompt. @@ -425,14 +411,14 @@ def encode_prompt( device=device, clean_caption=clean_caption, ) - + # get negative embeddings for classifier free guidance if do_classifier_free_guidance and negative_prompt_embeds is None: negative_prompt = negative_prompt if negative_prompt is not None else "" # normalize str to list negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt - + if prompt is not None and type(prompt) is not type(negative_prompt): raise TypeError( f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=" @@ -446,11 +432,9 @@ def encode_prompt( ) negative_prompt_embeds, negative_prompt_attention_mask = self._get_gemma_prompt_embeds( - prompt=prompt, - num_images_per_prompt=num_images_per_prompt, - device=device + prompt=prompt, num_images_per_prompt=num_images_per_prompt, device=device ) - + return prompt_embeds, prompt_attention_mask, negative_prompt_embeds, negative_prompt_attention_mask # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs @@ -460,17 +444,13 @@ def prepare_extra_step_kwargs(self, generator, eta): # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 # and should be between [0, 1] - accepts_eta = "eta" in set( - inspect.signature(self.scheduler.step).parameters.keys() - ) + accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) extra_step_kwargs = {} if accepts_eta: extra_step_kwargs["eta"] = eta # check if the scheduler accepts generator - accepts_generator = "generator" in set( - inspect.signature(self.scheduler.step).parameters.keys() - ) + accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys()) if accepts_generator: extra_step_kwargs["generator"] = generator return extra_step_kwargs @@ -487,9 +467,7 @@ def check_inputs( negative_prompt_attention_mask=None, ): if height % 8 != 0 or width % 8 != 0: - raise ValueError( - f"`height` and `width` have to be divisible by 8 but are {height} and {width}." - ) + raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.") if prompt is not None and prompt_embeds is not None: raise ValueError( @@ -500,12 +478,8 @@ def check_inputs( raise ValueError( "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined." ) - elif prompt is not None and ( - not isinstance(prompt, str) and not isinstance(prompt, list) - ): - raise ValueError( - f"`prompt` has to be of type `str` or `list` but is {type(prompt)}" - ) + elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)): + raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") if prompt is not None and negative_prompt_embeds is not None: raise ValueError( @@ -520,17 +494,10 @@ def check_inputs( ) if prompt_embeds is not None and prompt_attention_mask is None: - raise ValueError( - "Must provide `prompt_attention_mask` when specifying `prompt_embeds`." - ) + raise ValueError("Must provide `prompt_attention_mask` when specifying `prompt_embeds`.") - if ( - negative_prompt_embeds is not None - and negative_prompt_attention_mask is None - ): - raise ValueError( - "Must provide `negative_prompt_attention_mask` when specifying `negative_prompt_embeds`." - ) + if negative_prompt_embeds is not None and negative_prompt_attention_mask is None: + raise ValueError("Must provide `negative_prompt_attention_mask` when specifying `negative_prompt_embeds`.") if prompt_embeds is not None and negative_prompt_embeds is not None: if prompt_embeds.shape != negative_prompt_embeds.shape: @@ -549,16 +516,12 @@ def check_inputs( # Copied from diffusers.pipelines.deepfloyd_if.pipeline_if.IFPipeline._text_preprocessing def _text_preprocessing(self, text, clean_caption=False): if clean_caption and not is_bs4_available(): - logger.warning( - BACKENDS_MAPPING["bs4"][-1].format("Setting `clean_caption=True`") - ) + logger.warning(BACKENDS_MAPPING["bs4"][-1].format("Setting `clean_caption=True`")) logger.warning("Setting `clean_caption` to False...") clean_caption = False if clean_caption and not is_ftfy_available(): - logger.warning( - BACKENDS_MAPPING["ftfy"][-1].format("Setting `clean_caption=True`") - ) + logger.warning(BACKENDS_MAPPING["ftfy"][-1].format("Setting `clean_caption=True`")) logger.warning("Setting `clean_caption` to False...") clean_caption = False @@ -646,17 +609,13 @@ def _clean_caption(self, caption): # "123456.." caption = re.sub(r"\b\d{6,}\b", "", caption) # filenames: - caption = re.sub( - r"[\S]+\.(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)", "", caption - ) + caption = re.sub(r"[\S]+\.(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)", "", caption) # caption = re.sub(r"[\"\']{2,}", r'"', caption) # """AUSVERKAUFT""" caption = re.sub(r"[\.]{2,}", r" ", caption) # """AUSVERKAUFT""" - caption = re.sub( - self.bad_punct_regex, r" ", caption - ) # ***AUSVERKAUFT***, #AUSVERKAUFT + caption = re.sub(self.bad_punct_regex, r" ", caption) # ***AUSVERKAUFT***, #AUSVERKAUFT caption = re.sub(r"\s+\.\s+", r" ", caption) # " . " # this-is-my-cute-cat / this_is_my_cute_cat @@ -674,14 +633,10 @@ def _clean_caption(self, caption): caption = re.sub(r"(worldwide\s+)?(free\s+)?shipping", "", caption) caption = re.sub(r"(free\s)?download(\sfree)?", "", caption) caption = re.sub(r"\bclick\b\s(?:for|on)\s\w+", "", caption) - caption = re.sub( - r"\b(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)(\simage[s]?)?", "", caption - ) + caption = re.sub(r"\b(?:png|jpg|jpeg|bmp|webp|eps|pdf|apk|mp4)(\simage[s]?)?", "", caption) caption = re.sub(r"\bpage\s+\d+\b", "", caption) - caption = re.sub( - r"\b\d*[a-zA-Z]+\d+[a-zA-Z]+\d+[a-zA-Z\d]*\b", r" ", caption - ) # j2d1a2a... + caption = re.sub(r"\b\d*[a-zA-Z]+\d+[a-zA-Z]+\d+[a-zA-Z\d]*\b", r" ", caption) # j2d1a2a... caption = re.sub(r"\b\d+\.?\d*[xх×]\d+\.?\d*\b", "", caption) @@ -723,9 +678,7 @@ def prepare_latents( ) if latents is None: - latents = randn_tensor( - shape, generator=generator, device=device, dtype=dtype - ) + latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype) else: latents = latents.to(device) @@ -835,7 +788,7 @@ def __call__( Whether or not to clean the caption before creating embeddings. Requires `beautifulsoup4` and `ftfy` to be installed. If the dependencies are not installed, the embeddings will be created from the raw prompt. - max_sequence_length (`int` defaults to 120): + max_sequence_length (`int` defaults to 120): Maximum sequence length to use with the `prompt`. callback_on_step_end (`Callable`, *optional*): A function that calls at the end of each denoising steps during the inference. The function is called @@ -856,7 +809,7 @@ def __call__( """ height = height or self.default_sample_size * self.vae_scale_factor width = width or self.default_sample_size * self.vae_scale_factor - + # 1. Check inputs. Raise error if not correct self.check_inputs( prompt, @@ -929,24 +882,22 @@ def __call__( with self.progress_bar(total=num_inference_steps) as progress_bar: for i, t in enumerate(timesteps): # expand the latents if we are doing classifier free guidance - latent_model_input = ( - torch.cat([latents] * 2) if do_classifier_free_guidance else latents - ) + latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents # broadcast to batch dimension in a way that's compatible with ONNX/Core ML timestep = t.expand(latent_model_input.shape[0]) hidden_size = self.transformer.config.hidden_size num_attention_heads = self.transformer.config.num_attention_heads attention_head_dim = hidden_size // num_attention_heads - - scaling_factor = math.sqrt(width * height / 1024 ** 2) - + + scaling_factor = math.sqrt(width * height / 1024**2) + self.transformer.freqs_cis = self.transformer.precompute_freqs_cis( attention_head_dim, 384, scale_factor=scaling_factor, - scaling_watershed=scaling_watershed, - timestep=timesteps[0] + scaling_watershed=scaling_watershed, + timestep=timesteps[0], ) noise_pred = self.transformer( @@ -964,9 +915,7 @@ def __call__( # eps, rest = model_out[:, :self.in_channels], model_out[:, self.in_channels:] if do_classifier_free_guidance: noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) - noise_pred = noise_pred_uncond + guidance_scale * ( - noise_pred_text - noise_pred_uncond - ) + noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) current_timestep = t if not torch.is_tensor(current_timestep): @@ -983,9 +932,7 @@ def __call__( device=latent_model_input.device, ) elif len(current_timestep.shape) == 0: - current_timestep = current_timestep[None].to( - latent_model_input.device - ) + current_timestep = current_timestep[None].to(latent_model_input.device) # broadcast to batch dimension in a way that's compatible with ONNX/Core ML current_timestep = current_timestep.expand(latent_model_input.shape[0]) @@ -997,7 +944,6 @@ def __call__( caption_mask=prompt_attention_mask, return_dict=False, )[0] - # learned sigma if self.transformer.config.out_channels // 2 == latent_channels: @@ -1016,6 +962,7 @@ def __call__( # noise_pred, t, latents, **extra_step_kwargs, return_dict=False # )[0] + progress_bar.update() if not output_type == "latent": latents = latents / self.vae.config.scaling_factor diff --git a/src/diffusers/schedulers/scheduling_flow_match_heun_discrete.py b/src/diffusers/schedulers/scheduling_flow_match_heun_discrete.py index cebf6106744e..a200c94a6d1a 100644 --- a/src/diffusers/schedulers/scheduling_flow_match_heun_discrete.py +++ b/src/diffusers/schedulers/scheduling_flow_match_heun_discrete.py @@ -160,14 +160,14 @@ def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.devic timesteps = sigmas * self.config.num_train_timesteps timesteps = torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2)]) self.timesteps = timesteps.to(device=device) - + sigmas = torch.cat([sigmas, torch.zeros(1, device=sigmas.device)]) self.sigmas = torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2), sigmas[-1:]]) - + # empty dt and derivative self.prev_derivative = None self.dt = None - + self._step_index = None self._begin_index = None @@ -196,7 +196,7 @@ def _init_step_index(self, timestep): @property def state_in_first_order(self): return self.dt is None - + def step( self, model_output: torch.FloatTensor, @@ -255,7 +255,7 @@ def step( # Upcast to avoid precision issues when computing prev_sample sample = sample.to(torch.float32) - + if self.state_in_first_order: sigma = self.sigmas[self.step_index] sigma_next = self.sigmas[self.step_index + 1] @@ -263,7 +263,7 @@ def step( # 2nd order / Heun's method sigma = self.sigmas[self.step_index - 1] sigma_next = self.sigmas[self.step_index] - + gamma = min(s_churn / (len(self.sigmas) - 1), 2**0.5 - 1) if s_tmin <= sigma <= s_tmax else 0.0 noise = randn_tensor( @@ -280,13 +280,13 @@ def step( # NOTE: "original_sample" should not be an expected prediction_type but is left in for # backwards compatibility denoised = sample - model_output * sigma - + if self.state_in_first_order: # 2. Convert to an ODE derivative for 1st order derivative = (sample - denoised) / sigma_hat # 3. Delta timestep dt = self.sigmas_next - sigma_hat - + # store for 2nd order step self.prev_derivative = derivative self.dt = dt @@ -295,7 +295,7 @@ def step( # 2. 2nd order / Heun's method derivative = (sample - denoised) / sigma_next derivative = 0.5 * (self.prev_derivative + derivative) - + # 3. take prev timestep & sample dt = self.dt sample = self.sample @@ -305,7 +305,7 @@ def step( self.prev_derivative = None self.dt = None self.sample = None - + prev_sample = sample + derivative * dt # Cast sample back to model compatible dtype prev_sample = prev_sample.to(model_output.dtype) @@ -319,4 +319,4 @@ def step( return FlowMatchHeunDiscreteSchedulerOutput(prev_sample=prev_sample) def __len__(self): - return self.config.num_train_timesteps \ No newline at end of file + return self.config.num_train_timesteps diff --git a/tests/pipelines/lumina/test_lumina.py b/tests/pipelines/lumina/test_lumina_nextdit.py similarity index 98% rename from tests/pipelines/lumina/test_lumina.py rename to tests/pipelines/lumina/test_lumina_nextdit.py index 59ddfe2916e4..01857c615cef 100644 --- a/tests/pipelines/lumina/test_lumina.py +++ b/tests/pipelines/lumina/test_lumina_nextdit.py @@ -19,13 +19,13 @@ import numpy as np import torch -from transformers import AutoTokenizer, AutoModel +from transformers import AutoModel, AutoTokenizer from diffusers import ( AutoencoderKL, DDIMScheduler, + LuminaNextDiT2DModel, LuminaText2ImgPipeline, - Transformer2DModel, ) from diffusers.utils.testing_utils import ( enable_full_determinism, @@ -53,7 +53,7 @@ class LuminaText2ImgPipelineFastTests(PipelineTesterMixin, unittest.TestCase): def get_dummy_components(self): torch.manual_seed(0) - transformer = Transformer2DModel( + transformer = LuminaNextDiT2DModel( sample_size=8, num_layers=2, patch_size=2, @@ -75,7 +75,6 @@ def get_dummy_components(self): scheduler = DDIMScheduler() text_encoder = AutoModel.from_pretrained("google/gemma-2b") - tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b") components = { @@ -84,6 +83,8 @@ def get_dummy_components(self): "scheduler": scheduler, "text_encoder": text_encoder, "tokenizer": tokenizer, + "safety_checker": None, + "feature_extractor": None, } return components @@ -97,8 +98,8 @@ def get_dummy_inputs(self, device, seed=0): "generator": generator, "num_inference_steps": 2, "guidance_scale": 5.0, - "use_resolution_binning": False, "output_type": "np", + "use_resolution_binning": False, } return inputs From 1ad8e2b1ead26578959a945b85f39e9af11f2761 Mon Sep 17 00:00:00 2001 From: PommesPeter Date: Fri, 21 Jun 2024 00:15:26 +0800 Subject: [PATCH 13/92] Updated docs about lumina --- docs/source/en/_toctree.yml | 4 + docs/source/en/api/models/lumina_nextdit2d.md | 20 ++++ docs/source/en/api/pipelines/lumina.md | 106 ++++++++++++++++++ .../schedulers/flow_match_heun_discrete.md | 18 +++ src/diffusers/models/__init__.py | 2 +- src/diffusers/models/transformers/__init__.py | 2 +- ...mina_nextdit_2d.py => lumina_nextdit2d.py} | 0 .../pipelines/lumina/pipeline_lumina.py | 4 +- 8 files changed, 152 insertions(+), 4 deletions(-) create mode 100644 docs/source/en/api/models/lumina_nextdit2d.md create mode 100644 docs/source/en/api/pipelines/lumina.md create mode 100644 docs/source/en/api/schedulers/flow_match_heun_discrete.md rename src/diffusers/models/transformers/{lumina_nextdit_2d.py => lumina_nextdit2d.py} (100%) diff --git a/docs/source/en/_toctree.yml b/docs/source/en/_toctree.yml index c306e1eb99e7..1273e5c941be 100644 --- a/docs/source/en/_toctree.yml +++ b/docs/source/en/_toctree.yml @@ -245,6 +245,8 @@ title: DiTTransformer2DModel - local: api/models/hunyuan_transformer2d title: HunyuanDiT2DModel + - local: api/models/lumina_nextdit2d + title: LuminaNextDiT2DModel - local: api/models/transformer_temporal title: TransformerTemporalModel - local: api/models/sd3_transformer2d @@ -316,6 +318,8 @@ title: Latent Diffusion - local: api/pipelines/ledits_pp title: LEDITS++ + - local: api/pipelines/lumina + title: Lumina-T2X - local: api/pipelines/marigold title: Marigold - local: api/pipelines/panorama diff --git a/docs/source/en/api/models/lumina_nextdit2d.md b/docs/source/en/api/models/lumina_nextdit2d.md new file mode 100644 index 000000000000..54af42493407 --- /dev/null +++ b/docs/source/en/api/models/lumina_nextdit2d.md @@ -0,0 +1,20 @@ + + +# LuminaNextDiT2DModel + +A Diffusion Transformer model for 2D data from [Lumina-T2X](https://github.com/Alpha-VLLM/Lumina-T2X). + +## LuminaNextDiT2DModel + +[[autodoc]] LuminaNextDiT2DModel + diff --git a/docs/source/en/api/pipelines/lumina.md b/docs/source/en/api/pipelines/lumina.md new file mode 100644 index 000000000000..0a31983e2160 --- /dev/null +++ b/docs/source/en/api/pipelines/lumina.md @@ -0,0 +1,106 @@ + + +# Lumina-T2X +![concepts](https://github.com/Alpha-VLLM/Lumina-T2X/assets/54879512/9f52eabb-07dc-4881-8257-6d8a5f2a0a5a) + +[Lumina-Next : Making Lumina-T2X Stronger and Faster with Next-DiT](https://github.com/Alpha-VLLM/Lumina-T2X/blob/main/assets/lumina-next.pdf) from Alpha-VLLM, OpenGVLab, Shanghai AI Laboratory. + +The abstract from the paper is: + +*Lumina-T2X is a nascent family of Flow-based Large Diffusion Transformers (Flag-DiT) that establishes a unified framework for transforming noise into various modalities, such as images and videos, conditioned on text instructions. Despite its promising capabilities, Lumina-T2X still encounters challenges including training instability, slow inference, and extrapolation artifacts. In this paper, we present Lumina-Next, an improved version of Lumina-T2X, showcasing stronger generation performance with increased training and inference efficiency. We begin with a comprehensive analysis of the Flag-DiT architecture and identify several suboptimal components, which we address by introducing the Next-DiT architecture with 3D RoPE and sandwich normalizations. To enable better resolution extrapolation, we thoroughly compare different context extrapolation methods applied to text-to-image generation with 3D RoPE, and propose Frequency- and Time-Aware Scaled RoPE tailored for diffusion transformers. Additionally, we introduce a sigmoid time discretization schedule to reduce sampling steps in solving the Flow ODE and the Context Drop method to merge redundant visual tokens for faster network evaluation, effectively boosting the overall sampling speed. Thanks to these improvements, Lumina-Next not only improves the quality and efficiency of basic text-to-image generation but also demonstrates superior resolution extrapolation capabilities and multilingual generation using decoder-based LLMs as the text encoder, all in a zero-shot manner. To further validate Lumina-Next as a versatile generative framework, we instantiate it on diverse tasks including visual recognition, multi-view, audio, music, and point cloud generation, showcasing strong performance across these domains. By releasing all codes and model weights at https://github.com/Alpha-VLLM/Lumina-T2X, we aim to advance the development of next-generation generative AI capable of universal modeling.* + +**Highlights**: Lumina-Next is a next-generation Diffusion Transformer that significantly enhances text-to-image generation, multilingual generation, and multitask performance by introducing the Next-DiT architecture, 3D RoPE, and frequency- and time-aware RoPE, among other improvements. + +Lumina-Next has the following components: +* It improves sampling efficiency with fewer and faster Steps. +* It uses a Next-DiT as a transformer backbone with Sandwichnorm 3D RoPE, and Grouped-Query Attention. +* It uses a Frequency- and Time-Aware Scaled RoPE. + +--- + +[Lumina-T2X: Transforming Text into Any Modality, Resolution, and Duration via Flow-based Large Diffusion Transformers](https://arxiv.org/abs/2405.05945) from Alpha-VLLM, OpenGVLab, Shanghai AI Laboratory. + +The abstract from the paper is: + +*Sora unveils the potential of scaling Diffusion Transformer for generating photorealistic images and videos at arbitrary resolutions, aspect ratios, and durations, yet it still lacks sufficient implementation details. In this technical report, we introduce the Lumina-T2X family - a series of Flow-based Large Diffusion Transformers (Flag-DiT) equipped with zero-initialized attention, as a unified framework designed to transform noise into images, videos, multi-view 3D objects, and audio clips conditioned on text instructions. By tokenizing the latent spatial-temporal space and incorporating learnable placeholders such as [nextline] and [nextframe] tokens, Lumina-T2X seamlessly unifies the representations of different modalities across various spatial-temporal resolutions. This unified approach enables training within a single framework for different modalities and allows for flexible generation of multimodal data at any resolution, aspect ratio, and length during inference. Advanced techniques like RoPE, RMSNorm, and flow matching enhance the stability, flexibility, and scalability of Flag-DiT, enabling models of Lumina-T2X to scale up to 7 billion parameters and extend the context window to 128K tokens. This is particularly beneficial for creating ultra-high-definition images with our Lumina-T2I model and long 720p videos with our Lumina-T2V model. Remarkably, Lumina-T2I, powered by a 5-billion-parameter Flag-DiT, requires only 35% of the training computational costs of a 600-million-parameter naive DiT. Our further comprehensive analysis underscores Lumina-T2X's preliminary capability in resolution extrapolation, high-resolution editing, generating consistent 3D views, and synthesizing videos with seamless transitions. We expect that the open-sourcing of Lumina-T2X will further foster creativity, transparency, and diversity in the generative AI community.* + + +You can find the original codebase at [Alpha-VLLM](https://github.com/Alpha-VLLM/Lumina-T2X) and all the available checkpoints at [Alpha-VLLM Lumina Family](https://huggingface.co/collections/Alpha-VLLM/lumina-family-66423205bedb81171fd0644b). + +**Highlights**: Lumina-T2X supports Any Modality, Resolution, and Duration. + +Lumina-T2X has the following components: +* It uses a Flow-based Large Diffusion Transformer as the backbone +* It supports different any modalities with one backbone and corresponding encoder, decoder. + + + +Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers.md) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading.md#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines. + + + +### Inference (Text-to-Image) + +Use [`torch.compile`](https://huggingface.co/docs/diffusers/main/en/tutorials/fast_diffusion#torchcompile) to reduce the inference latency. + +First, load the pipeline: + +```python +from diffusers import LuminaText2ImgPipeline +import torch + +pipeline = LuminaText2ImgPipeline.from_pretrained( + "Alpha-VLLM/Lumina-Next-SFT-diffusers", torch_dtype=torch.bf16 +).to("cuda") +``` + +Then change the memory layout of the pipelines `transformer` and `vae` components to `torch.channels-last`: + +```python +pipeline.transformer.to(memory_format=torch.channels_last) +pipeline.vae.to(memory_format=torch.channels_last) +``` + +Finally, compile the components and run inference: + +```python +pipeline.transformer = torch.compile(pipeline.transformer, mode="max-autotune", fullgraph=True) +pipeline.vae.decode = torch.compile(pipeline.vae.decode, mode="max-autotune", fullgraph=True) + +image = pipeline(prompt="Upper body of a young woman in a Victorian-era outfit with brass goggles and leather straps. Background shows an industrial revolution cityscape with smoky skies and tall, metal structures").images[0] +``` + + + + + + +## LuminaText2ImgPipeline + +[[autodoc]] LuminaText2ImgPipeline + - all + - __call__ + diff --git a/docs/source/en/api/schedulers/flow_match_heun_discrete.md b/docs/source/en/api/schedulers/flow_match_heun_discrete.md new file mode 100644 index 000000000000..642f8ffc7dcc --- /dev/null +++ b/docs/source/en/api/schedulers/flow_match_heun_discrete.md @@ -0,0 +1,18 @@ + + +# FlowMatchHeunDiscreteScheduler + +`FlowMatchHeunDiscreteScheduler` is based on the flow-matching sampling introduced in [EDM](https://arxiv.org/abs/2403.03206). + +## FlowMatchHeunDiscreteScheduler +[[autodoc]] FlowMatchHeunDiscreteScheduler diff --git a/src/diffusers/models/__init__.py b/src/diffusers/models/__init__.py index 7ea74466c083..441524e548bc 100644 --- a/src/diffusers/models/__init__.py +++ b/src/diffusers/models/__init__.py @@ -40,7 +40,7 @@ _import_structure["transformers.dit_transformer_2d"] = ["DiTTransformer2DModel"] _import_structure["transformers.dual_transformer_2d"] = ["DualTransformer2DModel"] _import_structure["transformers.hunyuan_transformer_2d"] = ["HunyuanDiT2DModel"] - _import_structure["transformers.lumina_nextdit_2d"] = ["LuminaNextDiT2DModel"] + _import_structure["transformers.lumina_nextdit2d"] = ["LuminaNextDiT2DModel"] _import_structure["transformers.pixart_transformer_2d"] = ["PixArtTransformer2DModel"] _import_structure["transformers.prior_transformer"] = ["PriorTransformer"] _import_structure["transformers.t5_film_transformer"] = ["T5FilmDecoder"] diff --git a/src/diffusers/models/transformers/__init__.py b/src/diffusers/models/transformers/__init__.py index f4e41239b88c..533fb621269e 100644 --- a/src/diffusers/models/transformers/__init__.py +++ b/src/diffusers/models/transformers/__init__.py @@ -5,7 +5,7 @@ from .dit_transformer_2d import DiTTransformer2DModel from .dual_transformer_2d import DualTransformer2DModel from .hunyuan_transformer_2d import HunyuanDiT2DModel - from .lumina_nextdit_2d import LuminaNextDiT2DModel + from .lumina_nextdit2d import LuminaNextDiT2DModel from .pixart_transformer_2d import PixArtTransformer2DModel from .prior_transformer import PriorTransformer from .t5_film_transformer import T5FilmDecoder diff --git a/src/diffusers/models/transformers/lumina_nextdit_2d.py b/src/diffusers/models/transformers/lumina_nextdit2d.py similarity index 100% rename from src/diffusers/models/transformers/lumina_nextdit_2d.py rename to src/diffusers/models/transformers/lumina_nextdit2d.py diff --git a/src/diffusers/pipelines/lumina/pipeline_lumina.py b/src/diffusers/pipelines/lumina/pipeline_lumina.py index eaac27fd1a2e..783e0e7f15f4 100644 --- a/src/diffusers/pipelines/lumina/pipeline_lumina.py +++ b/src/diffusers/pipelines/lumina/pipeline_lumina.py @@ -24,7 +24,7 @@ from ...image_processor import VaeImageProcessor from ...models import AutoencoderKL -from ...models.transformers.lumina_nextdit_2d import LuminaNextDiT2DModel +from ...models.transformers.lumina_nextdit2d import LuminaNextDiT2DModel from ...schedulers import FlowMatchEulerDiscreteScheduler from ...utils import ( BACKENDS_MAPPING, @@ -917,7 +917,7 @@ def __call__( noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) - current_timestep = t + current_timestep = t / num_inference_steps if not torch.is_tensor(current_timestep): # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can # This would be a good case for the `match` statement (Python 3.10+) From 627b383648c3720d04847181a7716cd27a26ebb0 Mon Sep 17 00:00:00 2001 From: PommesPeter Date: Fri, 21 Jun 2024 00:18:35 +0800 Subject: [PATCH 14/92] Fixed timestep scale --- src/diffusers/pipelines/lumina/pipeline_lumina.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/diffusers/pipelines/lumina/pipeline_lumina.py b/src/diffusers/pipelines/lumina/pipeline_lumina.py index 783e0e7f15f4..704019c6f5f6 100644 --- a/src/diffusers/pipelines/lumina/pipeline_lumina.py +++ b/src/diffusers/pipelines/lumina/pipeline_lumina.py @@ -917,7 +917,7 @@ def __call__( noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) - current_timestep = t / num_inference_steps + current_timestep = t / self.scheduler.config.num_train_timesteps if not torch.is_tensor(current_timestep): # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can # This would be a good case for the `match` statement (Python 3.10+) From d50b85ea5bf0a7b65f270fc9db2f5ca644ff8e27 Mon Sep 17 00:00:00 2001 From: PommesPeter Date: Fri, 21 Jun 2024 00:44:43 +0800 Subject: [PATCH 15/92] Fixed import error --- src/diffusers/schedulers/__init__.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/src/diffusers/schedulers/__init__.py b/src/diffusers/schedulers/__init__.py index 33e48fc3b0dc..dfee479bfa96 100644 --- a/src/diffusers/schedulers/__init__.py +++ b/src/diffusers/schedulers/__init__.py @@ -57,6 +57,7 @@ _import_structure["scheduling_euler_ancestral_discrete"] = ["EulerAncestralDiscreteScheduler"] _import_structure["scheduling_euler_discrete"] = ["EulerDiscreteScheduler"] _import_structure["scheduling_flow_match_euler_discrete"] = ["FlowMatchEulerDiscreteScheduler"] + _import_structure["scheduling_flow_match_heun_discrete"] = ["FlowMatchHeunDiscreteScheduler"] _import_structure["scheduling_heun_discrete"] = ["HeunDiscreteScheduler"] _import_structure["scheduling_ipndm"] = ["IPNDMScheduler"] _import_structure["scheduling_k_dpm_2_ancestral_discrete"] = ["KDPM2AncestralDiscreteScheduler"] @@ -153,6 +154,7 @@ from .scheduling_euler_ancestral_discrete import EulerAncestralDiscreteScheduler from .scheduling_euler_discrete import EulerDiscreteScheduler from .scheduling_flow_match_euler_discrete import FlowMatchEulerDiscreteScheduler + from .scheduling_flow_match_heun_discrete import FlowMatchHeunDiscreteScheduler from .scheduling_heun_discrete import HeunDiscreteScheduler from .scheduling_ipndm import IPNDMScheduler from .scheduling_k_dpm_2_ancestral_discrete import KDPM2AncestralDiscreteScheduler From 18762c885dc1a08bab2bc0038af24e6838cf2cfe Mon Sep 17 00:00:00 2001 From: PommesPeter Date: Fri, 21 Jun 2024 01:59:12 +0800 Subject: [PATCH 16/92] Fixed bug on flow match heun --- .../models/transformers/lumina_nextdit2d.py | 3 +++ src/diffusers/pipelines/lumina/pipeline_lumina.py | 5 +++++ .../scheduling_flow_match_heun_discrete.py | 15 +++++++-------- 3 files changed, 15 insertions(+), 8 deletions(-) diff --git a/src/diffusers/models/transformers/lumina_nextdit2d.py b/src/diffusers/models/transformers/lumina_nextdit2d.py index e6e4224f2441..9282ffea11ef 100644 --- a/src/diffusers/models/transformers/lumina_nextdit2d.py +++ b/src/diffusers/models/transformers/lumina_nextdit2d.py @@ -521,6 +521,8 @@ class LuminaNextDiT2DModel(ModelMixin, ConfigMixin): Inherit ModelMixin and ConfigMixin to be compatible with the sampler StableDiffusionPipeline of diffusers. Parameters: + sample_size (`int`): The width of the latent images. This is fixed during training since + it is used to learn a number of position embeddings. patch_size (`int`, *optional*, (`int`, *optional*, defaults to 2): The size of each patch in the image. This parameter defines the resolution of patches fed into the model. in_channels (`int`, *optional*, defaults to 4): @@ -561,6 +563,7 @@ class LuminaNextDiT2DModel(ModelMixin, ConfigMixin): @register_to_config def __init__( self, + sample_size: int = 128, patch_size: Optional[int] = 2, in_channels: Optional[int] = 4, hidden_size: Optional[int] = 4096, diff --git a/src/diffusers/pipelines/lumina/pipeline_lumina.py b/src/diffusers/pipelines/lumina/pipeline_lumina.py index 704019c6f5f6..17a4ce9e1b69 100644 --- a/src/diffusers/pipelines/lumina/pipeline_lumina.py +++ b/src/diffusers/pipelines/lumina/pipeline_lumina.py @@ -297,6 +297,11 @@ def __init__( self.tokenizer_max_length = ( self.tokenizer.model_max_length if hasattr(self, "tokenizer") and self.tokenizer is not None else 256 ) + self.default_sample_size = ( + self.transformer.config.sample_size + if hasattr(self, "transformer") and self.transformer is not None + else 128 + ) def _get_gemma_prompt_embeds( self, diff --git a/src/diffusers/schedulers/scheduling_flow_match_heun_discrete.py b/src/diffusers/schedulers/scheduling_flow_match_heun_discrete.py index a200c94a6d1a..d9a3ca2d4b0a 100644 --- a/src/diffusers/schedulers/scheduling_flow_match_heun_discrete.py +++ b/src/diffusers/schedulers/scheduling_flow_match_heun_discrete.py @@ -59,7 +59,7 @@ class FlowMatchHeunDiscreteScheduler(SchedulerMixin, ConfigMixin): """ _compatibles = [] - order = 1 + order = 2 @register_to_config def __init__( @@ -276,22 +276,21 @@ def step( if gamma > 0: sample = sample + eps * (sigma_hat**2 - sigma**2) ** 0.5 - # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise - # NOTE: "original_sample" should not be an expected prediction_type but is left in for - # backwards compatibility - denoised = sample - model_output * sigma - if self.state_in_first_order: - # 2. Convert to an ODE derivative for 1st order + # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise + denoised = sample - model_output * sigma + # 2. convert to an ODE derivative for 1st order derivative = (sample - denoised) / sigma_hat # 3. Delta timestep - dt = self.sigmas_next - sigma_hat + dt = sigma_next - sigma_hat # store for 2nd order step self.prev_derivative = derivative self.dt = dt self.sample = sample else: + # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise + denoised = sample - model_output * sigma_next # 2. 2nd order / Heun's method derivative = (sample - denoised) / sigma_next derivative = 0.5 * (self.prev_derivative + derivative) From e3b20b1a493e442baa9ca85135e6bb9cd650a90d Mon Sep 17 00:00:00 2001 From: PommesPeter Date: Sat, 22 Jun 2024 00:23:39 +0800 Subject: [PATCH 17/92] Update: run the pipeline successfully --- debug.py | 8 ++ src/diffusers/models/embeddings.py | 2 +- .../models/transformers/lumina_nextdit2d.py | 57 +++----- .../pipelines/lumina/pipeline_lumina.py | 136 +++++++++--------- test.png | Bin 0 -> 2072895 bytes test_lumina.py | 7 + 6 files changed, 102 insertions(+), 108 deletions(-) create mode 100644 debug.py create mode 100644 test.png create mode 100644 test_lumina.py diff --git a/debug.py b/debug.py new file mode 100644 index 000000000000..e4818eb2df73 --- /dev/null +++ b/debug.py @@ -0,0 +1,8 @@ +from diffusers import LuminaText2ImgPipeline +import torch + +pipeline = LuminaText2ImgPipeline.from_pretrained( + "/mnt/hdd1/xiejunlin/checkpoints/Lumina-Next-SFT-diffusers", torch_dtype=torch.bfloat16 +).to("cuda") + +image = pipeline(prompt="Upper body of a young woman in a Victorian-era outfit with brass goggles and leather straps. Background shows an industrial revolution cityscape with smoky skies and tall, metal structures").images[0] \ No newline at end of file diff --git a/src/diffusers/models/embeddings.py b/src/diffusers/models/embeddings.py index 09fa4ed90d64..94c6d177b971 100644 --- a/src/diffusers/models/embeddings.py +++ b/src/diffusers/models/embeddings.py @@ -786,7 +786,7 @@ def __init__(self, hidden_size=4096, caption_dim=5120, frequency_embedding_size= def forward(self, timestep, caption_feat, caption_mask): # timestep embedding: time_freq = self.time_proj(timestep) - time_embed = self.timestep_embedder(time_freq.to(dtype=self.timestep_embedder.linear_1.dtype)) + time_embed = self.timestep_embedder(time_freq.to(dtype=self.timestep_embedder.linear_1.weight.dtype)) # caption condition embedding: caption_mask_float = caption_mask.float().unsqueeze(-1) diff --git a/src/diffusers/models/transformers/lumina_nextdit2d.py b/src/diffusers/models/transformers/lumina_nextdit2d.py index 9282ffea11ef..af7170185a60 100644 --- a/src/diffusers/models/transformers/lumina_nextdit2d.py +++ b/src/diffusers/models/transformers/lumina_nextdit2d.py @@ -264,7 +264,7 @@ def forward( xk = Attention.apply_rotary_emb(xk, freqs_cis=freqs_cis) xq, xk = xq.to(dtype), xk.to(dtype) - + if self.proportional_attn: softmax_scale = math.sqrt(math.log(seqlen, self.base_seqlen) / self.head_dim) else: @@ -315,6 +315,7 @@ def forward( ) if hasattr(self, "wk_cap"): + # print(self.wk_cap(caption_feat).shape, caption_feat.shape) yk = self.k_cap_norm(self.wk_cap(caption_feat)).view(bsz, -1, self.num_kv_heads, self.head_dim) yv = self.wv_cap(caption_feat).view(bsz, -1, self.num_kv_heads, self.head_dim) @@ -329,7 +330,7 @@ def forward( yk.permute(0, 2, 1, 3), yv.permute(0, 2, 1, 3), caption_mask.view(bsz, 1, 1, -1).expand(bsz, self.num_attention_heads, seqlen, -1), - ).permuste(0, 2, 1, 3) + ).permute(0, 2, 1, 3) output_caption = output_caption * self.gate.tanh().view(1, 1, -1, 1) output = output + output_caption @@ -555,7 +556,7 @@ class LuminaNextDiT2DModel(ModelMixin, ConfigMixin): caption_dim (`int`, *optional*, defaults to 2048): The dimensionality of the text embeddings. This parameter defines the size of the text representations used in the model. - scale_factor (`float`, *optional*, defaults to 1.0): + scaling_factor (`float`, *optional*, defaults to 1.0): A scaling factor applied to certain parameters or layers in the model. This can be used for adjusting the overall scale of the model's operations. """ @@ -576,7 +577,7 @@ def __init__( learn_sigma: Optional[bool] = True, qk_norm: Optional[bool] = False, caption_dim: Optional[int] = 2048, - scale_factor: Optional[float] = 1.0, + scaling_factor: Optional[float] = 1.0, ) -> None: super().__init__() self.patch_size = patch_size @@ -584,7 +585,7 @@ def __init__( self.out_channels = in_channels * 2 if learn_sigma else in_channels self.hidden_size = hidden_size self.num_attention_heads = num_attention_heads - self.scale_factor = scale_factor + self.scaling_factor = scaling_factor self.learn_sigma = learn_sigma self.patch_embedder = nn.Linear( @@ -621,10 +622,10 @@ def __init__( self.final_layer = FinalLayer(hidden_size, patch_size, self.out_channels) assert (hidden_size // num_attention_heads) % 4 == 0, "2d rope needs head dim to be divisible by 4" - self.freqs_cis = LuminaNextDiT2DModel.precompute_freqs_cis( + self.freqs_cis = self.precompute_freqs_cis( hidden_size // num_attention_heads, 384, - scale_factor=scale_factor, + scaling_factor=scaling_factor, ) def unpatchify(self, x: torch.Tensor, img_size: List[Tuple[int, int]], return_tensor=False) -> List[torch.Tensor]: @@ -774,13 +775,7 @@ def forward( x = layer(x, mask, freqs_cis, caption_feat, caption_mask, adaln_input=adaln_input) x = self.final_layer(x, adaln_input) - x = self.unpatchify(x, img_size, return_tensor=x_is_tensor) - - if self.learn_sigma: - if x_is_tensor: - output, _ = x.chunk(2, dim=1) - else: - output = [_.chunk(2, dim=0)[0] for _ in x] + output = self.unpatchify(x, img_size, return_tensor=x_is_tensor) if not return_dict: return (output,) @@ -794,8 +789,8 @@ def forward_with_cfg( caption_feat, caption_mask, cfg_scale, - scale_factor=1.0, - scale_watershed=1.0, + scaling_factor=1.0, + scaling_watershed=1.0, base_seqlen: Optional[int] = None, proportional_attn: bool = False, return_dict: bool = True, @@ -814,9 +809,9 @@ def forward_with_cfg( A mask for the caption features, indicating which elements are valid. cfg_scale: The classifier-free guidance scale, used to adjust the influence of the conditioning information. - scale_factor (float, optional, defaults to 1.0): + scaling_factor (float, optional, defaults to 1.0): A scaling factor applied to certain operations or parameters within the forward pass. - scale_watershed (float, optional, defaults to 1.0): + scaling_watershed (float, optional, defaults to 1.0): A specific scaling factor used in conjunction with the main scale factor, potentially for different stages of processing. base_seqlen (Optional[int], optional): @@ -830,8 +825,8 @@ def forward_with_cfg( self.freqs_cis = LuminaNextDiT2DModel.precompute_freqs_cis( self.hidden_size // self.num_attention_heads, 384, - scale_factor=scale_factor, - scale_watershed=scale_watershed, + scaling_factor=scaling_factor, + scaling_watershed=scaling_watershed, timestep=timestep[0].item(), ) @@ -863,14 +858,13 @@ def forward_with_cfg( return Transformer2DModelOutput(sample=output) - @classmethod def precompute_freqs_cis( - cls, + self, dim: int, end: int, theta: float = 10000.0, - scale_factor: float = 1.0, - scale_watershed: float = 1.0, + scaling_factor: float = 1.0, + scaling_watershed: float = 1.0, timestep: float = 1.0, ): """ @@ -891,12 +885,12 @@ def precompute_freqs_cis( exponentials. """ - if timestep < scale_watershed: - linear_factor = scale_factor + if timestep < scaling_watershed: + linear_factor = scaling_factor ntk_factor = 1.0 else: linear_factor = 1.0 - ntk_factor = scale_factor + ntk_factor = scaling_factor theta = theta * ntk_factor freqs = 1.0 / (theta ** (torch.arange(0, dim, 4)[: (dim // 4)].float().cuda() / dim)) / linear_factor @@ -924,12 +918,3 @@ def _recursive_count_params(module): _recursive_count_params(self) return total_params - - -# todo: delete it after creating hf format model -def LuminaNextDiT2DModel_2B_patch2(**kwargs): - return LuminaNextDiT2DModel(patch_size=2, dim=2304, n_layers=24, num_attention_heads=32, **kwargs) - - -def LuminaNextDiT2DModel_2B_GQA_patch2(**kwargs): - return LuminaNextDiT2DModel(patch_size=2, dim=2304, n_layers=24, num_attention_heads=32, num_kv_heads=8, **kwargs) diff --git a/src/diffusers/pipelines/lumina/pipeline_lumina.py b/src/diffusers/pipelines/lumina/pipeline_lumina.py index 17a4ce9e1b69..d53790f7ebe9 100644 --- a/src/diffusers/pipelines/lumina/pipeline_lumina.py +++ b/src/diffusers/pipelines/lumina/pipeline_lumina.py @@ -289,19 +289,16 @@ def __init__( transformer=transformer, scheduler=scheduler, ) - - self.vae_scale_factor = ( - 2 ** (len(self.vae.config.block_out_channels) - 1) if hasattr(self, "vae") and self.vae is not None else 8 - ) + self.vae_scale_factor = 8 self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor) - self.tokenizer_max_length = ( - self.tokenizer.model_max_length if hasattr(self, "tokenizer") and self.tokenizer is not None else 256 - ) + self.tokenizer_max_length = 256 self.default_sample_size = ( self.transformer.config.sample_size if hasattr(self, "transformer") and self.transformer is not None else 128 ) + self.default_image_size = self.default_sample_size * self.vae_scale_factor + self.base_seq_len = (self.default_image_size // 16) ** 2 def _get_gemma_prompt_embeds( self, @@ -335,13 +332,13 @@ def _get_gemma_prompt_embeds( f" {self.tokenizer_max_length} tokens: {removed_text}" ) - prompt_attention_mask = text_inputs.attention_mask - prompt_attention_mask = prompt_attention_mask.to(device) + prompt_attention_mask = text_inputs.attention_mask + prompt_attention_mask = prompt_attention_mask.to(device) - prompt_embeds = self.text_encoder( - text_input_ids.to(device), attention_mask=prompt_attention_mask, output_hidden_states=True - ) - prompt_embeds = prompt_embeds.hidden_states[-2] + prompt_embeds = self.text_encoder( + text_input_ids.to(device), attention_mask=prompt_attention_mask, output_hidden_states=True + ) + prompt_embeds = prompt_embeds.hidden_states[-2] if self.text_encoder is not None: dtype = self.text_encoder.dtype @@ -728,6 +725,7 @@ def __call__( clean_caption: bool = True, max_sequence_length: int = 120, scaling_watershed: Optional[float] = 0.3, + proportional_attn: Optional[bool] = True, callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None, callback_on_step_end_tensor_inputs: List[str] = ["latents"], ) -> Union[ImagePipelineOutput, Tuple]: @@ -861,10 +859,7 @@ def __call__( clean_caption=clean_caption, max_sequence_length=max_sequence_length, ) - # if do_classifier_free_guidance: - # prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0) - # prompt_attention_mask = torch.cat([negative_prompt_attention_mask, prompt_attention_mask], dim=0) - + # 4. Prepare timesteps timesteps, num_inference_steps = retrieve_timesteps( self.scheduler, num_inference_steps, device, timesteps, sigmas @@ -888,41 +883,8 @@ def __call__( for i, t in enumerate(timesteps): # expand the latents if we are doing classifier free guidance latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents - # broadcast to batch dimension in a way that's compatible with ONNX/Core ML - timestep = t.expand(latent_model_input.shape[0]) - - hidden_size = self.transformer.config.hidden_size - num_attention_heads = self.transformer.config.num_attention_heads - attention_head_dim = hidden_size // num_attention_heads - - scaling_factor = math.sqrt(width * height / 1024**2) - - self.transformer.freqs_cis = self.transformer.precompute_freqs_cis( - attention_head_dim, - 384, - scale_factor=scaling_factor, - scaling_watershed=scaling_watershed, - timestep=timesteps[0], - ) - - noise_pred = self.transformer( - x=latent_model_input, - timestep=timestep, - encoder_hidden_states=prompt_embeds, - joint_attention_kwargs=self.joint_attention_kwargs, - return_dict=False, - )[0] - - # perform guidance - # NOTE: For exact reproducibility reasons, we apply classifier-free guidance on only - # three channels by default. The standard approach to cfg applies it to all channels. - # This can be done by uncommenting the following line and commenting-out the line following that. - # eps, rest = model_out[:, :self.in_channels], model_out[:, self.in_channels:] - if do_classifier_free_guidance: - noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) - noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) - - current_timestep = t / self.scheduler.config.num_train_timesteps + + current_timestep = t if not torch.is_tensor(current_timestep): # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can # This would be a good case for the `match` statement (Python 3.10+) @@ -941,7 +903,32 @@ def __call__( # broadcast to batch dimension in a way that's compatible with ONNX/Core ML current_timestep = current_timestep.expand(latent_model_input.shape[0]) - # predict noise model_output + # reverse the timestep since Lumina uses t=0 as the noise and t=1 as the image + current_timestep = (1 - current_timestep / self.scheduler.config.num_train_timesteps) + + # dynamic scaling_factor for different resolution. + hidden_size = self.transformer.config.hidden_size + num_attention_heads = self.transformer.config.num_attention_heads + attention_head_dim = hidden_size // num_attention_heads + scaling_factor = math.sqrt(width * height / self.default_image_size ** 2) + + self.transformer.freqs_cis = self.transformer.precompute_freqs_cis( + attention_head_dim, + 384, + scaling_factor=scaling_factor, + scaling_watershed=scaling_watershed, + timestep=current_timestep[0].item(), + ) + if proportional_attn: + assert self.base_seq_len is not None + for layer in self.transformer.layers: + layer.attention.base_seqlen = self.base_seq_len + layer.attention.proportional_attn = proportional_attn + else: + for layer in self.transformer.layers: + layer.attention.base_seqlen = None + layer.attention.proportional_attn = proportional_attn + noise_pred = self.transformer( x=latent_model_input, timestep=current_timestep, @@ -949,37 +936,44 @@ def __call__( caption_mask=prompt_attention_mask, return_dict=False, )[0] - # learned sigma - if self.transformer.config.out_channels // 2 == latent_channels: + if self.transformer.config.learn_sigma: noise_pred = noise_pred.chunk(2, dim=1)[0] else: noise_pred = noise_pred - # # compute previous image: x_t -> x_t-1 - # if num_inference_steps == 1: - # # For DMD one step sampling: https://arxiv.org/abs/2311.18828 - # latents = self.scheduler.step( - # noise_pred, t, latents, **extra_step_kwargs - # ).pred_original_sample - # else: - # latents = self.scheduler.step( - # noise_pred, t, latents, **extra_step_kwargs, return_dict=False - # )[0] + # perform guidance scale + # NOTE: For exact reproducibility reasons, we apply classifier-free guidance on only + # three channels by default. The standard approach to cfg applies it to all channels. + # This can be done by uncommenting the following line and commenting-out the line following that. + # eps, rest = model_out[:, :self.in_channels], model_out[:, self.in_channels:] + if do_classifier_free_guidance: + noise_pred_eps, noise_pred_rest = noise_pred[:, :3], noise_pred[:, 3:] + noise_pred_cond_eps, noise_pred_uncond_eps = torch.split(noise_pred_eps, len(noise_pred_eps) // 2, dim=0) + noise_pred_half = noise_pred_uncond_eps + guidance_scale * (noise_pred_cond_eps - noise_pred_uncond_eps) + noise_pred_eps = torch.cat([noise_pred_half, noise_pred_half], dim=0) + + noise_pred = torch.cat([noise_pred_eps, noise_pred_rest], dim=1) + noise_pred, _ = noise_pred.chunk(2, dim=0) - progress_bar.update() + # compute the previous noisy sample x_t -> x_t-1 + latents_dtype = latents.dtype + latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0] + + if latents.dtype != latents_dtype: + if torch.backends.mps.is_available(): + # some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272 + latents = latents.to(latents_dtype) + + progress_bar.update() if not output_type == "latent": latents = latents / self.vae.config.scaling_factor image = self.vae.decode(latents, return_dict=False)[0] - image = (image + 1.0) / 2.0 - image.clamp_(0.0, 1.0) + image = self.image_processor.postprocess(image, output_type=output_type) else: image = latents - if not output_type == "latent": - image = self.image_processor.postprocess(image, output_type=output_type) - # Offload all models self.maybe_free_model_hooks() diff --git a/test.png b/test.png new file mode 100644 index 0000000000000000000000000000000000000000..99b483de898174c7ba38ab9d6036286bb2dc65b0 GIT binary patch literal 2072895 zcmV)7K*zs{P)d*Ywf+yIrm)iyvdigGPA0(e07&;-)I96O#p-=3<*L29Z1F~AO&Ya(r7da zx|oR}B`PLFjV45iq$nB;L6C%PpaC?{-d1;Y`K+qUth6tmfA6|uwpmL*?#pURkUMVP 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a/src/diffusers/pipelines/lumina/pipeline_lumina.py +++ b/src/diffusers/pipelines/lumina/pipeline_lumina.py @@ -51,8 +51,7 @@ >>> import torch >>> from diffusers import LuminaText2ImgPipeline - >>> # You can replace the checkpoint id with "Alpha-VLLM/Lumina-Next-T2I" too. - >>> pipe = LuminaText2ImgPipeline.from_pretrained("Alpha-VLLM/Lumina-T2I", torch_dtype=torch.bfloat16) + >>> pipe = LuminaText2ImgPipeline.from_pretrained("Alpha-VLLM/Lumina-Next-SFT-diffusers", torch_dtype=torch.bfloat16) >>> # Enable memory optimizations. >>> pipe.enable_model_cpu_offload() @@ -61,114 +60,6 @@ ``` """ -ASPECT_RATIO_1024_BIN = { - "0.25": [512.0, 2048.0], - "0.28": [512.0, 1856.0], - "0.32": [576.0, 1792.0], - "0.33": [576.0, 1728.0], - "0.35": [576.0, 1664.0], - "0.4": [640.0, 1600.0], - "0.42": [640.0, 1536.0], - "0.48": [704.0, 1472.0], - "0.5": [704.0, 1408.0], - "0.52": [704.0, 1344.0], - "0.57": [768.0, 1344.0], - "0.6": [768.0, 1280.0], - "0.68": [832.0, 1216.0], - "0.72": [832.0, 1152.0], - "0.78": [896.0, 1152.0], - "0.82": [896.0, 1088.0], - "0.88": [960.0, 1088.0], - "0.94": [960.0, 1024.0], - "1.0": [1024.0, 1024.0], - "1.07": [1024.0, 960.0], - "1.13": [1088.0, 960.0], - "1.21": [1088.0, 896.0], - "1.29": [1152.0, 896.0], - "1.38": [1152.0, 832.0], - "1.46": [1216.0, 832.0], - "1.67": [1280.0, 768.0], - "1.75": [1344.0, 768.0], - "2.0": [1408.0, 704.0], - "2.09": [1472.0, 704.0], - "2.4": [1536.0, 640.0], - "2.5": [1600.0, 640.0], - "3.0": [1728.0, 576.0], - "4.0": [2048.0, 512.0], -} - -ASPECT_RATIO_512_BIN = { - "0.25": [256.0, 1024.0], - "0.28": [256.0, 928.0], - "0.32": [288.0, 896.0], - "0.33": [288.0, 864.0], - "0.35": [288.0, 832.0], - "0.4": [320.0, 800.0], - "0.42": [320.0, 768.0], - "0.48": [352.0, 736.0], - "0.5": [352.0, 704.0], - "0.52": [352.0, 672.0], - "0.57": [384.0, 672.0], - "0.6": [384.0, 640.0], - "0.68": [416.0, 608.0], - "0.72": [416.0, 576.0], - "0.78": [448.0, 576.0], - "0.82": [448.0, 544.0], - "0.88": [480.0, 544.0], - "0.94": [480.0, 512.0], - "1.0": [512.0, 512.0], - "1.07": [512.0, 480.0], - "1.13": [544.0, 480.0], - "1.21": [544.0, 448.0], - "1.29": [576.0, 448.0], - "1.38": [576.0, 416.0], - "1.46": [608.0, 416.0], - "1.67": [640.0, 384.0], - "1.75": [672.0, 384.0], - "2.0": [704.0, 352.0], - "2.09": [736.0, 352.0], - "2.4": [768.0, 320.0], - "2.5": [800.0, 320.0], - "3.0": [864.0, 288.0], - "4.0": [1024.0, 256.0], -} - -ASPECT_RATIO_256_BIN = { - "0.25": [128.0, 512.0], - "0.28": [128.0, 464.0], - "0.32": [144.0, 448.0], - "0.33": [144.0, 432.0], - "0.35": [144.0, 416.0], - "0.4": [160.0, 400.0], - "0.42": [160.0, 384.0], - "0.48": [176.0, 368.0], - "0.5": [176.0, 352.0], - "0.52": [176.0, 336.0], - "0.57": [192.0, 336.0], - "0.6": [192.0, 320.0], - "0.68": [208.0, 304.0], - "0.72": [208.0, 288.0], - "0.78": [224.0, 288.0], - "0.82": [224.0, 272.0], - "0.88": [240.0, 272.0], - "0.94": [240.0, 256.0], - "1.0": [256.0, 256.0], - "1.07": [256.0, 240.0], - "1.13": [272.0, 240.0], - "1.21": [272.0, 224.0], - "1.29": [288.0, 224.0], - "1.38": [288.0, 208.0], - "1.46": [304.0, 208.0], - "1.67": [320.0, 192.0], - "1.75": [336.0, 192.0], - "2.0": [352.0, 176.0], - "2.09": [368.0, 176.0], - "2.4": [384.0, 160.0], - "2.5": [400.0, 160.0], - "3.0": [432.0, 144.0], - "4.0": [512.0, 128.0], -} - # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps def retrieve_timesteps( @@ -708,9 +599,9 @@ def __call__( prompt: Union[str, List[str]] = None, width: Optional[int] = None, height: Optional[int] = None, - num_inference_steps: int = 20, + num_inference_steps: int = 30, timesteps: List[int] = None, - guidance_scale: float = 4.5, + guidance_scale: float = 4.0, negative_prompt: str = "", sigmas: List[float] = None, num_images_per_prompt: Optional[int] = 1, @@ -723,11 +614,9 @@ def __call__( output_type: Optional[str] = "pil", return_dict: bool = True, clean_caption: bool = True, - max_sequence_length: int = 120, + max_sequence_length: int = 256, scaling_watershed: Optional[float] = 0.3, proportional_attn: Optional[bool] = True, - callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None, - callback_on_step_end_tensor_inputs: List[str] = ["latents"], ) -> Union[ImagePipelineOutput, Tuple]: """ Function invoked when calling the pipeline for generation. @@ -740,7 +629,7 @@ def __call__( The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). - num_inference_steps (`int`, *optional*, defaults to 100): + num_inference_steps (`int`, *optional*, defaults to 30): The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. timesteps (`List[int]`, *optional*): @@ -751,7 +640,7 @@ def __call__( Custom sigmas to use for the denoising process with schedulers which support a `sigmas` argument in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed will be used. - guidance_scale (`float`, *optional*, defaults to 4.5): + guidance_scale (`float`, *optional*, defaults to 4.0): Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). `guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > @@ -833,6 +722,16 @@ def __call__( else: batch_size = prompt_embeds.shape[0] + if proportional_attn: + assert self.base_seq_len is not None + for layer in self.transformer.layers: + layer.attention.base_seqlen = self.base_seq_len + layer.attention.proportional_attn = proportional_attn + else: + for layer in self.transformer.layers: + layer.attention.base_seqlen = None + layer.attention.proportional_attn = proportional_attn + device = self._execution_device # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) @@ -859,7 +758,10 @@ def __call__( clean_caption=clean_caption, max_sequence_length=max_sequence_length, ) - + if do_classifier_free_guidance: + prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0) + prompt_attention_mask = torch.cat([negative_prompt_attention_mask, prompt_attention_mask], dim=0) + # 4. Prepare timesteps timesteps, num_inference_steps = retrieve_timesteps( self.scheduler, num_inference_steps, device, timesteps, sigmas @@ -919,15 +821,6 @@ def __call__( scaling_watershed=scaling_watershed, timestep=current_timestep[0].item(), ) - if proportional_attn: - assert self.base_seq_len is not None - for layer in self.transformer.layers: - layer.attention.base_seqlen = self.base_seq_len - layer.attention.proportional_attn = proportional_attn - else: - for layer in self.transformer.layers: - layer.attention.base_seqlen = None - layer.attention.proportional_attn = proportional_attn noise_pred = self.transformer( x=latent_model_input, @@ -936,11 +829,7 @@ def __call__( caption_mask=prompt_attention_mask, return_dict=False, )[0] - # learned sigma - if self.transformer.config.learn_sigma: - noise_pred = noise_pred.chunk(2, dim=1)[0] - else: - noise_pred = noise_pred + noise_pred = noise_pred.chunk(2, dim=1)[0] # perform guidance scale # NOTE: For exact reproducibility reasons, we apply classifier-free guidance on only @@ -958,6 +847,7 @@ def __call__( # compute the previous noisy sample x_t -> x_t-1 latents_dtype = latents.dtype + noise_pred = -noise_pred latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0] if latents.dtype != latents_dtype: From 63331aeccdaccefaaf5e39578100506039294c7b Mon Sep 17 00:00:00 2001 From: PommesPeter Date: Sat, 22 Jun 2024 02:06:03 +0800 Subject: [PATCH 20/92] Fixed bugs --- .../pipelines/lumina/pipeline_lumina.py | 23 ++++++++---------- test.png | Bin 0 -> 2076084 bytes 2 files changed, 10 insertions(+), 13 deletions(-) create mode 100644 test.png diff --git a/src/diffusers/pipelines/lumina/pipeline_lumina.py b/src/diffusers/pipelines/lumina/pipeline_lumina.py index 907d86a78a79..c88350d5352a 100644 --- a/src/diffusers/pipelines/lumina/pipeline_lumina.py +++ b/src/diffusers/pipelines/lumina/pipeline_lumina.py @@ -51,11 +51,11 @@ >>> import torch >>> from diffusers import LuminaText2ImgPipeline - >>> pipe = LuminaText2ImgPipeline.from_pretrained("Alpha-VLLM/Lumina-Next-SFT-diffusers", torch_dtype=torch.bfloat16) + >>> pipe = LuminaText2ImgPipeline.from_pretrained("Alpha-VLLM/Lumina-Next-SFT-diffusers", torch_dtype=torch.bfloat16).cuda() >>> # Enable memory optimizations. >>> pipe.enable_model_cpu_offload() - >>> prompt = "A small cactus with a happy face in the Sahara desert." + >>> prompt = "Upper body of a young woman in a Victorian-era outfit with brass goggles and leather straps. Background shows an industrial revolution cityscape with smoky skies and tall, metal structures" >>> image = pipe(prompt).images[0] ``` """ @@ -182,7 +182,7 @@ def __init__( ) self.vae_scale_factor = 8 self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor) - self.tokenizer_max_length = 256 + self.max_sequence_length = 256 self.default_sample_size = ( self.transformer.config.sample_size if hasattr(self, "transformer") and self.transformer is not None @@ -196,8 +196,6 @@ def _get_gemma_prompt_embeds( prompt: Union[str, List[str]], num_images_per_prompt: int = 1, device: Optional[torch.device] = None, - clip_skip: Optional[int] = None, - clip_model_index: int = 0, clean_caption: Optional[bool] = False, ): device = device or self._execution_device @@ -209,7 +207,7 @@ def _get_gemma_prompt_embeds( prompt, padding=True, pad_to_multiple_of=8, - max_length=self.tokenizer_max_length, + max_length=self.max_sequence_length, truncation=True, return_tensors="pt", ) @@ -217,10 +215,10 @@ def _get_gemma_prompt_embeds( untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids): - removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer_max_length - 1 : -1]) + removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.max_sequence_length - 1 : -1]) logger.warning( "The following part of your input was truncated because T5 can only handle sequences up to" - f" {self.tokenizer_max_length} tokens: {removed_text}" + f" {self.max_sequence_length} tokens: {removed_text}" ) prompt_attention_mask = text_inputs.attention_mask @@ -260,7 +258,6 @@ def encode_prompt( prompt_attention_mask: Optional[torch.Tensor] = None, negative_prompt_attention_mask: Optional[torch.Tensor] = None, clean_caption: bool = False, - max_sequence_length: int = 256, **kwargs, ): r""" @@ -286,7 +283,7 @@ def encode_prompt( Pre-generated negative text embeddings. For Lumina-T2I, it's should be the embeddings of the "" string. clean_caption (`bool`, defaults to `False`): If `True`, the function will preprocess and clean the provided caption before encoding. - max_sequence_length (`int`, defaults to 120): Maximum sequence length to use for the prompt. + max_sequence_length (`int`, defaults to 256): Maximum sequence length to use for the prompt. """ if device is None: device = self._execution_device @@ -325,7 +322,7 @@ def encode_prompt( ) negative_prompt_embeds, negative_prompt_attention_mask = self._get_gemma_prompt_embeds( - prompt=prompt, num_images_per_prompt=num_images_per_prompt, device=device + prompt=negative_prompt, num_images_per_prompt=num_images_per_prompt, device=device ) return prompt_embeds, prompt_attention_mask, negative_prompt_embeds, negative_prompt_attention_mask @@ -759,8 +756,8 @@ def __call__( max_sequence_length=max_sequence_length, ) if do_classifier_free_guidance: - prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0) - prompt_attention_mask = torch.cat([negative_prompt_attention_mask, prompt_attention_mask], dim=0) + prompt_embeds = torch.cat([prompt_embeds, negative_prompt_embeds], dim=1) + prompt_attention_mask = torch.cat([prompt_attention_mask, negative_prompt_attention_mask], dim=1) # 4. 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Subject: [PATCH 21/92] Fixed prompt embedding bugs --- .../pipelines/lumina/pipeline_lumina.py | 59 +++++++++++++----- test.png | Bin 2076084 -> 0 bytes 2 files changed, 43 insertions(+), 16 deletions(-) delete mode 100644 test.png diff --git a/src/diffusers/pipelines/lumina/pipeline_lumina.py b/src/diffusers/pipelines/lumina/pipeline_lumina.py index c88350d5352a..e8ca17f5536b 100644 --- a/src/diffusers/pipelines/lumina/pipeline_lumina.py +++ b/src/diffusers/pipelines/lumina/pipeline_lumina.py @@ -189,7 +189,7 @@ def __init__( else 128 ) self.default_image_size = self.default_sample_size * self.vae_scale_factor - self.base_seq_len = (self.default_image_size // 16) ** 2 + self.base_sequence_length = (self.default_image_size // 16) ** 2 def _get_gemma_prompt_embeds( self, @@ -197,6 +197,7 @@ def _get_gemma_prompt_embeds( num_images_per_prompt: int = 1, device: Optional[torch.device] = None, clean_caption: Optional[bool] = False, + max_length: Optional[int] = None, ): device = device or self._execution_device prompt = [prompt] if isinstance(prompt, str) else prompt @@ -205,14 +206,13 @@ def _get_gemma_prompt_embeds( prompt = self._text_preprocessing(prompt, clean_caption=clean_caption) text_inputs = self.tokenizer( prompt, - padding=True, pad_to_multiple_of=8, max_length=self.max_sequence_length, truncation=True, return_tensors="pt", ) - text_input_ids = text_inputs.input_ids - untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids + text_input_ids = text_inputs.input_ids.to(device) + untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids.to(device) if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids): removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.max_sequence_length - 1 : -1]) @@ -221,11 +221,11 @@ def _get_gemma_prompt_embeds( f" {self.max_sequence_length} tokens: {removed_text}" ) - prompt_attention_mask = text_inputs.attention_mask - prompt_attention_mask = prompt_attention_mask.to(device) + prompt_attention_mask = text_inputs.attention_mask.to(device) + prompt_attention_mask = prompt_attention_mask.repeat(num_images_per_prompt, 1) prompt_embeds = self.text_encoder( - text_input_ids.to(device), attention_mask=prompt_attention_mask, output_hidden_states=True + text_input_ids, attention_mask=prompt_attention_mask, output_hidden_states=True ) prompt_embeds = prompt_embeds.hidden_states[-2] @@ -302,11 +302,11 @@ def encode_prompt( clean_caption=clean_caption, ) - # get negative embeddings for classifier free guidance + # Get negative embeddings for classifier free guidance if do_classifier_free_guidance and negative_prompt_embeds is None: negative_prompt = negative_prompt if negative_prompt is not None else "" - # normalize str to list + # Normalize str to list negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt if prompt is not None and type(prompt) is not type(negative_prompt): @@ -314,17 +314,42 @@ def encode_prompt( f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=" f" {type(prompt)}." ) + elif isinstance(negative_prompt, str): + negative_prompt = [negative_prompt] elif batch_size != len(negative_prompt): raise ValueError( f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:" f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches" " the batch size of `prompt`." ) - - negative_prompt_embeds, negative_prompt_attention_mask = self._get_gemma_prompt_embeds( - prompt=negative_prompt, num_images_per_prompt=num_images_per_prompt, device=device + # Padding negative prompt to the same length with prompt + prompt_max_length = prompt_embeds.shape[1] + negative_text_inputs = self.tokenizer( + negative_prompt, + padding="max_length", + max_length=prompt_max_length, + truncation=True, + return_tensors="pt", + ) + negtive_text_input_ids = negative_text_inputs.input_ids.to(device) + negative_prompt_attention_mask = negative_text_inputs.attention_mask.to(device) + negative_prompt_attention_mask = negative_prompt_attention_mask.repeat(num_images_per_prompt, 1) + # Get the negative prompt embeddings + negative_prompt_embeds = self.text_encoder( + negtive_text_input_ids, + attention_mask=negative_prompt_attention_mask, + output_hidden_states=True, ) + negative_dtype = self.text_encoder.dtype + negative_prompt_embeds = negative_prompt_embeds.hidden_states[-2] + _, seq_len, _ = negative_prompt_embeds.shape + + negative_prompt_embeds = negative_prompt_embeds.to(dtype=negative_dtype, device=device) + # duplicate text embeddings and attention mask for each generation per prompt, using mps friendly method + negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1) + negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1) + return prompt_embeds, prompt_attention_mask, negative_prompt_embeds, negative_prompt_attention_mask # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs @@ -720,9 +745,9 @@ def __call__( batch_size = prompt_embeds.shape[0] if proportional_attn: - assert self.base_seq_len is not None + assert self.base_sequence_length is not None for layer in self.transformer.layers: - layer.attention.base_seqlen = self.base_seq_len + layer.attention.base_seqlen = self.base_sequence_length layer.attention.proportional_attn = proportional_attn else: for layer in self.transformer.layers: @@ -755,9 +780,11 @@ def __call__( clean_caption=clean_caption, max_sequence_length=max_sequence_length, ) + import ipdb + ipdb.set_trace() if do_classifier_free_guidance: - prompt_embeds = torch.cat([prompt_embeds, negative_prompt_embeds], dim=1) - prompt_attention_mask = torch.cat([prompt_attention_mask, negative_prompt_attention_mask], dim=1) + prompt_embeds = torch.cat([prompt_embeds, negative_prompt_embeds], dim=0) + prompt_attention_mask = torch.cat([prompt_attention_mask, negative_prompt_attention_mask], dim=0) # 4. 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Subject: [PATCH 22/92] Removed unused code --- src/diffusers/pipelines/lumina/pipeline_lumina.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/src/diffusers/pipelines/lumina/pipeline_lumina.py b/src/diffusers/pipelines/lumina/pipeline_lumina.py index e8ca17f5536b..6922ee1b601d 100644 --- a/src/diffusers/pipelines/lumina/pipeline_lumina.py +++ b/src/diffusers/pipelines/lumina/pipeline_lumina.py @@ -780,8 +780,6 @@ def __call__( clean_caption=clean_caption, max_sequence_length=max_sequence_length, ) - import ipdb - ipdb.set_trace() if do_classifier_free_guidance: prompt_embeds = torch.cat([prompt_embeds, negative_prompt_embeds], dim=0) prompt_attention_mask = torch.cat([prompt_attention_mask, negative_prompt_attention_mask], dim=0) From 69b02cbf9dd56d9646adc340d3e5c3400f58ea01 Mon Sep 17 00:00:00 2001 From: zhuole1025 Date: Mon, 24 Jun 2024 04:46:47 +0000 Subject: [PATCH 23/92] Fix bugs --- src/diffusers/models/embeddings.py | 2 +- .../models/transformers/lumina_nextdit2d.py | 76 ------------------- .../pipelines/lumina/pipeline_lumina.py | 2 +- .../scheduling_flow_match_euler_discrete.py | 2 +- 4 files changed, 3 insertions(+), 79 deletions(-) diff --git a/src/diffusers/models/embeddings.py b/src/diffusers/models/embeddings.py index 94c6d177b971..1f6abf7a054f 100644 --- a/src/diffusers/models/embeddings.py +++ b/src/diffusers/models/embeddings.py @@ -762,7 +762,7 @@ class LuminaCombinedTimestepCaptionEmbedding(nn.Module): def __init__(self, hidden_size=4096, caption_dim=5120, frequency_embedding_size=256): super().__init__() self.time_proj = Timesteps( - num_channels=frequency_embedding_size, flip_sin_to_cos=False, downscale_freq_shift=0.0 + num_channels=frequency_embedding_size, flip_sin_to_cos=True, downscale_freq_shift=0.0 ) self.timestep_embedder = TimestepEmbedding(in_channels=frequency_embedding_size, time_embed_dim=hidden_size) diff --git a/src/diffusers/models/transformers/lumina_nextdit2d.py b/src/diffusers/models/transformers/lumina_nextdit2d.py index af7170185a60..1c27ded8ad46 100644 --- a/src/diffusers/models/transformers/lumina_nextdit2d.py +++ b/src/diffusers/models/transformers/lumina_nextdit2d.py @@ -782,82 +782,6 @@ def forward( return Transformer2DModelOutput(sample=output) - def forward_with_cfg( - self, - x, - timestep, - caption_feat, - caption_mask, - cfg_scale, - scaling_factor=1.0, - scaling_watershed=1.0, - base_seqlen: Optional[int] = None, - proportional_attn: bool = False, - return_dict: bool = True, - ): - """ - Forward pass of LuminaNextDiT, but also batches the unconditional forward pass for classifier-free guidance. - - Args: - x: - The input tensor, typically representing image data. - timestep: - A tensor representing the time steps or sequence positions. - caption_feat: - The caption features, which are used for conditioning the model. - caption_mask: - A mask for the caption features, indicating which elements are valid. - cfg_scale: - The classifier-free guidance scale, used to adjust the influence of the conditioning information. - scaling_factor (float, optional, defaults to 1.0): - A scaling factor applied to certain operations or parameters within the forward pass. - scaling_watershed (float, optional, defaults to 1.0): - A specific scaling factor used in conjunction with the main scale factor, potentially for different - stages of processing. - base_seqlen (Optional[int], optional): - The base sequence length, which can be used to set the length of sequences processed by the model. If - None, a default value is used. - proportional_attn (bool, optional, defaults to False): - Whether to use proportional attention mechanisms within the model, adjusting attention weights - proportionally. - """ - # https://github.com/openai/glide-text2im/blob/main/notebooks/text2im.ipynb - self.freqs_cis = LuminaNextDiT2DModel.precompute_freqs_cis( - self.hidden_size // self.num_attention_heads, - 384, - scaling_factor=scaling_factor, - scaling_watershed=scaling_watershed, - timestep=timestep[0].item(), - ) - - if proportional_attn: - assert base_seqlen is not None - for layer in self.layers: - layer.attention.base_seqlen = base_seqlen - layer.attention.proportional_attn = proportional_attn - else: - for layer in self.layers: - layer.attention.base_seqlen = None - layer.attention.proportional_attn = proportional_attn - - half = x[: len(x) // 2] - combined = torch.cat([half, half], dim=0) - model_out = self(combined, timestep, caption_feat, caption_mask) - # For exact reproducibility reasons, we apply classifier-free guidance on only - # three channels by default. The standard approach to cfg applies it to all channels. - # This can be done by uncommenting the following line and commenting-out the line following that. - # eps, rest = model_out[:, :self.in_channels], model_out[:, self.in_channels:] - eps, rest = model_out[:, :3], model_out[:, 3:] - cond_eps, uncond_eps = torch.split(eps, len(eps) // 2, dim=0) - half_eps = uncond_eps + cfg_scale * (cond_eps - uncond_eps) - eps = torch.cat([half_eps, half_eps], dim=0) - output = torch.cat([eps, rest], dim=1) - - if not return_dict: - return (output,) - - return Transformer2DModelOutput(sample=output) - def precompute_freqs_cis( self, dim: int, diff --git a/src/diffusers/pipelines/lumina/pipeline_lumina.py b/src/diffusers/pipelines/lumina/pipeline_lumina.py index 6922ee1b601d..6cc52ad079b4 100644 --- a/src/diffusers/pipelines/lumina/pipeline_lumina.py +++ b/src/diffusers/pipelines/lumina/pipeline_lumina.py @@ -217,7 +217,7 @@ def _get_gemma_prompt_embeds( if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids): removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.max_sequence_length - 1 : -1]) logger.warning( - "The following part of your input was truncated because T5 can only handle sequences up to" + "The following part of your input was truncated because Gemma can only handle sequences up to" f" {self.max_sequence_length} tokens: {removed_text}" ) diff --git a/src/diffusers/schedulers/scheduling_flow_match_euler_discrete.py b/src/diffusers/schedulers/scheduling_flow_match_euler_discrete.py index 08e9d4f3a95d..1725b213931c 100644 --- a/src/diffusers/schedulers/scheduling_flow_match_euler_discrete.py +++ b/src/diffusers/schedulers/scheduling_flow_match_euler_discrete.py @@ -156,7 +156,7 @@ def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.devic sigmas = timesteps / self.config.num_train_timesteps sigmas = self.config.shift * sigmas / (1 + (self.config.shift - 1) * sigmas) sigmas = torch.from_numpy(sigmas).to(dtype=torch.float32, device=device) - + timesteps = sigmas * self.config.num_train_timesteps self.timesteps = timesteps.to(device=device) self.sigmas = torch.cat([sigmas, torch.zeros(1, device=sigmas.device)]) From cf2da8b50d32fe8e2f9366d5b01bbae98beb0ccf Mon Sep 17 00:00:00 2001 From: zhuole1025 Date: Mon, 24 Jun 2024 17:55:05 +0000 Subject: [PATCH 24/92] Add lumina tests --- .../pipelines/lumina/pipeline_lumina.py | 18 +- tests/pipelines/lumina/test_lumina_nextdit.py | 449 ++++-------------- 2 files changed, 97 insertions(+), 370 deletions(-) diff --git a/src/diffusers/pipelines/lumina/pipeline_lumina.py b/src/diffusers/pipelines/lumina/pipeline_lumina.py index 6cc52ad079b4..4e8f6d56f009 100644 --- a/src/diffusers/pipelines/lumina/pipeline_lumina.py +++ b/src/diffusers/pipelines/lumina/pipeline_lumina.py @@ -160,7 +160,7 @@ class LuminaText2ImgPipeline(DiffusionPipeline): + r"]{1,}" ) # noqa - _optional_components = ["tokenizer", "text_encoder"] + _optional_components = [] model_cpu_offload_seq = "text_encoder->transformer->vae" def __init__( @@ -209,6 +209,7 @@ def _get_gemma_prompt_embeds( pad_to_multiple_of=8, max_length=self.max_sequence_length, truncation=True, + padding=True, return_tensors="pt", ) text_input_ids = text_inputs.input_ids.to(device) @@ -222,8 +223,6 @@ def _get_gemma_prompt_embeds( ) prompt_attention_mask = text_inputs.attention_mask.to(device) - prompt_attention_mask = prompt_attention_mask.repeat(num_images_per_prompt, 1) - prompt_embeds = self.text_encoder( text_input_ids, attention_mask=prompt_attention_mask, output_hidden_states=True ) @@ -242,6 +241,8 @@ def _get_gemma_prompt_embeds( # duplicate text embeddings and attention mask for each generation per prompt, using mps friendly method prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1) prompt_embeds = prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1) + prompt_attention_mask = prompt_attention_mask.repeat(num_images_per_prompt, 1) + prompt_attention_mask = prompt_attention_mask.view(batch_size * num_images_per_prompt, -1) return prompt_embeds, prompt_attention_mask @@ -250,7 +251,7 @@ def encode_prompt( self, prompt: Union[str, List[str]], do_classifier_free_guidance: bool = True, - negative_prompt: str = "", + negative_prompt: Union[str, List[str]] = None, num_images_per_prompt: int = 1, device: Optional[torch.device] = None, prompt_embeds: Optional[torch.Tensor] = None, @@ -331,12 +332,11 @@ def encode_prompt( truncation=True, return_tensors="pt", ) - negtive_text_input_ids = negative_text_inputs.input_ids.to(device) + negative_text_input_ids = negative_text_inputs.input_ids.to(device) negative_prompt_attention_mask = negative_text_inputs.attention_mask.to(device) - negative_prompt_attention_mask = negative_prompt_attention_mask.repeat(num_images_per_prompt, 1) # Get the negative prompt embeddings negative_prompt_embeds = self.text_encoder( - negtive_text_input_ids, + negative_text_input_ids, attention_mask=negative_prompt_attention_mask, output_hidden_states=True, ) @@ -349,6 +349,8 @@ def encode_prompt( # duplicate text embeddings and attention mask for each generation per prompt, using mps friendly method negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1) negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1) + negative_prompt_attention_mask = negative_prompt_attention_mask.repeat(num_images_per_prompt, 1) + negative_prompt_attention_mask = negative_prompt_attention_mask.view(batch_size * num_images_per_prompt, -1) return prompt_embeds, prompt_attention_mask, negative_prompt_embeds, negative_prompt_attention_mask @@ -624,7 +626,7 @@ def __call__( num_inference_steps: int = 30, timesteps: List[int] = None, guidance_scale: float = 4.0, - negative_prompt: str = "", + negative_prompt: Union[str, List[str]] = None, sigmas: List[float] = None, num_images_per_prompt: Optional[int] = 1, generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, diff --git a/tests/pipelines/lumina/test_lumina_nextdit.py b/tests/pipelines/lumina/test_lumina_nextdit.py index 01857c615cef..575b4596b1bb 100644 --- a/tests/pipelines/lumina/test_lumina_nextdit.py +++ b/tests/pipelines/lumina/test_lumina_nextdit.py @@ -1,79 +1,54 @@ -# coding=utf-8 -# Copyright 2024 HuggingFace Inc. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - import gc -import tempfile import unittest import numpy as np import torch -from transformers import AutoModel, AutoTokenizer +from transformers import AutoTokenizer, AutoModel -from diffusers import ( - AutoencoderKL, - DDIMScheduler, - LuminaNextDiT2DModel, - LuminaText2ImgPipeline, -) +from diffusers import AutoencoderKL, FlowMatchEulerDiscreteScheduler, LuminaNextDiT2DModel, LuminaText2ImgPipeline from diffusers.utils.testing_utils import ( - enable_full_determinism, numpy_cosine_similarity_distance, require_torch_gpu, slow, torch_device, ) -from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS -from ..test_pipelines_common import PipelineTesterMixin, to_np - - -enable_full_determinism() +from ..test_pipelines_common import PipelineTesterMixin -class LuminaText2ImgPipelineFastTests(PipelineTesterMixin, unittest.TestCase): +class LuminaText2ImgPipelinePipelineFastTests(unittest.TestCase, PipelineTesterMixin): pipeline_class = LuminaText2ImgPipeline - params = TEXT_TO_IMAGE_PARAMS - {"cross_attention_kwargs"} - batch_params = TEXT_TO_IMAGE_BATCH_PARAMS - image_params = TEXT_TO_IMAGE_IMAGE_PARAMS - image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS - - required_optional_params = PipelineTesterMixin.required_optional_params + params = frozenset( + [ + "prompt", + "height", + "width", + "guidance_scale", + "negative_prompt", + "prompt_embeds", + "negative_prompt_embeds", + ] + ) + batch_params = frozenset(["prompt", "negative_prompt"]) def get_dummy_components(self): torch.manual_seed(0) transformer = LuminaNextDiT2DModel( - sample_size=8, - num_layers=2, - patch_size=2, - attention_head_dim=8, - num_attention_heads=3, - caption_channels=32, + sample_size=32, + patch_size=1, in_channels=4, - cross_attention_dim=24, - out_channels=8, - attention_bias=True, - activation_fn="gelu-approximate", - num_embeds_ada_norm=1000, - norm_type="ada_norm_single", - norm_elementwise_affine=False, - norm_eps=1e-6, + num_layers=1, + hidden_size=32, + num_attention_heads=4, + multiple_of=32, + caption_dim=2048, + qk_norm=True, + learn_sigma=True, ) torch.manual_seed(0) vae = AutoencoderKL() - scheduler = DDIMScheduler() + scheduler = FlowMatchEulerDiscreteScheduler() text_encoder = AutoModel.from_pretrained("google/gemma-2b") tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b") @@ -83,8 +58,6 @@ def get_dummy_components(self): "scheduler": scheduler, "text_encoder": text_encoder, "tokenizer": tokenizer, - "safety_checker": None, - "feature_extractor": None, } return components @@ -92,246 +65,52 @@ def get_dummy_inputs(self, device, seed=0): if str(device).startswith("mps"): generator = torch.manual_seed(seed) else: - generator = torch.Generator(device=device).manual_seed(seed) + generator = torch.Generator(device="cpu").manual_seed(seed) + inputs = { "prompt": "A painting of a squirrel eating a burger", "generator": generator, "num_inference_steps": 2, "guidance_scale": 5.0, "output_type": "np", - "use_resolution_binning": False, } return inputs - def test_sequential_cpu_offload_forward_pass(self): - # TODO(PVP, Sayak) need to fix later - return + def test_lumina_prompt_embeds(self): + pipe = self.pipeline_class(**self.get_dummy_components()).to(torch_device) + inputs = self.get_dummy_inputs(torch_device) - def test_save_load_optional_components(self): - components = self.get_dummy_components() - pipe = self.pipeline_class(**components) - pipe.to(torch_device) - pipe.set_progress_bar_config(disable=None) + output_with_prompt = pipe(**inputs).images[0] inputs = self.get_dummy_inputs(torch_device) + prompt = inputs.pop("prompt") - prompt = inputs["prompt"] - generator = inputs["generator"] - num_inference_steps = inputs["num_inference_steps"] - output_type = inputs["output_type"] - + do_classifier_free_guidance = inputs["guidance_scale"] > 1 ( prompt_embeds, prompt_attention_mask, negative_prompt_embeds, negative_prompt_attention_mask, - ) = pipe.encode_prompt(prompt) - - # inputs with prompt converted to embeddings - inputs = { - "prompt_embeds": prompt_embeds, - "prompt_attention_mask": prompt_attention_mask, - "negative_prompt": None, - "negative_prompt_embeds": negative_prompt_embeds, - "negative_prompt_attention_mask": negative_prompt_attention_mask, - "generator": generator, - "num_inference_steps": num_inference_steps, - "output_type": output_type, - "use_resolution_binning": False, - } - - # set all optional components to None - for optional_component in pipe._optional_components: - setattr(pipe, optional_component, None) - - output = pipe(**inputs)[0] - - with tempfile.TemporaryDirectory() as tmpdir: - pipe.save_pretrained(tmpdir) - pipe_loaded = self.pipeline_class.from_pretrained(tmpdir) - pipe_loaded.to(torch_device) - pipe_loaded.set_progress_bar_config(disable=None) - - for optional_component in pipe._optional_components: - self.assertTrue( - getattr(pipe_loaded, optional_component) is None, - f"`{optional_component}` did not stay set to None after loading.", - ) - - inputs = self.get_dummy_inputs(torch_device) - - generator = inputs["generator"] - num_inference_steps = inputs["num_inference_steps"] - output_type = inputs["output_type"] - - # inputs with prompt converted to embeddings - inputs = { - "prompt_embeds": prompt_embeds, - "prompt_attention_mask": prompt_attention_mask, - "negative_prompt": None, - "negative_prompt_embeds": negative_prompt_embeds, - "negative_prompt_attention_mask": negative_prompt_attention_mask, - "generator": generator, - "num_inference_steps": num_inference_steps, - "output_type": output_type, - "use_resolution_binning": False, - } - - output_loaded = pipe_loaded(**inputs)[0] - - max_diff = np.abs(to_np(output) - to_np(output_loaded)).max() - self.assertLess(max_diff, 1e-4) - - def test_inference(self): - device = "cpu" - - components = self.get_dummy_components() - pipe = self.pipeline_class(**components) - pipe.to(device) - pipe.set_progress_bar_config(disable=None) - - inputs = self.get_dummy_inputs(device) - image = pipe(**inputs).images - image_slice = image[0, -3:, -3:, -1] - - self.assertEqual(image.shape, (1, 8, 8, 3)) - expected_slice = np.array([0.6319, 0.3526, 0.3806, 0.6327, 0.4639, 0.483, 0.2583, 0.5331, 0.4852]) - max_diff = np.abs(image_slice.flatten() - expected_slice).max() - self.assertLessEqual(max_diff, 1e-3) - - def test_inference_non_square_images(self): - device = "cpu" - - components = self.get_dummy_components() - pipe = self.pipeline_class(**components) - pipe.to(device) - pipe.set_progress_bar_config(disable=None) - - inputs = self.get_dummy_inputs(device) - image = pipe(**inputs, height=32, width=48).images - image_slice = image[0, -3:, -3:, -1] - self.assertEqual(image.shape, (1, 32, 48, 3)) - - expected_slice = np.array([0.6493, 0.537, 0.4081, 0.4762, 0.3695, 0.4711, 0.3026, 0.5218, 0.5263]) - max_diff = np.abs(image_slice.flatten() - expected_slice).max() - self.assertLessEqual(max_diff, 1e-3) - - def test_inference_with_embeddings_and_multiple_images(self): - components = self.get_dummy_components() - pipe = self.pipeline_class(**components) - pipe.to(torch_device) - pipe.set_progress_bar_config(disable=None) - - inputs = self.get_dummy_inputs(torch_device) - - prompt = inputs["prompt"] - generator = inputs["generator"] - num_inference_steps = inputs["num_inference_steps"] - output_type = inputs["output_type"] - - prompt_embeds, prompt_attn_mask, negative_prompt_embeds, neg_prompt_attn_mask = pipe.encode_prompt(prompt) - - # inputs with prompt converted to embeddings - inputs = { - "prompt_embeds": prompt_embeds, - "prompt_attention_mask": prompt_attn_mask, - "negative_prompt": None, - "negative_prompt_embeds": negative_prompt_embeds, - "negative_prompt_attention_mask": neg_prompt_attn_mask, - "generator": generator, - "num_inference_steps": num_inference_steps, - "output_type": output_type, - "num_images_per_prompt": 2, - "use_resolution_binning": False, - } - - # set all optional components to None - for optional_component in pipe._optional_components: - setattr(pipe, optional_component, None) - - output = pipe(**inputs)[0] - - with tempfile.TemporaryDirectory() as tmpdir: - pipe.save_pretrained(tmpdir) - pipe_loaded = self.pipeline_class.from_pretrained(tmpdir) - pipe_loaded.to(torch_device) - pipe_loaded.set_progress_bar_config(disable=None) - - for optional_component in pipe._optional_components: - self.assertTrue( - getattr(pipe_loaded, optional_component) is None, - f"`{optional_component}` did not stay set to None after loading.", - ) - - inputs = self.get_dummy_inputs(torch_device) - - generator = inputs["generator"] - num_inference_steps = inputs["num_inference_steps"] - output_type = inputs["output_type"] - - # inputs with prompt converted to embeddings - inputs = { - "prompt_embeds": prompt_embeds, - "prompt_attention_mask": prompt_attn_mask, - "negative_prompt": None, - "negative_prompt_embeds": negative_prompt_embeds, - "negative_prompt_attention_mask": neg_prompt_attn_mask, - "generator": generator, - "num_inference_steps": num_inference_steps, - "output_type": output_type, - "num_images_per_prompt": 2, - "use_resolution_binning": False, - } - - output_loaded = pipe_loaded(**inputs)[0] - - max_diff = np.abs(to_np(output) - to_np(output_loaded)).max() - self.assertLess(max_diff, 1e-4) - - def test_inference_with_multiple_images_per_prompt(self): - device = "cpu" - - components = self.get_dummy_components() - pipe = self.pipeline_class(**components) - pipe.to(device) - pipe.set_progress_bar_config(disable=None) - - inputs = self.get_dummy_inputs(device) - inputs["num_images_per_prompt"] = 2 - image = pipe(**inputs).images - image_slice = image[0, -3:, -3:, -1] - - self.assertEqual(image.shape, (2, 8, 8, 3)) - expected_slice = np.array([0.6319, 0.3526, 0.3806, 0.6327, 0.4639, 0.483, 0.2583, 0.5331, 0.4852]) - max_diff = np.abs(image_slice.flatten() - expected_slice).max() - self.assertLessEqual(max_diff, 1e-3) - - def test_raises_warning_for_mask_feature(self): - device = "cpu" - - components = self.get_dummy_components() - pipe = self.pipeline_class(**components) - pipe.to(device) - pipe.set_progress_bar_config(disable=None) - - inputs = self.get_dummy_inputs(device) - inputs.update({"mask_feature": True}) - - with self.assertWarns(FutureWarning) as warning_ctx: - _ = pipe(**inputs).images - - assert "mask_feature" in str(warning_ctx.warning) + ) = pipe.encode_prompt( + prompt, + do_classifier_free_guidance=do_classifier_free_guidance, + device=torch_device, + ) + output_with_embeds = pipe( + prompt_embeds=prompt_embeds, + prompt_attention_mask=prompt_attention_mask, + **inputs, + ).images[0] - def test_inference_batch_single_identical(self): - self._test_inference_batch_single_identical(expected_max_diff=1e-3) + max_diff = np.abs(output_with_prompt - output_with_embeds).max() + assert max_diff < 1e-4 @slow @require_torch_gpu -class LuminaText2ImgPipelineIntegrationTests(unittest.TestCase): - ckpt_id_1024 = "PixArt-alpha/PixArt-XL-2-1024-MS" - ckpt_id_512 = "PixArt-alpha/PixArt-XL-2-512x512" - prompt = "A small cactus with a happy face in the Sahara desert." +class LuminaText2ImgPipelineSlowTests(unittest.TestCase): + pipeline_class = LuminaText2ImgPipeline + repo_id = "Alpha-VLLM/Lumina-Next-SFT-diffusers" def setUp(self): super().setUp() @@ -343,101 +122,47 @@ def tearDown(self): gc.collect() torch.cuda.empty_cache() - def test_pixart_1024(self): - generator = torch.Generator("cpu").manual_seed(0) - - pipe = LuminaText2ImgPipeline.from_pretrained(self.ckpt_id_1024, torch_dtype=torch.float16) - pipe.enable_model_cpu_offload() - prompt = self.prompt - - image = pipe(prompt, generator=generator, num_inference_steps=2, output_type="np").images - - image_slice = image[0, -3:, -3:, -1] - expected_slice = np.array([0.0742, 0.0835, 0.2114, 0.0295, 0.0784, 0.2361, 0.1738, 0.2251, 0.3589]) - - max_diff = numpy_cosine_similarity_distance(image_slice.flatten(), expected_slice) - self.assertLessEqual(max_diff, 1e-4) - - def test_pixart_512(self): - generator = torch.Generator("cpu").manual_seed(0) - - pipe = LuminaText2ImgPipeline.from_pretrained(self.ckpt_id_512, torch_dtype=torch.float16) - pipe.enable_model_cpu_offload() - - prompt = self.prompt - - image = pipe(prompt, generator=generator, num_inference_steps=2, output_type="np").images - - image_slice = image[0, -3:, -3:, -1] - expected_slice = np.array([0.3477, 0.3882, 0.4541, 0.3413, 0.3821, 0.4463, 0.4001, 0.4409, 0.4958]) - - max_diff = numpy_cosine_similarity_distance(image_slice.flatten(), expected_slice) - self.assertLessEqual(max_diff, 1e-4) - - def test_pixart_1024_without_resolution_binning(self): - generator = torch.manual_seed(0) - - pipe = LuminaText2ImgPipeline.from_pretrained(self.ckpt_id_1024, torch_dtype=torch.float16) - pipe.enable_model_cpu_offload() - - prompt = self.prompt - height, width = 1024, 768 - num_inference_steps = 2 - - image = pipe( - prompt, - height=height, - width=width, - generator=generator, - num_inference_steps=num_inference_steps, - output_type="np", - ).images - image_slice = image[0, -3:, -3:, -1] - - generator = torch.manual_seed(0) - no_res_bin_image = pipe( - prompt, - height=height, - width=width, - generator=generator, - num_inference_steps=num_inference_steps, - output_type="np", - use_resolution_binning=False, - ).images - no_res_bin_image_slice = no_res_bin_image[0, -3:, -3:, -1] - - assert not np.allclose(image_slice, no_res_bin_image_slice, atol=1e-4, rtol=1e-4) + def get_inputs(self, device, seed=0): + if str(device).startswith("mps"): + generator = torch.manual_seed(seed) + else: + generator = torch.Generator(device="cpu").manual_seed(seed) - def test_pixart_512_without_resolution_binning(self): - generator = torch.manual_seed(0) + return { + "prompt": "A photo of a cat", + "num_inference_steps": 2, + "guidance_scale": 5.0, + "output_type": "np", + "generator": generator, + } - pipe = LuminaText2ImgPipeline.from_pretrained(self.ckpt_id_512, torch_dtype=torch.float16) + def test_lumina_inference(self): + pipe = self.pipeline_class.from_pretrained(self.repo_id, torch_dtype=torch.bfloat16) pipe.enable_model_cpu_offload() - prompt = self.prompt - height, width = 512, 768 - num_inference_steps = 2 - - image = pipe( - prompt, - height=height, - width=width, - generator=generator, - num_inference_steps=num_inference_steps, - output_type="np", - ).images - image_slice = image[0, -3:, -3:, -1] + inputs = self.get_inputs(torch_device) + + image = pipe(**inputs).images[0] + image_slice = image[0, :10, :10] + expected_slice = np.array( + [ + [0.36132812, 0.30004883, 0.25830078], + [0.36669922, 0.31103516, 0.23754883], + [0.34814453, 0.29248047, 0.23583984], + [0.35791016, 0.30981445, 0.23999023], + [0.36328125, 0.31274414, 0.2607422], + [0.37304688, 0.32177734, 0.26171875], + [0.3671875, 0.31933594, 0.25756836], + [0.36035156, 0.31103516, 0.2578125], + [0.3857422, 0.33789062, 0.27563477], + [0.3701172, 0.31982422, 0.265625], + ], + dtype=np.float32, + ) - generator = torch.manual_seed(0) - no_res_bin_image = pipe( - prompt, - height=height, - width=width, - generator=generator, - num_inference_steps=num_inference_steps, - output_type="np", - use_resolution_binning=False, - ).images - no_res_bin_image_slice = no_res_bin_image[0, -3:, -3:, -1] + max_diff = numpy_cosine_similarity_distance(expected_slice.flatten(), image_slice.flatten()) - assert not np.allclose(image_slice, no_res_bin_image_slice, atol=1e-4, rtol=1e-4) + assert max_diff < 1e-4 + +if __name__ == "__main__": + unittest.main() \ No newline at end of file From 759781ee9f25cab111a9686dfa3ef25b8be2428c Mon Sep 17 00:00:00 2001 From: PommesPeter Date: Tue, 25 Jun 2024 03:13:10 +0800 Subject: [PATCH 25/92] Implement attention in diffusres --- src/diffusers/models/attention_processor.py | 159 +++++++ src/diffusers/models/embeddings.py | 6 +- .../models/transformers/lumina_nextdit2d.py | 419 +++--------------- .../pipelines/lumina/pipeline_lumina.py | 24 +- .../scheduling_flow_match_euler_discrete.py | 2 +- 5 files changed, 244 insertions(+), 366 deletions(-) diff --git a/src/diffusers/models/attention_processor.py b/src/diffusers/models/attention_processor.py index d36319493980..fb2030f1d883 100644 --- a/src/diffusers/models/attention_processor.py +++ b/src/diffusers/models/attention_processor.py @@ -1699,6 +1699,165 @@ def __call__( return hidden_states +class LuminaAttnProcessor2_0(nn.Module): + r""" + Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0). This is + used in the LuminaNextDiT model. It applies a s normalization layer and rotary embedding on query and key vector. + """ + + def __init__( + self, + hidden_size: int = 4096, + encoder_hidden_size: int = 2048, + heads: int = 32, + kv_heads: int = 8, + ): + super().__init__() + if not hasattr(F, "scaled_dot_product_attention"): + raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.") + self.head_dim = hidden_size // heads + + self.to_q = nn.Linear( + hidden_size, + heads * self.head_dim, + bias=False, + ) + self.to_k = nn.Linear( + hidden_size, + kv_heads * self.head_dim, + bias=False, + ) + self.to_v = nn.Linear( + hidden_size, + kv_heads * self.head_dim, + bias=False, + ) + + self.norm_q = nn.LayerNorm(heads * self.head_dim) + self.norm_k = nn.LayerNorm(kv_heads * self.head_dim) + + self.key_cap = nn.Linear( + encoder_hidden_size, + kv_heads * self.head_dim, + bias=False, + ) + self.value_cap = nn.Linear( + encoder_hidden_size, + kv_heads * self.head_dim, + bias=False, + ) + self.norm_k_cap = nn.LayerNorm(kv_heads * self.head_dim) + + self.gate = nn.Parameter(torch.zeros([heads])) + + def __call__( + self, + attn: Attention, + hidden_states: torch.Tensor, + encoder_hidden_states: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + encoder_mask: Optional[torch.Tensor] = None, + image_rotary_emb: Optional[torch.Tensor] = None, + proportional_attn: Optional[bool] = True, + base_sequence_length: Optional[int] = 4096, + ) -> torch.Tensor: + from .embeddings import apply_rotary_emb + + input_ndim = hidden_states.ndim + + if input_ndim == 4: + batch_size, channel, height, width = hidden_states.shape + hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2) + + batch_size, sequence_length, _ = ( + hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape + ) + + if attention_mask is not None: + attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size) + # scaled_dot_product_attention expects attention_mask shape to be + # (batch, heads, source_length, target_length) + attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1]) + + # Get Query-Key-Value Pair + query = self.to_q(hidden_states) + + key = self.to_k(hidden_states) + value = self.to_v(hidden_states) + + query_dim = query.shape[-1] + inner_dim = key.shape[-1] + head_dim = query_dim // attn.heads + dtype = query.dtype + + # Get key-value head + kv_heads = inner_dim // attn.heads + + # Apply Query-Key Norm if needed + if self.norm_q is not None: + query = self.norm_q(query) + if self.norm_k is not None: + key = self.norm_k(key) + + query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) + + key = key.view(batch_size, -1, kv_heads, head_dim).transpose(1, 2) + value = value.view(batch_size, -1, kv_heads, head_dim).transpose(1, 2) + + # Apply RoPE if needed + if image_rotary_emb is not None: + query = apply_rotary_emb(query, image_rotary_emb) + key = apply_rotary_emb(key, image_rotary_emb) + + query, key = query.to(dtype), key.to(dtype) + + # Apply proportional attention if true + if proportional_attn: + softmax_scale = math.sqrt(math.log(sequence_length, base_sequence_length)) * attn.scale + else: + softmax_scale = attn.scale + + # perform Grouped-qurey Attention (GQA) + n_rep = attn.heads // kv_heads + if n_rep >= 1: + key = key.unsqueeze(3).repeat(1, 1, 1, n_rep, 1).flatten(2, 3) + value = value.unsqueeze(3).repeat(1, 1, 1, n_rep, 1).flatten(2, 3) + + attention_mask = ( + attention_mask.bool().view(batch_size, 1, 1, sequence_length).expand(-1, attn.heads, sequence_length, -1) + ) + # the output of sdp = (batch, num_heads, seq_len, head_dim) + # TODO: add support for attn.scale when we move to Torch 2.1 + hidden_states = F.scaled_dot_product_attention( + query, key, value, attn_mask=attention_mask, scale=softmax_scale + ) + hidden_states = hidden_states.permute(0, 2, 1, 3).to(dtype) + + # Cross Attention + key_cap = self.key_cap(encoder_hidden_states) + # Apply Key Norm to encoder_hidden_state + key_cap = self.norm_k_cap(key_cap).view(batch_size, -1, kv_heads, head_dim).transpose(1, 2) + value_cap = self.value_cap(encoder_hidden_states).view(batch_size, -1, kv_heads, head_dim).transpose(1, 2) + + if n_rep >= 1: + key_cap = key_cap.unsqueeze(3).repeat(1, 1, 1, n_rep, 1).flatten(2, 3) + value_cap = value_cap.unsqueeze(3).repeat(1, 1, 1, n_rep, 1).flatten(2, 3) + + encoder_mask = (encoder_mask.view(batch_size, 1, 1, -1).expand(batch_size, attn.heads, sequence_length, -1),) + hidden_states_caption = F.scaled_dot_product_attention(query, key_cap, value_cap, encoder_mask) + hidden_states_caption = hidden_states_caption.permute(0, 2, 1, 3) + + hidden_states_caption = hidden_states_caption * self.gate.tanh().view(1, 1, -1, 1) + hidden_states = hidden_states + hidden_states_caption + + hidden_states = hidden_states.flatten(-2) + + # linear proj + hidden_states = attn.to_out[0](hidden_states) + + return hidden_states + + class FusedAttnProcessor2_0: r""" Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0). It uses diff --git a/src/diffusers/models/embeddings.py b/src/diffusers/models/embeddings.py index 1f6abf7a054f..73fe4b64ce99 100644 --- a/src/diffusers/models/embeddings.py +++ b/src/diffusers/models/embeddings.py @@ -759,7 +759,7 @@ def forward(self, timestep, encoder_hidden_states, image_meta_size, style, hidde class LuminaCombinedTimestepCaptionEmbedding(nn.Module): - def __init__(self, hidden_size=4096, caption_dim=5120, frequency_embedding_size=256): + def __init__(self, hidden_size=4096, encoder_hidden_size=2048, frequency_embedding_size=256): super().__init__() self.time_proj = Timesteps( num_channels=frequency_embedding_size, flip_sin_to_cos=True, downscale_freq_shift=0.0 @@ -773,9 +773,9 @@ def __init__(self, hidden_size=4096, caption_dim=5120, frequency_embedding_size= nn.init.zeros_(self.timestep_embedder.linear_2.bias) self.caption_embedder = nn.Sequential( - nn.LayerNorm(caption_dim), + nn.LayerNorm(encoder_hidden_size), nn.Linear( - caption_dim, + encoder_hidden_size, hidden_size, bias=True, ), diff --git a/src/diffusers/models/transformers/lumina_nextdit2d.py b/src/diffusers/models/transformers/lumina_nextdit2d.py index 1c27ded8ad46..37db0cce66d1 100644 --- a/src/diffusers/models/transformers/lumina_nextdit2d.py +++ b/src/diffusers/models/transformers/lumina_nextdit2d.py @@ -12,18 +12,15 @@ # See the License for the specific language governing permissions and # limitations under the License. -import math from typing import List, Optional, Tuple import torch import torch.nn as nn -import torch.nn.functional as F -from flash_attn import flash_attn_varlen_func -from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa from ...configuration_utils import ConfigMixin, register_to_config from ...utils import logging from ..attention import LuminaFeedForward +from ..attention_processor import Attention, LuminaAttnProcessor2_0 from ..embeddings import ( LuminaCombinedTimestepCaptionEmbedding, ) @@ -39,307 +36,6 @@ def modulate(x, scale): return x * (1 + scale.unsqueeze(1)) -class Attention(nn.Module): - """ - Initialize the Multi-head attention module. - - Args: - hidden_size (int): Number of input dimensions. - num_attention_heads (int): Number of heads. - num_kv_heads (Optional[int]): Number of kv heads, if using GQA. - """ - - def __init__( - self, - hidden_size: int, - num_attention_heads: int, - num_kv_heads: Optional[int], - qk_norm: bool, - caption_dim: int, - ): - super().__init__() - self.num_kv_heads = num_attention_heads if num_kv_heads is None else num_kv_heads - self.num_attention_heads = num_attention_heads - self.num_kv_heads = self.num_kv_heads - self.n_rep = self.num_attention_heads // self.num_kv_heads - self.head_dim = hidden_size // num_attention_heads - - self.wq = nn.Linear( - hidden_size, - num_attention_heads * self.head_dim, - bias=False, - ) - nn.init.xavier_uniform_(self.wq.weight) - self.wk = nn.Linear( - hidden_size, - self.num_kv_heads * self.head_dim, - bias=False, - ) - nn.init.xavier_uniform_(self.wk.weight) - self.wv = nn.Linear( - hidden_size, - self.num_kv_heads * self.head_dim, - bias=False, - ) - nn.init.xavier_uniform_(self.wv.weight) - if caption_dim > 0: - self.wk_cap = nn.Linear( - caption_dim, - self.num_kv_heads * self.head_dim, - bias=False, - ) - nn.init.xavier_uniform_(self.wk_cap.weight) - self.wv_cap = nn.Linear( - caption_dim, - self.num_kv_heads * self.head_dim, - bias=False, - ) - nn.init.xavier_uniform_(self.wv_cap.weight) - self.gate = nn.Parameter(torch.zeros([self.num_attention_heads])) - - self.wo = nn.Linear( - num_attention_heads * self.head_dim, - hidden_size, - bias=False, - ) - nn.init.xavier_uniform_(self.wo.weight) - - if qk_norm: - self.q_norm = nn.LayerNorm(self.num_attention_heads * self.head_dim) - self.k_norm = nn.LayerNorm(self.num_kv_heads * self.head_dim) - if caption_dim > 0: - self.k_cap_norm = nn.LayerNorm(self.num_kv_heads * self.head_dim) - else: - self.k_cap_norm = nn.Identity() - else: - self.q_norm = self.k_norm = nn.Identity() - self.k_cap_norm = nn.Identity() - - # for proportional attention computation - self.base_seqlen = None - self.proportional_attn = False - self.use_flash_attn = True - - @staticmethod - def reshape_for_broadcast(freqs_cis: torch.Tensor, x: torch.Tensor): - """ - Reshape frequency tensor for broadcasting it with another tensor. - - This function reshapes the frequency tensor to have the same shape as the target tensor 'x' for the purpose of - broadcasting the frequency tensor during element-wise operations. - - Args: - freqs_cis (torch.Tensor): Frequency tensor to be reshaped. - x (torch.Tensor): Target tensor for broadcasting compatibility. - - Returns: - torch.Tensor: Reshaped frequency tensor. - - Raises: - AssertionError: If the frequency tensor doesn't match the expected - shape. - AssertionError: If the target tensor 'x' doesn't have the expected - number of dimensions. - """ - ndim = x.ndim - assert 0 <= 1 < ndim - assert freqs_cis.shape == (x.shape[1], x.shape[-1]) - shape = [d if i == 1 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)] - return freqs_cis.view(*shape) - - @staticmethod - def apply_rotary_emb( - x_in: torch.Tensor, - freqs_cis: torch.Tensor, - ) -> torch.Tensor: - """ - Apply rotary embeddings to input tensors using the given frequency tensor. - - This function applies rotary embeddings to the given query 'xq' and key 'xk' tensors using the provided - frequency tensor 'freqs_cis'. The input tensors are reshaped as complex numbers, and the frequency tensor is - reshaped for broadcasting compatibility. The resulting tensors contain rotary embeddings and are returned as - real tensors. - - Args: - x_in (torch.Tensor): Query or Key tensor to apply rotary embeddings. - freqs_cis (torch.Tensor): Precomputed frequency tensor for complex - exponentials. - - Returns: - Tuple[torch.Tensor, torch.Tensor]: Tuple of modified query tensor - and key tensor with rotary embeddings. - """ - with torch.cuda.amp.autocast(enabled=False): - x = torch.view_as_complex(x_in.float().reshape(*x_in.shape[:-1], -1, 2)) - freqs_cis = freqs_cis.unsqueeze(2) - x_out = torch.view_as_real(x * freqs_cis).flatten(3) - return x_out.type_as(x_in) - - # copied from huggingface modeling_llama.py - def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length): - def _get_unpad_data(attention_mask): - seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32) - indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten() - max_seqlen_in_batch = seqlens_in_batch.max().item() - cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0)) - return ( - indices, - cu_seqlens, - max_seqlen_in_batch, - ) - - indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask) - batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape - - key_layer = index_first_axis( - key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), - indices_k, - ) - value_layer = index_first_axis( - value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), - indices_k, - ) - if query_length == kv_seq_len: - query_layer = index_first_axis( - query_layer.reshape(batch_size * kv_seq_len, self.num_attention_heads, head_dim), - indices_k, - ) - cu_seqlens_q = cu_seqlens_k - max_seqlen_in_batch_q = max_seqlen_in_batch_k - indices_q = indices_k - elif query_length == 1: - max_seqlen_in_batch_q = 1 - cu_seqlens_q = torch.arange( - batch_size + 1, dtype=torch.int32, device=query_layer.device - ) # There is a memcpy here, that is very bad. - indices_q = cu_seqlens_q[:-1] - query_layer = query_layer.squeeze(1) - else: - # The -q_len: slice assumes left padding. - attention_mask = attention_mask[:, -query_length:] - query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask) - - return ( - query_layer, - key_layer, - value_layer, - indices_q, - (cu_seqlens_q, cu_seqlens_k), - (max_seqlen_in_batch_q, max_seqlen_in_batch_k), - ) - - def forward( - self, - x: torch.Tensor, - x_mask: torch.Tensor, - freqs_cis: torch.Tensor, - caption_feat: torch.Tensor, - caption_mask: torch.Tensor, - ) -> torch.Tensor: - """ - Apply the attention mechanism to the input tensors. - - Args: - x (torch.Tensor): Input tensor. - x_mask (torch.Tensor): Mask for the input tensor. - freqs_cis (torch.Tensor): Frequency tensor for complex exponentials. - caption_feat (torch.Tensor): Additional input tensor. - caption_mask (torch.Tensor): Mask for the additional input tensor. - - Returns: - torch.Tensor: Output tensor after applying the attention mechanism. - """ - bsz, seqlen, _ = x.shape - xq, xk, xv = self.wq(x), self.wk(x), self.wv(x) - dtype = xq.dtype - - xq = self.q_norm(xq) - xk = self.k_norm(xk) - - xq = xq.view(bsz, seqlen, self.num_attention_heads, self.head_dim) - xk = xk.view(bsz, seqlen, self.num_kv_heads, self.head_dim) - xv = xv.view(bsz, seqlen, self.num_kv_heads, self.head_dim) - - xq = Attention.apply_rotary_emb(xq, freqs_cis=freqs_cis) - xk = Attention.apply_rotary_emb(xk, freqs_cis=freqs_cis) - - xq, xk = xq.to(dtype), xk.to(dtype) - - if self.proportional_attn: - softmax_scale = math.sqrt(math.log(seqlen, self.base_seqlen) / self.head_dim) - else: - softmax_scale = math.sqrt(1 / self.head_dim) - - if self.use_flash_attn and dtype in [torch.float16, torch.bfloat16]: - ( - query_states, - key_states, - value_states, - indices_q, - cu_seq_lens, - max_seq_lens, - ) = self._upad_input(xq, xk, xv, x_mask, seqlen) - - cu_seqlens_q, cu_seqlens_k = cu_seq_lens - max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens - - attn_output_unpad = flash_attn_varlen_func( - query_states, - key_states, - value_states, - cu_seqlens_q=cu_seqlens_q, - cu_seqlens_k=cu_seqlens_k, - max_seqlen_q=max_seqlen_in_batch_q, - max_seqlen_k=max_seqlen_in_batch_k, - dropout_p=0.0, - causal=False, - softmax_scale=softmax_scale, - ) - output = pad_input(attn_output_unpad, indices_q, bsz, seqlen) - - else: - n_rep = self.num_attention_heads // self.num_kv_heads - if n_rep >= 1: - xk = xk.unsqueeze(3).repeat(1, 1, 1, n_rep, 1).flatten(2, 3) - xv = xv.unsqueeze(3).repeat(1, 1, 1, n_rep, 1).flatten(2, 3) - output = ( - F.scaled_dot_product_attention( - xq.permute(0, 2, 1, 3), - xk.permute(0, 2, 1, 3), - xv.permute(0, 2, 1, 3), - attn_mask=x_mask.bool().view(bsz, 1, 1, seqlen).expand(-1, self.num_attention_heads, seqlen, -1), - scale=softmax_scale, - ) - .permute(0, 2, 1, 3) - .to(dtype) - ) - - if hasattr(self, "wk_cap"): - # print(self.wk_cap(caption_feat).shape, caption_feat.shape) - yk = self.k_cap_norm(self.wk_cap(caption_feat)).view(bsz, -1, self.num_kv_heads, self.head_dim) - yv = self.wv_cap(caption_feat).view(bsz, -1, self.num_kv_heads, self.head_dim) - - n_rep = self.num_attention_heads // self.num_kv_heads - - if n_rep >= 1: - yk = yk.unsqueeze(3).repeat(1, 1, 1, n_rep, 1).flatten(2, 3) - yv = yv.unsqueeze(3).repeat(1, 1, 1, n_rep, 1).flatten(2, 3) - - output_caption = F.scaled_dot_product_attention( - xq.permute(0, 2, 1, 3), - yk.permute(0, 2, 1, 3), - yv.permute(0, 2, 1, 3), - caption_mask.view(bsz, 1, 1, -1).expand(bsz, self.num_attention_heads, seqlen, -1), - ).permute(0, 2, 1, 3) - - output_caption = output_caption * self.gate.tanh().view(1, 1, -1, 1) - output = output + output_caption - - output = output.flatten(-2) - - return self.wo(output) - - class FinalLayer(nn.Module): """ The final layer of LuminaNextDiT. @@ -431,13 +127,29 @@ def __init__( ffn_dim_multiplier: float, norm_eps: float, qk_norm: bool, - caption_dim: int, - elementwise_affine: bool = True, + encoder_hidden_size: int, + norm_elementwise_affine: bool = True, ) -> None: super().__init__() self.hidden_size = hidden_size self.head_dim = hidden_size // num_attention_heads - self.attention = Attention(hidden_size, num_attention_heads, num_kv_heads, qk_norm, caption_dim) + self.attention = Attention( + query_dim=hidden_size, + cross_attention_dim=None, + dim_head=hidden_size // num_attention_heads, + heads=num_attention_heads, + qk_norm="layer_norm" if qk_norm else None, + eps=1e-5, + bias=False, + out_bias=False, + processor=LuminaAttnProcessor2_0( + hidden_size=hidden_size, + encoder_hidden_size=encoder_hidden_size, + heads=num_attention_heads, + kv_heads=num_kv_heads, + ), + ) + self.feed_forward = LuminaFeedForward( hidden_size=hidden_size, intermediate_size=4 * hidden_size, @@ -445,11 +157,11 @@ def __init__( ffn_dim_multiplier=ffn_dim_multiplier, ) self.layer_id = layer_id - self.attention_norm1 = RMSNorm(hidden_size, eps=norm_eps, elementwise_affine=elementwise_affine) - self.ffn_norm1 = RMSNorm(hidden_size, eps=norm_eps, elementwise_affine=elementwise_affine) + self.attention_norm1 = RMSNorm(hidden_size, eps=norm_eps, elementwise_affine=norm_elementwise_affine) + self.ffn_norm1 = RMSNorm(hidden_size, eps=norm_eps, elementwise_affine=norm_elementwise_affine) - self.attention_norm2 = RMSNorm(hidden_size, eps=norm_eps, elementwise_affine=elementwise_affine) - self.ffn_norm2 = RMSNorm(hidden_size, eps=norm_eps, elementwise_affine=elementwise_affine) + self.attention_norm2 = RMSNorm(hidden_size, eps=norm_eps, elementwise_affine=norm_elementwise_affine) + self.ffn_norm2 = RMSNorm(hidden_size, eps=norm_eps, elementwise_affine=norm_elementwise_affine) self.adaLN_modulation = nn.Sequential( nn.SiLU(), @@ -462,22 +174,22 @@ def __init__( nn.init.zeros_(self.adaLN_modulation[1].weight) nn.init.zeros_(self.adaLN_modulation[1].bias) - self.attention_caption_norm = RMSNorm(caption_dim, eps=norm_eps, elementwise_affine=elementwise_affine) + self.attention_caption_norm = RMSNorm(encoder_hidden_size, eps=norm_eps, elementwise_affine=norm_elementwise_affine) def forward( self, - x: torch.Tensor, - x_mask: torch.Tensor, + hidden_state: torch.Tensor, + attention_mask: torch.Tensor, freqs_cis: torch.Tensor, - caption_feat: torch.Tensor, - caption_mask: torch.Tensor, + encoder_hidden_state: torch.Tensor, + encoder_mask: torch.Tensor, adaln_input: Optional[torch.Tensor] = None, ): """ Perform a forward pass through the LuminaNextDiTBlock. Args: - x (torch.Tensor): Input tensor. + hidden_state (torch.Tensor): Input tensor. freqs_cis (torch.Tensor): Precomputed cosine and sine frequencies. Returns: torch.Tensor: Output tensor after applying attention and @@ -486,33 +198,33 @@ def forward( if adaln_input is not None: scale_msa, gate_msa, scale_mlp, gate_mlp = self.adaLN_modulation(adaln_input).chunk(4, dim=1) - x = x + gate_msa.unsqueeze(1).tanh() * self.attention_norm2( + hidden_state = hidden_state + gate_msa.unsqueeze(1).tanh() * self.attention_norm2( self.attention( - modulate(self.attention_norm1(x), scale_msa), - x_mask, - freqs_cis, - self.attention_caption_norm(caption_feat), - caption_mask, + hidden_states=modulate(self.attention_norm1(hidden_state), scale_msa), + encoder_hidden_state=self.attention_caption_norm(encoder_hidden_state), + attention_mask=attention_mask, + encoder_mask=encoder_mask, + image_rotary_emb=freqs_cis, ) ) - x = x + gate_mlp.unsqueeze(1).tanh() * self.ffn_norm2( + hidden_state = hidden_state + gate_mlp.unsqueeze(1).tanh() * self.ffn_norm2( self.feed_forward( - modulate(self.ffn_norm1(x), scale_mlp), + modulate(self.ffn_norm1(hidden_state), scale_mlp), ) ) else: - x = x + self.attention_norm2( + hidden_state = hidden_state + self.attention_norm2( self.attention( - self.attention_norm1(x), - x_mask, - freqs_cis, - self.attention_caption_norm(caption_feat), - caption_mask, + hidden_states=modulate(self.attention_norm1(hidden_state), scale_msa), + encoder_hidden_state=self.attention_caption_norm(encoder_hidden_state), + attention_mask=attention_mask, + encoder_mask=encoder_mask, + image_rotary_emb=freqs_cis, ) ) - x = x + self.ffn_norm2(self.feed_forward(self.ffn_norm1(x))) + hidden_state = hidden_state + self.ffn_norm2(self.feed_forward(self.ffn_norm1(hidden_state))) - return x + return hidden_state class LuminaNextDiT2DModel(ModelMixin, ConfigMixin): @@ -553,7 +265,7 @@ class LuminaNextDiT2DModel(ModelMixin, ConfigMixin): predictions. qk_norm (`bool`, *optional*, defaults to True): Indicates if the queries and keys in the attention mechanism should be normalized. - caption_dim (`int`, *optional*, defaults to 2048): + encoder_hidden_size (`int`, *optional*, defaults to 2048): The dimensionality of the text embeddings. This parameter defines the size of the text representations used in the model. scaling_factor (`float`, *optional*, defaults to 1.0): @@ -567,7 +279,7 @@ def __init__( sample_size: int = 128, patch_size: Optional[int] = 2, in_channels: Optional[int] = 4, - hidden_size: Optional[int] = 4096, + hidden_size: Optional[int] = 2304, num_layers: Optional[int] = 32, num_attention_heads: Optional[int] = 32, num_kv_heads: Optional[int] = None, @@ -576,10 +288,11 @@ def __init__( norm_eps: Optional[float] = 1e-5, learn_sigma: Optional[bool] = True, qk_norm: Optional[bool] = False, - caption_dim: Optional[int] = 2048, + encoder_hidden_size: Optional[int] = 2048, scaling_factor: Optional[float] = 1.0, ) -> None: super().__init__() + self.sample_size = sample_size self.patch_size = patch_size self.in_channels = in_channels self.out_channels = in_channels * 2 if learn_sigma else in_channels @@ -600,7 +313,7 @@ def __init__( nn.init.normal_(self.pad_token, std=0.02) self.time_caption_embed = LuminaCombinedTimestepCaptionEmbedding( - hidden_size=min(hidden_size, 1024), caption_dim=caption_dim + hidden_size=min(hidden_size, 1024), encoder_hidden_size=encoder_hidden_size ) self.layers = nn.ModuleList( @@ -614,7 +327,7 @@ def __init__( ffn_dim_multiplier, norm_eps, qk_norm, - caption_dim, + encoder_hidden_size, ) for layer_id in range(num_layers) ] @@ -746,36 +459,36 @@ def patchify_and_embed( def forward( self, - x: torch.Tensor, + hidden_states: torch.Tensor, timestep: torch.Tensor, - caption_feat: torch.Tensor, - caption_mask: torch.Tensor, + encoder_hidden_state: torch.Tensor, + encoder_mask: torch.Tensor, return_dict=True, ) -> torch.Tensor: """ Forward pass of LuminaNextDiT. Args: - x (torch.Tensor): Input tensor of shape (N, C, H, W). + hidden_states (torch.Tensor): Input tensor of shape (N, C, H, W). timestep (torch.Tensor): Tensor of diffusion timesteps of shape (N,). - caption_feat (torch.Tensor): Tensor of caption features of shape (N, D). - caption_mask (torch.Tensor): Tensor of caption masks of shape (N, L). + encoder_hidden_state (torch.Tensor): Tensor of caption features of shape (N, D). + encoder_mask (torch.Tensor): Tensor of caption masks of shape (N, L). Returns: torch.Tensor: Output tensor of shape (N, C, H, W). """ - x_is_tensor = isinstance(x, torch.Tensor) - x, mask, img_size, freqs_cis = self.patchify_and_embed(x) - freqs_cis = freqs_cis.to(x.device) + x_is_tensor = isinstance(hidden_states, torch.Tensor) + hidden_states, mask, img_size, freqs_cis = self.patchify_and_embed(hidden_states) + freqs_cis = freqs_cis.to(hidden_states.device) - adaln_input = self.time_caption_embed(timestep, caption_feat, caption_mask) + adaln_input = self.time_caption_embed(timestep, encoder_hidden_state, encoder_mask) - caption_mask = caption_mask.bool() + encoder_mask = encoder_mask.bool() for layer in self.layers: - x = layer(x, mask, freqs_cis, caption_feat, caption_mask, adaln_input=adaln_input) + hidden_states = layer(hidden_states, mask, freqs_cis, encoder_hidden_state, encoder_mask, adaln_input=adaln_input) - x = self.final_layer(x, adaln_input) - output = self.unpatchify(x, img_size, return_tensor=x_is_tensor) + hidden_states = self.final_layer(hidden_states, adaln_input) + output = self.unpatchify(hidden_states, img_size, return_tensor=x_is_tensor) if not return_dict: return (output,) diff --git a/src/diffusers/pipelines/lumina/pipeline_lumina.py b/src/diffusers/pipelines/lumina/pipeline_lumina.py index 6cc52ad079b4..60beb2858e25 100644 --- a/src/diffusers/pipelines/lumina/pipeline_lumina.py +++ b/src/diffusers/pipelines/lumina/pipeline_lumina.py @@ -17,7 +17,7 @@ import math import re import urllib.parse as ul -from typing import Callable, Dict, List, Optional, Tuple, Union +from typing import List, Optional, Tuple, Union import torch from transformers import AutoModel, AutoTokenizer @@ -51,7 +51,9 @@ >>> import torch >>> from diffusers import LuminaText2ImgPipeline - >>> pipe = LuminaText2ImgPipeline.from_pretrained("Alpha-VLLM/Lumina-Next-SFT-diffusers", torch_dtype=torch.bfloat16).cuda() + >>> pipe = LuminaText2ImgPipeline.from_pretrained( + ... "Alpha-VLLM/Lumina-Next-SFT-diffusers", torch_dtype=torch.bfloat16 + ... ).cuda() >>> # Enable memory optimizations. >>> pipe.enable_model_cpu_offload() @@ -807,7 +809,7 @@ def __call__( for i, t in enumerate(timesteps): # expand the latents if we are doing classifier free guidance latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents - + current_timestep = t if not torch.is_tensor(current_timestep): # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can @@ -828,14 +830,14 @@ def __call__( current_timestep = current_timestep.expand(latent_model_input.shape[0]) # reverse the timestep since Lumina uses t=0 as the noise and t=1 as the image - current_timestep = (1 - current_timestep / self.scheduler.config.num_train_timesteps) + current_timestep = 1 - current_timestep / self.scheduler.config.num_train_timesteps # dynamic scaling_factor for different resolution. hidden_size = self.transformer.config.hidden_size num_attention_heads = self.transformer.config.num_attention_heads attention_head_dim = hidden_size // num_attention_heads - scaling_factor = math.sqrt(width * height / self.default_image_size ** 2) - + scaling_factor = math.sqrt(width * height / self.default_image_size**2) + self.transformer.freqs_cis = self.transformer.precompute_freqs_cis( attention_head_dim, 384, @@ -860,10 +862,14 @@ def __call__( # eps, rest = model_out[:, :self.in_channels], model_out[:, self.in_channels:] if do_classifier_free_guidance: noise_pred_eps, noise_pred_rest = noise_pred[:, :3], noise_pred[:, 3:] - noise_pred_cond_eps, noise_pred_uncond_eps = torch.split(noise_pred_eps, len(noise_pred_eps) // 2, dim=0) - noise_pred_half = noise_pred_uncond_eps + guidance_scale * (noise_pred_cond_eps - noise_pred_uncond_eps) + noise_pred_cond_eps, noise_pred_uncond_eps = torch.split( + noise_pred_eps, len(noise_pred_eps) // 2, dim=0 + ) + noise_pred_half = noise_pred_uncond_eps + guidance_scale * ( + noise_pred_cond_eps - noise_pred_uncond_eps + ) noise_pred_eps = torch.cat([noise_pred_half, noise_pred_half], dim=0) - + noise_pred = torch.cat([noise_pred_eps, noise_pred_rest], dim=1) noise_pred, _ = noise_pred.chunk(2, dim=0) diff --git a/src/diffusers/schedulers/scheduling_flow_match_euler_discrete.py b/src/diffusers/schedulers/scheduling_flow_match_euler_discrete.py index 1725b213931c..08e9d4f3a95d 100644 --- a/src/diffusers/schedulers/scheduling_flow_match_euler_discrete.py +++ b/src/diffusers/schedulers/scheduling_flow_match_euler_discrete.py @@ -156,7 +156,7 @@ def set_timesteps(self, num_inference_steps: int, device: Union[str, torch.devic sigmas = timesteps / self.config.num_train_timesteps sigmas = self.config.shift * sigmas / (1 + (self.config.shift - 1) * sigmas) sigmas = torch.from_numpy(sigmas).to(dtype=torch.float32, device=device) - + timesteps = sigmas * self.config.num_train_timesteps self.timesteps = timesteps.to(device=device) self.sigmas = torch.cat([sigmas, torch.zeros(1, device=sigmas.device)]) From 367e9f97e970e93df31c1445cfcb9afb84c50e24 Mon Sep 17 00:00:00 2001 From: PommesPeter Date: Wed, 26 Jun 2024 03:44:26 +0800 Subject: [PATCH 26/92] Fixed AttnProcessor --- src/diffusers/models/attention_processor.py | 86 +++++++++---------- src/diffusers/models/embeddings.py | 24 ++++-- .../models/transformers/lumina_nextdit2d.py | 17 +--- .../pipelines/lumina/pipeline_lumina.py | 6 +- 4 files changed, 60 insertions(+), 73 deletions(-) diff --git a/src/diffusers/models/attention_processor.py b/src/diffusers/models/attention_processor.py index fb2030f1d883..54d7d68f6de0 100644 --- a/src/diffusers/models/attention_processor.py +++ b/src/diffusers/models/attention_processor.py @@ -96,6 +96,7 @@ def __init__( query_dim: int, cross_attention_dim: Optional[int] = None, heads: int = 8, + kv_heads: int = 1, dim_head: int = 64, dropout: float = 0.0, bias: bool = False, @@ -120,6 +121,7 @@ def __init__( ): super().__init__() self.inner_dim = out_dim if out_dim is not None else dim_head * heads + self.inner_kv_dim = out_dim if out_dim is not None else dim_head * kv_heads self.query_dim = query_dim self.use_bias = bias self.is_cross_attention = cross_attention_dim is not None @@ -200,15 +202,15 @@ def __init__( if not self.only_cross_attention: # only relevant for the `AddedKVProcessor` classes - self.to_k = nn.Linear(self.cross_attention_dim, self.inner_dim, bias=bias) - self.to_v = nn.Linear(self.cross_attention_dim, self.inner_dim, bias=bias) + self.to_k = nn.Linear(self.cross_attention_dim, self.inner_kv_dim, bias=bias) + self.to_v = nn.Linear(self.cross_attention_dim, self.inner_kv_dim, bias=bias) else: self.to_k = None self.to_v = None if self.added_kv_proj_dim is not None: - self.add_k_proj = nn.Linear(added_kv_proj_dim, self.inner_dim) - self.add_v_proj = nn.Linear(added_kv_proj_dim, self.inner_dim) + self.add_k_proj = nn.Linear(added_kv_proj_dim, self.inner_kv_dim) + self.add_v_proj = nn.Linear(added_kv_proj_dim, self.inner_kv_dim) if self.context_pre_only is not None: self.add_q_proj = nn.Linear(added_kv_proj_dim, self.inner_dim) @@ -1716,23 +1718,6 @@ def __init__( if not hasattr(F, "scaled_dot_product_attention"): raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.") self.head_dim = hidden_size // heads - - self.to_q = nn.Linear( - hidden_size, - heads * self.head_dim, - bias=False, - ) - self.to_k = nn.Linear( - hidden_size, - kv_heads * self.head_dim, - bias=False, - ) - self.to_v = nn.Linear( - hidden_size, - kv_heads * self.head_dim, - bias=False, - ) - self.norm_q = nn.LayerNorm(heads * self.head_dim) self.norm_k = nn.LayerNorm(kv_heads * self.head_dim) @@ -1769,29 +1754,21 @@ def __call__( batch_size, channel, height, width = hidden_states.shape hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2) - batch_size, sequence_length, _ = ( - hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape - ) - - if attention_mask is not None: - attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size) - # scaled_dot_product_attention expects attention_mask shape to be - # (batch, heads, source_length, target_length) - attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1]) + batch_size, sequence_length, _ = hidden_states.shape # Get Query-Key-Value Pair - query = self.to_q(hidden_states) + query = attn.to_q(hidden_states) - key = self.to_k(hidden_states) - value = self.to_v(hidden_states) + key = attn.to_k(hidden_states) + value = attn.to_v(hidden_states) query_dim = query.shape[-1] inner_dim = key.shape[-1] head_dim = query_dim // attn.heads dtype = query.dtype - # Get key-value head - kv_heads = inner_dim // attn.heads + # Get key-value heads + kv_heads = inner_dim // self.head_dim # Apply Query-Key Norm if needed if self.norm_q is not None: @@ -1799,15 +1776,15 @@ def __call__( if self.norm_k is not None: key = self.norm_k(key) - query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) + query = query.view(batch_size, sequence_length, attn.heads, head_dim) - key = key.view(batch_size, -1, kv_heads, head_dim).transpose(1, 2) - value = value.view(batch_size, -1, kv_heads, head_dim).transpose(1, 2) + key = key.view(batch_size, sequence_length, kv_heads, head_dim) + value = value.view(batch_size, sequence_length, kv_heads, head_dim) # Apply RoPE if needed if image_rotary_emb is not None: - query = apply_rotary_emb(query, image_rotary_emb) - key = apply_rotary_emb(key, image_rotary_emb) + query = apply_rotary_emb(query, image_rotary_emb, use_real=False) + key = apply_rotary_emb(key, image_rotary_emb, use_real=False) query, key = query.to(dtype), key.to(dtype) @@ -1823,29 +1800,44 @@ def __call__( key = key.unsqueeze(3).repeat(1, 1, 1, n_rep, 1).flatten(2, 3) value = value.unsqueeze(3).repeat(1, 1, 1, n_rep, 1).flatten(2, 3) + # scaled_dot_product_attention expects attention_mask shape to be + # (batch, heads, source_length, target_length) attention_mask = ( - attention_mask.bool().view(batch_size, 1, 1, sequence_length).expand(-1, attn.heads, sequence_length, -1) + attention_mask.bool().view(batch_size, 1, 1, sequence_length) ) + attention_mask = attention_mask.expand(-1, attn.heads, sequence_length, -1) + + query = query.transpose(1, 2) + key = key.transpose(1, 2) + value = value.transpose(1, 2) + # the output of sdp = (batch, num_heads, seq_len, head_dim) # TODO: add support for attn.scale when we move to Torch 2.1 hidden_states = F.scaled_dot_product_attention( query, key, value, attn_mask=attention_mask, scale=softmax_scale ) - hidden_states = hidden_states.permute(0, 2, 1, 3).to(dtype) + hidden_states = hidden_states.transpose(1, 2).to(dtype) # Cross Attention key_cap = self.key_cap(encoder_hidden_states) # Apply Key Norm to encoder_hidden_state - key_cap = self.norm_k_cap(key_cap).view(batch_size, -1, kv_heads, head_dim).transpose(1, 2) - value_cap = self.value_cap(encoder_hidden_states).view(batch_size, -1, kv_heads, head_dim).transpose(1, 2) + key_cap = self.norm_k_cap(key_cap).view(batch_size, -1, kv_heads, head_dim) + value_cap = self.value_cap(encoder_hidden_states).view(batch_size, -1, kv_heads, head_dim) if n_rep >= 1: key_cap = key_cap.unsqueeze(3).repeat(1, 1, 1, n_rep, 1).flatten(2, 3) value_cap = value_cap.unsqueeze(3).repeat(1, 1, 1, n_rep, 1).flatten(2, 3) - encoder_mask = (encoder_mask.view(batch_size, 1, 1, -1).expand(batch_size, attn.heads, sequence_length, -1),) - hidden_states_caption = F.scaled_dot_product_attention(query, key_cap, value_cap, encoder_mask) - hidden_states_caption = hidden_states_caption.permute(0, 2, 1, 3) + key_cap = key_cap.transpose(1, 2) + value_cap = value_cap.transpose(1, 2) + + # scaled_dot_product_attention expects attention_mask shape to be + # (batch, heads, source_length, target_length) + encoder_mask = encoder_mask.view(batch_size, 1, 1, -1) + encoder_mask = encoder_mask.expand(batch_size, attn.heads, sequence_length, -1) + + hidden_states_caption = F.scaled_dot_product_attention(query, key_cap, value_cap, attn_mask=encoder_mask) + hidden_states_caption = hidden_states_caption.transpose(1, 2) hidden_states_caption = hidden_states_caption * self.gate.tanh().view(1, 1, -1, 1) hidden_states = hidden_states + hidden_states_caption diff --git a/src/diffusers/models/embeddings.py b/src/diffusers/models/embeddings.py index 73fe4b64ce99..ac3cb224c137 100644 --- a/src/diffusers/models/embeddings.py +++ b/src/diffusers/models/embeddings.py @@ -310,6 +310,7 @@ def get_1d_rotary_pos_embed(dim: int, pos: Union[np.ndarray, int], theta: float def apply_rotary_emb( x: torch.Tensor, freqs_cis: Union[torch.Tensor, Tuple[torch.Tensor]], + use_real: bool = True, ) -> Tuple[torch.Tensor, torch.Tensor]: """ Apply rotary embeddings to input tensors using the given frequency tensor. This function applies rotary embeddings @@ -325,16 +326,23 @@ def apply_rotary_emb( Returns: Tuple[torch.Tensor, torch.Tensor]: Tuple of modified query tensor and key tensor with rotary embeddings. """ - cos, sin = freqs_cis # [S, D] - cos = cos[None, None] - sin = sin[None, None] - cos, sin = cos.to(x.device), sin.to(x.device) + if use_real: + cos, sin = freqs_cis # [S, D] + cos = cos[None, None] + sin = sin[None, None] + cos, sin = cos.to(x.device), sin.to(x.device) - x_real, x_imag = x.reshape(*x.shape[:-1], -1, 2).unbind(-1) # [B, S, H, D//2] - x_rotated = torch.stack([-x_imag, x_real], dim=-1).flatten(3) - out = (x.float() * cos + x_rotated.float() * sin).to(x.dtype) + x_real, x_imag = x.reshape(*x.shape[:-1], -1, 2).unbind(-1) # [B, S, H, D//2] + x_rotated = torch.stack([-x_imag, x_real], dim=-1).flatten(3) + out = (x.float() * cos + x_rotated.float() * sin).to(x.dtype) - return out + return out + else: + x_rotated = torch.view_as_complex(x.float().reshape(*x.shape[:-1], -1, 2)) + freqs_cis = freqs_cis.unsqueeze(2) + x_out = torch.view_as_real(x_rotated * freqs_cis).flatten(3) + + return x_out.type_as(x) class TimestepEmbedding(nn.Module): diff --git a/src/diffusers/models/transformers/lumina_nextdit2d.py b/src/diffusers/models/transformers/lumina_nextdit2d.py index 37db0cce66d1..38589d9cd75c 100644 --- a/src/diffusers/models/transformers/lumina_nextdit2d.py +++ b/src/diffusers/models/transformers/lumina_nextdit2d.py @@ -138,7 +138,7 @@ def __init__( cross_attention_dim=None, dim_head=hidden_size // num_attention_heads, heads=num_attention_heads, - qk_norm="layer_norm" if qk_norm else None, + kv_heads=num_kv_heads, eps=1e-5, bias=False, out_bias=False, @@ -201,7 +201,7 @@ def forward( hidden_state = hidden_state + gate_msa.unsqueeze(1).tanh() * self.attention_norm2( self.attention( hidden_states=modulate(self.attention_norm1(hidden_state), scale_msa), - encoder_hidden_state=self.attention_caption_norm(encoder_hidden_state), + encoder_hidden_states=self.attention_caption_norm(encoder_hidden_state), attention_mask=attention_mask, encoder_mask=encoder_mask, image_rotary_emb=freqs_cis, @@ -542,16 +542,3 @@ def precompute_freqs_cis( freqs_cis = torch.cat([freqs_cis_h, freqs_cis_w], dim=-1).flatten(2) return freqs_cis - - def parameter_count(self) -> int: - total_params = 0 - - def _recursive_count_params(module): - nonlocal total_params - for param in module.parameters(recurse=False): - total_params += param.numel() - for submodule in module.children(): - _recursive_count_params(submodule) - - _recursive_count_params(self) - return total_params diff --git a/src/diffusers/pipelines/lumina/pipeline_lumina.py b/src/diffusers/pipelines/lumina/pipeline_lumina.py index 33cdf68fab98..81ac3a14aff7 100644 --- a/src/diffusers/pipelines/lumina/pipeline_lumina.py +++ b/src/diffusers/pipelines/lumina/pipeline_lumina.py @@ -849,10 +849,10 @@ def __call__( ) noise_pred = self.transformer( - x=latent_model_input, + hidden_states=latent_model_input, timestep=current_timestep, - caption_feat=prompt_embeds, - caption_mask=prompt_attention_mask, + encoder_hidden_state=prompt_embeds, + encoder_mask=prompt_attention_mask, return_dict=False, )[0] noise_pred = noise_pred.chunk(2, dim=1)[0] From 2da4cbbb673e906cbcd540eb9b184b67c546a31d Mon Sep 17 00:00:00 2001 From: PommesPeter <54879512+PommesPeter@users.noreply.github.com> Date: Wed, 26 Jun 2024 03:45:15 +0800 Subject: [PATCH 27/92] Delete debug.py --- debug.py | 8 -------- 1 file changed, 8 deletions(-) delete mode 100644 debug.py diff --git a/debug.py b/debug.py deleted file mode 100644 index e4818eb2df73..000000000000 --- a/debug.py +++ /dev/null @@ -1,8 +0,0 @@ -from diffusers import LuminaText2ImgPipeline -import torch - -pipeline = LuminaText2ImgPipeline.from_pretrained( - "/mnt/hdd1/xiejunlin/checkpoints/Lumina-Next-SFT-diffusers", torch_dtype=torch.bfloat16 -).to("cuda") - -image = pipeline(prompt="Upper body of a young woman in a Victorian-era outfit with brass goggles and leather straps. Background shows an industrial revolution cityscape with smoky skies and tall, metal structures").images[0] \ No newline at end of file From 21999fc3b71dfc8e4575c1a357793331f40e9e3d Mon Sep 17 00:00:00 2001 From: PommesPeter Date: Wed, 26 Jun 2024 03:46:50 +0800 Subject: [PATCH 28/92] Fixed convert scripts --- scripts/convert_lumina_to_diffusers.py | 80 ++++++++++++-------------- 1 file changed, 37 insertions(+), 43 deletions(-) diff --git a/scripts/convert_lumina_to_diffusers.py b/scripts/convert_lumina_to_diffusers.py index 2d95df7d0e0e..5cc028393ade 100644 --- a/scripts/convert_lumina_to_diffusers.py +++ b/scripts/convert_lumina_to_diffusers.py @@ -12,15 +12,14 @@ def main(args): # checkpoint from https://huggingface.co/Alpha-VLLM/Lumina-Next-SFT or https://huggingface.co/Alpha-VLLM/Lumina-Next-T2I all_sd = load_file(args.origin_ckpt_path, device="cpu") converted_state_dict = {} - - # Pad token + # pad token converted_state_dict["pad_token"] = all_sd["pad_token"] - # Patch Embed + # patch embed converted_state_dict["patch_embedder.weight"] = all_sd["x_embedder.weight"] converted_state_dict["patch_embedder.bias"] = all_sd["x_embedder.bias"] - # Timestep and Caption Embed + # time and caption embed converted_state_dict["time_caption_embed.timestep_embedder.linear_1.weight"] = all_sd["t_embedder.mlp.0.weight"] converted_state_dict["time_caption_embed.timestep_embedder.linear_1.bias"] = all_sd["t_embedder.mlp.0.bias"] converted_state_dict["time_caption_embed.timestep_embedder.linear_2.weight"] = all_sd["t_embedder.mlp.2.weight"] @@ -31,54 +30,49 @@ def main(args): converted_state_dict["time_caption_embed.caption_embedder.1.bias"] = all_sd["cap_embedder.1.bias"] for i in range(24): - # adaLN - converted_state_dict[f"layers.{i}.attention.gate"] = all_sd[f"layers.{i}.attention.gate"] + # adaln + converted_state_dict[f"layers.{i}.attention.processor.gate"] = all_sd[f"layers.{i}.attention.gate"] converted_state_dict[f"layers.{i}.adaLN_modulation.1.weight"] = all_sd[f"layers.{i}.adaLN_modulation.1.weight"] converted_state_dict[f"layers.{i}.adaLN_modulation.1.bias"] = all_sd[f"layers.{i}.adaLN_modulation.1.bias"] - - # QKV - converted_state_dict[f"layers.{i}.attention.wq.weight"] = all_sd[f"layers.{i}.attention.wq.weight"] - converted_state_dict[f"layers.{i}.attention.wk.weight"] = all_sd[f"layers.{i}.attention.wk.weight"] - converted_state_dict[f"layers.{i}.attention.wv.weight"] = all_sd[f"layers.{i}.attention.wv.weight"] - - # Caption KV - converted_state_dict[f"layers.{i}.attention.wk_cap.weight"] = all_sd[f"layers.{i}.attention.wk_y.weight"] - converted_state_dict[f"layers.{i}.attention.wv_cap.weight"] = all_sd[f"layers.{i}.attention.wv_y.weight"] - - # Output Layer - converted_state_dict[f"layers.{i}.attention.wo.weight"] = all_sd[f"layers.{i}.attention.wo.weight"] - - # Attention - # QK Norm - converted_state_dict[f"layers.{i}.attention.q_norm.weight"] = all_sd[f"layers.{i}.attention.q_norm.weight"] - converted_state_dict[f"layers.{i}.attention.q_norm.bias"] = all_sd[f"layers.{i}.attention.q_norm.bias"] - - converted_state_dict[f"layers.{i}.attention.k_norm.weight"] = all_sd[f"layers.{i}.attention.k_norm.weight"] - converted_state_dict[f"layers.{i}.attention.k_norm.bias"] = all_sd[f"layers.{i}.attention.k_norm.bias"] - - # Caption K Norm - converted_state_dict[f"layers.{i}.attention.k_cap_norm.weight"] = all_sd[ - f"layers.{i}.attention.ky_norm.weight" - ] - converted_state_dict[f"layers.{i}.attention.k_cap_norm.bias"] = all_sd[f"layers.{i}.attention.ky_norm.bias"] - - # Attention Norm + + # qkv + converted_state_dict[f"layers.{i}.attention.to_q.weight"] = all_sd[f"layers.{i}.attention.wq.weight"] + converted_state_dict[f"layers.{i}.attention.to_k.weight"] = all_sd[f"layers.{i}.attention.wk.weight"] + converted_state_dict[f"layers.{i}.attention.to_v.weight"] = all_sd[f"layers.{i}.attention.wv.weight"] + + # cap + converted_state_dict[f"layers.{i}.attention.processor.key_cap.weight"] = all_sd[f"layers.{i}.attention.wk_y.weight"] + converted_state_dict[f"layers.{i}.attention.processor.value_cap.weight"] = all_sd[f"layers.{i}.attention.wv_y.weight"] + + # output + converted_state_dict[f"layers.{i}.attention.to_out.0.weight"] = all_sd[f"layers.{i}.attention.wo.weight"] + + # attention + # qk norm + converted_state_dict[f"layers.{i}.attention.processor.norm_q.weight"] = all_sd[f"layers.{i}.attention.q_norm.weight"] + converted_state_dict[f"layers.{i}.attention.processor.norm_q.bias"] = all_sd[f"layers.{i}.attention.q_norm.bias"] + + converted_state_dict[f"layers.{i}.attention.processor.norm_k.weight"] = all_sd[f"layers.{i}.attention.k_norm.weight"] + converted_state_dict[f"layers.{i}.attention.processor.norm_k.bias"] = all_sd[f"layers.{i}.attention.k_norm.bias"] + + converted_state_dict[f"layers.{i}.attention.processor.norm_k_cap.weight"] = all_sd[f"layers.{i}.attention.ky_norm.weight"] + converted_state_dict[f"layers.{i}.attention.processor.norm_k_cap.bias"] = all_sd[f"layers.{i}.attention.ky_norm.bias"] + + # attention norm converted_state_dict[f"layers.{i}.attention_norm1.weight"] = all_sd[f"layers.{i}.attention_norm1.weight"] converted_state_dict[f"layers.{i}.attention_norm2.weight"] = all_sd[f"layers.{i}.attention_norm2.weight"] - converted_state_dict[f"layers.{i}.attention_caption_norm.weight"] = all_sd[ - f"layers.{i}.attention_y_norm.weight" - ] - - # Feed Forward layer + converted_state_dict[f"layers.{i}.attention_caption_norm.weight"] = all_sd[f"layers.{i}.attention_y_norm.weight"] + + # feed forward converted_state_dict[f"layers.{i}.feed_forward.w1.weight"] = all_sd[f"layers.{i}.feed_forward.w1.weight"] converted_state_dict[f"layers.{i}.feed_forward.w2.weight"] = all_sd[f"layers.{i}.feed_forward.w2.weight"] converted_state_dict[f"layers.{i}.feed_forward.w3.weight"] = all_sd[f"layers.{i}.feed_forward.w3.weight"] - - # Feed Forward Norm + + # feed forward norm converted_state_dict[f"layers.{i}.ffn_norm1.weight"] = all_sd[f"layers.{i}.ffn_norm1.weight"] converted_state_dict[f"layers.{i}.ffn_norm2.weight"] = all_sd[f"layers.{i}.ffn_norm2.weight"] - - # Final Layer + + # final layer converted_state_dict["final_layer.linear.weight"] = all_sd["final_layer.linear.weight"] converted_state_dict["final_layer.linear.bias"] = all_sd["final_layer.linear.bias"] From 8b0d0968044abbb0a3492b88aca123fb2620ffc7 Mon Sep 17 00:00:00 2001 From: PommesPeter Date: Wed, 26 Jun 2024 03:48:33 +0800 Subject: [PATCH 29/92] Format code quality and style --- scripts/convert_lumina_to_diffusers.py | 58 ++++++++++++------- src/diffusers/models/attention_processor.py | 10 ++-- src/diffusers/models/embeddings.py | 2 +- .../models/transformers/lumina_nextdit2d.py | 8 ++- .../pipelines/lumina/pipeline_lumina.py | 4 +- tests/pipelines/lumina/test_lumina_nextdit.py | 7 ++- 6 files changed, 56 insertions(+), 33 deletions(-) diff --git a/scripts/convert_lumina_to_diffusers.py b/scripts/convert_lumina_to_diffusers.py index 5cc028393ade..9d17d3101b59 100644 --- a/scripts/convert_lumina_to_diffusers.py +++ b/scripts/convert_lumina_to_diffusers.py @@ -12,7 +12,7 @@ def main(args): # checkpoint from https://huggingface.co/Alpha-VLLM/Lumina-Next-SFT or https://huggingface.co/Alpha-VLLM/Lumina-Next-T2I all_sd = load_file(args.origin_ckpt_path, device="cpu") converted_state_dict = {} - # pad token + # pad token converted_state_dict["pad_token"] = all_sd["pad_token"] # patch embed @@ -34,44 +34,62 @@ def main(args): converted_state_dict[f"layers.{i}.attention.processor.gate"] = all_sd[f"layers.{i}.attention.gate"] converted_state_dict[f"layers.{i}.adaLN_modulation.1.weight"] = all_sd[f"layers.{i}.adaLN_modulation.1.weight"] converted_state_dict[f"layers.{i}.adaLN_modulation.1.bias"] = all_sd[f"layers.{i}.adaLN_modulation.1.bias"] - + # qkv converted_state_dict[f"layers.{i}.attention.to_q.weight"] = all_sd[f"layers.{i}.attention.wq.weight"] converted_state_dict[f"layers.{i}.attention.to_k.weight"] = all_sd[f"layers.{i}.attention.wk.weight"] converted_state_dict[f"layers.{i}.attention.to_v.weight"] = all_sd[f"layers.{i}.attention.wv.weight"] - + # cap - converted_state_dict[f"layers.{i}.attention.processor.key_cap.weight"] = all_sd[f"layers.{i}.attention.wk_y.weight"] - converted_state_dict[f"layers.{i}.attention.processor.value_cap.weight"] = all_sd[f"layers.{i}.attention.wv_y.weight"] - + converted_state_dict[f"layers.{i}.attention.processor.key_cap.weight"] = all_sd[ + f"layers.{i}.attention.wk_y.weight" + ] + converted_state_dict[f"layers.{i}.attention.processor.value_cap.weight"] = all_sd[ + f"layers.{i}.attention.wv_y.weight" + ] + # output converted_state_dict[f"layers.{i}.attention.to_out.0.weight"] = all_sd[f"layers.{i}.attention.wo.weight"] - + # attention # qk norm - converted_state_dict[f"layers.{i}.attention.processor.norm_q.weight"] = all_sd[f"layers.{i}.attention.q_norm.weight"] - converted_state_dict[f"layers.{i}.attention.processor.norm_q.bias"] = all_sd[f"layers.{i}.attention.q_norm.bias"] - - converted_state_dict[f"layers.{i}.attention.processor.norm_k.weight"] = all_sd[f"layers.{i}.attention.k_norm.weight"] - converted_state_dict[f"layers.{i}.attention.processor.norm_k.bias"] = all_sd[f"layers.{i}.attention.k_norm.bias"] - - converted_state_dict[f"layers.{i}.attention.processor.norm_k_cap.weight"] = all_sd[f"layers.{i}.attention.ky_norm.weight"] - converted_state_dict[f"layers.{i}.attention.processor.norm_k_cap.bias"] = all_sd[f"layers.{i}.attention.ky_norm.bias"] - + converted_state_dict[f"layers.{i}.attention.processor.norm_q.weight"] = all_sd[ + f"layers.{i}.attention.q_norm.weight" + ] + converted_state_dict[f"layers.{i}.attention.processor.norm_q.bias"] = all_sd[ + f"layers.{i}.attention.q_norm.bias" + ] + + converted_state_dict[f"layers.{i}.attention.processor.norm_k.weight"] = all_sd[ + f"layers.{i}.attention.k_norm.weight" + ] + converted_state_dict[f"layers.{i}.attention.processor.norm_k.bias"] = all_sd[ + f"layers.{i}.attention.k_norm.bias" + ] + + converted_state_dict[f"layers.{i}.attention.processor.norm_k_cap.weight"] = all_sd[ + f"layers.{i}.attention.ky_norm.weight" + ] + converted_state_dict[f"layers.{i}.attention.processor.norm_k_cap.bias"] = all_sd[ + f"layers.{i}.attention.ky_norm.bias" + ] + # attention norm converted_state_dict[f"layers.{i}.attention_norm1.weight"] = all_sd[f"layers.{i}.attention_norm1.weight"] converted_state_dict[f"layers.{i}.attention_norm2.weight"] = all_sd[f"layers.{i}.attention_norm2.weight"] - converted_state_dict[f"layers.{i}.attention_caption_norm.weight"] = all_sd[f"layers.{i}.attention_y_norm.weight"] - + converted_state_dict[f"layers.{i}.attention_caption_norm.weight"] = all_sd[ + f"layers.{i}.attention_y_norm.weight" + ] + # feed forward converted_state_dict[f"layers.{i}.feed_forward.w1.weight"] = all_sd[f"layers.{i}.feed_forward.w1.weight"] converted_state_dict[f"layers.{i}.feed_forward.w2.weight"] = all_sd[f"layers.{i}.feed_forward.w2.weight"] converted_state_dict[f"layers.{i}.feed_forward.w3.weight"] = all_sd[f"layers.{i}.feed_forward.w3.weight"] - + # feed forward norm converted_state_dict[f"layers.{i}.ffn_norm1.weight"] = all_sd[f"layers.{i}.ffn_norm1.weight"] converted_state_dict[f"layers.{i}.ffn_norm2.weight"] = all_sd[f"layers.{i}.ffn_norm2.weight"] - + # final layer converted_state_dict["final_layer.linear.weight"] = all_sd["final_layer.linear.weight"] converted_state_dict["final_layer.linear.bias"] = all_sd["final_layer.linear.bias"] diff --git a/src/diffusers/models/attention_processor.py b/src/diffusers/models/attention_processor.py index 54d7d68f6de0..2a9ddb34812a 100644 --- a/src/diffusers/models/attention_processor.py +++ b/src/diffusers/models/attention_processor.py @@ -1802,15 +1802,13 @@ def __call__( # scaled_dot_product_attention expects attention_mask shape to be # (batch, heads, source_length, target_length) - attention_mask = ( - attention_mask.bool().view(batch_size, 1, 1, sequence_length) - ) + attention_mask = attention_mask.bool().view(batch_size, 1, 1, sequence_length) attention_mask = attention_mask.expand(-1, attn.heads, sequence_length, -1) - + query = query.transpose(1, 2) key = key.transpose(1, 2) value = value.transpose(1, 2) - + # the output of sdp = (batch, num_heads, seq_len, head_dim) # TODO: add support for attn.scale when we move to Torch 2.1 hidden_states = F.scaled_dot_product_attention( @@ -1835,7 +1833,7 @@ def __call__( # (batch, heads, source_length, target_length) encoder_mask = encoder_mask.view(batch_size, 1, 1, -1) encoder_mask = encoder_mask.expand(batch_size, attn.heads, sequence_length, -1) - + hidden_states_caption = F.scaled_dot_product_attention(query, key_cap, value_cap, attn_mask=encoder_mask) hidden_states_caption = hidden_states_caption.transpose(1, 2) diff --git a/src/diffusers/models/embeddings.py b/src/diffusers/models/embeddings.py index ac3cb224c137..d443cad779fc 100644 --- a/src/diffusers/models/embeddings.py +++ b/src/diffusers/models/embeddings.py @@ -341,7 +341,7 @@ def apply_rotary_emb( x_rotated = torch.view_as_complex(x.float().reshape(*x.shape[:-1], -1, 2)) freqs_cis = freqs_cis.unsqueeze(2) x_out = torch.view_as_real(x_rotated * freqs_cis).flatten(3) - + return x_out.type_as(x) diff --git a/src/diffusers/models/transformers/lumina_nextdit2d.py b/src/diffusers/models/transformers/lumina_nextdit2d.py index 38589d9cd75c..48f2ef01ac66 100644 --- a/src/diffusers/models/transformers/lumina_nextdit2d.py +++ b/src/diffusers/models/transformers/lumina_nextdit2d.py @@ -174,7 +174,9 @@ def __init__( nn.init.zeros_(self.adaLN_modulation[1].weight) nn.init.zeros_(self.adaLN_modulation[1].bias) - self.attention_caption_norm = RMSNorm(encoder_hidden_size, eps=norm_eps, elementwise_affine=norm_elementwise_affine) + self.attention_caption_norm = RMSNorm( + encoder_hidden_size, eps=norm_eps, elementwise_affine=norm_elementwise_affine + ) def forward( self, @@ -485,7 +487,9 @@ def forward( encoder_mask = encoder_mask.bool() for layer in self.layers: - hidden_states = layer(hidden_states, mask, freqs_cis, encoder_hidden_state, encoder_mask, adaln_input=adaln_input) + hidden_states = layer( + hidden_states, mask, freqs_cis, encoder_hidden_state, encoder_mask, adaln_input=adaln_input + ) hidden_states = self.final_layer(hidden_states, adaln_input) output = self.unpatchify(hidden_states, img_size, return_tensor=x_is_tensor) diff --git a/src/diffusers/pipelines/lumina/pipeline_lumina.py b/src/diffusers/pipelines/lumina/pipeline_lumina.py index 81ac3a14aff7..66bd6469e42d 100644 --- a/src/diffusers/pipelines/lumina/pipeline_lumina.py +++ b/src/diffusers/pipelines/lumina/pipeline_lumina.py @@ -352,7 +352,9 @@ def encode_prompt( negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1) negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1) negative_prompt_attention_mask = negative_prompt_attention_mask.repeat(num_images_per_prompt, 1) - negative_prompt_attention_mask = negative_prompt_attention_mask.view(batch_size * num_images_per_prompt, -1) + negative_prompt_attention_mask = negative_prompt_attention_mask.view( + batch_size * num_images_per_prompt, -1 + ) return prompt_embeds, prompt_attention_mask, negative_prompt_embeds, negative_prompt_attention_mask diff --git a/tests/pipelines/lumina/test_lumina_nextdit.py b/tests/pipelines/lumina/test_lumina_nextdit.py index 575b4596b1bb..84d99e7d863e 100644 --- a/tests/pipelines/lumina/test_lumina_nextdit.py +++ b/tests/pipelines/lumina/test_lumina_nextdit.py @@ -3,7 +3,7 @@ import numpy as np import torch -from transformers import AutoTokenizer, AutoModel +from transformers import AutoModel, AutoTokenizer from diffusers import AutoencoderKL, FlowMatchEulerDiscreteScheduler, LuminaNextDiT2DModel, LuminaText2ImgPipeline from diffusers.utils.testing_utils import ( @@ -163,6 +163,7 @@ def test_lumina_inference(self): max_diff = numpy_cosine_similarity_distance(expected_slice.flatten(), image_slice.flatten()) assert max_diff < 1e-4 - + + if __name__ == "__main__": - unittest.main() \ No newline at end of file + unittest.main() From 47cf46464cae3f584ec3442c483b528549200ea7 Mon Sep 17 00:00:00 2001 From: zhuole1025 Date: Wed, 26 Jun 2024 06:36:26 +0000 Subject: [PATCH 30/92] Refactor qknorm in attention processor --- src/diffusers/models/attention_processor.py | 8 ++++++-- src/diffusers/models/transformers/lumina_nextdit2d.py | 3 +++ 2 files changed, 9 insertions(+), 2 deletions(-) diff --git a/src/diffusers/models/attention_processor.py b/src/diffusers/models/attention_processor.py index 2a9ddb34812a..b297d06b22d7 100644 --- a/src/diffusers/models/attention_processor.py +++ b/src/diffusers/models/attention_processor.py @@ -105,6 +105,8 @@ def __init__( cross_attention_norm: Optional[str] = None, cross_attention_norm_num_groups: int = 32, qk_norm: Optional[str] = None, + q_norm_dim: Optional[int] = None, + k_norm_dim: Optional[int] = None, added_kv_proj_dim: Optional[int] = None, norm_num_groups: Optional[int] = None, spatial_norm_dim: Optional[int] = None, @@ -170,8 +172,10 @@ def __init__( self.norm_q = None self.norm_k = None elif qk_norm == "layer_norm": - self.norm_q = nn.LayerNorm(dim_head, eps=eps) - self.norm_k = nn.LayerNorm(dim_head, eps=eps) + q_norm_dim = q_norm_dim if q_norm_dim is not None else dim_head + k_norm_dim = k_norm_dim if k_norm_dim is not None else dim_head + self.norm_q = nn.LayerNorm(q_norm_dim, eps=eps) + self.norm_k = nn.LayerNorm(k_norm_dim, eps=eps) else: raise ValueError(f"unknown qk_norm: {qk_norm}. Should be None or 'layer_norm'") diff --git a/src/diffusers/models/transformers/lumina_nextdit2d.py b/src/diffusers/models/transformers/lumina_nextdit2d.py index 48f2ef01ac66..3da7fc113e1b 100644 --- a/src/diffusers/models/transformers/lumina_nextdit2d.py +++ b/src/diffusers/models/transformers/lumina_nextdit2d.py @@ -139,6 +139,9 @@ def __init__( dim_head=hidden_size // num_attention_heads, heads=num_attention_heads, kv_heads=num_kv_heads, + qk_norm=qk_norm, + q_norm_dim=num_attention_heads * self.head_dim, + k_norm_dim=num_kv_heads * self.head_dim, eps=1e-5, bias=False, out_bias=False, From 947e002e944f1e1bc6ab716eaa43c17c568d11f5 Mon Sep 17 00:00:00 2001 From: PommesPeter Date: Wed, 26 Jun 2024 23:11:44 +0800 Subject: [PATCH 31/92] Updated attention implementation and models --- src/diffusers/models/attention_processor.py | 108 ++++++--------- .../models/transformers/lumina_nextdit2d.py | 127 ++++++++++++------ 2 files changed, 126 insertions(+), 109 deletions(-) diff --git a/src/diffusers/models/attention_processor.py b/src/diffusers/models/attention_processor.py index 2a9ddb34812a..dc9112c4ddd4 100644 --- a/src/diffusers/models/attention_processor.py +++ b/src/diffusers/models/attention_processor.py @@ -14,7 +14,7 @@ import inspect import math from importlib import import_module -from typing import Callable, List, Optional, Union +from typing import Callable, Dict, List, Optional, Union import torch import torch.nn.functional as F @@ -105,6 +105,8 @@ def __init__( cross_attention_norm: Optional[str] = None, cross_attention_norm_num_groups: int = 32, qk_norm: Optional[str] = None, + q_norm_dim: Optional[int] = None, + k_norm_dim: Optional[int] = None, added_kv_proj_dim: Optional[int] = None, norm_num_groups: Optional[int] = None, spatial_norm_dim: Optional[int] = None, @@ -118,11 +120,14 @@ def __init__( processor: Optional["AttnProcessor"] = None, out_dim: int = None, context_pre_only=None, + added_learnable_params: Dict[str, torch.Tensor] = None, ): super().__init__() self.inner_dim = out_dim if out_dim is not None else dim_head * heads self.inner_kv_dim = out_dim if out_dim is not None else dim_head * kv_heads self.query_dim = query_dim + self.q_norm_dim = q_norm_dim if q_norm_dim is not None else dim_head + self.k_norm_dim = k_norm_dim if k_norm_dim is not None else dim_head self.use_bias = bias self.is_cross_attention = cross_attention_dim is not None self.cross_attention_dim = cross_attention_dim if cross_attention_dim is not None else query_dim @@ -170,8 +175,8 @@ def __init__( self.norm_q = None self.norm_k = None elif qk_norm == "layer_norm": - self.norm_q = nn.LayerNorm(dim_head, eps=eps) - self.norm_k = nn.LayerNorm(dim_head, eps=eps) + self.norm_q = nn.LayerNorm(self.q_norm_dim, eps=eps) + self.norm_k = nn.LayerNorm(self.k_norm_dim, eps=eps) else: raise ValueError(f"unknown qk_norm: {qk_norm}. Should be None or 'layer_norm'") @@ -221,6 +226,11 @@ def __init__( if self.context_pre_only is not None and not self.context_pre_only: self.to_add_out = nn.Linear(self.inner_dim, self.out_dim, bias=out_bias) + # Added learnable parameters into Attention + if added_learnable_params is not None: + for key, value in added_learnable_params.items(): + setattr(self, key, nn.Parameter(value)) + # set attention processor # We use the AttnProcessor2_0 by default when torch 2.x is used which uses # torch.nn.functional.scaled_dot_product_attention for native Flash/memory_efficient_attention @@ -1701,39 +1711,15 @@ def __call__( return hidden_states -class LuminaAttnProcessor2_0(nn.Module): +class LuminaAttnProcessor2_0: r""" Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0). This is used in the LuminaNextDiT model. It applies a s normalization layer and rotary embedding on query and key vector. """ - def __init__( - self, - hidden_size: int = 4096, - encoder_hidden_size: int = 2048, - heads: int = 32, - kv_heads: int = 8, - ): - super().__init__() + def __init__(self): if not hasattr(F, "scaled_dot_product_attention"): raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.") - self.head_dim = hidden_size // heads - self.norm_q = nn.LayerNorm(heads * self.head_dim) - self.norm_k = nn.LayerNorm(kv_heads * self.head_dim) - - self.key_cap = nn.Linear( - encoder_hidden_size, - kv_heads * self.head_dim, - bias=False, - ) - self.value_cap = nn.Linear( - encoder_hidden_size, - kv_heads * self.head_dim, - bias=False, - ) - self.norm_k_cap = nn.LayerNorm(kv_heads * self.head_dim) - - self.gate = nn.Parameter(torch.zeros([heads])) def __call__( self, @@ -1741,13 +1727,15 @@ def __call__( hidden_states: torch.Tensor, encoder_hidden_states: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, - encoder_mask: Optional[torch.Tensor] = None, image_rotary_emb: Optional[torch.Tensor] = None, proportional_attn: Optional[bool] = True, base_sequence_length: Optional[int] = 4096, ) -> torch.Tensor: from .embeddings import apply_rotary_emb + if encoder_hidden_states is not None: + residual = hidden_states + input_ndim = hidden_states.ndim if input_ndim == 4: @@ -1759,8 +1747,12 @@ def __call__( # Get Query-Key-Value Pair query = attn.to_q(hidden_states) - key = attn.to_k(hidden_states) - value = attn.to_v(hidden_states) + # For caption Key-Value + if encoder_hidden_states is None: + encoder_hidden_states = hidden_states + + key = attn.to_k(encoder_hidden_states) + value = attn.to_v(encoder_hidden_states) query_dim = query.shape[-1] inner_dim = key.shape[-1] @@ -1768,13 +1760,13 @@ def __call__( dtype = query.dtype # Get key-value heads - kv_heads = inner_dim // self.head_dim + kv_heads = inner_dim // head_dim # Apply Query-Key Norm if needed - if self.norm_q is not None: - query = self.norm_q(query) - if self.norm_k is not None: - key = self.norm_k(key) + if attn.norm_q is not None: + query = attn.norm_q(query) + if attn.norm_k is not None: + key = attn.norm_k(key) query = query.view(batch_size, sequence_length, attn.heads, head_dim) @@ -1789,10 +1781,13 @@ def __call__( query, key = query.to(dtype), key.to(dtype) # Apply proportional attention if true - if proportional_attn: - softmax_scale = math.sqrt(math.log(sequence_length, base_sequence_length)) * attn.scale + if encoder_hidden_states is not None: + softmax_scale = None else: - softmax_scale = attn.scale + if proportional_attn: + softmax_scale = math.sqrt(math.log(sequence_length, base_sequence_length)) * attn.scale + else: + softmax_scale = attn.scale # perform Grouped-qurey Attention (GQA) n_rep = attn.heads // kv_heads @@ -1816,34 +1811,13 @@ def __call__( ) hidden_states = hidden_states.transpose(1, 2).to(dtype) - # Cross Attention - key_cap = self.key_cap(encoder_hidden_states) - # Apply Key Norm to encoder_hidden_state - key_cap = self.norm_k_cap(key_cap).view(batch_size, -1, kv_heads, head_dim) - value_cap = self.value_cap(encoder_hidden_states).view(batch_size, -1, kv_heads, head_dim) - - if n_rep >= 1: - key_cap = key_cap.unsqueeze(3).repeat(1, 1, 1, n_rep, 1).flatten(2, 3) - value_cap = value_cap.unsqueeze(3).repeat(1, 1, 1, n_rep, 1).flatten(2, 3) - - key_cap = key_cap.transpose(1, 2) - value_cap = value_cap.transpose(1, 2) - - # scaled_dot_product_attention expects attention_mask shape to be - # (batch, heads, source_length, target_length) - encoder_mask = encoder_mask.view(batch_size, 1, 1, -1) - encoder_mask = encoder_mask.expand(batch_size, attn.heads, sequence_length, -1) - - hidden_states_caption = F.scaled_dot_product_attention(query, key_cap, value_cap, attn_mask=encoder_mask) - hidden_states_caption = hidden_states_caption.transpose(1, 2) - - hidden_states_caption = hidden_states_caption * self.gate.tanh().view(1, 1, -1, 1) - hidden_states = hidden_states + hidden_states_caption - - hidden_states = hidden_states.flatten(-2) + if encoder_hidden_states is not None: + hidden_states = hidden_states * attn.gate.tanh().view(1, 1, -1, 1) + hidden_states = residual + hidden_states + hidden_states = hidden_states.flatten(-2) - # linear proj - hidden_states = attn.to_out[0](hidden_states) + # linear proj + hidden_states = attn.to_out[0](hidden_states) return hidden_states diff --git a/src/diffusers/models/transformers/lumina_nextdit2d.py b/src/diffusers/models/transformers/lumina_nextdit2d.py index 48f2ef01ac66..1cae8c7457ea 100644 --- a/src/diffusers/models/transformers/lumina_nextdit2d.py +++ b/src/diffusers/models/transformers/lumina_nextdit2d.py @@ -131,24 +131,45 @@ def __init__( norm_elementwise_affine: bool = True, ) -> None: super().__init__() + self.layer_id = layer_id self.hidden_size = hidden_size self.head_dim = hidden_size // num_attention_heads - self.attention = Attention( + + learnable_params = {"gate": torch.zeros([num_attention_heads])} + + # Self-attention + self.attn = Attention( query_dim=hidden_size, cross_attention_dim=None, dim_head=hidden_size // num_attention_heads, + q_norm_dim=hidden_size, + k_norm_dim=num_kv_heads * self.head_dim, + qk_norm="layer_norm" if qk_norm else None, heads=num_attention_heads, kv_heads=num_kv_heads, eps=1e-5, bias=False, out_bias=False, - processor=LuminaAttnProcessor2_0( - hidden_size=hidden_size, - encoder_hidden_size=encoder_hidden_size, - heads=num_attention_heads, - kv_heads=num_kv_heads, - ), + processor=LuminaAttnProcessor2_0(), + ) + self.attn.to_out = nn.Identity() + + # Cross-attention + self.cross_attn = Attention( + query_dim=hidden_size, + cross_attention_dim=encoder_hidden_size, + dim_head=hidden_size // num_attention_heads, + k_norm_dim=num_kv_heads * self.head_dim, + qk_norm="layer_norm" if qk_norm else None, + heads=num_attention_heads, + kv_heads=num_kv_heads, + eps=1e-5, + bias=False, + out_bias=False, + added_learnable_params=learnable_params, + processor=LuminaAttnProcessor2_0(), ) + self.cross_attn.norm_q = nn.Identity() # disable cross attention norm_q self.feed_forward = LuminaFeedForward( hidden_size=hidden_size, @@ -156,11 +177,11 @@ def __init__( multiple_of=multiple_of, ffn_dim_multiplier=ffn_dim_multiplier, ) - self.layer_id = layer_id - self.attention_norm1 = RMSNorm(hidden_size, eps=norm_eps, elementwise_affine=norm_elementwise_affine) + + self.attn_norm1 = RMSNorm(hidden_size, eps=norm_eps, elementwise_affine=norm_elementwise_affine) self.ffn_norm1 = RMSNorm(hidden_size, eps=norm_eps, elementwise_affine=norm_elementwise_affine) - self.attention_norm2 = RMSNorm(hidden_size, eps=norm_eps, elementwise_affine=norm_elementwise_affine) + self.attn_norm2 = RMSNorm(hidden_size, eps=norm_eps, elementwise_affine=norm_elementwise_affine) self.ffn_norm2 = RMSNorm(hidden_size, eps=norm_eps, elementwise_affine=norm_elementwise_affine) self.adaLN_modulation = nn.Sequential( @@ -174,16 +195,16 @@ def __init__( nn.init.zeros_(self.adaLN_modulation[1].weight) nn.init.zeros_(self.adaLN_modulation[1].bias) - self.attention_caption_norm = RMSNorm( + self.attn_encoder_hidden_states_norm = RMSNorm( encoder_hidden_size, eps=norm_eps, elementwise_affine=norm_elementwise_affine ) def forward( self, - hidden_state: torch.Tensor, + hidden_states: torch.Tensor, attention_mask: torch.Tensor, freqs_cis: torch.Tensor, - encoder_hidden_state: torch.Tensor, + encoder_hidden_states: torch.Tensor, encoder_mask: torch.Tensor, adaln_input: Optional[torch.Tensor] = None, ): @@ -191,42 +212,65 @@ def forward( Perform a forward pass through the LuminaNextDiTBlock. Args: - hidden_state (torch.Tensor): Input tensor. + hidden_states (torch.Tensor): Input tensor. freqs_cis (torch.Tensor): Precomputed cosine and sine frequencies. Returns: torch.Tensor: Output tensor after applying attention and feedforward layers. """ + residual = hidden_states + + encoder_hidden_states = self.attn_encoder_hidden_states_norm(encoder_hidden_states) + if adaln_input is not None: scale_msa, gate_msa, scale_mlp, gate_mlp = self.adaLN_modulation(adaln_input).chunk(4, dim=1) - hidden_state = hidden_state + gate_msa.unsqueeze(1).tanh() * self.attention_norm2( - self.attention( - hidden_states=modulate(self.attention_norm1(hidden_state), scale_msa), - encoder_hidden_states=self.attention_caption_norm(encoder_hidden_state), - attention_mask=attention_mask, - encoder_mask=encoder_mask, - image_rotary_emb=freqs_cis, - ) + # Self-attention + # TODO (2024/06/26 14:00): current implementation is using self-attn hidden_states to get wq again + # TODO: may occur some problems. it may need to use previous init hidden_states + hidden_states = self.attn( + hidden_states=modulate(self.attn_norm1(hidden_states), scale_msa), + encoder_hidden_states=None, + attention_mask=attention_mask, + image_rotary_emb=freqs_cis, ) - hidden_state = hidden_state + gate_mlp.unsqueeze(1).tanh() * self.ffn_norm2( - self.feed_forward( - modulate(self.ffn_norm1(hidden_state), scale_mlp), - ) + + # Cross-attention + hidden_states = self.cross_attn( + hidden_states=hidden_states, + encoder_hidden_states=encoder_hidden_states, + attention_mask=encoder_mask, + image_rotary_emb=None, ) + + hidden_states = residual + gate_msa.unsqueeze(1).tanh() * self.attn_norm2(hidden_states) + + hidden_states = self.feed_forward( + modulate(self.ffn_norm1(hidden_states), scale_mlp), + ) + hidden_states = hidden_states + gate_mlp.unsqueeze(1).tanh() * self.ffn_norm2(hidden_states) else: - hidden_state = hidden_state + self.attention_norm2( - self.attention( - hidden_states=modulate(self.attention_norm1(hidden_state), scale_msa), - encoder_hidden_state=self.attention_caption_norm(encoder_hidden_state), - attention_mask=attention_mask, - encoder_mask=encoder_mask, - image_rotary_emb=freqs_cis, - ) + # Self-attention + hidden_states = self.attn( + hidden_states=self.attn_norm1(hidden_states), + encoder_hidden_states=None, + attention_mask=attention_mask, + image_rotary_emb=freqs_cis, ) - hidden_state = hidden_state + self.ffn_norm2(self.feed_forward(self.ffn_norm1(hidden_state))) - return hidden_state + # Cross-attention + hidden_states = self.cross_attn( + hidden_states=hidden_states, + encoder_hidden_states=encoder_hidden_states, + attention_mask=encoder_mask, + image_rotary_emb=None, + ) + hidden_states = residual + self.attn_norm2(hidden_states) + + hidden_states = self.feed_forward(self.ffn_norm1(hidden_states)) + hidden_states = hidden_states + self.ffn_norm2(hidden_states) + + return hidden_states class LuminaNextDiT2DModel(ModelMixin, ConfigMixin): @@ -289,7 +333,7 @@ def __init__( ffn_dim_multiplier: Optional[float] = None, norm_eps: Optional[float] = 1e-5, learn_sigma: Optional[bool] = True, - qk_norm: Optional[bool] = False, + qk_norm: Optional[bool] = True, encoder_hidden_size: Optional[int] = 2048, scaling_factor: Optional[float] = 1.0, ) -> None: @@ -301,7 +345,6 @@ def __init__( self.hidden_size = hidden_size self.num_attention_heads = num_attention_heads self.scaling_factor = scaling_factor - self.learn_sigma = learn_sigma self.patch_embedder = nn.Linear( in_features=patch_size * patch_size * in_channels, @@ -463,7 +506,7 @@ def forward( self, hidden_states: torch.Tensor, timestep: torch.Tensor, - encoder_hidden_state: torch.Tensor, + encoder_hidden_states: torch.Tensor, encoder_mask: torch.Tensor, return_dict=True, ) -> torch.Tensor: @@ -473,7 +516,7 @@ def forward( Args: hidden_states (torch.Tensor): Input tensor of shape (N, C, H, W). timestep (torch.Tensor): Tensor of diffusion timesteps of shape (N,). - encoder_hidden_state (torch.Tensor): Tensor of caption features of shape (N, D). + encoder_hidden_states (torch.Tensor): Tensor of caption features of shape (N, D). encoder_mask (torch.Tensor): Tensor of caption masks of shape (N, L). Returns: @@ -483,12 +526,12 @@ def forward( hidden_states, mask, img_size, freqs_cis = self.patchify_and_embed(hidden_states) freqs_cis = freqs_cis.to(hidden_states.device) - adaln_input = self.time_caption_embed(timestep, encoder_hidden_state, encoder_mask) + adaln_input = self.time_caption_embed(timestep, encoder_hidden_states, encoder_mask) encoder_mask = encoder_mask.bool() for layer in self.layers: hidden_states = layer( - hidden_states, mask, freqs_cis, encoder_hidden_state, encoder_mask, adaln_input=adaln_input + hidden_states, mask, freqs_cis, encoder_hidden_states, encoder_mask, adaln_input=adaln_input ) hidden_states = self.final_layer(hidden_states, adaln_input) From 11b54e7ec2281625b205e7cdc2cc61a978551b42 Mon Sep 17 00:00:00 2001 From: PommesPeter <54879512+PommesPeter@users.noreply.github.com> Date: Wed, 26 Jun 2024 23:13:21 +0800 Subject: [PATCH 32/92] Update src/diffusers/models/attention.py Co-authored-by: YiYi Xu --- src/diffusers/models/attention.py | 3 --- 1 file changed, 3 deletions(-) diff --git a/src/diffusers/models/attention.py b/src/diffusers/models/attention.py index 5aeadab580c7..f85a38f32caf 100644 --- a/src/diffusers/models/attention.py +++ b/src/diffusers/models/attention.py @@ -571,9 +571,6 @@ def __init__( intermediate_size, bias=False, ) - nn.init.xavier_uniform_(self.w1.weight) - nn.init.xavier_uniform_(self.w2.weight) - nn.init.xavier_uniform_(self.w3.weight) # @torch.compile def _forward_silu_gating(self, x1, x3): From 097c5fa85a28c940beae16549c2f1df0ec27611d Mon Sep 17 00:00:00 2001 From: PommesPeter Date: Wed, 26 Jun 2024 23:17:22 +0800 Subject: [PATCH 33/92] Updated attention implementation and models --- src/diffusers/models/transformers/lumina_nextdit2d.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/src/diffusers/models/transformers/lumina_nextdit2d.py b/src/diffusers/models/transformers/lumina_nextdit2d.py index ff36813805b3..6ad1bc29f031 100644 --- a/src/diffusers/models/transformers/lumina_nextdit2d.py +++ b/src/diffusers/models/transformers/lumina_nextdit2d.py @@ -148,8 +148,6 @@ def __init__( heads=num_attention_heads, kv_heads=num_kv_heads, qk_norm=qk_norm, - q_norm_dim=num_attention_heads * self.head_dim, - k_norm_dim=num_kv_heads * self.head_dim, eps=1e-5, bias=False, out_bias=False, From d78f0e03588f5a23b99247d38c7991784fa5f767 Mon Sep 17 00:00:00 2001 From: PommesPeter Date: Wed, 26 Jun 2024 23:37:30 +0800 Subject: [PATCH 34/92] Updated attention implementation and models --- src/diffusers/models/attention_processor.py | 9 +++++++-- src/diffusers/models/transformers/lumina_nextdit2d.py | 3 ++- src/diffusers/pipelines/lumina/pipeline_lumina.py | 10 +++++----- 3 files changed, 14 insertions(+), 8 deletions(-) diff --git a/src/diffusers/models/attention_processor.py b/src/diffusers/models/attention_processor.py index dc9112c4ddd4..870235e40fd1 100644 --- a/src/diffusers/models/attention_processor.py +++ b/src/diffusers/models/attention_processor.py @@ -1735,6 +1735,8 @@ def __call__( if encoder_hidden_states is not None: residual = hidden_states + else: + residual = encoder_hidden_states input_ndim = hidden_states.ndim @@ -1781,7 +1783,7 @@ def __call__( query, key = query.to(dtype), key.to(dtype) # Apply proportional attention if true - if encoder_hidden_states is not None: + if residual is not None: softmax_scale = None else: if proportional_attn: @@ -1810,8 +1812,11 @@ def __call__( query, key, value, attn_mask=attention_mask, scale=softmax_scale ) hidden_states = hidden_states.transpose(1, 2).to(dtype) + + import ipdb + ipdb.set_trace() - if encoder_hidden_states is not None: + if residual is not None: hidden_states = hidden_states * attn.gate.tanh().view(1, 1, -1, 1) hidden_states = residual + hidden_states hidden_states = hidden_states.flatten(-2) diff --git a/src/diffusers/models/transformers/lumina_nextdit2d.py b/src/diffusers/models/transformers/lumina_nextdit2d.py index 6ad1bc29f031..5b0395cc82bf 100644 --- a/src/diffusers/models/transformers/lumina_nextdit2d.py +++ b/src/diffusers/models/transformers/lumina_nextdit2d.py @@ -147,7 +147,6 @@ def __init__( qk_norm="layer_norm" if qk_norm else None, heads=num_attention_heads, kv_heads=num_kv_heads, - qk_norm=qk_norm, eps=1e-5, bias=False, out_bias=False, @@ -229,6 +228,8 @@ def forward( # Self-attention # TODO (2024/06/26 14:00): current implementation is using self-attn hidden_states to get wq again # TODO: may occur some problems. it may need to use previous init hidden_states + import ipdb + ipdb.set_trace() hidden_states = self.attn( hidden_states=modulate(self.attn_norm1(hidden_states), scale_msa), encoder_hidden_states=None, diff --git a/src/diffusers/pipelines/lumina/pipeline_lumina.py b/src/diffusers/pipelines/lumina/pipeline_lumina.py index 66bd6469e42d..44e0a93c2874 100644 --- a/src/diffusers/pipelines/lumina/pipeline_lumina.py +++ b/src/diffusers/pipelines/lumina/pipeline_lumina.py @@ -753,12 +753,12 @@ def __call__( if proportional_attn: assert self.base_sequence_length is not None for layer in self.transformer.layers: - layer.attention.base_seqlen = self.base_sequence_length - layer.attention.proportional_attn = proportional_attn + layer.attn.base_seqlen = self.base_sequence_length + layer.attn.proportional_attn = proportional_attn else: for layer in self.transformer.layers: - layer.attention.base_seqlen = None - layer.attention.proportional_attn = proportional_attn + layer.attn.base_seqlen = None + layer.attn.proportional_attn = proportional_attn device = self._execution_device @@ -853,7 +853,7 @@ def __call__( noise_pred = self.transformer( hidden_states=latent_model_input, timestep=current_timestep, - encoder_hidden_state=prompt_embeds, + encoder_hidden_states=prompt_embeds, encoder_mask=prompt_attention_mask, return_dict=False, )[0] From 0b559a073058761ffca06ef1e0cef27ca27a59a0 Mon Sep 17 00:00:00 2001 From: PommesPeter Date: Thu, 27 Jun 2024 01:35:27 +0800 Subject: [PATCH 35/92] Fixed bugs --- src/diffusers/models/attention_processor.py | 23 +++++++++++-------- .../models/transformers/lumina_nextdit2d.py | 2 -- 2 files changed, 13 insertions(+), 12 deletions(-) diff --git a/src/diffusers/models/attention_processor.py b/src/diffusers/models/attention_processor.py index 61de4d82a31d..3b47a37ae790 100644 --- a/src/diffusers/models/attention_processor.py +++ b/src/diffusers/models/attention_processor.py @@ -1631,8 +1631,10 @@ def __call__( from .embeddings import apply_rotary_emb if encoder_hidden_states is not None: + # self residual = hidden_states else: + # cross residual = encoder_hidden_states input_ndim = hidden_states.ndim @@ -1642,11 +1644,15 @@ def __call__( hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2) batch_size, sequence_length, _ = hidden_states.shape - + # Get Query-Key-Value Pair - query = attn.to_q(hidden_states) + if residual is None: + query = attn.to_q(hidden_states) + else: + query = hidden_states.transpose(1, 2) + batch_size, _, sequence_length = hidden_states.shape - # For caption Key-Value + # For encoder Key-Value if encoder_hidden_states is None: encoder_hidden_states = hidden_states @@ -1667,10 +1673,10 @@ def __call__( if attn.norm_k is not None: key = attn.norm_k(key) - query = query.view(batch_size, sequence_length, attn.heads, head_dim) + query = query.view(batch_size, -1, attn.heads, head_dim) - key = key.view(batch_size, sequence_length, kv_heads, head_dim) - value = value.view(batch_size, sequence_length, kv_heads, head_dim) + key = key.view(batch_size, -1, kv_heads, head_dim) + value = value.view(batch_size, -1, kv_heads, head_dim) # Apply RoPE if needed if image_rotary_emb is not None: @@ -1696,7 +1702,7 @@ def __call__( # scaled_dot_product_attention expects attention_mask shape to be # (batch, heads, source_length, target_length) - attention_mask = attention_mask.bool().view(batch_size, 1, 1, sequence_length) + attention_mask = attention_mask.bool().view(batch_size, 1, 1, -1) attention_mask = attention_mask.expand(-1, attn.heads, sequence_length, -1) query = query.transpose(1, 2) @@ -1709,9 +1715,6 @@ def __call__( query, key, value, attn_mask=attention_mask, scale=softmax_scale ) hidden_states = hidden_states.transpose(1, 2).to(dtype) - - import ipdb - ipdb.set_trace() if residual is not None: hidden_states = hidden_states * attn.gate.tanh().view(1, 1, -1, 1) diff --git a/src/diffusers/models/transformers/lumina_nextdit2d.py b/src/diffusers/models/transformers/lumina_nextdit2d.py index 5b0395cc82bf..1cae8c7457ea 100644 --- a/src/diffusers/models/transformers/lumina_nextdit2d.py +++ b/src/diffusers/models/transformers/lumina_nextdit2d.py @@ -228,8 +228,6 @@ def forward( # Self-attention # TODO (2024/06/26 14:00): current implementation is using self-attn hidden_states to get wq again # TODO: may occur some problems. it may need to use previous init hidden_states - import ipdb - ipdb.set_trace() hidden_states = self.attn( hidden_states=modulate(self.attn_norm1(hidden_states), scale_msa), encoder_hidden_states=None, From 36f7e114fd3a6544270789598a627379bfc7ec33 Mon Sep 17 00:00:00 2001 From: PommesPeter Date: Thu, 27 Jun 2024 02:06:12 +0800 Subject: [PATCH 36/92] Format code --- scripts/convert_lumina_to_diffusers.py | 49 +++++++-------------- src/diffusers/models/attention_processor.py | 4 +- 2 files changed, 18 insertions(+), 35 deletions(-) diff --git a/scripts/convert_lumina_to_diffusers.py b/scripts/convert_lumina_to_diffusers.py index 9d17d3101b59..c2679e462315 100644 --- a/scripts/convert_lumina_to_diffusers.py +++ b/scripts/convert_lumina_to_diffusers.py @@ -31,53 +31,38 @@ def main(args): for i in range(24): # adaln - converted_state_dict[f"layers.{i}.attention.processor.gate"] = all_sd[f"layers.{i}.attention.gate"] + converted_state_dict[f"layers.{i}.cross_attn.gate"] = all_sd[f"layers.{i}.attention.gate"] converted_state_dict[f"layers.{i}.adaLN_modulation.1.weight"] = all_sd[f"layers.{i}.adaLN_modulation.1.weight"] converted_state_dict[f"layers.{i}.adaLN_modulation.1.bias"] = all_sd[f"layers.{i}.adaLN_modulation.1.bias"] # qkv - converted_state_dict[f"layers.{i}.attention.to_q.weight"] = all_sd[f"layers.{i}.attention.wq.weight"] - converted_state_dict[f"layers.{i}.attention.to_k.weight"] = all_sd[f"layers.{i}.attention.wk.weight"] - converted_state_dict[f"layers.{i}.attention.to_v.weight"] = all_sd[f"layers.{i}.attention.wv.weight"] + converted_state_dict[f"layers.{i}.attn.to_q.weight"] = all_sd[f"layers.{i}.attention.wq.weight"] + converted_state_dict[f"layers.{i}.attn.to_k.weight"] = all_sd[f"layers.{i}.attention.wk.weight"] + converted_state_dict[f"layers.{i}.attn.to_v.weight"] = all_sd[f"layers.{i}.attention.wv.weight"] # cap - converted_state_dict[f"layers.{i}.attention.processor.key_cap.weight"] = all_sd[ - f"layers.{i}.attention.wk_y.weight" - ] - converted_state_dict[f"layers.{i}.attention.processor.value_cap.weight"] = all_sd[ - f"layers.{i}.attention.wv_y.weight" - ] + converted_state_dict[f"layers.{i}.cross_attn.to_q.weight"] = all_sd[f"layers.{i}.attention.wq.weight"] + converted_state_dict[f"layers.{i}.cross_attn.to_k.weight"] = all_sd[f"layers.{i}.attention.wk_y.weight"] + converted_state_dict[f"layers.{i}.cross_attn.to_v.weight"] = all_sd[f"layers.{i}.attention.wv_y.weight"] # output - converted_state_dict[f"layers.{i}.attention.to_out.0.weight"] = all_sd[f"layers.{i}.attention.wo.weight"] + converted_state_dict[f"layers.{i}.cross_attn.to_out.0.weight"] = all_sd[f"layers.{i}.attention.wo.weight"] # attention # qk norm - converted_state_dict[f"layers.{i}.attention.processor.norm_q.weight"] = all_sd[ - f"layers.{i}.attention.q_norm.weight" - ] - converted_state_dict[f"layers.{i}.attention.processor.norm_q.bias"] = all_sd[ - f"layers.{i}.attention.q_norm.bias" - ] + converted_state_dict[f"layers.{i}.attn.norm_q.weight"] = all_sd[f"layers.{i}.attention.q_norm.weight"] + converted_state_dict[f"layers.{i}.attn.norm_q.bias"] = all_sd[f"layers.{i}.attention.q_norm.bias"] - converted_state_dict[f"layers.{i}.attention.processor.norm_k.weight"] = all_sd[ - f"layers.{i}.attention.k_norm.weight" - ] - converted_state_dict[f"layers.{i}.attention.processor.norm_k.bias"] = all_sd[ - f"layers.{i}.attention.k_norm.bias" - ] + converted_state_dict[f"layers.{i}.attn.norm_k.weight"] = all_sd[f"layers.{i}.attention.k_norm.weight"] + converted_state_dict[f"layers.{i}.attn.norm_k.bias"] = all_sd[f"layers.{i}.attention.k_norm.bias"] - converted_state_dict[f"layers.{i}.attention.processor.norm_k_cap.weight"] = all_sd[ - f"layers.{i}.attention.ky_norm.weight" - ] - converted_state_dict[f"layers.{i}.attention.processor.norm_k_cap.bias"] = all_sd[ - f"layers.{i}.attention.ky_norm.bias" - ] + converted_state_dict[f"layers.{i}.cross_attn.norm_k.weight"] = all_sd[f"layers.{i}.attention.ky_norm.weight"] + converted_state_dict[f"layers.{i}.cross_attn.norm_k.bias"] = all_sd[f"layers.{i}.attention.ky_norm.bias"] # attention norm - converted_state_dict[f"layers.{i}.attention_norm1.weight"] = all_sd[f"layers.{i}.attention_norm1.weight"] - converted_state_dict[f"layers.{i}.attention_norm2.weight"] = all_sd[f"layers.{i}.attention_norm2.weight"] - converted_state_dict[f"layers.{i}.attention_caption_norm.weight"] = all_sd[ + converted_state_dict[f"layers.{i}.attn_norm1.weight"] = all_sd[f"layers.{i}.attention_norm1.weight"] + converted_state_dict[f"layers.{i}.attn_norm2.weight"] = all_sd[f"layers.{i}.attention_norm2.weight"] + converted_state_dict[f"layers.{i}.attn_encoder_hidden_states_norm.weight"] = all_sd[ f"layers.{i}.attention_y_norm.weight" ] diff --git a/src/diffusers/models/attention_processor.py b/src/diffusers/models/attention_processor.py index 3b47a37ae790..d25e3949beee 100644 --- a/src/diffusers/models/attention_processor.py +++ b/src/diffusers/models/attention_processor.py @@ -13,8 +13,6 @@ # limitations under the License. import inspect import math - -from importlib import import_module from typing import Callable, Dict, List, Optional, Union import torch @@ -1644,7 +1642,7 @@ def __call__( hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2) batch_size, sequence_length, _ = hidden_states.shape - + # Get Query-Key-Value Pair if residual is None: query = attn.to_q(hidden_states) From 940769df65dafa06d72614a199e2cc70a688320a Mon Sep 17 00:00:00 2001 From: PommesPeter <54879512+PommesPeter@users.noreply.github.com> Date: Thu, 27 Jun 2024 09:42:16 +0800 Subject: [PATCH 37/92] Update src/diffusers/models/transformers/lumina_nextdit2d.py Co-authored-by: YiYi Xu --- src/diffusers/models/transformers/lumina_nextdit2d.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/diffusers/models/transformers/lumina_nextdit2d.py b/src/diffusers/models/transformers/lumina_nextdit2d.py index 1cae8c7457ea..ebef8f7a77c3 100644 --- a/src/diffusers/models/transformers/lumina_nextdit2d.py +++ b/src/diffusers/models/transformers/lumina_nextdit2d.py @@ -425,7 +425,7 @@ def unpatchify(self, x: torch.Tensor, img_size: List[Tuple[int, int]], return_te return imgs def patchify_and_embed( - self, x: List[torch.Tensor] | torch.Tensor + self, x: Union[List[torch.Tensor], torch.Tensor] ) -> Tuple[torch.Tensor, torch.Tensor, List[Tuple[int, int]], torch.Tensor]: """ Patchifies and embeds the input tensor(s). From cc692ee96934c24b0e2d59fcbaa13b120228a1f2 Mon Sep 17 00:00:00 2001 From: PommesPeter <54879512+PommesPeter@users.noreply.github.com> Date: Thu, 27 Jun 2024 09:49:50 +0800 Subject: [PATCH 38/92] Update src/diffusers/pipelines/lumina/pipeline_lumina.py Co-authored-by: YiYi Xu --- src/diffusers/pipelines/lumina/pipeline_lumina.py | 9 --------- 1 file changed, 9 deletions(-) diff --git a/src/diffusers/pipelines/lumina/pipeline_lumina.py b/src/diffusers/pipelines/lumina/pipeline_lumina.py index 44e0a93c2874..67ea35e16e75 100644 --- a/src/diffusers/pipelines/lumina/pipeline_lumina.py +++ b/src/diffusers/pipelines/lumina/pipeline_lumina.py @@ -750,15 +750,6 @@ def __call__( else: batch_size = prompt_embeds.shape[0] - if proportional_attn: - assert self.base_sequence_length is not None - for layer in self.transformer.layers: - layer.attn.base_seqlen = self.base_sequence_length - layer.attn.proportional_attn = proportional_attn - else: - for layer in self.transformer.layers: - layer.attn.base_seqlen = None - layer.attn.proportional_attn = proportional_attn device = self._execution_device From 599a1ed4c88eea742d546368b5c4964dce673f10 Mon Sep 17 00:00:00 2001 From: PommesPeter <54879512+PommesPeter@users.noreply.github.com> Date: Thu, 27 Jun 2024 09:51:08 +0800 Subject: [PATCH 39/92] Update src/diffusers/models/transformers/lumina_nextdit2d.py Co-authored-by: YiYi Xu --- src/diffusers/models/transformers/lumina_nextdit2d.py | 1 + 1 file changed, 1 insertion(+) diff --git a/src/diffusers/models/transformers/lumina_nextdit2d.py b/src/diffusers/models/transformers/lumina_nextdit2d.py index ebef8f7a77c3..0d98904b1d3e 100644 --- a/src/diffusers/models/transformers/lumina_nextdit2d.py +++ b/src/diffusers/models/transformers/lumina_nextdit2d.py @@ -508,6 +508,7 @@ def forward( timestep: torch.Tensor, encoder_hidden_states: torch.Tensor, encoder_mask: torch.Tensor, + cross_attention_kwargs: Dict[str, Any] = None, return_dict=True, ) -> torch.Tensor: """ From 072629968d246f85fbd4c337d7b17ffc6f62b13f Mon Sep 17 00:00:00 2001 From: PommesPeter <54879512+PommesPeter@users.noreply.github.com> Date: Thu, 27 Jun 2024 09:51:27 +0800 Subject: [PATCH 40/92] Update src/diffusers/models/transformers/lumina_nextdit2d.py Co-authored-by: YiYi Xu --- src/diffusers/models/transformers/lumina_nextdit2d.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/src/diffusers/models/transformers/lumina_nextdit2d.py b/src/diffusers/models/transformers/lumina_nextdit2d.py index 0d98904b1d3e..0e00a40090e3 100644 --- a/src/diffusers/models/transformers/lumina_nextdit2d.py +++ b/src/diffusers/models/transformers/lumina_nextdit2d.py @@ -532,7 +532,8 @@ def forward( encoder_mask = encoder_mask.bool() for layer in self.layers: hidden_states = layer( - hidden_states, mask, freqs_cis, encoder_hidden_states, encoder_mask, adaln_input=adaln_input + hidden_states, mask, freqs_cis, encoder_hidden_states, encoder_mask, adaln_input=adaln_input,\ + cross_attention_kwargs=cross_attention_kwargs, ) hidden_states = self.final_layer(hidden_states, adaln_input) From fd6e9ed73b4d077ad0a9694831124dc7b7220915 Mon Sep 17 00:00:00 2001 From: PommesPeter <54879512+PommesPeter@users.noreply.github.com> Date: Thu, 27 Jun 2024 09:51:38 +0800 Subject: [PATCH 41/92] Update src/diffusers/models/transformers/lumina_nextdit2d.py Co-authored-by: YiYi Xu --- src/diffusers/models/transformers/lumina_nextdit2d.py | 1 + 1 file changed, 1 insertion(+) diff --git a/src/diffusers/models/transformers/lumina_nextdit2d.py b/src/diffusers/models/transformers/lumina_nextdit2d.py index 0e00a40090e3..15bd12ada48a 100644 --- a/src/diffusers/models/transformers/lumina_nextdit2d.py +++ b/src/diffusers/models/transformers/lumina_nextdit2d.py @@ -207,6 +207,7 @@ def forward( encoder_hidden_states: torch.Tensor, encoder_mask: torch.Tensor, adaln_input: Optional[torch.Tensor] = None, + cross_attention_kwargs: Dict[str, Any] = None, ): """ Perform a forward pass through the LuminaNextDiTBlock. From b5e76d64a62541bed6dd0e382eecdaa35a3d0d75 Mon Sep 17 00:00:00 2001 From: PommesPeter <54879512+PommesPeter@users.noreply.github.com> Date: Thu, 27 Jun 2024 09:52:00 +0800 Subject: [PATCH 42/92] Update src/diffusers/models/transformers/lumina_nextdit2d.py Co-authored-by: YiYi Xu --- src/diffusers/models/transformers/lumina_nextdit2d.py | 1 + 1 file changed, 1 insertion(+) diff --git a/src/diffusers/models/transformers/lumina_nextdit2d.py b/src/diffusers/models/transformers/lumina_nextdit2d.py index 15bd12ada48a..47f86e423b4d 100644 --- a/src/diffusers/models/transformers/lumina_nextdit2d.py +++ b/src/diffusers/models/transformers/lumina_nextdit2d.py @@ -234,6 +234,7 @@ def forward( encoder_hidden_states=None, attention_mask=attention_mask, image_rotary_emb=freqs_cis, + **cross_attention_kwargs, ) # Cross-attention From 4379b31aaed51ac92c92f9c223bb2b31480ca561 Mon Sep 17 00:00:00 2001 From: PommesPeter <54879512+PommesPeter@users.noreply.github.com> Date: Thu, 27 Jun 2024 09:52:13 +0800 Subject: [PATCH 43/92] Update src/diffusers/pipelines/lumina/pipeline_lumina.py Co-authored-by: YiYi Xu --- src/diffusers/pipelines/lumina/pipeline_lumina.py | 1 + 1 file changed, 1 insertion(+) diff --git a/src/diffusers/pipelines/lumina/pipeline_lumina.py b/src/diffusers/pipelines/lumina/pipeline_lumina.py index 67ea35e16e75..6a8928f9a0e3 100644 --- a/src/diffusers/pipelines/lumina/pipeline_lumina.py +++ b/src/diffusers/pipelines/lumina/pipeline_lumina.py @@ -847,6 +847,7 @@ def __call__( encoder_hidden_states=prompt_embeds, encoder_mask=prompt_attention_mask, return_dict=False, + cross_attention_kwargs=self.cross_attention_kwargs, )[0] noise_pred = noise_pred.chunk(2, dim=1)[0] From b692707ebd799f1bae72665f4ee440612a46961f Mon Sep 17 00:00:00 2001 From: PommesPeter Date: Thu, 27 Jun 2024 10:15:52 +0800 Subject: [PATCH 44/92] Refactor proportational attention --- .../models/transformers/lumina_nextdit2d.py | 14 +++++++++++--- src/diffusers/pipelines/lumina/pipeline_lumina.py | 9 +++++++-- 2 files changed, 18 insertions(+), 5 deletions(-) diff --git a/src/diffusers/models/transformers/lumina_nextdit2d.py b/src/diffusers/models/transformers/lumina_nextdit2d.py index 47f86e423b4d..7780c53e157f 100644 --- a/src/diffusers/models/transformers/lumina_nextdit2d.py +++ b/src/diffusers/models/transformers/lumina_nextdit2d.py @@ -12,7 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. -from typing import List, Optional, Tuple +from typing import Any, Dict, List, Optional, Tuple, Union import torch import torch.nn as nn @@ -243,6 +243,7 @@ def forward( encoder_hidden_states=encoder_hidden_states, attention_mask=encoder_mask, image_rotary_emb=None, + **cross_attention_kwargs, ) hidden_states = residual + gate_msa.unsqueeze(1).tanh() * self.attn_norm2(hidden_states) @@ -258,6 +259,7 @@ def forward( encoder_hidden_states=None, attention_mask=attention_mask, image_rotary_emb=freqs_cis, + **cross_attention_kwargs, ) # Cross-attention @@ -266,6 +268,7 @@ def forward( encoder_hidden_states=encoder_hidden_states, attention_mask=encoder_mask, image_rotary_emb=None, + **cross_attention_kwargs, ) hidden_states = residual + self.attn_norm2(hidden_states) @@ -534,8 +537,13 @@ def forward( encoder_mask = encoder_mask.bool() for layer in self.layers: hidden_states = layer( - hidden_states, mask, freqs_cis, encoder_hidden_states, encoder_mask, adaln_input=adaln_input,\ - cross_attention_kwargs=cross_attention_kwargs, + hidden_states, + mask, + freqs_cis, + encoder_hidden_states, + encoder_mask, + adaln_input=adaln_input, + cross_attention_kwargs=cross_attention_kwargs, ) hidden_states = self.final_layer(hidden_states, adaln_input) diff --git a/src/diffusers/pipelines/lumina/pipeline_lumina.py b/src/diffusers/pipelines/lumina/pipeline_lumina.py index 6a8928f9a0e3..3ac15b78d645 100644 --- a/src/diffusers/pipelines/lumina/pipeline_lumina.py +++ b/src/diffusers/pipelines/lumina/pipeline_lumina.py @@ -191,7 +191,6 @@ def __init__( else 128 ) self.default_image_size = self.default_sample_size * self.vae_scale_factor - self.base_sequence_length = (self.default_image_size // 16) ** 2 def _get_gemma_prompt_embeds( self, @@ -741,6 +740,7 @@ def __call__( prompt_attention_mask=prompt_attention_mask, negative_prompt_attention_mask=negative_prompt_attention_mask, ) + cross_attention_kwargs = {} # 2. Define call parameters if prompt is not None and isinstance(prompt, str): @@ -750,6 +750,11 @@ def __call__( else: batch_size = prompt_embeds.shape[0] + if proportional_attn: + cross_attention_kwargs["proportational_attn"] = True + cross_attention_kwargs["base_sequence_length"] = (self.default_image_size // 16) ** 2 + else: + cross_attention_kwargs["proportational_attn"] = False device = self._execution_device @@ -847,7 +852,7 @@ def __call__( encoder_hidden_states=prompt_embeds, encoder_mask=prompt_attention_mask, return_dict=False, - cross_attention_kwargs=self.cross_attention_kwargs, + cross_attention_kwargs=cross_attention_kwargs, )[0] noise_pred = noise_pred.chunk(2, dim=1)[0] From 4f73fbef9c15dcb8414050f8b5ebcbb29b993b69 Mon Sep 17 00:00:00 2001 From: PommesPeter Date: Thu, 27 Jun 2024 12:01:22 +0800 Subject: [PATCH 45/92] Refactor freqs_cis --- src/diffusers/models/attention_processor.py | 1 - .../models/transformers/lumina_nextdit2d.py | 15 +++++++++++++-- .../pipelines/lumina/pipeline_lumina.py | 16 +++++----------- 3 files changed, 18 insertions(+), 14 deletions(-) diff --git a/src/diffusers/models/attention_processor.py b/src/diffusers/models/attention_processor.py index d25e3949beee..3fd6d96474d3 100644 --- a/src/diffusers/models/attention_processor.py +++ b/src/diffusers/models/attention_processor.py @@ -299,7 +299,6 @@ def set_use_memory_efficient_attention_xformers( elif not torch.cuda.is_available(): raise ValueError( "torch.cuda.is_available() should be True but is False. xformers' memory efficient attention is" - " only available for GPU " ) else: try: diff --git a/src/diffusers/models/transformers/lumina_nextdit2d.py b/src/diffusers/models/transformers/lumina_nextdit2d.py index 7780c53e157f..e027ae40b712 100644 --- a/src/diffusers/models/transformers/lumina_nextdit2d.py +++ b/src/diffusers/models/transformers/lumina_nextdit2d.py @@ -349,6 +349,7 @@ def __init__( self.out_channels = in_channels * 2 if learn_sigma else in_channels self.hidden_size = hidden_size self.num_attention_heads = num_attention_heads + self.head_dim = hidden_size // num_attention_heads self.scaling_factor = scaling_factor self.patch_embedder = nn.Linear( @@ -513,6 +514,8 @@ def forward( timestep: torch.Tensor, encoder_hidden_states: torch.Tensor, encoder_mask: torch.Tensor, + scaling_factor: Optional[int] = 1, + scaling_watershed: Optional[float] = 0.3, cross_attention_kwargs: Dict[str, Any] = None, return_dict=True, ) -> torch.Tensor: @@ -528,6 +531,14 @@ def forward( Returns: torch.Tensor: Output tensor of shape (N, C, H, W). """ + # For inference + self.freqs_cis = self.precompute_freqs_cis( + self.head_dim, + 384, + scaling_factor=scaling_factor, + scaling_watershed=scaling_watershed, + timestep=timestep[0].item(), + ) x_is_tensor = isinstance(hidden_states, torch.Tensor) hidden_states, mask, img_size, freqs_cis = self.patchify_and_embed(hidden_states) freqs_cis = freqs_cis.to(hidden_states.device) @@ -591,9 +602,9 @@ def precompute_freqs_cis( theta = theta * ntk_factor freqs = 1.0 / (theta ** (torch.arange(0, dim, 4)[: (dim // 4)].float().cuda() / dim)) / linear_factor - timestep = torch.arange(end, device=freqs.device, dtype=torch.float) # type: ignore + timestep = torch.arange(end, device=freqs.device, dtype=torch.float) - freqs = torch.outer(timestep, freqs).float() # type: ignore + freqs = torch.outer(timestep, freqs).float() freqs_cis = torch.polar(torch.ones_like(freqs), freqs) # complex64 freqs_cis_h = freqs_cis.view(end, 1, dim // 4, 1).repeat(1, end, 1, 1) diff --git a/src/diffusers/pipelines/lumina/pipeline_lumina.py b/src/diffusers/pipelines/lumina/pipeline_lumina.py index 3ac15b78d645..fe39ca303c53 100644 --- a/src/diffusers/pipelines/lumina/pipeline_lumina.py +++ b/src/diffusers/pipelines/lumina/pipeline_lumina.py @@ -751,7 +751,7 @@ def __call__( batch_size = prompt_embeds.shape[0] if proportional_attn: - cross_attention_kwargs["proportational_attn"] = True + cross_attention_kwargs["proportional_attn"] = True cross_attention_kwargs["base_sequence_length"] = (self.default_image_size // 16) ** 2 else: cross_attention_kwargs["proportational_attn"] = False @@ -836,23 +836,17 @@ def __call__( hidden_size = self.transformer.config.hidden_size num_attention_heads = self.transformer.config.num_attention_heads attention_head_dim = hidden_size // num_attention_heads - scaling_factor = math.sqrt(width * height / self.default_image_size**2) - - self.transformer.freqs_cis = self.transformer.precompute_freqs_cis( - attention_head_dim, - 384, - scaling_factor=scaling_factor, - scaling_watershed=scaling_watershed, - timestep=current_timestep[0].item(), - ) + scaling_factor = math.sqrt(width * height / self.default_image_size ** 2) noise_pred = self.transformer( hidden_states=latent_model_input, timestep=current_timestep, encoder_hidden_states=prompt_embeds, encoder_mask=prompt_attention_mask, - return_dict=False, + scaling_factor=scaling_factor, + scaling_watershed=scaling_watershed, cross_attention_kwargs=cross_attention_kwargs, + return_dict=False, )[0] noise_pred = noise_pred.chunk(2, dim=1)[0] From 388e07caef738352ced3ab51eec60f8fed29d4a6 Mon Sep 17 00:00:00 2001 From: PommesPeter Date: Thu, 27 Jun 2024 12:06:23 +0800 Subject: [PATCH 46/92] Fxied typo --- src/diffusers/models/attention_processor.py | 1 + src/diffusers/pipelines/lumina/pipeline_lumina.py | 5 +---- 2 files changed, 2 insertions(+), 4 deletions(-) diff --git a/src/diffusers/models/attention_processor.py b/src/diffusers/models/attention_processor.py index 3fd6d96474d3..d25e3949beee 100644 --- a/src/diffusers/models/attention_processor.py +++ b/src/diffusers/models/attention_processor.py @@ -299,6 +299,7 @@ def set_use_memory_efficient_attention_xformers( elif not torch.cuda.is_available(): raise ValueError( "torch.cuda.is_available() should be True but is False. xformers' memory efficient attention is" + " only available for GPU " ) else: try: diff --git a/src/diffusers/pipelines/lumina/pipeline_lumina.py b/src/diffusers/pipelines/lumina/pipeline_lumina.py index fe39ca303c53..b491c3408aef 100644 --- a/src/diffusers/pipelines/lumina/pipeline_lumina.py +++ b/src/diffusers/pipelines/lumina/pipeline_lumina.py @@ -833,10 +833,7 @@ def __call__( current_timestep = 1 - current_timestep / self.scheduler.config.num_train_timesteps # dynamic scaling_factor for different resolution. - hidden_size = self.transformer.config.hidden_size - num_attention_heads = self.transformer.config.num_attention_heads - attention_head_dim = hidden_size // num_attention_heads - scaling_factor = math.sqrt(width * height / self.default_image_size ** 2) + scaling_factor = math.sqrt(width * height / self.default_image_size**2) noise_pred = self.transformer( hidden_states=latent_model_input, From 73f69b7a45035f2b7a0830d43fcaf53bcb680d90 Mon Sep 17 00:00:00 2001 From: PommesPeter Date: Thu, 27 Jun 2024 12:15:51 +0800 Subject: [PATCH 47/92] Removed init weight distribution and typo --- src/diffusers/__init__.py | 1 - src/diffusers/models/embeddings.py | 7 ------- .../models/transformers/lumina_nextdit2d.py | 15 +++------------ 3 files changed, 3 insertions(+), 20 deletions(-) diff --git a/src/diffusers/__init__.py b/src/diffusers/__init__.py index 9b47750b5448..65b86cfab5ec 100644 --- a/src/diffusers/__init__.py +++ b/src/diffusers/__init__.py @@ -272,7 +272,6 @@ "LEditsPPPipelineStableDiffusion", "LEditsPPPipelineStableDiffusionXL", "LuminaText2ImgPipeline", - "LuminaText2ImgPipeline", "MarigoldDepthPipeline", "MarigoldNormalsPipeline", "MusicLDMPipeline", diff --git a/src/diffusers/models/embeddings.py b/src/diffusers/models/embeddings.py index fe5b80478ec4..70262e63fb81 100644 --- a/src/diffusers/models/embeddings.py +++ b/src/diffusers/models/embeddings.py @@ -775,11 +775,6 @@ def __init__(self, hidden_size=4096, encoder_hidden_size=2048, frequency_embeddi self.timestep_embedder = TimestepEmbedding(in_channels=frequency_embedding_size, time_embed_dim=hidden_size) - nn.init.normal_(self.timestep_embedder.linear_1.weight, std=0.02) - nn.init.zeros_(self.timestep_embedder.linear_1.bias) - nn.init.normal_(self.timestep_embedder.linear_2.weight, std=0.02) - nn.init.zeros_(self.timestep_embedder.linear_2.bias) - self.caption_embedder = nn.Sequential( nn.LayerNorm(encoder_hidden_size), nn.Linear( @@ -788,8 +783,6 @@ def __init__(self, hidden_size=4096, encoder_hidden_size=2048, frequency_embeddi bias=True, ), ) - nn.init.zeros_(self.caption_embedder[1].weight) - nn.init.zeros_(self.caption_embedder[1].bias) def forward(self, timestep, caption_feat, caption_mask): # timestep embedding: diff --git a/src/diffusers/models/transformers/lumina_nextdit2d.py b/src/diffusers/models/transformers/lumina_nextdit2d.py index e027ae40b712..c3f91e5a1803 100644 --- a/src/diffusers/models/transformers/lumina_nextdit2d.py +++ b/src/diffusers/models/transformers/lumina_nextdit2d.py @@ -36,7 +36,7 @@ def modulate(x, scale): return x * (1 + scale.unsqueeze(1)) -class FinalLayer(nn.Module): +class LuminaFinalLayer(nn.Module): """ The final layer of LuminaNextDiT. @@ -60,8 +60,6 @@ def __init__(self, hidden_size, patch_size, out_channels): patch_size * patch_size * out_channels, bias=True, ) - nn.init.zeros_(self.linear.weight) - nn.init.zeros_(self.linear.bias) self.adaLN_modulation = nn.Sequential( nn.SiLU(), @@ -71,12 +69,10 @@ def __init__(self, hidden_size, patch_size, out_channels): bias=True, ), ) - nn.init.zeros_(self.adaLN_modulation[1].weight) - nn.init.zeros_(self.adaLN_modulation[1].bias) def forward(self, x, c): """ - Forward pass of the FinalLayer. + Forward pass of the LuminaFinalLayer. Args: x (torch.Tensor): The input tensor. @@ -192,8 +188,6 @@ def __init__( bias=True, ), ) - nn.init.zeros_(self.adaLN_modulation[1].weight) - nn.init.zeros_(self.adaLN_modulation[1].bias) self.attn_encoder_hidden_states_norm = RMSNorm( encoder_hidden_size, eps=norm_eps, elementwise_affine=norm_elementwise_affine @@ -357,11 +351,8 @@ def __init__( out_features=hidden_size, bias=True, ) - nn.init.xavier_uniform_(self.patch_embedder.weight) - nn.init.constant_(self.patch_embedder.bias, 0.0) self.pad_token = nn.Parameter(torch.empty(hidden_size)) - nn.init.normal_(self.pad_token, std=0.02) self.time_caption_embed = LuminaCombinedTimestepCaptionEmbedding( hidden_size=min(hidden_size, 1024), encoder_hidden_size=encoder_hidden_size @@ -383,7 +374,7 @@ def __init__( for layer_id in range(num_layers) ] ) - self.final_layer = FinalLayer(hidden_size, patch_size, self.out_channels) + self.final_layer = LuminaFinalLayer(hidden_size, patch_size, self.out_channels) assert (hidden_size // num_attention_heads) % 4 == 0, "2d rope needs head dim to be divisible by 4" self.freqs_cis = self.precompute_freqs_cis( From 91c934ca2524a37d52a95d21f63085aaa936aace Mon Sep 17 00:00:00 2001 From: zhuole1025 Date: Fri, 28 Jun 2024 07:44:01 +0000 Subject: [PATCH 48/92] Fix some bugs in attnetion --- src/diffusers/models/attention_processor.py | 33 ++++++----------- .../models/transformers/lumina_nextdit2d.py | 36 +++++++++++-------- 2 files changed, 31 insertions(+), 38 deletions(-) diff --git a/src/diffusers/models/attention_processor.py b/src/diffusers/models/attention_processor.py index d25e3949beee..f7e9350e4b26 100644 --- a/src/diffusers/models/attention_processor.py +++ b/src/diffusers/models/attention_processor.py @@ -1620,21 +1620,16 @@ def __call__( self, attn: Attention, hidden_states: torch.Tensor, - encoder_hidden_states: Optional[torch.Tensor] = None, + encoder_hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, - image_rotary_emb: Optional[torch.Tensor] = None, + query_rotary_emb: Optional[torch.Tensor] = None, + key_rotary_emb: Optional[torch.Tensor] = None, proportional_attn: Optional[bool] = True, base_sequence_length: Optional[int] = 4096, + residual: Optional[torch.Tensor] = None, ) -> torch.Tensor: from .embeddings import apply_rotary_emb - if encoder_hidden_states is not None: - # self - residual = hidden_states - else: - # cross - residual = encoder_hidden_states - input_ndim = hidden_states.ndim if input_ndim == 4: @@ -1644,16 +1639,7 @@ def __call__( batch_size, sequence_length, _ = hidden_states.shape # Get Query-Key-Value Pair - if residual is None: - query = attn.to_q(hidden_states) - else: - query = hidden_states.transpose(1, 2) - batch_size, _, sequence_length = hidden_states.shape - - # For encoder Key-Value - if encoder_hidden_states is None: - encoder_hidden_states = hidden_states - + query = attn.to_q(hidden_states) key = attn.to_k(encoder_hidden_states) value = attn.to_v(encoder_hidden_states) @@ -1677,9 +1663,10 @@ def __call__( value = value.view(batch_size, -1, kv_heads, head_dim) # Apply RoPE if needed - if image_rotary_emb is not None: - query = apply_rotary_emb(query, image_rotary_emb, use_real=False) - key = apply_rotary_emb(key, image_rotary_emb, use_real=False) + if query_rotary_emb is not None: + query = apply_rotary_emb(query, query_rotary_emb, use_real=False) + if key_rotary_emb is not None: + key = apply_rotary_emb(key, key_rotary_emb, use_real=False) query, key = query.to(dtype), key.to(dtype) @@ -1718,7 +1705,7 @@ def __call__( hidden_states = hidden_states * attn.gate.tanh().view(1, 1, -1, 1) hidden_states = residual + hidden_states hidden_states = hidden_states.flatten(-2) - + # linear proj hidden_states = attn.to_out[0](hidden_states) diff --git a/src/diffusers/models/transformers/lumina_nextdit2d.py b/src/diffusers/models/transformers/lumina_nextdit2d.py index c3f91e5a1803..f65033ad8640 100644 --- a/src/diffusers/models/transformers/lumina_nextdit2d.py +++ b/src/diffusers/models/transformers/lumina_nextdit2d.py @@ -155,6 +155,7 @@ def __init__( query_dim=hidden_size, cross_attention_dim=encoder_hidden_size, dim_head=hidden_size // num_attention_heads, + q_norm_dim=hidden_size, k_norm_dim=num_kv_heads * self.head_dim, qk_norm="layer_norm" if qk_norm else None, heads=num_attention_heads, @@ -215,41 +216,45 @@ def forward( """ residual = hidden_states - encoder_hidden_states = self.attn_encoder_hidden_states_norm(encoder_hidden_states) - if adaln_input is not None: scale_msa, gate_msa, scale_mlp, gate_mlp = self.adaLN_modulation(adaln_input).chunk(4, dim=1) - + # Self-attention # TODO (2024/06/26 14:00): current implementation is using self-attn hidden_states to get wq again # TODO: may occur some problems. it may need to use previous init hidden_states - hidden_states = self.attn( - hidden_states=modulate(self.attn_norm1(hidden_states), scale_msa), - encoder_hidden_states=None, + hidden_states=modulate(self.attn_norm1(hidden_states), scale_msa) + self_hidden_states = self.attn( + hidden_states=hidden_states, + encoder_hidden_states=hidden_states, attention_mask=attention_mask, - image_rotary_emb=freqs_cis, + query_rotary_emb=freqs_cis, + key_rotary_emb=freqs_cis, **cross_attention_kwargs, ) - + # Cross-attention + self.cross_attn.norm_q = self.attn.norm_q hidden_states = self.cross_attn( hidden_states=hidden_states, - encoder_hidden_states=encoder_hidden_states, + encoder_hidden_states=self.attn_encoder_hidden_states_norm(encoder_hidden_states), attention_mask=encoder_mask, - image_rotary_emb=None, + residual=self_hidden_states, + query_rotary_emb=freqs_cis, + key_rotary_emb=None, **cross_attention_kwargs, ) hidden_states = residual + gate_msa.unsqueeze(1).tanh() * self.attn_norm2(hidden_states) - + hidden_states = self.feed_forward( modulate(self.ffn_norm1(hidden_states), scale_mlp), ) hidden_states = hidden_states + gate_mlp.unsqueeze(1).tanh() * self.ffn_norm2(hidden_states) else: + hidden_states = self.attn_norm1(hidden_states) # Self-attention - hidden_states = self.attn( - hidden_states=self.attn_norm1(hidden_states), + self_hidden_states = self.attn( + hidden_states=hidden_states, encoder_hidden_states=None, attention_mask=attention_mask, image_rotary_emb=freqs_cis, @@ -259,9 +264,10 @@ def forward( # Cross-attention hidden_states = self.cross_attn( hidden_states=hidden_states, - encoder_hidden_states=encoder_hidden_states, + encoder_hidden_states=self.attn_encoder_hidden_states_norm(encoder_hidden_states), attention_mask=encoder_mask, image_rotary_emb=None, + residual=self_hidden_states, **cross_attention_kwargs, ) hidden_states = residual + self.attn_norm2(hidden_states) @@ -535,7 +541,7 @@ def forward( freqs_cis = freqs_cis.to(hidden_states.device) adaln_input = self.time_caption_embed(timestep, encoder_hidden_states, encoder_mask) - + encoder_mask = encoder_mask.bool() for layer in self.layers: hidden_states = layer( From f84ab691289303103acfecb74d4331fcdb7aa9d6 Mon Sep 17 00:00:00 2001 From: zhuole1025 Date: Sat, 29 Jun 2024 16:55:42 +0000 Subject: [PATCH 49/92] Fix bugs in attention --- .../models/transformers/lumina_nextdit2d.py | 28 ++++++++----------- 1 file changed, 12 insertions(+), 16 deletions(-) diff --git a/src/diffusers/models/transformers/lumina_nextdit2d.py b/src/diffusers/models/transformers/lumina_nextdit2d.py index f65033ad8640..24780d68453a 100644 --- a/src/diffusers/models/transformers/lumina_nextdit2d.py +++ b/src/diffusers/models/transformers/lumina_nextdit2d.py @@ -166,7 +166,6 @@ def __init__( added_learnable_params=learnable_params, processor=LuminaAttnProcessor2_0(), ) - self.cross_attn.norm_q = nn.Identity() # disable cross attention norm_q self.feed_forward = LuminaFeedForward( hidden_size=hidden_size, @@ -220,10 +219,8 @@ def forward( scale_msa, gate_msa, scale_mlp, gate_mlp = self.adaLN_modulation(adaln_input).chunk(4, dim=1) # Self-attention - # TODO (2024/06/26 14:00): current implementation is using self-attn hidden_states to get wq again - # TODO: may occur some problems. it may need to use previous init hidden_states hidden_states=modulate(self.attn_norm1(hidden_states), scale_msa) - self_hidden_states = self.attn( + self_attn_output = self.attn( hidden_states=hidden_states, encoder_hidden_states=hidden_states, attention_mask=attention_mask, @@ -233,27 +230,26 @@ def forward( ) # Cross-attention - self.cross_attn.norm_q = self.attn.norm_q - hidden_states = self.cross_attn( + cross_attn_output = self.cross_attn( hidden_states=hidden_states, encoder_hidden_states=self.attn_encoder_hidden_states_norm(encoder_hidden_states), attention_mask=encoder_mask, - residual=self_hidden_states, + residual=self_attn_output, query_rotary_emb=freqs_cis, key_rotary_emb=None, **cross_attention_kwargs, ) - - hidden_states = residual + gate_msa.unsqueeze(1).tanh() * self.attn_norm2(hidden_states) - hidden_states = self.feed_forward( + hidden_states = residual + gate_msa.unsqueeze(1).tanh() * self.attn_norm2(cross_attn_output) + + mlp_output = self.feed_forward( modulate(self.ffn_norm1(hidden_states), scale_mlp), ) - hidden_states = hidden_states + gate_mlp.unsqueeze(1).tanh() * self.ffn_norm2(hidden_states) + hidden_states = hidden_states + gate_mlp.unsqueeze(1).tanh() * self.ffn_norm2(mlp_output) else: hidden_states = self.attn_norm1(hidden_states) # Self-attention - self_hidden_states = self.attn( + self_attn_output = self.attn( hidden_states=hidden_states, encoder_hidden_states=None, attention_mask=attention_mask, @@ -262,17 +258,17 @@ def forward( ) # Cross-attention - hidden_states = self.cross_attn( + cross_attn_output = self.cross_attn( hidden_states=hidden_states, encoder_hidden_states=self.attn_encoder_hidden_states_norm(encoder_hidden_states), attention_mask=encoder_mask, image_rotary_emb=None, - residual=self_hidden_states, + residual=self_attn_output, **cross_attention_kwargs, ) - hidden_states = residual + self.attn_norm2(hidden_states) + hidden_states = residual + self.attn_norm2(cross_attn_output) - hidden_states = self.feed_forward(self.ffn_norm1(hidden_states)) + mlp_output = self.feed_forward(self.ffn_norm1(hidden_states)) hidden_states = hidden_states + self.ffn_norm2(hidden_states) return hidden_states From 327c31e8ae842d92df3281d41b444d675f1f5da9 Mon Sep 17 00:00:00 2001 From: PommesPeter Date: Sun, 30 Jun 2024 01:05:54 +0800 Subject: [PATCH 50/92] Fixed convert weight scripts --- scripts/convert_lumina_to_diffusers.py | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/scripts/convert_lumina_to_diffusers.py b/scripts/convert_lumina_to_diffusers.py index c2679e462315..90fa7b600817 100644 --- a/scripts/convert_lumina_to_diffusers.py +++ b/scripts/convert_lumina_to_diffusers.py @@ -12,7 +12,7 @@ def main(args): # checkpoint from https://huggingface.co/Alpha-VLLM/Lumina-Next-SFT or https://huggingface.co/Alpha-VLLM/Lumina-Next-T2I all_sd = load_file(args.origin_ckpt_path, device="cpu") converted_state_dict = {} - # pad token + # pad token converted_state_dict["pad_token"] = all_sd["pad_token"] # patch embed @@ -56,15 +56,16 @@ def main(args): converted_state_dict[f"layers.{i}.attn.norm_k.weight"] = all_sd[f"layers.{i}.attention.k_norm.weight"] converted_state_dict[f"layers.{i}.attn.norm_k.bias"] = all_sd[f"layers.{i}.attention.k_norm.bias"] + converted_state_dict[f"layers.{i}.cross_attn.norm_q.weight"] = all_sd[f"layers.{i}.attention.q_norm.weight"] + converted_state_dict[f"layers.{i}.cross_attn.norm_q.bias"] = all_sd[f"layers.{i}.attention.q_norm.bias"] + converted_state_dict[f"layers.{i}.cross_attn.norm_k.weight"] = all_sd[f"layers.{i}.attention.ky_norm.weight"] converted_state_dict[f"layers.{i}.cross_attn.norm_k.bias"] = all_sd[f"layers.{i}.attention.ky_norm.bias"] # attention norm converted_state_dict[f"layers.{i}.attn_norm1.weight"] = all_sd[f"layers.{i}.attention_norm1.weight"] converted_state_dict[f"layers.{i}.attn_norm2.weight"] = all_sd[f"layers.{i}.attention_norm2.weight"] - converted_state_dict[f"layers.{i}.attn_encoder_hidden_states_norm.weight"] = all_sd[ - f"layers.{i}.attention_y_norm.weight" - ] + converted_state_dict[f"layers.{i}.attn_encoder_hidden_states_norm.weight"] = all_sd[f"layers.{i}.attention_y_norm.weight"] # feed forward converted_state_dict[f"layers.{i}.feed_forward.w1.weight"] = all_sd[f"layers.{i}.feed_forward.w1.weight"] From 899a5c050f7dbf85308a446cbf2aa1921b626d7b Mon Sep 17 00:00:00 2001 From: PommesPeter Date: Sun, 30 Jun 2024 01:06:20 +0800 Subject: [PATCH 51/92] Fixed typo --- scripts/convert_lumina_to_diffusers.py | 6 ++++-- src/diffusers/models/attention_processor.py | 2 +- .../models/transformers/lumina_nextdit2d.py | 12 ++++++------ 3 files changed, 11 insertions(+), 9 deletions(-) diff --git a/scripts/convert_lumina_to_diffusers.py b/scripts/convert_lumina_to_diffusers.py index 90fa7b600817..e1753b25d59f 100644 --- a/scripts/convert_lumina_to_diffusers.py +++ b/scripts/convert_lumina_to_diffusers.py @@ -12,7 +12,7 @@ def main(args): # checkpoint from https://huggingface.co/Alpha-VLLM/Lumina-Next-SFT or https://huggingface.co/Alpha-VLLM/Lumina-Next-T2I all_sd = load_file(args.origin_ckpt_path, device="cpu") converted_state_dict = {} - # pad token + # pad token converted_state_dict["pad_token"] = all_sd["pad_token"] # patch embed @@ -65,7 +65,9 @@ def main(args): # attention norm converted_state_dict[f"layers.{i}.attn_norm1.weight"] = all_sd[f"layers.{i}.attention_norm1.weight"] converted_state_dict[f"layers.{i}.attn_norm2.weight"] = all_sd[f"layers.{i}.attention_norm2.weight"] - converted_state_dict[f"layers.{i}.attn_encoder_hidden_states_norm.weight"] = all_sd[f"layers.{i}.attention_y_norm.weight"] + converted_state_dict[f"layers.{i}.attn_encoder_hidden_states_norm.weight"] = all_sd[ + f"layers.{i}.attention_y_norm.weight" + ] # feed forward converted_state_dict[f"layers.{i}.feed_forward.w1.weight"] = all_sd[f"layers.{i}.feed_forward.w1.weight"] diff --git a/src/diffusers/models/attention_processor.py b/src/diffusers/models/attention_processor.py index f7e9350e4b26..095778e266c8 100644 --- a/src/diffusers/models/attention_processor.py +++ b/src/diffusers/models/attention_processor.py @@ -1705,7 +1705,7 @@ def __call__( hidden_states = hidden_states * attn.gate.tanh().view(1, 1, -1, 1) hidden_states = residual + hidden_states hidden_states = hidden_states.flatten(-2) - + # linear proj hidden_states = attn.to_out[0](hidden_states) diff --git a/src/diffusers/models/transformers/lumina_nextdit2d.py b/src/diffusers/models/transformers/lumina_nextdit2d.py index 24780d68453a..ba9b4bd38ac6 100644 --- a/src/diffusers/models/transformers/lumina_nextdit2d.py +++ b/src/diffusers/models/transformers/lumina_nextdit2d.py @@ -217,9 +217,9 @@ def forward( if adaln_input is not None: scale_msa, gate_msa, scale_mlp, gate_mlp = self.adaLN_modulation(adaln_input).chunk(4, dim=1) - + # Self-attention - hidden_states=modulate(self.attn_norm1(hidden_states), scale_msa) + hidden_states = modulate(self.attn_norm1(hidden_states), scale_msa) self_attn_output = self.attn( hidden_states=hidden_states, encoder_hidden_states=hidden_states, @@ -228,7 +228,7 @@ def forward( key_rotary_emb=freqs_cis, **cross_attention_kwargs, ) - + # Cross-attention cross_attn_output = self.cross_attn( hidden_states=hidden_states, @@ -239,9 +239,9 @@ def forward( key_rotary_emb=None, **cross_attention_kwargs, ) - + hidden_states = residual + gate_msa.unsqueeze(1).tanh() * self.attn_norm2(cross_attn_output) - + mlp_output = self.feed_forward( modulate(self.ffn_norm1(hidden_states), scale_mlp), ) @@ -537,7 +537,7 @@ def forward( freqs_cis = freqs_cis.to(hidden_states.device) adaln_input = self.time_caption_embed(timestep, encoder_hidden_states, encoder_mask) - + encoder_mask = encoder_mask.bool() for layer in self.layers: hidden_states = layer( From 38cbaf9ba01feca3e9d5e1ddb18b6a29790d9cac Mon Sep 17 00:00:00 2001 From: PommesPeter <54879512+PommesPeter@users.noreply.github.com> Date: Sun, 30 Jun 2024 13:42:08 +0800 Subject: [PATCH 52/92] Update src/diffusers/models/attention_processor.py Co-authored-by: YiYi Xu --- src/diffusers/models/attention_processor.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/diffusers/models/attention_processor.py b/src/diffusers/models/attention_processor.py index 095778e266c8..8dde981ffdfe 100644 --- a/src/diffusers/models/attention_processor.py +++ b/src/diffusers/models/attention_processor.py @@ -122,7 +122,7 @@ def __init__( ): super().__init__() self.inner_dim = out_dim if out_dim is not None else dim_head * heads - self.inner_kv_dim = out_dim if out_dim is not None else dim_head * kv_heads + self.inner_kv_dim = self.inner_dim if kv_heads is None else dim_head * kv_heads self.query_dim = query_dim self.q_norm_dim = q_norm_dim if q_norm_dim is not None else dim_head self.k_norm_dim = k_norm_dim if k_norm_dim is not None else dim_head From 082665c25f738e9a39f47f165535e97fa78de09a Mon Sep 17 00:00:00 2001 From: PommesPeter <54879512+PommesPeter@users.noreply.github.com> Date: Sun, 30 Jun 2024 13:42:58 +0800 Subject: [PATCH 53/92] Update src/diffusers/models/attention_processor.py Co-authored-by: YiYi Xu --- src/diffusers/models/attention_processor.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/diffusers/models/attention_processor.py b/src/diffusers/models/attention_processor.py index 8dde981ffdfe..d5b7735f667c 100644 --- a/src/diffusers/models/attention_processor.py +++ b/src/diffusers/models/attention_processor.py @@ -94,7 +94,7 @@ def __init__( query_dim: int, cross_attention_dim: Optional[int] = None, heads: int = 8, - kv_heads: int = 1, + kv_heads: Optional[int] = None, dim_head: int = 64, dropout: float = 0.0, bias: bool = False, From f6c5a1824aba01fe08692f9da6e8e23d0f7ca10f Mon Sep 17 00:00:00 2001 From: PommesPeter <54879512+PommesPeter@users.noreply.github.com> Date: Sun, 30 Jun 2024 13:43:45 +0800 Subject: [PATCH 54/92] Update src/diffusers/models/attention_processor.py Co-authored-by: YiYi Xu --- src/diffusers/models/attention_processor.py | 4 ---- 1 file changed, 4 deletions(-) diff --git a/src/diffusers/models/attention_processor.py b/src/diffusers/models/attention_processor.py index d5b7735f667c..22f30cb7d584 100644 --- a/src/diffusers/models/attention_processor.py +++ b/src/diffusers/models/attention_processor.py @@ -224,10 +224,6 @@ def __init__( if self.context_pre_only is not None and not self.context_pre_only: self.to_add_out = nn.Linear(self.inner_dim, self.out_dim, bias=out_bias) - # Added learnable parameters into Attention - if added_learnable_params is not None: - for key, value in added_learnable_params.items(): - setattr(self, key, nn.Parameter(value)) # set attention processor # We use the AttnProcessor2_0 by default when torch 2.x is used which uses From a9410c861bbdf21a730e2b895a538eb14def9d20 Mon Sep 17 00:00:00 2001 From: PommesPeter <54879512+PommesPeter@users.noreply.github.com> Date: Sun, 30 Jun 2024 13:44:01 +0800 Subject: [PATCH 55/92] Update src/diffusers/models/attention_processor.py Co-authored-by: YiYi Xu --- src/diffusers/models/attention_processor.py | 1 - 1 file changed, 1 deletion(-) diff --git a/src/diffusers/models/attention_processor.py b/src/diffusers/models/attention_processor.py index 22f30cb7d584..c23c5dbfb6a6 100644 --- a/src/diffusers/models/attention_processor.py +++ b/src/diffusers/models/attention_processor.py @@ -118,7 +118,6 @@ def __init__( processor: Optional["AttnProcessor"] = None, out_dim: int = None, context_pre_only=None, - added_learnable_params: Dict[str, torch.Tensor] = None, ): super().__init__() self.inner_dim = out_dim if out_dim is not None else dim_head * heads From 02378b008aacc6e370c406e0ebd54863fd8e5576 Mon Sep 17 00:00:00 2001 From: PommesPeter <54879512+PommesPeter@users.noreply.github.com> Date: Sun, 30 Jun 2024 13:49:39 +0800 Subject: [PATCH 56/92] Update src/diffusers/models/attention_processor.py Co-authored-by: YiYi Xu --- src/diffusers/models/attention_processor.py | 11 +++++++++-- 1 file changed, 9 insertions(+), 2 deletions(-) diff --git a/src/diffusers/models/attention_processor.py b/src/diffusers/models/attention_processor.py index c23c5dbfb6a6..9332f586429b 100644 --- a/src/diffusers/models/attention_processor.py +++ b/src/diffusers/models/attention_processor.py @@ -172,8 +172,15 @@ def __init__( self.norm_q = None self.norm_k = None elif qk_norm == "layer_norm": - self.norm_q = nn.LayerNorm(self.q_norm_dim, eps=eps) - self.norm_k = nn.LayerNorm(self.k_norm_dim, eps=eps) + if kv_heads is None: + q_norm_dim = dim_head + k_norm_dim = dim_head + else: + # Lumina applys qk norm across all heads + q_norm_dim = dim_head * heads + k_norm_dim = dim_head * kv_heads + self.norm_q = nn.LayerNorm(q_norm_dim, eps=eps) + self.norm_k = nn.LayerNorm(k_norm_dim, eps=eps) else: raise ValueError(f"unknown qk_norm: {qk_norm}. Should be None or 'layer_norm'") From a81c554dd4618105ee49399cd45b1e9d8e456d2e Mon Sep 17 00:00:00 2001 From: PommesPeter <54879512+PommesPeter@users.noreply.github.com> Date: Sun, 30 Jun 2024 13:49:46 +0800 Subject: [PATCH 57/92] Update src/diffusers/models/transformers/lumina_nextdit2d.py Co-authored-by: YiYi Xu --- src/diffusers/models/transformers/lumina_nextdit2d.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/src/diffusers/models/transformers/lumina_nextdit2d.py b/src/diffusers/models/transformers/lumina_nextdit2d.py index ba9b4bd38ac6..1ed3ba4a4b3c 100644 --- a/src/diffusers/models/transformers/lumina_nextdit2d.py +++ b/src/diffusers/models/transformers/lumina_nextdit2d.py @@ -155,8 +155,6 @@ def __init__( query_dim=hidden_size, cross_attention_dim=encoder_hidden_size, dim_head=hidden_size // num_attention_heads, - q_norm_dim=hidden_size, - k_norm_dim=num_kv_heads * self.head_dim, qk_norm="layer_norm" if qk_norm else None, heads=num_attention_heads, kv_heads=num_kv_heads, From aee650dd0bfca1be02745281b8fff46e03fb0afd Mon Sep 17 00:00:00 2001 From: PommesPeter <54879512+PommesPeter@users.noreply.github.com> Date: Sun, 30 Jun 2024 13:49:55 +0800 Subject: [PATCH 58/92] Update src/diffusers/models/attention_processor.py Co-authored-by: YiYi Xu --- src/diffusers/models/attention_processor.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/src/diffusers/models/attention_processor.py b/src/diffusers/models/attention_processor.py index 9332f586429b..b62b543be0a8 100644 --- a/src/diffusers/models/attention_processor.py +++ b/src/diffusers/models/attention_processor.py @@ -103,8 +103,6 @@ def __init__( cross_attention_norm: Optional[str] = None, cross_attention_norm_num_groups: int = 32, qk_norm: Optional[str] = None, - q_norm_dim: Optional[int] = None, - k_norm_dim: Optional[int] = None, added_kv_proj_dim: Optional[int] = None, norm_num_groups: Optional[int] = None, spatial_norm_dim: Optional[int] = None, From e9a45c3ea538610aeef5c991facb1fbf8ee8dd44 Mon Sep 17 00:00:00 2001 From: PommesPeter <54879512+PommesPeter@users.noreply.github.com> Date: Sun, 30 Jun 2024 13:50:04 +0800 Subject: [PATCH 59/92] Update src/diffusers/models/transformers/lumina_nextdit2d.py Co-authored-by: YiYi Xu --- src/diffusers/models/transformers/lumina_nextdit2d.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/src/diffusers/models/transformers/lumina_nextdit2d.py b/src/diffusers/models/transformers/lumina_nextdit2d.py index 1ed3ba4a4b3c..ba2c10807ed5 100644 --- a/src/diffusers/models/transformers/lumina_nextdit2d.py +++ b/src/diffusers/models/transformers/lumina_nextdit2d.py @@ -138,8 +138,6 @@ def __init__( query_dim=hidden_size, cross_attention_dim=None, dim_head=hidden_size // num_attention_heads, - q_norm_dim=hidden_size, - k_norm_dim=num_kv_heads * self.head_dim, qk_norm="layer_norm" if qk_norm else None, heads=num_attention_heads, kv_heads=num_kv_heads, From 98eb745f7ee2cd5631b6ff0470969a997520a071 Mon Sep 17 00:00:00 2001 From: PommesPeter <54879512+PommesPeter@users.noreply.github.com> Date: Sun, 30 Jun 2024 13:50:16 +0800 Subject: [PATCH 60/92] Update src/diffusers/models/attention_processor.py Co-authored-by: YiYi Xu --- src/diffusers/models/attention_processor.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/src/diffusers/models/attention_processor.py b/src/diffusers/models/attention_processor.py index b62b543be0a8..c1064ee08775 100644 --- a/src/diffusers/models/attention_processor.py +++ b/src/diffusers/models/attention_processor.py @@ -121,8 +121,6 @@ def __init__( self.inner_dim = out_dim if out_dim is not None else dim_head * heads self.inner_kv_dim = self.inner_dim if kv_heads is None else dim_head * kv_heads self.query_dim = query_dim - self.q_norm_dim = q_norm_dim if q_norm_dim is not None else dim_head - self.k_norm_dim = k_norm_dim if k_norm_dim is not None else dim_head self.use_bias = bias self.is_cross_attention = cross_attention_dim is not None self.cross_attention_dim = cross_attention_dim if cross_attention_dim is not None else query_dim From df2b7d05d38693ec29105afc55e133ce90ecf11c Mon Sep 17 00:00:00 2001 From: PommesPeter Date: Sun, 30 Jun 2024 15:25:59 +0800 Subject: [PATCH 61/92] Refactor attention output and Removed residual in Attn --- scripts/convert_lumina_to_diffusers.py | 2 +- src/diffusers/models/attention_processor.py | 14 ++------------ .../models/transformers/lumina_nextdit2d.py | 13 +++++++++---- 3 files changed, 12 insertions(+), 17 deletions(-) diff --git a/scripts/convert_lumina_to_diffusers.py b/scripts/convert_lumina_to_diffusers.py index e1753b25d59f..7e670decb1ed 100644 --- a/scripts/convert_lumina_to_diffusers.py +++ b/scripts/convert_lumina_to_diffusers.py @@ -31,7 +31,7 @@ def main(args): for i in range(24): # adaln - converted_state_dict[f"layers.{i}.cross_attn.gate"] = all_sd[f"layers.{i}.attention.gate"] + converted_state_dict[f"layers.{i}.gate"] = all_sd[f"layers.{i}.attention.gate"] converted_state_dict[f"layers.{i}.adaLN_modulation.1.weight"] = all_sd[f"layers.{i}.adaLN_modulation.1.weight"] converted_state_dict[f"layers.{i}.adaLN_modulation.1.bias"] = all_sd[f"layers.{i}.adaLN_modulation.1.bias"] diff --git a/src/diffusers/models/attention_processor.py b/src/diffusers/models/attention_processor.py index c1064ee08775..498cf0229e59 100644 --- a/src/diffusers/models/attention_processor.py +++ b/src/diffusers/models/attention_processor.py @@ -13,7 +13,7 @@ # limitations under the License. import inspect import math -from typing import Callable, Dict, List, Optional, Union +from typing import Callable, List, Optional, Union import torch import torch.nn.functional as F @@ -226,7 +226,6 @@ def __init__( if self.context_pre_only is not None and not self.context_pre_only: self.to_add_out = nn.Linear(self.inner_dim, self.out_dim, bias=out_bias) - # set attention processor # We use the AttnProcessor2_0 by default when torch 2.x is used which uses # torch.nn.functional.scaled_dot_product_attention for native Flash/memory_efficient_attention @@ -1624,7 +1623,6 @@ def __call__( key_rotary_emb: Optional[torch.Tensor] = None, proportional_attn: Optional[bool] = True, base_sequence_length: Optional[int] = 4096, - residual: Optional[torch.Tensor] = None, ) -> torch.Tensor: from .embeddings import apply_rotary_emb @@ -1669,7 +1667,7 @@ def __call__( query, key = query.to(dtype), key.to(dtype) # Apply proportional attention if true - if residual is not None: + if key_rotary_emb is None: softmax_scale = None else: if proportional_attn: @@ -1699,14 +1697,6 @@ def __call__( ) hidden_states = hidden_states.transpose(1, 2).to(dtype) - if residual is not None: - hidden_states = hidden_states * attn.gate.tanh().view(1, 1, -1, 1) - hidden_states = residual + hidden_states - hidden_states = hidden_states.flatten(-2) - - # linear proj - hidden_states = attn.to_out[0](hidden_states) - return hidden_states diff --git a/src/diffusers/models/transformers/lumina_nextdit2d.py b/src/diffusers/models/transformers/lumina_nextdit2d.py index ba2c10807ed5..716037b1fc7a 100644 --- a/src/diffusers/models/transformers/lumina_nextdit2d.py +++ b/src/diffusers/models/transformers/lumina_nextdit2d.py @@ -131,7 +131,7 @@ def __init__( self.hidden_size = hidden_size self.head_dim = hidden_size // num_attention_heads - learnable_params = {"gate": torch.zeros([num_attention_heads])} + self.gate = nn.Parameter(torch.zeros([num_attention_heads])) # Self-attention self.attn = Attention( @@ -159,7 +159,6 @@ def __init__( eps=1e-5, bias=False, out_bias=False, - added_learnable_params=learnable_params, processor=LuminaAttnProcessor2_0(), ) @@ -226,17 +225,23 @@ def forward( ) # Cross-attention + norm_encoder_hidden_states = self.attn_encoder_hidden_states_norm(encoder_hidden_states) cross_attn_output = self.cross_attn( hidden_states=hidden_states, - encoder_hidden_states=self.attn_encoder_hidden_states_norm(encoder_hidden_states), + encoder_hidden_states=norm_encoder_hidden_states, attention_mask=encoder_mask, residual=self_attn_output, query_rotary_emb=freqs_cis, key_rotary_emb=None, **cross_attention_kwargs, ) + cross_attn_output = cross_attn_output * self.gate.tanh().view(1, 1, -1, 1) + mixed_attn_output = self_attn_output + cross_attn_output + mixed_attn_output = mixed_attn_output.flatten(-2) + # linear proj + hidden_states = self.cross_attn.to_out[0](mixed_attn_output) - hidden_states = residual + gate_msa.unsqueeze(1).tanh() * self.attn_norm2(cross_attn_output) + hidden_states = residual + gate_msa.unsqueeze(1).tanh() * self.attn_norm2(hidden_states) mlp_output = self.feed_forward( modulate(self.ffn_norm1(hidden_states), scale_mlp), From 6cd9936d94c8de7b260d23d150e5d965ff256727 Mon Sep 17 00:00:00 2001 From: PommesPeter <54879512+PommesPeter@users.noreply.github.com> Date: Mon, 1 Jul 2024 11:13:52 +0800 Subject: [PATCH 62/92] Apply suggestions from code review - Removed extra input parameter - Fixed typo on Feed_Forward Co-authored-by: YiYi Xu --- src/diffusers/models/attention.py | 4 ++-- src/diffusers/models/attention_processor.py | 5 ++--- 2 files changed, 4 insertions(+), 5 deletions(-) diff --git a/src/diffusers/models/attention.py b/src/diffusers/models/attention.py index f85a38f32caf..2b7ab2a2c339 100644 --- a/src/diffusers/models/attention.py +++ b/src/diffusers/models/attention.py @@ -544,8 +544,8 @@ class LuminaFeedForward(nn.Module): def __init__( self, - hidden_size: int, - intermediate_size: int, + dim: int, + inner_dim: int, multiple_of: Optional[int] = 256, ffn_dim_multiplier: Optional[float] = None, ): diff --git a/src/diffusers/models/attention_processor.py b/src/diffusers/models/attention_processor.py index 498cf0229e59..2df581520d20 100644 --- a/src/diffusers/models/attention_processor.py +++ b/src/diffusers/models/attention_processor.py @@ -1621,8 +1621,7 @@ def __call__( attention_mask: Optional[torch.Tensor] = None, query_rotary_emb: Optional[torch.Tensor] = None, key_rotary_emb: Optional[torch.Tensor] = None, - proportional_attn: Optional[bool] = True, - base_sequence_length: Optional[int] = 4096, + base_sequence_length: Optional[int] = None, ) -> torch.Tensor: from .embeddings import apply_rotary_emb @@ -1670,7 +1669,7 @@ def __call__( if key_rotary_emb is None: softmax_scale = None else: - if proportional_attn: + if base_sequence_length is not None: softmax_scale = math.sqrt(math.log(sequence_length, base_sequence_length)) * attn.scale else: softmax_scale = attn.scale From 127d1df1e6935f12d67f42b3763c79070a71cecd Mon Sep 17 00:00:00 2001 From: PommesPeter <54879512+PommesPeter@users.noreply.github.com> Date: Mon, 1 Jul 2024 13:16:55 +0800 Subject: [PATCH 63/92] Update src/diffusers/models/transformers/lumina_nextdit2d.py Co-authored-by: YiYi Xu --- src/diffusers/models/transformers/lumina_nextdit2d.py | 1 - 1 file changed, 1 deletion(-) diff --git a/src/diffusers/models/transformers/lumina_nextdit2d.py b/src/diffusers/models/transformers/lumina_nextdit2d.py index 716037b1fc7a..ed40249290c4 100644 --- a/src/diffusers/models/transformers/lumina_nextdit2d.py +++ b/src/diffusers/models/transformers/lumina_nextdit2d.py @@ -115,7 +115,6 @@ class LuminaNextDiTBlock(nn.Module): def __init__( self, - layer_id: int, hidden_size: int, num_attention_heads: int, num_kv_heads: int, From f51c75cbffd13cc50e78af808ca98b7ed6d811a1 Mon Sep 17 00:00:00 2001 From: PommesPeter <54879512+PommesPeter@users.noreply.github.com> Date: Mon, 1 Jul 2024 13:21:38 +0800 Subject: [PATCH 64/92] Apply suggestions from code review Co-authored-by: YiYi Xu --- src/diffusers/models/transformers/lumina_nextdit2d.py | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/src/diffusers/models/transformers/lumina_nextdit2d.py b/src/diffusers/models/transformers/lumina_nextdit2d.py index ed40249290c4..299ffef8a50b 100644 --- a/src/diffusers/models/transformers/lumina_nextdit2d.py +++ b/src/diffusers/models/transformers/lumina_nextdit2d.py @@ -115,14 +115,14 @@ class LuminaNextDiTBlock(nn.Module): def __init__( self, - hidden_size: int, + dim: int, num_attention_heads: int, num_kv_heads: int, multiple_of: int, ffn_dim_multiplier: float, norm_eps: float, qk_norm: bool, - encoder_hidden_size: int, + cross_attention_dim: int, norm_elementwise_affine: bool = True, ) -> None: super().__init__() @@ -229,7 +229,6 @@ def forward( hidden_states=hidden_states, encoder_hidden_states=norm_encoder_hidden_states, attention_mask=encoder_mask, - residual=self_attn_output, query_rotary_emb=freqs_cis, key_rotary_emb=None, **cross_attention_kwargs, @@ -263,7 +262,6 @@ def forward( encoder_hidden_states=self.attn_encoder_hidden_states_norm(encoder_hidden_states), attention_mask=encoder_mask, image_rotary_emb=None, - residual=self_attn_output, **cross_attention_kwargs, ) hidden_states = residual + self.attn_norm2(cross_attn_output) From cc881011212418609c7c2cc87d3777bd0fda7600 Mon Sep 17 00:00:00 2001 From: PommesPeter Date: Mon, 1 Jul 2024 13:22:18 +0800 Subject: [PATCH 65/92] Fixed name of FFN --- scripts/convert_lumina_to_diffusers.py | 6 ++--- src/diffusers/models/attention.py | 33 ++++++++++++-------------- 2 files changed, 18 insertions(+), 21 deletions(-) diff --git a/scripts/convert_lumina_to_diffusers.py b/scripts/convert_lumina_to_diffusers.py index 7e670decb1ed..710839920d76 100644 --- a/scripts/convert_lumina_to_diffusers.py +++ b/scripts/convert_lumina_to_diffusers.py @@ -70,9 +70,9 @@ def main(args): ] # feed forward - converted_state_dict[f"layers.{i}.feed_forward.w1.weight"] = all_sd[f"layers.{i}.feed_forward.w1.weight"] - converted_state_dict[f"layers.{i}.feed_forward.w2.weight"] = all_sd[f"layers.{i}.feed_forward.w2.weight"] - converted_state_dict[f"layers.{i}.feed_forward.w3.weight"] = all_sd[f"layers.{i}.feed_forward.w3.weight"] + converted_state_dict[f"layers.{i}.feed_forward.linear_1.weight"] = all_sd[f"layers.{i}.feed_forward.w1.weight"] + converted_state_dict[f"layers.{i}.feed_forward.linear_2.weight"] = all_sd[f"layers.{i}.feed_forward.w2.weight"] + converted_state_dict[f"layers.{i}.feed_forward.linear_3.weight"] = all_sd[f"layers.{i}.feed_forward.w3.weight"] # feed forward norm converted_state_dict[f"layers.{i}.ffn_norm1.weight"] = all_sd[f"layers.{i}.ffn_norm1.weight"] diff --git a/src/diffusers/models/attention.py b/src/diffusers/models/attention.py index 2b7ab2a2c339..9ff3d489b8a4 100644 --- a/src/diffusers/models/attention.py +++ b/src/diffusers/models/attention.py @@ -19,7 +19,7 @@ from ..utils import deprecate, logging from ..utils.torch_utils import maybe_allow_in_graph -from .activations import GEGLU, GELU, ApproximateGELU +from .activations import GEGLU, GELU, ApproximateGELU, FP32SiLU from .attention_processor import Attention, JointAttnProcessor2_0 from .embeddings import SinusoidalPositionalEmbedding from .normalization import AdaLayerNorm, AdaLayerNormContinuous, AdaLayerNormZero, RMSNorm @@ -550,34 +550,31 @@ def __init__( ffn_dim_multiplier: Optional[float] = None, ): super().__init__() - intermediate_size = int(2 * intermediate_size / 3) + inner_dim = int(2 * inner_dim / 3) # custom hidden_size factor multiplier if ffn_dim_multiplier is not None: - intermediate_size = int(ffn_dim_multiplier * intermediate_size) - intermediate_size = multiple_of * ((intermediate_size + multiple_of - 1) // multiple_of) + inner_dim = int(ffn_dim_multiplier * inner_dim) + inner_dim = multiple_of * ((inner_dim + multiple_of - 1) // multiple_of) - self.w1 = nn.Linear( - hidden_size, - intermediate_size, + self.linear_1 = nn.Linear( + dim, + inner_dim, bias=False, ) - self.w2 = nn.Linear( - intermediate_size, - hidden_size, + self.linear_2 = nn.Linear( + inner_dim, + dim, bias=False, ) - self.w3 = nn.Linear( - hidden_size, - intermediate_size, + self.linear_3 = nn.Linear( + dim, + inner_dim, bias=False, ) - - # @torch.compile - def _forward_silu_gating(self, x1, x3): - return F.silu(x1) * x3 + self.silu = FP32SiLU() def forward(self, x): - return self.w2(self._forward_silu_gating(self.w1(x), self.w3(x))) + return self.linear_2(self.silu(self.linear_1(x)) * self.linear_3(x)) @maybe_allow_in_graph From c70694f9fec1447a482f45f0cf5eaa109705c91f Mon Sep 17 00:00:00 2001 From: PommesPeter <54879512+PommesPeter@users.noreply.github.com> Date: Mon, 1 Jul 2024 13:26:37 +0800 Subject: [PATCH 66/92] Apply suggestions from code review Co-authored-by: YiYi Xu --- src/diffusers/models/transformers/lumina_nextdit2d.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/src/diffusers/models/transformers/lumina_nextdit2d.py b/src/diffusers/models/transformers/lumina_nextdit2d.py index 299ffef8a50b..691060c30358 100644 --- a/src/diffusers/models/transformers/lumina_nextdit2d.py +++ b/src/diffusers/models/transformers/lumina_nextdit2d.py @@ -333,7 +333,7 @@ def __init__( norm_eps: Optional[float] = 1e-5, learn_sigma: Optional[bool] = True, qk_norm: Optional[bool] = True, - encoder_hidden_size: Optional[int] = 2048, + cross_attention_dim: Optional[int] = 2048, scaling_factor: Optional[float] = 1.0, ) -> None: super().__init__() @@ -530,7 +530,6 @@ def forward( scaling_watershed=scaling_watershed, timestep=timestep[0].item(), ) - x_is_tensor = isinstance(hidden_states, torch.Tensor) hidden_states, mask, img_size, freqs_cis = self.patchify_and_embed(hidden_states) freqs_cis = freqs_cis.to(hidden_states.device) From f0904b138ad823a9e2cc2f183501207c11f7e06d Mon Sep 17 00:00:00 2001 From: PommesPeter Date: Mon, 1 Jul 2024 13:27:21 +0800 Subject: [PATCH 67/92] Renamed input name --- .../models/transformers/lumina_nextdit2d.py | 33 +++++++++---------- 1 file changed, 15 insertions(+), 18 deletions(-) diff --git a/src/diffusers/models/transformers/lumina_nextdit2d.py b/src/diffusers/models/transformers/lumina_nextdit2d.py index 299ffef8a50b..4f994c028d04 100644 --- a/src/diffusers/models/transformers/lumina_nextdit2d.py +++ b/src/diffusers/models/transformers/lumina_nextdit2d.py @@ -126,17 +126,15 @@ def __init__( norm_elementwise_affine: bool = True, ) -> None: super().__init__() - self.layer_id = layer_id - self.hidden_size = hidden_size - self.head_dim = hidden_size // num_attention_heads + self.head_dim = dim // num_attention_heads self.gate = nn.Parameter(torch.zeros([num_attention_heads])) # Self-attention self.attn = Attention( - query_dim=hidden_size, + query_dim=dim, cross_attention_dim=None, - dim_head=hidden_size // num_attention_heads, + dim_head=dim // num_attention_heads, qk_norm="layer_norm" if qk_norm else None, heads=num_attention_heads, kv_heads=num_kv_heads, @@ -149,9 +147,9 @@ def __init__( # Cross-attention self.cross_attn = Attention( - query_dim=hidden_size, - cross_attention_dim=encoder_hidden_size, - dim_head=hidden_size // num_attention_heads, + query_dim=dim, + cross_attention_dim=cross_attention_dim, + dim_head=dim // num_attention_heads, qk_norm="layer_norm" if qk_norm else None, heads=num_attention_heads, kv_heads=num_kv_heads, @@ -162,29 +160,29 @@ def __init__( ) self.feed_forward = LuminaFeedForward( - hidden_size=hidden_size, - intermediate_size=4 * hidden_size, + hidden_size=dim, + intermediate_size=4 * dim, multiple_of=multiple_of, ffn_dim_multiplier=ffn_dim_multiplier, ) - self.attn_norm1 = RMSNorm(hidden_size, eps=norm_eps, elementwise_affine=norm_elementwise_affine) - self.ffn_norm1 = RMSNorm(hidden_size, eps=norm_eps, elementwise_affine=norm_elementwise_affine) + self.attn_norm1 = RMSNorm(dim, eps=norm_eps, elementwise_affine=norm_elementwise_affine) + self.ffn_norm1 = RMSNorm(dim, eps=norm_eps, elementwise_affine=norm_elementwise_affine) - self.attn_norm2 = RMSNorm(hidden_size, eps=norm_eps, elementwise_affine=norm_elementwise_affine) - self.ffn_norm2 = RMSNorm(hidden_size, eps=norm_eps, elementwise_affine=norm_elementwise_affine) + self.attn_norm2 = RMSNorm(dim, eps=norm_eps, elementwise_affine=norm_elementwise_affine) + self.ffn_norm2 = RMSNorm(dim, eps=norm_eps, elementwise_affine=norm_elementwise_affine) self.adaLN_modulation = nn.Sequential( nn.SiLU(), nn.Linear( - min(hidden_size, 1024), - 4 * hidden_size, + min(dim, 1024), + 4 * dim, bias=True, ), ) self.attn_encoder_hidden_states_norm = RMSNorm( - encoder_hidden_size, eps=norm_eps, elementwise_affine=norm_elementwise_affine + cross_attention_dim, eps=norm_eps, elementwise_affine=norm_elementwise_affine ) def forward( @@ -361,7 +359,6 @@ def __init__( self.layers = nn.ModuleList( [ LuminaNextDiTBlock( - layer_id, hidden_size, num_attention_heads, num_kv_heads, From c589ce6505b5c137699b3e3b1e11aec2e3851da2 Mon Sep 17 00:00:00 2001 From: PommesPeter Date: Mon, 1 Jul 2024 14:26:36 +0800 Subject: [PATCH 68/92] Updated rotary emb --- src/diffusers/models/embeddings.py | 52 ++++++- .../models/transformers/lumina_nextdit2d.py | 138 +++--------------- .../pipelines/lumina/pipeline_lumina.py | 18 ++- 3 files changed, 83 insertions(+), 125 deletions(-) diff --git a/src/diffusers/models/embeddings.py b/src/diffusers/models/embeddings.py index 70262e63fb81..9d44ac2c440c 100644 --- a/src/diffusers/models/embeddings.py +++ b/src/diffusers/models/embeddings.py @@ -230,6 +230,52 @@ def forward(self, latent): return (latent + pos_embed).to(latent.dtype) +class LuminaPatchEmbed(nn.Module): + """2D Image to Patch Embedding with support for Lumina-T2X""" + + def __init__(self, patch_size=2, in_channels=4, embed_dim=768, bias=True): + super().__init__() + self.patch_size = patch_size + self.proj = nn.Linear( + in_features=patch_size * patch_size * in_channels, + out_features=embed_dim, + bias=bias, + ) + + def forward(self, x, freqs_cis): + """ + Patchifies and embeds the input tensor(s). + + Args: + x (List[torch.Tensor] | torch.Tensor): The input tensor(s) to be patchified and embedded. + + Returns: + Tuple[torch.Tensor, torch.Tensor, List[Tuple[int, int]], torch.Tensor]: A tuple containing the patchified + and embedded tensor(s), the mask indicating the valid patches, the original image size(s), and the + frequency tensor(s). + """ + freqs_cis = freqs_cis.to(x[0].device) + patch_height = patch_width = self.patch_size + batch_size, channel, height, width = x.size() + height_tokens, width_tokens = height // patch_height, width // patch_width + + x = x.view(batch_size, channel, height_tokens, patch_height, width_tokens, patch_width).permute( + 0, 2, 4, 1, 3, 5 + ) + x = x.flatten(3) + x = self.proj(x) + x = x.flatten(1, 2) + + mask = torch.ones(x.shape[0], x.shape[1], dtype=torch.int32, device=x.device) + + return ( + x, + mask, + [(height, width)] * batch_size, + freqs_cis[:height_tokens, :width_tokens].flatten(0, 1).unsqueeze(0), + ) + + def get_2d_rotary_pos_embed(embed_dim, crops_coords, grid_size, use_real=True): """ RoPE for image tokens with 2d structure. @@ -767,7 +813,7 @@ def forward(self, timestep, encoder_hidden_states, image_meta_size, style, hidde class LuminaCombinedTimestepCaptionEmbedding(nn.Module): - def __init__(self, hidden_size=4096, encoder_hidden_size=2048, frequency_embedding_size=256): + def __init__(self, hidden_size=4096, cross_attention_dim=2048, frequency_embedding_size=256): super().__init__() self.time_proj = Timesteps( num_channels=frequency_embedding_size, flip_sin_to_cos=True, downscale_freq_shift=0.0 @@ -776,9 +822,9 @@ def __init__(self, hidden_size=4096, encoder_hidden_size=2048, frequency_embeddi self.timestep_embedder = TimestepEmbedding(in_channels=frequency_embedding_size, time_embed_dim=hidden_size) self.caption_embedder = nn.Sequential( - nn.LayerNorm(encoder_hidden_size), + nn.LayerNorm(cross_attention_dim), nn.Linear( - encoder_hidden_size, + cross_attention_dim, hidden_size, bias=True, ), diff --git a/src/diffusers/models/transformers/lumina_nextdit2d.py b/src/diffusers/models/transformers/lumina_nextdit2d.py index dd46fa123c02..0a9d00b41efc 100644 --- a/src/diffusers/models/transformers/lumina_nextdit2d.py +++ b/src/diffusers/models/transformers/lumina_nextdit2d.py @@ -12,7 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. -from typing import Any, Dict, List, Optional, Tuple, Union +from typing import Any, Dict, List, Optional, Tuple import torch import torch.nn as nn @@ -23,6 +23,7 @@ from ..attention_processor import Attention, LuminaAttnProcessor2_0 from ..embeddings import ( LuminaCombinedTimestepCaptionEmbedding, + LuminaPatchEmbed, ) from ..modeling_outputs import Transformer2DModelOutput from ..modeling_utils import ModelMixin @@ -160,8 +161,8 @@ def __init__( ) self.feed_forward = LuminaFeedForward( - hidden_size=dim, - intermediate_size=4 * dim, + dim=dim, + inner_dim=4 * dim, multiple_of=multiple_of, ffn_dim_multiplier=ffn_dim_multiplier, ) @@ -189,7 +190,7 @@ def forward( self, hidden_states: torch.Tensor, attention_mask: torch.Tensor, - freqs_cis: torch.Tensor, + image_rotary_emb: torch.Tensor, encoder_hidden_states: torch.Tensor, encoder_mask: torch.Tensor, adaln_input: Optional[torch.Tensor] = None, @@ -200,7 +201,7 @@ def forward( Args: hidden_states (torch.Tensor): Input tensor. - freqs_cis (torch.Tensor): Precomputed cosine and sine frequencies. + image_rotary_emb (torch.Tensor): Precomputed cosine and sine frequencies. Returns: torch.Tensor: Output tensor after applying attention and feedforward layers. @@ -216,8 +217,8 @@ def forward( hidden_states=hidden_states, encoder_hidden_states=hidden_states, attention_mask=attention_mask, - query_rotary_emb=freqs_cis, - key_rotary_emb=freqs_cis, + query_rotary_emb=image_rotary_emb, + key_rotary_emb=image_rotary_emb, **cross_attention_kwargs, ) @@ -227,7 +228,7 @@ def forward( hidden_states=hidden_states, encoder_hidden_states=norm_encoder_hidden_states, attention_mask=encoder_mask, - query_rotary_emb=freqs_cis, + query_rotary_emb=image_rotary_emb, key_rotary_emb=None, **cross_attention_kwargs, ) @@ -250,7 +251,7 @@ def forward( hidden_states=hidden_states, encoder_hidden_states=None, attention_mask=attention_mask, - image_rotary_emb=freqs_cis, + image_rotary_emb=image_rotary_emb, **cross_attention_kwargs, ) @@ -308,7 +309,7 @@ class LuminaNextDiT2DModel(ModelMixin, ConfigMixin): predictions. qk_norm (`bool`, *optional*, defaults to True): Indicates if the queries and keys in the attention mechanism should be normalized. - encoder_hidden_size (`int`, *optional*, defaults to 2048): + cross_attention_dim (`int`, *optional*, defaults to 2048): The dimensionality of the text embeddings. This parameter defines the size of the text representations used in the model. scaling_factor (`float`, *optional*, defaults to 1.0): @@ -344,16 +345,14 @@ def __init__( self.head_dim = hidden_size // num_attention_heads self.scaling_factor = scaling_factor - self.patch_embedder = nn.Linear( - in_features=patch_size * patch_size * in_channels, - out_features=hidden_size, - bias=True, + self.patch_embedder = LuminaPatchEmbed( + patch_size=patch_size, in_channels=in_channels, embed_dim=hidden_size, bias=True ) self.pad_token = nn.Parameter(torch.empty(hidden_size)) self.time_caption_embed = LuminaCombinedTimestepCaptionEmbedding( - hidden_size=min(hidden_size, 1024), encoder_hidden_size=encoder_hidden_size + hidden_size=min(hidden_size, 1024), cross_attention_dim=cross_attention_dim ) self.layers = nn.ModuleList( @@ -366,19 +365,14 @@ def __init__( ffn_dim_multiplier, norm_eps, qk_norm, - encoder_hidden_size, + cross_attention_dim, ) - for layer_id in range(num_layers) + for _ in range(num_layers) ] ) self.final_layer = LuminaFinalLayer(hidden_size, patch_size, self.out_channels) assert (hidden_size // num_attention_heads) % 4 == 0, "2d rope needs head dim to be divisible by 4" - self.freqs_cis = self.precompute_freqs_cis( - hidden_size // num_attention_heads, - 384, - scaling_factor=scaling_factor, - ) def unpatchify(self, x: torch.Tensor, img_size: List[Tuple[int, int]], return_tensor=False) -> List[torch.Tensor]: """ @@ -418,92 +412,13 @@ def unpatchify(self, x: torch.Tensor, img_size: List[Tuple[int, int]], return_te ) return imgs - def patchify_and_embed( - self, x: Union[List[torch.Tensor], torch.Tensor] - ) -> Tuple[torch.Tensor, torch.Tensor, List[Tuple[int, int]], torch.Tensor]: - """ - Patchifies and embeds the input tensor(s). - - Args: - x (List[torch.Tensor] | torch.Tensor): The input tensor(s) to be patchified and embedded. - - Returns: - Tuple[torch.Tensor, torch.Tensor, List[Tuple[int, int]], torch.Tensor]: A tuple containing the patchified - and embedded tensor(s), the mask indicating the valid patches, the original image size(s), and the - frequency tensor(s). - """ - self.freqs_cis = self.freqs_cis.to(x[0].device) - if isinstance(x, torch.Tensor): - pH = pW = self.patch_size - B, C, H, W = x.size() - x = x.view(B, C, H // pH, pH, W // pW, pW).permute(0, 2, 4, 1, 3, 5).flatten(3) - x = self.patch_embedder(x) - x = x.flatten(1, 2) - - mask = torch.ones(x.shape[0], x.shape[1], dtype=torch.int32, device=x.device) - - return ( - x, - mask, - [(H, W)] * B, - self.freqs_cis[: H // pH, : W // pW].flatten(0, 1).unsqueeze(0), - ) - else: - pH = pW = self.patch_size - x_embed = [] - freqs_cis = [] - img_size = [] - l_effective_seq_len = [] - - for img in x: - C, H, W = img.size() - item_freqs_cis = self.freqs_cis[: H // pH, : W // pW] - freqs_cis.append(item_freqs_cis.flatten(0, 1)) - img_size.append((H, W)) - img = img.view(C, H // pH, pH, W // pW, pW).permute(1, 3, 0, 2, 4).flatten(2) - img = self.x_embedder(img) - img = img.flatten(0, 1) - l_effective_seq_len.append(len(img)) - x_embed.append(img) - - max_seq_len = max(l_effective_seq_len) - mask = torch.zeros(len(x), max_seq_len, dtype=torch.int32, device=x[0].device) - padded_x_embed = [] - padded_freqs_cis = [] - for i, (item_embed, item_freqs_cis, item_seq_len) in enumerate( - zip(x_embed, freqs_cis, l_effective_seq_len) - ): - item_embed = torch.cat( - [ - item_embed, - self.pad_token.view(1, -1).expand(max_seq_len - item_seq_len, -1), - ], - dim=0, - ) - item_freqs_cis = torch.cat( - [ - item_freqs_cis, - item_freqs_cis[-1:].expand(max_seq_len - item_seq_len, -1), - ], - dim=0, - ) - padded_x_embed.append(item_embed) - padded_freqs_cis.append(item_freqs_cis) - mask[i][:item_seq_len] = 1 - - x_embed = torch.stack(padded_x_embed, dim=0) - freqs_cis = torch.stack(padded_freqs_cis, dim=0) - - return x_embed, mask, img_size, freqs_cis - def forward( self, hidden_states: torch.Tensor, timestep: torch.Tensor, encoder_hidden_states: torch.Tensor, encoder_mask: torch.Tensor, - scaling_factor: Optional[int] = 1, - scaling_watershed: Optional[float] = 0.3, + image_rotary_emb: torch.Tensor, cross_attention_kwargs: Dict[str, Any] = None, return_dict=True, ) -> torch.Tensor: @@ -519,16 +434,8 @@ def forward( Returns: torch.Tensor: Output tensor of shape (N, C, H, W). """ - # For inference - self.freqs_cis = self.precompute_freqs_cis( - self.head_dim, - 384, - scaling_factor=scaling_factor, - scaling_watershed=scaling_watershed, - timestep=timestep[0].item(), - ) - hidden_states, mask, img_size, freqs_cis = self.patchify_and_embed(hidden_states) - freqs_cis = freqs_cis.to(hidden_states.device) + hidden_states, mask, img_size, image_rotary_emb = self.patch_embedder(hidden_states, image_rotary_emb) + image_rotary_emb = image_rotary_emb.to(hidden_states.device) adaln_input = self.time_caption_embed(timestep, encoder_hidden_states, encoder_mask) @@ -537,7 +444,7 @@ def forward( hidden_states = layer( hidden_states, mask, - freqs_cis, + image_rotary_emb, encoder_hidden_states, encoder_mask, adaln_input=adaln_input, @@ -545,7 +452,7 @@ def forward( ) hidden_states = self.final_layer(hidden_states, adaln_input) - output = self.unpatchify(hidden_states, img_size, return_tensor=x_is_tensor) + output = self.unpatchify(hidden_states, img_size, return_tensor=True) if not return_dict: return (output,) @@ -554,7 +461,6 @@ def forward( def precompute_freqs_cis( self, - dim: int, end: int, theta: float = 10000.0, scaling_factor: float = 1.0, @@ -578,7 +484,7 @@ def precompute_freqs_cis( torch.Tensor: Precomputed frequency tensor with complex exponentials. """ - + dim = self.head_dim if timestep < scaling_watershed: linear_factor = scaling_factor ntk_factor = 1.0 diff --git a/src/diffusers/pipelines/lumina/pipeline_lumina.py b/src/diffusers/pipelines/lumina/pipeline_lumina.py index b491c3408aef..5d004f93ab3a 100644 --- a/src/diffusers/pipelines/lumina/pipeline_lumina.py +++ b/src/diffusers/pipelines/lumina/pipeline_lumina.py @@ -803,8 +803,18 @@ def __call__( generator, latents, ) + + # 6. Prepare image_rotary_emb + # Dynamic scaling_factor for different resolution. + scaling_factor = math.sqrt(width * height / self.default_image_size**2) + image_rotary_emb = self.transformer.precompute_freqs_cis( + 384, + scaling_factor=scaling_factor, + scaling_watershed=scaling_watershed, + timestep=timesteps[0].item(), + ) - # 6. Denoising loop + # 7. Denoising loop with self.progress_bar(total=num_inference_steps) as progress_bar: for i, t in enumerate(timesteps): # expand the latents if we are doing classifier free guidance @@ -832,16 +842,12 @@ def __call__( # reverse the timestep since Lumina uses t=0 as the noise and t=1 as the image current_timestep = 1 - current_timestep / self.scheduler.config.num_train_timesteps - # dynamic scaling_factor for different resolution. - scaling_factor = math.sqrt(width * height / self.default_image_size**2) - noise_pred = self.transformer( hidden_states=latent_model_input, timestep=current_timestep, encoder_hidden_states=prompt_embeds, encoder_mask=prompt_attention_mask, - scaling_factor=scaling_factor, - scaling_watershed=scaling_watershed, + image_rotary_emb=image_rotary_emb, cross_attention_kwargs=cross_attention_kwargs, return_dict=False, )[0] From 0712910252ac3da5affdce1784da8df2a6f5da81 Mon Sep 17 00:00:00 2001 From: zhuole1025 Date: Mon, 1 Jul 2024 09:44:03 +0000 Subject: [PATCH 69/92] Remove useless codes --- src/diffusers/pipelines/lumina/pipeline_lumina.py | 3 --- 1 file changed, 3 deletions(-) diff --git a/src/diffusers/pipelines/lumina/pipeline_lumina.py b/src/diffusers/pipelines/lumina/pipeline_lumina.py index 5d004f93ab3a..192682f5fc47 100644 --- a/src/diffusers/pipelines/lumina/pipeline_lumina.py +++ b/src/diffusers/pipelines/lumina/pipeline_lumina.py @@ -751,10 +751,7 @@ def __call__( batch_size = prompt_embeds.shape[0] if proportional_attn: - cross_attention_kwargs["proportional_attn"] = True cross_attention_kwargs["base_sequence_length"] = (self.default_image_size // 16) ** 2 - else: - cross_attention_kwargs["proportational_attn"] = False device = self._execution_device From 32a163c1dfc1a45752a1a8c8f827999bc07b6513 Mon Sep 17 00:00:00 2001 From: PommesPeter <54879512+PommesPeter@users.noreply.github.com> Date: Tue, 2 Jul 2024 13:03:12 +0800 Subject: [PATCH 70/92] Apply suggestions from code review Co-authored-by: YiYi Xu --- .../models/transformers/lumina_nextdit2d.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/src/diffusers/models/transformers/lumina_nextdit2d.py b/src/diffusers/models/transformers/lumina_nextdit2d.py index 0a9d00b41efc..ec58a5cd2534 100644 --- a/src/diffusers/models/transformers/lumina_nextdit2d.py +++ b/src/diffusers/models/transformers/lumina_nextdit2d.py @@ -193,7 +193,7 @@ def forward( image_rotary_emb: torch.Tensor, encoder_hidden_states: torch.Tensor, encoder_mask: torch.Tensor, - adaln_input: Optional[torch.Tensor] = None, + temb: Optional[torch.Tensor] = None, cross_attention_kwargs: Dict[str, Any] = None, ): """ @@ -213,8 +213,8 @@ def forward( # Self-attention hidden_states = modulate(self.attn_norm1(hidden_states), scale_msa) - self_attn_output = self.attn( - hidden_states=hidden_states, + self_attn_output = self.attn1( + hidden_states=norm_hidden_states, encoder_hidden_states=hidden_states, attention_mask=attention_mask, query_rotary_emb=image_rotary_emb, @@ -223,8 +223,8 @@ def forward( ) # Cross-attention - norm_encoder_hidden_states = self.attn_encoder_hidden_states_norm(encoder_hidden_states) - cross_attn_output = self.cross_attn( + norm_encoder_hidden_states = self.norm1_context(encoder_hidden_states) + cross_attn_output = self.attn2( hidden_states=hidden_states, encoder_hidden_states=norm_encoder_hidden_states, attention_mask=encoder_mask, @@ -437,7 +437,7 @@ def forward( hidden_states, mask, img_size, image_rotary_emb = self.patch_embedder(hidden_states, image_rotary_emb) image_rotary_emb = image_rotary_emb.to(hidden_states.device) - adaln_input = self.time_caption_embed(timestep, encoder_hidden_states, encoder_mask) + temb = self.time_caption_embed(timestep, encoder_hidden_states, encoder_mask) encoder_mask = encoder_mask.bool() for layer in self.layers: From d57cc16eb875c152eb07c2c86d6f1ee4074b5108 Mon Sep 17 00:00:00 2001 From: PommesPeter Date: Tue, 2 Jul 2024 13:44:56 +0800 Subject: [PATCH 71/92] Updated variable name --- scripts/convert_lumina_to_diffusers.py | 32 +++--- .../models/transformers/lumina_nextdit2d.py | 104 +++++++----------- 2 files changed, 56 insertions(+), 80 deletions(-) diff --git a/scripts/convert_lumina_to_diffusers.py b/scripts/convert_lumina_to_diffusers.py index 710839920d76..70ac105a4ee8 100644 --- a/scripts/convert_lumina_to_diffusers.py +++ b/scripts/convert_lumina_to_diffusers.py @@ -36,36 +36,36 @@ def main(args): converted_state_dict[f"layers.{i}.adaLN_modulation.1.bias"] = all_sd[f"layers.{i}.adaLN_modulation.1.bias"] # qkv - converted_state_dict[f"layers.{i}.attn.to_q.weight"] = all_sd[f"layers.{i}.attention.wq.weight"] - converted_state_dict[f"layers.{i}.attn.to_k.weight"] = all_sd[f"layers.{i}.attention.wk.weight"] - converted_state_dict[f"layers.{i}.attn.to_v.weight"] = all_sd[f"layers.{i}.attention.wv.weight"] + converted_state_dict[f"layers.{i}.attn1.to_q.weight"] = all_sd[f"layers.{i}.attention.wq.weight"] + converted_state_dict[f"layers.{i}.attn1.to_k.weight"] = all_sd[f"layers.{i}.attention.wk.weight"] + converted_state_dict[f"layers.{i}.attn1.to_v.weight"] = all_sd[f"layers.{i}.attention.wv.weight"] # cap - converted_state_dict[f"layers.{i}.cross_attn.to_q.weight"] = all_sd[f"layers.{i}.attention.wq.weight"] - converted_state_dict[f"layers.{i}.cross_attn.to_k.weight"] = all_sd[f"layers.{i}.attention.wk_y.weight"] - converted_state_dict[f"layers.{i}.cross_attn.to_v.weight"] = all_sd[f"layers.{i}.attention.wv_y.weight"] + converted_state_dict[f"layers.{i}.attn2.to_q.weight"] = all_sd[f"layers.{i}.attention.wq.weight"] + converted_state_dict[f"layers.{i}.attn2.to_k.weight"] = all_sd[f"layers.{i}.attention.wk_y.weight"] + converted_state_dict[f"layers.{i}.attn2.to_v.weight"] = all_sd[f"layers.{i}.attention.wv_y.weight"] # output - converted_state_dict[f"layers.{i}.cross_attn.to_out.0.weight"] = all_sd[f"layers.{i}.attention.wo.weight"] + converted_state_dict[f"layers.{i}.attn2.to_out.0.weight"] = all_sd[f"layers.{i}.attention.wo.weight"] # attention # qk norm - converted_state_dict[f"layers.{i}.attn.norm_q.weight"] = all_sd[f"layers.{i}.attention.q_norm.weight"] - converted_state_dict[f"layers.{i}.attn.norm_q.bias"] = all_sd[f"layers.{i}.attention.q_norm.bias"] + converted_state_dict[f"layers.{i}.attn1.norm_q.weight"] = all_sd[f"layers.{i}.attention.q_norm.weight"] + converted_state_dict[f"layers.{i}.attn1.norm_q.bias"] = all_sd[f"layers.{i}.attention.q_norm.bias"] - converted_state_dict[f"layers.{i}.attn.norm_k.weight"] = all_sd[f"layers.{i}.attention.k_norm.weight"] - converted_state_dict[f"layers.{i}.attn.norm_k.bias"] = all_sd[f"layers.{i}.attention.k_norm.bias"] + converted_state_dict[f"layers.{i}.attn1.norm_k.weight"] = all_sd[f"layers.{i}.attention.k_norm.weight"] + converted_state_dict[f"layers.{i}.attn1.norm_k.bias"] = all_sd[f"layers.{i}.attention.k_norm.bias"] - converted_state_dict[f"layers.{i}.cross_attn.norm_q.weight"] = all_sd[f"layers.{i}.attention.q_norm.weight"] - converted_state_dict[f"layers.{i}.cross_attn.norm_q.bias"] = all_sd[f"layers.{i}.attention.q_norm.bias"] + converted_state_dict[f"layers.{i}.attn2.norm_q.weight"] = all_sd[f"layers.{i}.attention.q_norm.weight"] + converted_state_dict[f"layers.{i}.attn2.norm_q.bias"] = all_sd[f"layers.{i}.attention.q_norm.bias"] - converted_state_dict[f"layers.{i}.cross_attn.norm_k.weight"] = all_sd[f"layers.{i}.attention.ky_norm.weight"] - converted_state_dict[f"layers.{i}.cross_attn.norm_k.bias"] = all_sd[f"layers.{i}.attention.ky_norm.bias"] + converted_state_dict[f"layers.{i}.attn2.norm_k.weight"] = all_sd[f"layers.{i}.attention.ky_norm.weight"] + converted_state_dict[f"layers.{i}.attn2.norm_k.bias"] = all_sd[f"layers.{i}.attention.ky_norm.bias"] # attention norm converted_state_dict[f"layers.{i}.attn_norm1.weight"] = all_sd[f"layers.{i}.attention_norm1.weight"] converted_state_dict[f"layers.{i}.attn_norm2.weight"] = all_sd[f"layers.{i}.attention_norm2.weight"] - converted_state_dict[f"layers.{i}.attn_encoder_hidden_states_norm.weight"] = all_sd[ + converted_state_dict[f"layers.{i}.norm1_context.weight"] = all_sd[ f"layers.{i}.attention_y_norm.weight" ] diff --git a/src/diffusers/models/transformers/lumina_nextdit2d.py b/src/diffusers/models/transformers/lumina_nextdit2d.py index ec58a5cd2534..10eab85ceb91 100644 --- a/src/diffusers/models/transformers/lumina_nextdit2d.py +++ b/src/diffusers/models/transformers/lumina_nextdit2d.py @@ -132,7 +132,7 @@ def __init__( self.gate = nn.Parameter(torch.zeros([num_attention_heads])) # Self-attention - self.attn = Attention( + self.attn1 = Attention( query_dim=dim, cross_attention_dim=None, dim_head=dim // num_attention_heads, @@ -144,10 +144,10 @@ def __init__( out_bias=False, processor=LuminaAttnProcessor2_0(), ) - self.attn.to_out = nn.Identity() + self.attn1.to_out = nn.Identity() # Cross-attention - self.cross_attn = Attention( + self.attn2 = Attention( query_dim=dim, cross_attention_dim=cross_attention_dim, dim_head=dim // num_attention_heads, @@ -167,10 +167,10 @@ def __init__( ffn_dim_multiplier=ffn_dim_multiplier, ) - self.attn_norm1 = RMSNorm(dim, eps=norm_eps, elementwise_affine=norm_elementwise_affine) + self.norm1 = RMSNorm(dim, eps=norm_eps, elementwise_affine=norm_elementwise_affine) self.ffn_norm1 = RMSNorm(dim, eps=norm_eps, elementwise_affine=norm_elementwise_affine) - self.attn_norm2 = RMSNorm(dim, eps=norm_eps, elementwise_affine=norm_elementwise_affine) + self.norm2 = RMSNorm(dim, eps=norm_eps, elementwise_affine=norm_elementwise_affine) self.ffn_norm2 = RMSNorm(dim, eps=norm_eps, elementwise_affine=norm_elementwise_affine) self.adaLN_modulation = nn.Sequential( @@ -182,7 +182,7 @@ def __init__( ), ) - self.attn_encoder_hidden_states_norm = RMSNorm( + self.norm1_context = RMSNorm( cross_attention_dim, eps=norm_eps, elementwise_affine=norm_elementwise_affine ) @@ -193,7 +193,7 @@ def forward( image_rotary_emb: torch.Tensor, encoder_hidden_states: torch.Tensor, encoder_mask: torch.Tensor, - temb: Optional[torch.Tensor] = None, + temb: torch.Tensor = None, cross_attention_kwargs: Dict[str, Any] = None, ): """ @@ -208,65 +208,41 @@ def forward( """ residual = hidden_states - if adaln_input is not None: - scale_msa, gate_msa, scale_mlp, gate_mlp = self.adaLN_modulation(adaln_input).chunk(4, dim=1) - - # Self-attention - hidden_states = modulate(self.attn_norm1(hidden_states), scale_msa) - self_attn_output = self.attn1( - hidden_states=norm_hidden_states, - encoder_hidden_states=hidden_states, - attention_mask=attention_mask, - query_rotary_emb=image_rotary_emb, - key_rotary_emb=image_rotary_emb, - **cross_attention_kwargs, - ) - - # Cross-attention - norm_encoder_hidden_states = self.norm1_context(encoder_hidden_states) - cross_attn_output = self.attn2( - hidden_states=hidden_states, - encoder_hidden_states=norm_encoder_hidden_states, - attention_mask=encoder_mask, - query_rotary_emb=image_rotary_emb, - key_rotary_emb=None, - **cross_attention_kwargs, - ) - cross_attn_output = cross_attn_output * self.gate.tanh().view(1, 1, -1, 1) - mixed_attn_output = self_attn_output + cross_attn_output - mixed_attn_output = mixed_attn_output.flatten(-2) - # linear proj - hidden_states = self.cross_attn.to_out[0](mixed_attn_output) + scale_msa, gate_msa, scale_mlp, gate_mlp = self.adaLN_modulation(temb).chunk(4, dim=1) - hidden_states = residual + gate_msa.unsqueeze(1).tanh() * self.attn_norm2(hidden_states) + # Self-attention + norm_hidden_states = modulate(self.norm1(hidden_states), scale_msa) + self_attn_output = self.attn1( + hidden_states=norm_hidden_states, + encoder_hidden_states=norm_hidden_states, + attention_mask=attention_mask, + query_rotary_emb=image_rotary_emb, + key_rotary_emb=image_rotary_emb, + **cross_attention_kwargs, + ) - mlp_output = self.feed_forward( - modulate(self.ffn_norm1(hidden_states), scale_mlp), - ) - hidden_states = hidden_states + gate_mlp.unsqueeze(1).tanh() * self.ffn_norm2(mlp_output) - else: - hidden_states = self.attn_norm1(hidden_states) - # Self-attention - self_attn_output = self.attn( - hidden_states=hidden_states, - encoder_hidden_states=None, - attention_mask=attention_mask, - image_rotary_emb=image_rotary_emb, - **cross_attention_kwargs, - ) + # Cross-attention + norm_encoder_hidden_states = self.norm1_context(encoder_hidden_states) + cross_attn_output = self.attn2( + hidden_states=norm_hidden_states, + encoder_hidden_states=norm_encoder_hidden_states, + attention_mask=encoder_mask, + query_rotary_emb=image_rotary_emb, + key_rotary_emb=None, + **cross_attention_kwargs, + ) + cross_attn_output = cross_attn_output * self.gate.tanh().view(1, 1, -1, 1) + mixed_attn_output = self_attn_output + cross_attn_output + mixed_attn_output = mixed_attn_output.flatten(-2) + # linear proj + hidden_states = self.attn2.to_out[0](mixed_attn_output) - # Cross-attention - cross_attn_output = self.cross_attn( - hidden_states=hidden_states, - encoder_hidden_states=self.attn_encoder_hidden_states_norm(encoder_hidden_states), - attention_mask=encoder_mask, - image_rotary_emb=None, - **cross_attention_kwargs, - ) - hidden_states = residual + self.attn_norm2(cross_attn_output) + hidden_states = residual + gate_msa.unsqueeze(1).tanh() * self.norm2(hidden_states) - mlp_output = self.feed_forward(self.ffn_norm1(hidden_states)) - hidden_states = hidden_states + self.ffn_norm2(hidden_states) + mlp_output = self.feed_forward( + modulate(self.ffn_norm1(hidden_states), scale_mlp), + ) + hidden_states = hidden_states + gate_mlp.unsqueeze(1).tanh() * self.ffn_norm2(mlp_output) return hidden_states @@ -447,11 +423,11 @@ def forward( image_rotary_emb, encoder_hidden_states, encoder_mask, - adaln_input=adaln_input, + temb=temb, cross_attention_kwargs=cross_attention_kwargs, ) - hidden_states = self.final_layer(hidden_states, adaln_input) + hidden_states = self.final_layer(hidden_states, temb) output = self.unpatchify(hidden_states, img_size, return_tensor=True) if not return_dict: From 0b197c45716b109852dce89ad8b50b5559ad3a69 Mon Sep 17 00:00:00 2001 From: zhuole1025 Date: Tue, 2 Jul 2024 12:26:06 +0000 Subject: [PATCH 72/92] Refactor positional embedding --- src/diffusers/models/embeddings.py | 26 ++++++++-- .../models/transformers/lumina_nextdit2d.py | 49 +------------------ .../pipelines/lumina/pipeline_lumina.py | 31 +++++++----- 3 files changed, 43 insertions(+), 63 deletions(-) diff --git a/src/diffusers/models/embeddings.py b/src/diffusers/models/embeddings.py index 9d44ac2c440c..49477f437c10 100644 --- a/src/diffusers/models/embeddings.py +++ b/src/diffusers/models/embeddings.py @@ -276,7 +276,7 @@ def forward(self, x, freqs_cis): ) -def get_2d_rotary_pos_embed(embed_dim, crops_coords, grid_size, use_real=True): +def get_2d_rotary_pos_embed(embed_dim, crops_coords, grid_size, use_real=True, reverse=False): """ RoPE for image tokens with 2d structure. @@ -297,6 +297,8 @@ def get_2d_rotary_pos_embed(embed_dim, crops_coords, grid_size, use_real=True): grid_h = np.linspace(start[0], stop[0], grid_size[0], endpoint=False, dtype=np.float32) grid_w = np.linspace(start[1], stop[1], grid_size[1], endpoint=False, dtype=np.float32) grid = np.meshgrid(grid_w, grid_h) # here w goes first + if reverse: + grid = grid[::-1] # reverse the order of grid_h and grid_w grid = np.stack(grid, axis=0) # [2, W, H] grid = grid.reshape([2, 1, *grid.shape[1:]]) @@ -318,9 +320,21 @@ def get_2d_rotary_pos_embed_from_grid(embed_dim, grid, use_real=False): else: emb = torch.cat([emb_h, emb_w], dim=1) # (H*W, D/2) return emb + +def get_2d_rotary_pos_embed_lumina(embed_dim, len_h, len_w, linear_factor=1.0, ntk_factor=1.0): + assert embed_dim % 4 == 0 + + emb_h = get_1d_rotary_pos_embed(embed_dim // 2, len_h, linear_factor=linear_factor, ntk_factor=ntk_factor) # (H, D/4) + emb_w = get_1d_rotary_pos_embed(embed_dim // 2, len_w, linear_factor=linear_factor, ntk_factor=ntk_factor) # (W, D/4) + emb_h = emb_h.view(len_h, 1, embed_dim // 4, 1).repeat(1, len_w, 1, 1) # (H, W, D/4, 1) + emb_w = emb_w.view(1, len_w, embed_dim // 4, 1).repeat(len_h, 1, 1, 1) # (H, W, D/4, 1) + + emb = torch.cat([emb_h, emb_w], dim=-1).flatten(2) # (H, W, D/2) + return emb -def get_1d_rotary_pos_embed(dim: int, pos: Union[np.ndarray, int], theta: float = 10000.0, use_real=False): + +def get_1d_rotary_pos_embed(dim: int, pos: Union[np.ndarray, int], theta: float = 10000.0, use_real=False, linear_factor=1.0, ntk_factor=1.0): """ Precompute the frequency tensor for complex exponentials (cis) with given dimensions. @@ -335,13 +349,17 @@ def get_1d_rotary_pos_embed(dim: int, pos: Union[np.ndarray, int], theta: float Scaling factor for frequency computation. Defaults to 10000.0. use_real (`bool`, *optional*): If True, return real part and imaginary part separately. Otherwise, return complex numbers. - + linear_factor (`float`, *optional*, defaults to 1.0): + Scaling factor for the context extrapolation. Defaults to 1.0. + ntk_factor (`float`, *optional*, defaults to 1.0): + Scaling factor for the NTK-Aware RoPE. Defaults to 1.0. Returns: `torch.Tensor`: Precomputed frequency tensor with complex exponentials. [S, D/2] """ if isinstance(pos, int): pos = np.arange(pos) - freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim)) # [D/2] + theta = theta * ntk_factor + freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim)) / linear_factor # [D/2] t = torch.from_numpy(pos).to(freqs.device) # type: ignore # [S] freqs = torch.outer(t, freqs).float() # type: ignore # [S, D/2] if use_real: diff --git a/src/diffusers/models/transformers/lumina_nextdit2d.py b/src/diffusers/models/transformers/lumina_nextdit2d.py index ec58a5cd2534..a7b7bb8668fe 100644 --- a/src/diffusers/models/transformers/lumina_nextdit2d.py +++ b/src/diffusers/models/transformers/lumina_nextdit2d.py @@ -457,51 +457,4 @@ def forward( if not return_dict: return (output,) - return Transformer2DModelOutput(sample=output) - - def precompute_freqs_cis( - self, - end: int, - theta: float = 10000.0, - scaling_factor: float = 1.0, - scaling_watershed: float = 1.0, - timestep: float = 1.0, - ): - """ - Precompute the frequency tensor for complex exponentials (cis) with given dimensions. - - This function calculates a frequency tensor with complex exponentials using the given dimension 'dim' and the - end index 'end'. The 'theta' parameter scales the frequencies. The returned tensor contains complex values in - complex64 data type. - - Args: - dim (int): Dimension of the frequency tensor. - end (int): End index for precomputing frequencies. - theta (float, optional): Scaling factor for frequency computation. - Defaults to 10000.0. - - Returns: - torch.Tensor: Precomputed frequency tensor with complex - exponentials. - """ - dim = self.head_dim - if timestep < scaling_watershed: - linear_factor = scaling_factor - ntk_factor = 1.0 - else: - linear_factor = 1.0 - ntk_factor = scaling_factor - - theta = theta * ntk_factor - freqs = 1.0 / (theta ** (torch.arange(0, dim, 4)[: (dim // 4)].float().cuda() / dim)) / linear_factor - - timestep = torch.arange(end, device=freqs.device, dtype=torch.float) - - freqs = torch.outer(timestep, freqs).float() - freqs_cis = torch.polar(torch.ones_like(freqs), freqs) # complex64 - - freqs_cis_h = freqs_cis.view(end, 1, dim // 4, 1).repeat(1, end, 1, 1) - freqs_cis_w = freqs_cis.view(1, end, dim // 4, 1).repeat(end, 1, 1, 1) - freqs_cis = torch.cat([freqs_cis_h, freqs_cis_w], dim=-1).flatten(2) - - return freqs_cis + return Transformer2DModelOutput(sample=output) \ No newline at end of file diff --git a/src/diffusers/pipelines/lumina/pipeline_lumina.py b/src/diffusers/pipelines/lumina/pipeline_lumina.py index 192682f5fc47..a06cf9dfa26d 100644 --- a/src/diffusers/pipelines/lumina/pipeline_lumina.py +++ b/src/diffusers/pipelines/lumina/pipeline_lumina.py @@ -25,6 +25,7 @@ from ...image_processor import VaeImageProcessor from ...models import AutoencoderKL from ...models.transformers.lumina_nextdit2d import LuminaNextDiT2DModel +from ...models.embeddings import get_2d_rotary_pos_embed_lumina from ...schedulers import FlowMatchEulerDiscreteScheduler from ...utils import ( BACKENDS_MAPPING, @@ -642,7 +643,7 @@ def __call__( return_dict: bool = True, clean_caption: bool = True, max_sequence_length: int = 256, - scaling_watershed: Optional[float] = 0.3, + scaling_watershed: Optional[float] = 1.0, proportional_attn: Optional[bool] = True, ) -> Union[ImagePipelineOutput, Tuple]: """ @@ -801,17 +802,8 @@ def __call__( latents, ) - # 6. Prepare image_rotary_emb - # Dynamic scaling_factor for different resolution. - scaling_factor = math.sqrt(width * height / self.default_image_size**2) - image_rotary_emb = self.transformer.precompute_freqs_cis( - 384, - scaling_factor=scaling_factor, - scaling_watershed=scaling_watershed, - timestep=timesteps[0].item(), - ) - # 7. Denoising loop + # 6. Denoising loop with self.progress_bar(total=num_inference_steps) as progress_bar: for i, t in enumerate(timesteps): # expand the latents if we are doing classifier free guidance @@ -838,6 +830,23 @@ def __call__( # reverse the timestep since Lumina uses t=0 as the noise and t=1 as the image current_timestep = 1 - current_timestep / self.scheduler.config.num_train_timesteps + + # prepare image_rotary_emb for positional encoding + # dynamic scaling_factor for different resolution. + scaling_factor = math.sqrt(width * height / self.default_image_size**2) + if current_timestep < scaling_watershed: + linear_factor = scaling_factor + ntk_factor = 1.0 + else: + linear_factor = 1.0 + ntk_factor = scaling_factor + image_rotary_emb = get_2d_rotary_pos_embed_lumina( + self.transformer.head_dim, + 384, + 384, + linear_factor=linear_factor, + ntk_factor=ntk_factor, + ) noise_pred = self.transformer( hidden_states=latent_model_input, From e232b8c1c00c2124f9c2e50e8cf2ea110ec6d173 Mon Sep 17 00:00:00 2001 From: zhuole1025 Date: Tue, 2 Jul 2024 12:34:04 +0000 Subject: [PATCH 73/92] Refactor positional embedding --- src/diffusers/models/embeddings.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/src/diffusers/models/embeddings.py b/src/diffusers/models/embeddings.py index 49477f437c10..ed73f2d541b7 100644 --- a/src/diffusers/models/embeddings.py +++ b/src/diffusers/models/embeddings.py @@ -276,7 +276,7 @@ def forward(self, x, freqs_cis): ) -def get_2d_rotary_pos_embed(embed_dim, crops_coords, grid_size, use_real=True, reverse=False): +def get_2d_rotary_pos_embed(embed_dim, crops_coords, grid_size, use_real=True): """ RoPE for image tokens with 2d structure. @@ -297,8 +297,6 @@ def get_2d_rotary_pos_embed(embed_dim, crops_coords, grid_size, use_real=True, r grid_h = np.linspace(start[0], stop[0], grid_size[0], endpoint=False, dtype=np.float32) grid_w = np.linspace(start[1], stop[1], grid_size[1], endpoint=False, dtype=np.float32) grid = np.meshgrid(grid_w, grid_h) # here w goes first - if reverse: - grid = grid[::-1] # reverse the order of grid_h and grid_w grid = np.stack(grid, axis=0) # [2, W, H] grid = grid.reshape([2, 1, *grid.shape[1:]]) From 8ea9c27648f4ca9216c2df4d503322c5b221f1cc Mon Sep 17 00:00:00 2001 From: PommesPeter Date: Tue, 2 Jul 2024 23:50:14 +0800 Subject: [PATCH 74/92] Updated AdaLN --- src/diffusers/models/normalization.py | 76 ++++++++++++++++++- .../models/transformers/lumina_nextdit2d.py | 33 ++++---- 2 files changed, 92 insertions(+), 17 deletions(-) diff --git a/src/diffusers/models/normalization.py b/src/diffusers/models/normalization.py index a1a7ce91d754..9c360c6360a8 100644 --- a/src/diffusers/models/normalization.py +++ b/src/diffusers/models/normalization.py @@ -22,7 +22,7 @@ from ..utils import is_torch_version from .activations import get_activation -from .embeddings import CombinedTimestepLabelEmbeddings, PixArtAlphaCombinedTimestepSizeEmbeddings +from .embeddings import CombinedTimestepLabelEmbeddings, PixArtAlphaCombinedTimestepSizeEmbeddings, LuminaCombinedTimestepCaptionEmbedding class AdaLayerNorm(nn.Module): @@ -84,6 +84,36 @@ def forward( return x, gate_msa, shift_mlp, scale_mlp, gate_mlp +class LuminaRMSNormZero(nn.Module): + """ + Norm layer adaptive rms norm zero (adaLN-Zero). + + Parameters: + embedding_dim (`int`): The size of each embedding vector. + """ + def __init__(self, embedding_dim: int, norm_eps: float, norm_elementwise_affine: bool): + super().__init__() + self.silu = nn.SiLU() + self.linear = nn.Linear( + min(embedding_dim, 1024), + 4 * embedding_dim, + bias=True, + ) + self.norm = RMSNorm(embedding_dim, eps=norm_eps, elementwise_affine=norm_elementwise_affine) + + def forward( + self, + x: torch.Tensor, + emb: Optional[torch.Tensor] = None, + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + # emb = self.emb(timestep, encoder_hidden_states, encoder_mask) + emb = self.linear(self.silu(emb)) + scale_msa, gate_msa, scale_mlp, gate_mlp = emb.chunk(4, dim=1) + x = self.norm(x) * (1 + scale_msa[:, None]) + + return x, gate_msa, scale_mlp, gate_mlp + + class AdaLayerNormSingle(nn.Module): r""" Norm layer adaptive layer norm single (adaLN-single). @@ -187,6 +217,50 @@ def forward(self, x: torch.Tensor, conditioning_embedding: torch.Tensor) -> torc x = self.norm(x) * (1 + scale)[:, None, :] + shift[:, None, :] return x +class LuminaLayerNormContinuous(nn.Module): + def __init__( + self, + embedding_dim: int, + conditioning_embedding_dim: int, + # NOTE: It is a bit weird that the norm layer can be configured to have scale and shift parameters + # because the output is immediately scaled and shifted by the projected conditioning embeddings. + # Note that AdaLayerNorm does not let the norm layer have scale and shift parameters. + # However, this is how it was implemented in the original code, and it's rather likely you should + # set `elementwise_affine` to False. + elementwise_affine=True, + eps=1e-5, + bias=True, + norm_type="layer_norm", + out_dim: Optional[int] = None, + ): + super().__init__() + # AdaLN + self.silu = nn.SiLU() + self.linear_1 = nn.Linear(conditioning_embedding_dim, embedding_dim, bias=bias) + if norm_type == "layer_norm": + self.norm = LayerNorm(embedding_dim, eps, elementwise_affine, bias) + else: + raise ValueError(f"unknown norm_type {norm_type}") + # linear_2 + if out_dim is not None: + self.linear_2 = nn.Linear( + embedding_dim, + out_dim, + bias=bias, + ) + + def forward(self, x: torch.Tensor, conditioning_embedding: torch.Tensor,) -> torch.Tensor: + # convert back to the original dtype in case `conditioning_embedding`` is upcasted to float32 (needed for hunyuanDiT) + emb = self.linear_1(self.silu(conditioning_embedding).to(x.dtype)) + scale = emb + x = self.norm(x) * (1 + scale)[:, None, :] + + if self.linear_2 is not None: + x = self.linear_2(x) + + return x + + if is_torch_version(">=", "2.1.0"): LayerNorm = nn.LayerNorm diff --git a/src/diffusers/models/transformers/lumina_nextdit2d.py b/src/diffusers/models/transformers/lumina_nextdit2d.py index 10eab85ceb91..de667cebca92 100644 --- a/src/diffusers/models/transformers/lumina_nextdit2d.py +++ b/src/diffusers/models/transformers/lumina_nextdit2d.py @@ -27,7 +27,7 @@ ) from ..modeling_outputs import Transformer2DModelOutput from ..modeling_utils import ModelMixin -from ..normalization import RMSNorm +from ..normalization import RMSNorm, LuminaRMSNormZero, LuminaLayerNormContinuous logger = logging.get_logger(__name__) # pylint: disable=invalid-name @@ -167,21 +167,16 @@ def __init__( ffn_dim_multiplier=ffn_dim_multiplier, ) - self.norm1 = RMSNorm(dim, eps=norm_eps, elementwise_affine=norm_elementwise_affine) + self.norm1 = LuminaRMSNormZero( + embedding_dim=dim, + norm_eps=norm_eps, + norm_elementwise_affine=norm_elementwise_affine, + ) self.ffn_norm1 = RMSNorm(dim, eps=norm_eps, elementwise_affine=norm_elementwise_affine) self.norm2 = RMSNorm(dim, eps=norm_eps, elementwise_affine=norm_elementwise_affine) self.ffn_norm2 = RMSNorm(dim, eps=norm_eps, elementwise_affine=norm_elementwise_affine) - self.adaLN_modulation = nn.Sequential( - nn.SiLU(), - nn.Linear( - min(dim, 1024), - 4 * dim, - bias=True, - ), - ) - self.norm1_context = RMSNorm( cross_attention_dim, eps=norm_eps, elementwise_affine=norm_elementwise_affine ) @@ -208,10 +203,8 @@ def forward( """ residual = hidden_states - scale_msa, gate_msa, scale_mlp, gate_mlp = self.adaLN_modulation(temb).chunk(4, dim=1) - # Self-attention - norm_hidden_states = modulate(self.norm1(hidden_states), scale_msa) + norm_hidden_states, gate_msa, scale_mlp, gate_mlp = self.norm1(hidden_states, temb) self_attn_output = self.attn1( hidden_states=norm_hidden_states, encoder_hidden_states=norm_hidden_states, @@ -346,7 +339,15 @@ def __init__( for _ in range(num_layers) ] ) - self.final_layer = LuminaFinalLayer(hidden_size, patch_size, self.out_channels) + self.norm_out = LuminaLayerNormContinuous( + embedding_dim=hidden_size, + conditioning_embedding_dim=min(hidden_size, 1024), + elementwise_affine=False, + eps=1e-6, + bias=True, + out_dim=patch_size * patch_size * self.out_channels, + ) + # self.final_layer = LuminaFinalLayer(hidden_size, patch_size, self.out_channels) assert (hidden_size // num_attention_heads) % 4 == 0, "2d rope needs head dim to be divisible by 4" @@ -427,7 +428,7 @@ def forward( cross_attention_kwargs=cross_attention_kwargs, ) - hidden_states = self.final_layer(hidden_states, temb) + hidden_states = self.norm_out(hidden_states, temb) output = self.unpatchify(hidden_states, img_size, return_tensor=True) if not return_dict: From eb94171d75c6dac644ea70573df72a7ca013f847 Mon Sep 17 00:00:00 2001 From: PommesPeter Date: Wed, 3 Jul 2024 02:35:50 +0800 Subject: [PATCH 75/92] Added comment about time-aware denosing and Fixed a bug from typo --- src/diffusers/pipelines/lumina/pipeline_lumina.py | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/src/diffusers/pipelines/lumina/pipeline_lumina.py b/src/diffusers/pipelines/lumina/pipeline_lumina.py index a06cf9dfa26d..bcb11257725e 100644 --- a/src/diffusers/pipelines/lumina/pipeline_lumina.py +++ b/src/diffusers/pipelines/lumina/pipeline_lumina.py @@ -753,6 +753,8 @@ def __call__( if proportional_attn: cross_attention_kwargs["base_sequence_length"] = (self.default_image_size // 16) ** 2 + + scaling_factor = math.sqrt(width * height / self.default_image_size**2) device = self._execution_device @@ -833,8 +835,10 @@ def __call__( # prepare image_rotary_emb for positional encoding # dynamic scaling_factor for different resolution. - scaling_factor = math.sqrt(width * height / self.default_image_size**2) - if current_timestep < scaling_watershed: + # NOTE: For `Time-aware` denosing mechanism from Lumina-Next + # https://arxiv.org/abs/2406.18583, Sec 2.3 + # NOTE: We should compute different image_rotary_emb with different timestep. + if current_timestep[0] < scaling_watershed: linear_factor = scaling_factor ntk_factor = 1.0 else: From 780c94579de6cbc1b63d3e52ab50254cd7a89651 Mon Sep 17 00:00:00 2001 From: PommesPeter Date: Wed, 3 Jul 2024 02:49:32 +0800 Subject: [PATCH 76/92] Fixed code format and Removed unused code --- scripts/convert_lumina_to_diffusers.py | 4 +- src/diffusers/models/embeddings.py | 22 ++++--- src/diffusers/models/normalization.py | 16 +++-- .../models/transformers/lumina_nextdit2d.py | 60 +------------------ .../pipelines/lumina/pipeline_lumina.py | 9 ++- 5 files changed, 34 insertions(+), 77 deletions(-) diff --git a/scripts/convert_lumina_to_diffusers.py b/scripts/convert_lumina_to_diffusers.py index 70ac105a4ee8..245adf6fcc8c 100644 --- a/scripts/convert_lumina_to_diffusers.py +++ b/scripts/convert_lumina_to_diffusers.py @@ -65,9 +65,7 @@ def main(args): # attention norm converted_state_dict[f"layers.{i}.attn_norm1.weight"] = all_sd[f"layers.{i}.attention_norm1.weight"] converted_state_dict[f"layers.{i}.attn_norm2.weight"] = all_sd[f"layers.{i}.attention_norm2.weight"] - converted_state_dict[f"layers.{i}.norm1_context.weight"] = all_sd[ - f"layers.{i}.attention_y_norm.weight" - ] + converted_state_dict[f"layers.{i}.norm1_context.weight"] = all_sd[f"layers.{i}.attention_y_norm.weight"] # feed forward converted_state_dict[f"layers.{i}.feed_forward.linear_1.weight"] = all_sd[f"layers.{i}.feed_forward.w1.weight"] diff --git a/src/diffusers/models/embeddings.py b/src/diffusers/models/embeddings.py index ed73f2d541b7..b77179b521ea 100644 --- a/src/diffusers/models/embeddings.py +++ b/src/diffusers/models/embeddings.py @@ -318,21 +318,27 @@ def get_2d_rotary_pos_embed_from_grid(embed_dim, grid, use_real=False): else: emb = torch.cat([emb_h, emb_w], dim=1) # (H*W, D/2) return emb - + def get_2d_rotary_pos_embed_lumina(embed_dim, len_h, len_w, linear_factor=1.0, ntk_factor=1.0): assert embed_dim % 4 == 0 - emb_h = get_1d_rotary_pos_embed(embed_dim // 2, len_h, linear_factor=linear_factor, ntk_factor=ntk_factor) # (H, D/4) - emb_w = get_1d_rotary_pos_embed(embed_dim // 2, len_w, linear_factor=linear_factor, ntk_factor=ntk_factor) # (W, D/4) - emb_h = emb_h.view(len_h, 1, embed_dim // 4, 1).repeat(1, len_w, 1, 1) # (H, W, D/4, 1) - emb_w = emb_w.view(1, len_w, embed_dim // 4, 1).repeat(len_h, 1, 1, 1) # (H, W, D/4, 1) - + emb_h = get_1d_rotary_pos_embed( + embed_dim // 2, len_h, linear_factor=linear_factor, ntk_factor=ntk_factor + ) # (H, D/4) + emb_w = get_1d_rotary_pos_embed( + embed_dim // 2, len_w, linear_factor=linear_factor, ntk_factor=ntk_factor + ) # (W, D/4) + emb_h = emb_h.view(len_h, 1, embed_dim // 4, 1).repeat(1, len_w, 1, 1) # (H, W, D/4, 1) + emb_w = emb_w.view(1, len_w, embed_dim // 4, 1).repeat(len_h, 1, 1, 1) # (H, W, D/4, 1) + emb = torch.cat([emb_h, emb_w], dim=-1).flatten(2) # (H, W, D/2) return emb -def get_1d_rotary_pos_embed(dim: int, pos: Union[np.ndarray, int], theta: float = 10000.0, use_real=False, linear_factor=1.0, ntk_factor=1.0): +def get_1d_rotary_pos_embed( + dim: int, pos: Union[np.ndarray, int], theta: float = 10000.0, use_real=False, linear_factor=1.0, ntk_factor=1.0 +): """ Precompute the frequency tensor for complex exponentials (cis) with given dimensions. @@ -357,7 +363,7 @@ def get_1d_rotary_pos_embed(dim: int, pos: Union[np.ndarray, int], theta: float if isinstance(pos, int): pos = np.arange(pos) theta = theta * ntk_factor - freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim)) / linear_factor # [D/2] + freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim)) / linear_factor # [D/2] t = torch.from_numpy(pos).to(freqs.device) # type: ignore # [S] freqs = torch.outer(t, freqs).float() # type: ignore # [S, D/2] if use_real: diff --git a/src/diffusers/models/normalization.py b/src/diffusers/models/normalization.py index 9c360c6360a8..594360308989 100644 --- a/src/diffusers/models/normalization.py +++ b/src/diffusers/models/normalization.py @@ -22,7 +22,10 @@ from ..utils import is_torch_version from .activations import get_activation -from .embeddings import CombinedTimestepLabelEmbeddings, PixArtAlphaCombinedTimestepSizeEmbeddings, LuminaCombinedTimestepCaptionEmbedding +from .embeddings import ( + CombinedTimestepLabelEmbeddings, + PixArtAlphaCombinedTimestepSizeEmbeddings, +) class AdaLayerNorm(nn.Module): @@ -91,6 +94,7 @@ class LuminaRMSNormZero(nn.Module): Parameters: embedding_dim (`int`): The size of each embedding vector. """ + def __init__(self, embedding_dim: int, norm_eps: float, norm_elementwise_affine: bool): super().__init__() self.silu = nn.SiLU() @@ -99,7 +103,7 @@ def __init__(self, embedding_dim: int, norm_eps: float, norm_elementwise_affine: 4 * embedding_dim, bias=True, ) - self.norm = RMSNorm(embedding_dim, eps=norm_eps, elementwise_affine=norm_elementwise_affine) + self.norm = RMSNorm(embedding_dim, eps=norm_eps, elementwise_affine=norm_elementwise_affine) def forward( self, @@ -217,6 +221,7 @@ def forward(self, x: torch.Tensor, conditioning_embedding: torch.Tensor) -> torc x = self.norm(x) * (1 + scale)[:, None, :] + shift[:, None, :] return x + class LuminaLayerNormContinuous(nn.Module): def __init__( self, @@ -249,7 +254,11 @@ def __init__( bias=bias, ) - def forward(self, x: torch.Tensor, conditioning_embedding: torch.Tensor,) -> torch.Tensor: + def forward( + self, + x: torch.Tensor, + conditioning_embedding: torch.Tensor, + ) -> torch.Tensor: # convert back to the original dtype in case `conditioning_embedding`` is upcasted to float32 (needed for hunyuanDiT) emb = self.linear_1(self.silu(conditioning_embedding).to(x.dtype)) scale = emb @@ -261,7 +270,6 @@ def forward(self, x: torch.Tensor, conditioning_embedding: torch.Tensor,) -> tor return x - if is_torch_version(">=", "2.1.0"): LayerNorm = nn.LayerNorm else: diff --git a/src/diffusers/models/transformers/lumina_nextdit2d.py b/src/diffusers/models/transformers/lumina_nextdit2d.py index e847642db74c..8ea4648570b9 100644 --- a/src/diffusers/models/transformers/lumina_nextdit2d.py +++ b/src/diffusers/models/transformers/lumina_nextdit2d.py @@ -27,7 +27,7 @@ ) from ..modeling_outputs import Transformer2DModelOutput from ..modeling_utils import ModelMixin -from ..normalization import RMSNorm, LuminaRMSNormZero, LuminaLayerNormContinuous +from ..normalization import LuminaLayerNormContinuous, LuminaRMSNormZero, RMSNorm logger = logging.get_logger(__name__) # pylint: disable=invalid-name @@ -37,58 +37,6 @@ def modulate(x, scale): return x * (1 + scale.unsqueeze(1)) -class LuminaFinalLayer(nn.Module): - """ - The final layer of LuminaNextDiT. - - Parameters: - hidden_size (`int`): - The dimensionality of the hidden layers in the model. This parameter determines the width of the model's - hidden representations. - patch_size (`int`): The patch size of noise. - out_channels (`int`): The number of output channels. - """ - - def __init__(self, hidden_size, patch_size, out_channels): - super().__init__() - self.norm_final = nn.LayerNorm( - hidden_size, - elementwise_affine=False, - eps=1e-6, - ) - self.linear = nn.Linear( - hidden_size, - patch_size * patch_size * out_channels, - bias=True, - ) - - self.adaLN_modulation = nn.Sequential( - nn.SiLU(), - nn.Linear( - min(hidden_size, 1024), - hidden_size, - bias=True, - ), - ) - - def forward(self, x, c): - """ - Forward pass of the LuminaFinalLayer. - - Args: - x (torch.Tensor): The input tensor. - c (torch.Tensor): The conditioning tensor. - Returns: - torch.Tensor: The output tensor. - """ - scale = self.adaLN_modulation(c) - - x = modulate(self.norm_final(x), scale) - x = self.linear(x) - - return x - - class LuminaNextDiTBlock(nn.Module): """ Initialize a LuminaNextDiTBlock. @@ -177,9 +125,7 @@ def __init__( self.norm2 = RMSNorm(dim, eps=norm_eps, elementwise_affine=norm_elementwise_affine) self.ffn_norm2 = RMSNorm(dim, eps=norm_eps, elementwise_affine=norm_elementwise_affine) - self.norm1_context = RMSNorm( - cross_attention_dim, eps=norm_eps, elementwise_affine=norm_elementwise_affine - ) + self.norm1_context = RMSNorm(cross_attention_dim, eps=norm_eps, elementwise_affine=norm_elementwise_affine) def forward( self, @@ -434,4 +380,4 @@ def forward( if not return_dict: return (output,) - return Transformer2DModelOutput(sample=output) \ No newline at end of file + return Transformer2DModelOutput(sample=output) diff --git a/src/diffusers/pipelines/lumina/pipeline_lumina.py b/src/diffusers/pipelines/lumina/pipeline_lumina.py index bcb11257725e..24f7435b45b2 100644 --- a/src/diffusers/pipelines/lumina/pipeline_lumina.py +++ b/src/diffusers/pipelines/lumina/pipeline_lumina.py @@ -24,8 +24,8 @@ from ...image_processor import VaeImageProcessor from ...models import AutoencoderKL -from ...models.transformers.lumina_nextdit2d import LuminaNextDiT2DModel from ...models.embeddings import get_2d_rotary_pos_embed_lumina +from ...models.transformers.lumina_nextdit2d import LuminaNextDiT2DModel from ...schedulers import FlowMatchEulerDiscreteScheduler from ...utils import ( BACKENDS_MAPPING, @@ -753,7 +753,7 @@ def __call__( if proportional_attn: cross_attention_kwargs["base_sequence_length"] = (self.default_image_size // 16) ** 2 - + scaling_factor = math.sqrt(width * height / self.default_image_size**2) device = self._execution_device @@ -803,7 +803,6 @@ def __call__( generator, latents, ) - # 6. Denoising loop with self.progress_bar(total=num_inference_steps) as progress_bar: @@ -832,10 +831,10 @@ def __call__( # reverse the timestep since Lumina uses t=0 as the noise and t=1 as the image current_timestep = 1 - current_timestep / self.scheduler.config.num_train_timesteps - + # prepare image_rotary_emb for positional encoding # dynamic scaling_factor for different resolution. - # NOTE: For `Time-aware` denosing mechanism from Lumina-Next + # NOTE: For `Time-aware` denosing mechanism from Lumina-Next # https://arxiv.org/abs/2406.18583, Sec 2.3 # NOTE: We should compute different image_rotary_emb with different timestep. if current_timestep[0] < scaling_watershed: From 0f596b6afb00545eb410a74bea9c6392c1f1640d Mon Sep 17 00:00:00 2001 From: PommesPeter Date: Wed, 3 Jul 2024 02:51:54 +0800 Subject: [PATCH 77/92] Fixed code format and Removed unused code --- src/diffusers/models/transformers/lumina_nextdit2d.py | 9 ++------- 1 file changed, 2 insertions(+), 7 deletions(-) diff --git a/src/diffusers/models/transformers/lumina_nextdit2d.py b/src/diffusers/models/transformers/lumina_nextdit2d.py index 8ea4648570b9..83dea373717d 100644 --- a/src/diffusers/models/transformers/lumina_nextdit2d.py +++ b/src/diffusers/models/transformers/lumina_nextdit2d.py @@ -33,10 +33,6 @@ logger = logging.get_logger(__name__) # pylint: disable=invalid-name -def modulate(x, scale): - return x * (1 + scale.unsqueeze(1)) - - class LuminaNextDiTBlock(nn.Module): """ Initialize a LuminaNextDiTBlock. @@ -178,9 +174,8 @@ def forward( hidden_states = residual + gate_msa.unsqueeze(1).tanh() * self.norm2(hidden_states) - mlp_output = self.feed_forward( - modulate(self.ffn_norm1(hidden_states), scale_mlp), - ) + mlp_output = self.feed_forward(self.ffn_norm1(hidden_states) * (1 + scale_mlp.unsqueeze(0))) + hidden_states = hidden_states + gate_mlp.unsqueeze(1).tanh() * self.ffn_norm2(mlp_output) return hidden_states From cf1f23794bd880a23d7e4165e17a698cce636c27 Mon Sep 17 00:00:00 2001 From: PommesPeter Date: Wed, 3 Jul 2024 02:55:57 +0800 Subject: [PATCH 78/92] Removed unpatchify --- .../models/transformers/lumina_nextdit2d.py | 30 +++++-------------- 1 file changed, 8 insertions(+), 22 deletions(-) diff --git a/src/diffusers/models/transformers/lumina_nextdit2d.py b/src/diffusers/models/transformers/lumina_nextdit2d.py index 83dea373717d..ef253c5f5341 100644 --- a/src/diffusers/models/transformers/lumina_nextdit2d.py +++ b/src/diffusers/models/transformers/lumina_nextdit2d.py @@ -308,27 +308,6 @@ def unpatchify(self, x: torch.Tensor, img_size: List[Tuple[int, int]], return_te If return_tensor is False, the reconstructed images will be returned as a list of tensors, where each tensor has shape (H, W, C). """ - pH = pW = self.patch_size - if return_tensor: - H, W = img_size[0] - B = x.size(0) - L = (H // pH) * (W // pW) - x = x[:, :L].view(B, H // pH, W // pW, pH, pW, self.out_channels) - x = x.permute(0, 5, 1, 3, 2, 4).flatten(4, 5).flatten(2, 3) - return x - else: - imgs = [] - for i in range(x.size(0)): - H, W = img_size[i] - L = (H // pH) * (W // pW) - imgs.append( - x[i][:L] - .view(H // pH, W // pW, pH, pW, self.out_channels) - .permute(4, 0, 2, 1, 3) - .flatten(3, 4) - .flatten(1, 2) - ) - return imgs def forward( self, @@ -370,7 +349,14 @@ def forward( ) hidden_states = self.norm_out(hidden_states, temb) - output = self.unpatchify(hidden_states, img_size, return_tensor=True) + + # unpatchify + height_tokens = width_tokens = self.patch_size + height, width = img_size[0] + batch_size = hidden_states.size(0) + sequence_length = (height // height_tokens) * (width // width_tokens) + hidden_states = hidden_states[:, :sequence_length].view(batch_size, height // height_tokens, width // width_tokens, height_tokens, width_tokens, self.out_channels) + output = hidden_states.permute(0, 5, 1, 3, 2, 4).flatten(4, 5).flatten(2, 3) if not return_dict: return (output,) From b2a834c0f98e5e3e3f1471ef4ac2e0f8ddeb87a3 Mon Sep 17 00:00:00 2001 From: PommesPeter <54879512+PommesPeter@users.noreply.github.com> Date: Wed, 3 Jul 2024 14:41:23 +0800 Subject: [PATCH 79/92] Update src/diffusers/models/transformers/lumina_nextdit2d.py Co-authored-by: YiYi Xu --- .../models/transformers/lumina_nextdit2d.py | 15 --------------- 1 file changed, 15 deletions(-) diff --git a/src/diffusers/models/transformers/lumina_nextdit2d.py b/src/diffusers/models/transformers/lumina_nextdit2d.py index ef253c5f5341..dea2b0fd6375 100644 --- a/src/diffusers/models/transformers/lumina_nextdit2d.py +++ b/src/diffusers/models/transformers/lumina_nextdit2d.py @@ -292,21 +292,6 @@ def __init__( assert (hidden_size // num_attention_heads) % 4 == 0, "2d rope needs head dim to be divisible by 4" - def unpatchify(self, x: torch.Tensor, img_size: List[Tuple[int, int]], return_tensor=False) -> List[torch.Tensor]: - """ - Reconstructs the original images from the patchified tensor. - - Args: - x (torch.Tensor): The patchified tensor of shape (N, T, patch_size**2 * C). - img_size (List[Tuple[int, int]]): The list of image sizes for each image in the batch. - return_tensor (bool, optional): Whether to return the reconstructed images as a tensor. - If False, the reconstructed images will be returned as a list of tensors. Defaults to False. - - Returns: - List[torch.Tensor] or torch.Tensor: The reconstructed images. - If return_tensor is True, the reconstructed images will be returned as a tensor of shape (N, C, H, W). - If return_tensor is False, the reconstructed images will be returned as a list of tensors, where each - tensor has shape (H, W, C). """ def forward( From 0da4a17fb1632f22aa14d5f194911ab54eb10f10 Mon Sep 17 00:00:00 2001 From: PommesPeter <54879512+PommesPeter@users.noreply.github.com> Date: Thu, 4 Jul 2024 14:25:41 +0800 Subject: [PATCH 80/92] Update src/diffusers/models/attention_processor.py Co-authored-by: YiYi Xu --- src/diffusers/models/attention_processor.py | 13 +++++-------- 1 file changed, 5 insertions(+), 8 deletions(-) diff --git a/src/diffusers/models/attention_processor.py b/src/diffusers/models/attention_processor.py index 2df581520d20..0565cfd371d4 100644 --- a/src/diffusers/models/attention_processor.py +++ b/src/diffusers/models/attention_processor.py @@ -168,15 +168,12 @@ def __init__( self.norm_q = None self.norm_k = None elif qk_norm == "layer_norm": - if kv_heads is None: - q_norm_dim = dim_head - k_norm_dim = dim_head - else: + self.norm_q = nn.LayerNorm(dim_head, eps=eps) + self.norm_k = nn.LayerNorm(dim_head, eps=eps) + elif qk_norm == "layer_norm_across_heads": # Lumina applys qk norm across all heads - q_norm_dim = dim_head * heads - k_norm_dim = dim_head * kv_heads - self.norm_q = nn.LayerNorm(q_norm_dim, eps=eps) - self.norm_k = nn.LayerNorm(k_norm_dim, eps=eps) + self.norm_q = nn.LayerNorm(dim_head * heads, eps=eps) + self.norm_k = nn.LayerNorm(dim_head * kv_heads, eps=eps) else: raise ValueError(f"unknown qk_norm: {qk_norm}. Should be None or 'layer_norm'") From 2981da0c1e509105f923ac72b95b97efed8e2536 Mon Sep 17 00:00:00 2001 From: PommesPeter Date: Thu, 4 Jul 2024 15:54:34 +0800 Subject: [PATCH 81/92] Fixed typo --- src/diffusers/models/attention_processor.py | 8 ++++---- src/diffusers/models/transformers/lumina_nextdit2d.py | 7 +++---- 2 files changed, 7 insertions(+), 8 deletions(-) diff --git a/src/diffusers/models/attention_processor.py b/src/diffusers/models/attention_processor.py index 0565cfd371d4..8d23182ff4cf 100644 --- a/src/diffusers/models/attention_processor.py +++ b/src/diffusers/models/attention_processor.py @@ -170,10 +170,10 @@ def __init__( elif qk_norm == "layer_norm": self.norm_q = nn.LayerNorm(dim_head, eps=eps) self.norm_k = nn.LayerNorm(dim_head, eps=eps) - elif qk_norm == "layer_norm_across_heads": - # Lumina applys qk norm across all heads - self.norm_q = nn.LayerNorm(dim_head * heads, eps=eps) - self.norm_k = nn.LayerNorm(dim_head * kv_heads, eps=eps) + elif qk_norm == "layer_norm_across_heads": + # Lumina applys qk norm across all heads + self.norm_q = nn.LayerNorm(dim_head * heads, eps=eps) + self.norm_k = nn.LayerNorm(dim_head * kv_heads, eps=eps) else: raise ValueError(f"unknown qk_norm: {qk_norm}. Should be None or 'layer_norm'") diff --git a/src/diffusers/models/transformers/lumina_nextdit2d.py b/src/diffusers/models/transformers/lumina_nextdit2d.py index dea2b0fd6375..d5d41eb8eaf2 100644 --- a/src/diffusers/models/transformers/lumina_nextdit2d.py +++ b/src/diffusers/models/transformers/lumina_nextdit2d.py @@ -80,7 +80,7 @@ def __init__( query_dim=dim, cross_attention_dim=None, dim_head=dim // num_attention_heads, - qk_norm="layer_norm" if qk_norm else None, + qk_norm="layer_norm_across_heads" if qk_norm else None, heads=num_attention_heads, kv_heads=num_kv_heads, eps=1e-5, @@ -95,7 +95,7 @@ def __init__( query_dim=dim, cross_attention_dim=cross_attention_dim, dim_head=dim // num_attention_heads, - qk_norm="layer_norm" if qk_norm else None, + qk_norm="layer_norm_across_heads" if qk_norm else None, heads=num_attention_heads, kv_heads=num_kv_heads, eps=1e-5, @@ -174,7 +174,7 @@ def forward( hidden_states = residual + gate_msa.unsqueeze(1).tanh() * self.norm2(hidden_states) - mlp_output = self.feed_forward(self.ffn_norm1(hidden_states) * (1 + scale_mlp.unsqueeze(0))) + mlp_output = self.feed_forward(self.ffn_norm1(hidden_states) * (1 + scale_mlp.unsqueeze(1))) hidden_states = hidden_states + gate_mlp.unsqueeze(1).tanh() * self.ffn_norm2(mlp_output) @@ -292,7 +292,6 @@ def __init__( assert (hidden_size // num_attention_heads) % 4 == 0, "2d rope needs head dim to be divisible by 4" - """ def forward( self, From 3694034ce2ab518d0f642de129f9d74231d3084e Mon Sep 17 00:00:00 2001 From: PommesPeter Date: Fri, 5 Jul 2024 01:27:19 +0800 Subject: [PATCH 82/92] Run style and fix-copies --- .../models/transformers/lumina_nextdit2d.py | 7 ++++--- src/diffusers/pipelines/lumina/pipeline_lumina.py | 14 +++----------- src/diffusers/utils/dummy_pt_objects.py | 15 +++++++++++++++ .../utils/dummy_torch_and_transformers_objects.py | 6 +++--- 4 files changed, 25 insertions(+), 17 deletions(-) diff --git a/src/diffusers/models/transformers/lumina_nextdit2d.py b/src/diffusers/models/transformers/lumina_nextdit2d.py index d5d41eb8eaf2..7159d6bab52b 100644 --- a/src/diffusers/models/transformers/lumina_nextdit2d.py +++ b/src/diffusers/models/transformers/lumina_nextdit2d.py @@ -12,7 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. -from typing import Any, Dict, List, Optional, Tuple +from typing import Any, Dict, Optional import torch import torch.nn as nn @@ -292,7 +292,6 @@ def __init__( assert (hidden_size // num_attention_heads) % 4 == 0, "2d rope needs head dim to be divisible by 4" - def forward( self, hidden_states: torch.Tensor, @@ -339,7 +338,9 @@ def forward( height, width = img_size[0] batch_size = hidden_states.size(0) sequence_length = (height // height_tokens) * (width // width_tokens) - hidden_states = hidden_states[:, :sequence_length].view(batch_size, height // height_tokens, width // width_tokens, height_tokens, width_tokens, self.out_channels) + hidden_states = hidden_states[:, :sequence_length].view( + batch_size, height // height_tokens, width // width_tokens, height_tokens, width_tokens, self.out_channels + ) output = hidden_states.permute(0, 5, 1, 3, 2, 4).flatten(4, 5).flatten(2, 3) if not return_dict: diff --git a/src/diffusers/pipelines/lumina/pipeline_lumina.py b/src/diffusers/pipelines/lumina/pipeline_lumina.py index 24f7435b45b2..7f5f2b8f37c4 100644 --- a/src/diffusers/pipelines/lumina/pipeline_lumina.py +++ b/src/diffusers/pipelines/lumina/pipeline_lumina.py @@ -575,17 +575,7 @@ def _clean_caption(self, caption): return caption.strip() # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents - def prepare_latents( - self, - batch_size, - num_channels_latents, - height, - width, - dtype, - device, - generator, - latents=None, - ): + def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None): shape = ( batch_size, num_channels_latents, @@ -603,6 +593,8 @@ def prepare_latents( else: latents = latents.to(device) + # scale the initial noise by the standard deviation required by the scheduler + latents = latents * self.scheduler.init_noise_sigma return latents @property diff --git a/src/diffusers/utils/dummy_pt_objects.py b/src/diffusers/utils/dummy_pt_objects.py index 354ce7e0ba34..65f1f9ab17b4 100644 --- a/src/diffusers/utils/dummy_pt_objects.py +++ b/src/diffusers/utils/dummy_pt_objects.py @@ -197,6 +197,21 @@ def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch"]) +class LuminaNextDiT2DModel(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + @classmethod + def from_config(cls, *args, **kwargs): + requires_backends(cls, ["torch"]) + + @classmethod + def from_pretrained(cls, *args, **kwargs): + requires_backends(cls, ["torch"]) + + class ModelMixin(metaclass=DummyObject): _backends = ["torch"] diff --git a/src/diffusers/utils/dummy_torch_and_transformers_objects.py b/src/diffusers/utils/dummy_torch_and_transformers_objects.py index 85f146f905c2..282a13e678f3 100644 --- a/src/diffusers/utils/dummy_torch_and_transformers_objects.py +++ b/src/diffusers/utils/dummy_torch_and_transformers_objects.py @@ -722,7 +722,7 @@ def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers"]) -class MarigoldDepthPipeline(metaclass=DummyObject): +class LuminaText2ImgPipeline(metaclass=DummyObject): _backends = ["torch", "transformers"] def __init__(self, *args, **kwargs): @@ -737,7 +737,7 @@ def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers"]) -class MarigoldNormalsPipeline(metaclass=DummyObject): +class MarigoldDepthPipeline(metaclass=DummyObject): _backends = ["torch", "transformers"] def __init__(self, *args, **kwargs): @@ -752,7 +752,7 @@ def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers"]) -class LuminaText2ImgPipeline(metaclass=DummyObject): +class MarigoldNormalsPipeline(metaclass=DummyObject): _backends = ["torch", "transformers"] def __init__(self, *args, **kwargs): From 800dfeb41100ff49996370dff227d498452c1df8 Mon Sep 17 00:00:00 2001 From: PommesPeter Date: Fri, 5 Jul 2024 02:07:36 +0800 Subject: [PATCH 83/92] Fixed typo and docs --- docs/source/en/_toctree.yml | 2 + docs/source/en/api/models/lumina_nextdit2d.md | 2 +- docs/source/en/api/pipelines/lumina.md | 20 +----- src/diffusers/models/normalization.py | 2 +- .../models/transformers/lumina_nextdit2d.py | 61 ++++++++----------- .../pipelines/lumina/pipeline_lumina.py | 2 - 6 files changed, 31 insertions(+), 58 deletions(-) diff --git a/docs/source/en/_toctree.yml b/docs/source/en/_toctree.yml index 4f842cc8c39a..7f378a34bde9 100644 --- a/docs/source/en/_toctree.yml +++ b/docs/source/en/_toctree.yml @@ -439,6 +439,8 @@ title: EulerDiscreteScheduler - local: api/schedulers/flow_match_euler_discrete title: FlowMatchEulerDiscreteScheduler + - local: api/schedulers/flow_match_heun_discrete + title: FlowMatchHeunDiscreteScheduler - local: api/schedulers/heun title: HeunDiscreteScheduler - local: api/schedulers/ipndm diff --git a/docs/source/en/api/models/lumina_nextdit2d.md b/docs/source/en/api/models/lumina_nextdit2d.md index 54af42493407..fe28918e2b58 100644 --- a/docs/source/en/api/models/lumina_nextdit2d.md +++ b/docs/source/en/api/models/lumina_nextdit2d.md @@ -12,7 +12,7 @@ specific language governing permissions and limitations under the License. # LuminaNextDiT2DModel -A Diffusion Transformer model for 2D data from [Lumina-T2X](https://github.com/Alpha-VLLM/Lumina-T2X). +A Next Version of Diffusion Transformer model for 2D data from [Lumina-T2X](https://github.com/Alpha-VLLM/Lumina-T2X). ## LuminaNextDiT2DModel diff --git a/docs/source/en/api/pipelines/lumina.md b/docs/source/en/api/pipelines/lumina.md index 0a31983e2160..d26a27ded2fb 100644 --- a/docs/source/en/api/pipelines/lumina.md +++ b/docs/source/en/api/pipelines/lumina.md @@ -60,7 +60,7 @@ from diffusers import LuminaText2ImgPipeline import torch pipeline = LuminaText2ImgPipeline.from_pretrained( - "Alpha-VLLM/Lumina-Next-SFT-diffusers", torch_dtype=torch.bf16 + "Alpha-VLLM/Lumina-Next-SFT-diffusers", torch_dtype=torch.bfloat16 ).to("cuda") ``` @@ -80,24 +80,6 @@ pipeline.vae.decode = torch.compile(pipeline.vae.decode, mode="max-autotune", fu image = pipeline(prompt="Upper body of a young woman in a Victorian-era outfit with brass goggles and leather straps. Background shows an industrial revolution cityscape with smoky skies and tall, metal structures").images[0] ``` - - - - - ## LuminaText2ImgPipeline [[autodoc]] LuminaText2ImgPipeline diff --git a/src/diffusers/models/normalization.py b/src/diffusers/models/normalization.py index 594360308989..16d76faad0c5 100644 --- a/src/diffusers/models/normalization.py +++ b/src/diffusers/models/normalization.py @@ -89,7 +89,7 @@ def forward( class LuminaRMSNormZero(nn.Module): """ - Norm layer adaptive rms norm zero (adaLN-Zero). + Norm layer adaptive RMS normalization zero. Parameters: embedding_dim (`int`): The size of each embedding vector. diff --git a/src/diffusers/models/transformers/lumina_nextdit2d.py b/src/diffusers/models/transformers/lumina_nextdit2d.py index 7159d6bab52b..d4f5b4658542 100644 --- a/src/diffusers/models/transformers/lumina_nextdit2d.py +++ b/src/diffusers/models/transformers/lumina_nextdit2d.py @@ -35,27 +35,19 @@ class LuminaNextDiTBlock(nn.Module): """ - Initialize a LuminaNextDiTBlock. - - Args: - layer_id (int): Identifier for the layer. - hidden_size (int): Embedding dimension of the input features. - num_attention_heads (int): Number of attention heads. - num_kv_heads (Optional[int]): Number of attention heads in key and - value features (if using GQA), or set to None for the same as query. - multiple_of (int): - ffn_dim_multiplier (float): - norm_eps (float): - - Attributes: - num_attention_heads (int): Number of attention heads. - hidden_size (int): Dimension size of the model. - head_dim (int): Dimension size of each attention head. - attention (Attention): Attention module. - feed_forward (FeedForward): FeedForward module. - layer_id (int): Identifier for the layer. - attention_norm (RMSNorm): Layer normalization for attention output. - ffn_norm (RMSNorm): Layer normalization for feedforward output. + A LuminaNextDiTBlock for LuminaNextDiT2DModel. + + Parameters: + dim (`int`): Embedding dimension of the input features. + num_attention_heads (`int`): Number of attention heads. + num_kv_heads (`int`): + Number of attention heads in key and value features (if using GQA), or set to None for the same as query. + multiple_of (`int`): The number of multiple of ffn layer. + ffn_dim_multiplier (`float`): The multipier factor of ffn layer dimension. + norm_eps (`float`): The eps for norm layer. + qk_norm (`bool`): normalization for query and key. + cross_attention_dim (`int`): Cross attention embedding dimension of the input text prompt hidden_states. + norm_elementwise_affine (`bool`, *optional*, defaults to True), """ def __init__( @@ -130,18 +122,20 @@ def forward( image_rotary_emb: torch.Tensor, encoder_hidden_states: torch.Tensor, encoder_mask: torch.Tensor, - temb: torch.Tensor = None, - cross_attention_kwargs: Dict[str, Any] = None, + temb: torch.Tensor, + cross_attention_kwargs: Optional[Dict[str, Any]] = None, ): """ Perform a forward pass through the LuminaNextDiTBlock. - Args: - hidden_states (torch.Tensor): Input tensor. - image_rotary_emb (torch.Tensor): Precomputed cosine and sine frequencies. - Returns: - torch.Tensor: Output tensor after applying attention and - feedforward layers. + Parameters: + hidden_states (`torch.Tensor`): The input of hidden_states for LuminaNextDiTBlock. + attention_mask (`torch.Tensor): The input of hidden_states corresponse attention mask. + image_rotary_emb (`torch.Tensor`): Precomputed cosine and sine frequencies. + encoder_hidden_states: (`torch.Tensor`): The hidden_states of text prompt are processed by Gemma encoder. + encoder_mask (`torch.Tensor`): The hidden_states of text prompt attention mask. + temb (`torch.Tensor`): Timestep embedding with text prompt embedding. + cross_attention_kwargs (`Dict[str, Any]`): kwargs for cross attention. """ residual = hidden_states @@ -212,9 +206,9 @@ class LuminaNextDiT2DModel(ModelMixin, ConfigMixin): ffn_dim_multiplier (`float`, *optional*): A multiplier for the dimensionality of the feed-forward network. If None, it uses a default value based on the model configuration. - norm_eps float = (`float`, *optional*, defaults to 1e-5): + norm_eps (`float`, *optional*, defaults to 1e-5): A small value added to the denominator for numerical stability in normalization layers. - learn_sigma bool = (`bool`, *optional*, defaults to True): + learn_sigma (`bool`, *optional*, defaults to True): Whether the model should learn the sigma parameter, which might be related to uncertainty or variance in predictions. qk_norm (`bool`, *optional*, defaults to True): @@ -305,14 +299,11 @@ def forward( """ Forward pass of LuminaNextDiT. - Args: + Parameters: hidden_states (torch.Tensor): Input tensor of shape (N, C, H, W). timestep (torch.Tensor): Tensor of diffusion timesteps of shape (N,). encoder_hidden_states (torch.Tensor): Tensor of caption features of shape (N, D). encoder_mask (torch.Tensor): Tensor of caption masks of shape (N, L). - - Returns: - torch.Tensor: Output tensor of shape (N, C, H, W). """ hidden_states, mask, img_size, image_rotary_emb = self.patch_embedder(hidden_states, image_rotary_emb) image_rotary_emb = image_rotary_emb.to(hidden_states.device) diff --git a/src/diffusers/pipelines/lumina/pipeline_lumina.py b/src/diffusers/pipelines/lumina/pipeline_lumina.py index 7f5f2b8f37c4..20df169ca410 100644 --- a/src/diffusers/pipelines/lumina/pipeline_lumina.py +++ b/src/diffusers/pipelines/lumina/pipeline_lumina.py @@ -593,8 +593,6 @@ def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype else: latents = latents.to(device) - # scale the initial noise by the standard deviation required by the scheduler - latents = latents * self.scheduler.init_noise_sigma return latents @property From 5c1a965c7dbf596bd5ba9f8fcf87a41e98fae493 Mon Sep 17 00:00:00 2001 From: PommesPeter Date: Fri, 5 Jul 2024 15:49:18 +0800 Subject: [PATCH 84/92] added new scheduler --- src/diffusers/__init__.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/src/diffusers/__init__.py b/src/diffusers/__init__.py index 65b86cfab5ec..8bab7fbd4812 100644 --- a/src/diffusers/__init__.py +++ b/src/diffusers/__init__.py @@ -163,6 +163,7 @@ "EulerAncestralDiscreteScheduler", "EulerDiscreteScheduler", "FlowMatchEulerDiscreteScheduler", + "FlowMatchHeunDiscreteScheduler", "HeunDiscreteScheduler", "IPNDMScheduler", "KarrasVeScheduler", @@ -582,6 +583,7 @@ EulerAncestralDiscreteScheduler, EulerDiscreteScheduler, FlowMatchEulerDiscreteScheduler, + FlowMatchHeunDiscreteScheduler, HeunDiscreteScheduler, IPNDMScheduler, KarrasVeScheduler, From dc821ed1d89e22e8445d06dd7eb017b0bc45c5f6 Mon Sep 17 00:00:00 2001 From: PommesPeter Date: Fri, 5 Jul 2024 16:15:17 +0800 Subject: [PATCH 85/92] updated fix-copies --- src/diffusers/pipelines/lumina/pipeline_lumina.py | 1 - src/diffusers/utils/dummy_pt_objects.py | 15 +++++++++++++++ 2 files changed, 15 insertions(+), 1 deletion(-) diff --git a/src/diffusers/pipelines/lumina/pipeline_lumina.py b/src/diffusers/pipelines/lumina/pipeline_lumina.py index 20df169ca410..41c88cbd2b1e 100644 --- a/src/diffusers/pipelines/lumina/pipeline_lumina.py +++ b/src/diffusers/pipelines/lumina/pipeline_lumina.py @@ -574,7 +574,6 @@ def _clean_caption(self, caption): return caption.strip() - # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None): shape = ( batch_size, diff --git a/src/diffusers/utils/dummy_pt_objects.py b/src/diffusers/utils/dummy_pt_objects.py index 65f1f9ab17b4..cd55c075f8bf 100644 --- a/src/diffusers/utils/dummy_pt_objects.py +++ b/src/diffusers/utils/dummy_pt_objects.py @@ -1110,6 +1110,21 @@ def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch"]) +class FlowMatchHeunDiscreteScheduler(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + @classmethod + def from_config(cls, *args, **kwargs): + requires_backends(cls, ["torch"]) + + @classmethod + def from_pretrained(cls, *args, **kwargs): + requires_backends(cls, ["torch"]) + + class HeunDiscreteScheduler(metaclass=DummyObject): _backends = ["torch"] From 021d5a662790c3ebaf697d86d3d21e5df53b7181 Mon Sep 17 00:00:00 2001 From: PommesPeter Date: Sat, 6 Jul 2024 00:06:03 +0800 Subject: [PATCH 86/92] updated fix-copies --- scripts/convert_lumina_to_diffusers.py | 7 ++++--- tests/pipelines/lumina/test_lumina_nextdit.py | 20 +++++++++++-------- 2 files changed, 16 insertions(+), 11 deletions(-) diff --git a/scripts/convert_lumina_to_diffusers.py b/scripts/convert_lumina_to_diffusers.py index 245adf6fcc8c..6fa275e65538 100644 --- a/scripts/convert_lumina_to_diffusers.py +++ b/scripts/convert_lumina_to_diffusers.py @@ -85,7 +85,8 @@ def main(args): # Lumina-Next-SFT 2B transformer = LuminaNextDiT2DModel( - patch_size=2, + sample_size=128, + patch_size=2, in_channels=4, hidden_size=2304, num_layers=24, @@ -96,8 +97,8 @@ def main(args): norm_eps=1e-5, learn_sigma=True, qk_norm=True, - caption_dim=2048, - scale_factor=1.0, + cross_attention_dim=2048, + scaling_factor=1.0, ) transformer.load_state_dict(converted_state_dict, strict=True) diff --git a/tests/pipelines/lumina/test_lumina_nextdit.py b/tests/pipelines/lumina/test_lumina_nextdit.py index 84d99e7d863e..e4e6b629ae8c 100644 --- a/tests/pipelines/lumina/test_lumina_nextdit.py +++ b/tests/pipelines/lumina/test_lumina_nextdit.py @@ -34,16 +34,20 @@ class LuminaText2ImgPipelinePipelineFastTests(unittest.TestCase, PipelineTesterM def get_dummy_components(self): torch.manual_seed(0) transformer = LuminaNextDiT2DModel( - sample_size=32, - patch_size=1, + sample_size=128, + patch_size=2, in_channels=4, - num_layers=1, - hidden_size=32, - num_attention_heads=4, - multiple_of=32, - caption_dim=2048, - qk_norm=True, + hidden_size=2304, + num_layers=24, + num_attention_heads=32, + num_kv_heads=8, + multiple_of=256, + ffn_dim_multiplier=None, + norm_eps=1e-5, learn_sigma=True, + qk_norm=True, + cross_attention_dim=2048, + scaling_factor=1.0, ) torch.manual_seed(0) vae = AutoencoderKL() From 5e19d48635d9b10e0c797d4cc9f3174918b20b39 Mon Sep 17 00:00:00 2001 From: PommesPeter Date: Sat, 6 Jul 2024 01:03:40 +0800 Subject: [PATCH 87/92] Fixed style --- scripts/convert_lumina_to_diffusers.py | 2 +- tests/pipelines/lumina/test_lumina_nextdit.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/scripts/convert_lumina_to_diffusers.py b/scripts/convert_lumina_to_diffusers.py index 6fa275e65538..a12625d1376f 100644 --- a/scripts/convert_lumina_to_diffusers.py +++ b/scripts/convert_lumina_to_diffusers.py @@ -86,7 +86,7 @@ def main(args): # Lumina-Next-SFT 2B transformer = LuminaNextDiT2DModel( sample_size=128, - patch_size=2, + patch_size=2, in_channels=4, hidden_size=2304, num_layers=24, diff --git a/tests/pipelines/lumina/test_lumina_nextdit.py b/tests/pipelines/lumina/test_lumina_nextdit.py index e4e6b629ae8c..8d09b9ae1624 100644 --- a/tests/pipelines/lumina/test_lumina_nextdit.py +++ b/tests/pipelines/lumina/test_lumina_nextdit.py @@ -35,7 +35,7 @@ def get_dummy_components(self): torch.manual_seed(0) transformer = LuminaNextDiT2DModel( sample_size=128, - patch_size=2, + patch_size=2, in_channels=4, hidden_size=2304, num_layers=24, From 425d6928b5e1e143b99ce4933e8a12d66e2fe932 Mon Sep 17 00:00:00 2001 From: PommesPeter Date: Sat, 6 Jul 2024 01:38:09 +0800 Subject: [PATCH 88/92] Refactor test func with dummy model --- tests/pipelines/lumina/test_lumina_nextdit.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/tests/pipelines/lumina/test_lumina_nextdit.py b/tests/pipelines/lumina/test_lumina_nextdit.py index 8d09b9ae1624..cfd8f043c542 100644 --- a/tests/pipelines/lumina/test_lumina_nextdit.py +++ b/tests/pipelines/lumina/test_lumina_nextdit.py @@ -39,7 +39,7 @@ def get_dummy_components(self): in_channels=4, hidden_size=2304, num_layers=24, - num_attention_heads=32, + num_atqtention_heads=32, num_kv_heads=8, multiple_of=256, ffn_dim_multiplier=None, @@ -53,8 +53,8 @@ def get_dummy_components(self): vae = AutoencoderKL() scheduler = FlowMatchEulerDiscreteScheduler() - text_encoder = AutoModel.from_pretrained("google/gemma-2b") - tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b") + text_encoder = AutoModel.from_pretrained("hf-internal-testing/dummy-gemma") + tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/dummy-gemma") components = { "transformer": transformer.eval(), From 7e4583734bcee73c0f03b83aec2fb19d84b24228 Mon Sep 17 00:00:00 2001 From: YiYi Xu Date: Fri, 5 Jul 2024 08:54:39 -1000 Subject: [PATCH 89/92] Update tests/pipelines/lumina/test_lumina_nextdit.py --- tests/pipelines/lumina/test_lumina_nextdit.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/pipelines/lumina/test_lumina_nextdit.py b/tests/pipelines/lumina/test_lumina_nextdit.py index cfd8f043c542..18e800312299 100644 --- a/tests/pipelines/lumina/test_lumina_nextdit.py +++ b/tests/pipelines/lumina/test_lumina_nextdit.py @@ -39,7 +39,7 @@ def get_dummy_components(self): in_channels=4, hidden_size=2304, num_layers=24, - num_atqtention_heads=32, + num_attention_heads=32, num_kv_heads=8, multiple_of=256, ffn_dim_multiplier=None, From 0547f4a2f08569e73044ba2eb5e573e0f98aa5ad Mon Sep 17 00:00:00 2001 From: yiyixuxu Date: Sat, 6 Jul 2024 10:06:37 +0200 Subject: [PATCH 90/92] update test --- tests/pipelines/lumina/test_lumina_nextdit.py | 34 +++++++++++-------- 1 file changed, 20 insertions(+), 14 deletions(-) diff --git a/tests/pipelines/lumina/test_lumina_nextdit.py b/tests/pipelines/lumina/test_lumina_nextdit.py index 18e800312299..9f028eb3bbc9 100644 --- a/tests/pipelines/lumina/test_lumina_nextdit.py +++ b/tests/pipelines/lumina/test_lumina_nextdit.py @@ -3,7 +3,7 @@ import numpy as np import torch -from transformers import AutoModel, AutoTokenizer +from transformers import AutoTokenizer, GemmaConfig, GemmaForCausalLM from diffusers import AutoencoderKL, FlowMatchEulerDiscreteScheduler, LuminaNextDiT2DModel, LuminaText2ImgPipeline from diffusers.utils.testing_utils import ( @@ -34,33 +34,43 @@ class LuminaText2ImgPipelinePipelineFastTests(unittest.TestCase, PipelineTesterM def get_dummy_components(self): torch.manual_seed(0) transformer = LuminaNextDiT2DModel( - sample_size=128, + sample_size=16, patch_size=2, in_channels=4, - hidden_size=2304, - num_layers=24, - num_attention_heads=32, - num_kv_heads=8, - multiple_of=256, + hidden_size=24, + num_layers=2, + num_attention_heads=3, + num_kv_heads=1, + multiple_of=16, ffn_dim_multiplier=None, norm_eps=1e-5, learn_sigma=True, qk_norm=True, - cross_attention_dim=2048, + cross_attention_dim=32, scaling_factor=1.0, ) torch.manual_seed(0) vae = AutoencoderKL() scheduler = FlowMatchEulerDiscreteScheduler() - text_encoder = AutoModel.from_pretrained("hf-internal-testing/dummy-gemma") tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/dummy-gemma") + torch.manual_seed(0) + config = GemmaConfig( + head_dim=4, + hidden_size=32, + intermediate_size=37, + num_attention_heads=4, + num_hidden_layers=2, + num_key_value_heads=4, + ) + text_encoder = GemmaForCausalLM(config) + components = { "transformer": transformer.eval(), "vae": vae.eval(), "scheduler": scheduler, - "text_encoder": text_encoder, + "text_encoder": text_encoder.eval(), "tokenizer": tokenizer, } return components @@ -167,7 +177,3 @@ def test_lumina_inference(self): max_diff = numpy_cosine_similarity_distance(expected_slice.flatten(), image_slice.flatten()) assert max_diff < 1e-4 - - -if __name__ == "__main__": - unittest.main() From 19aa58532d7c804efd517d128a20328b4c3b6348 Mon Sep 17 00:00:00 2001 From: PommesPeter Date: Mon, 8 Jul 2024 10:14:14 +0800 Subject: [PATCH 91/92] Updated slow test case --- tests/pipelines/lumina/test_lumina_nextdit.py | 20 +++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/tests/pipelines/lumina/test_lumina_nextdit.py b/tests/pipelines/lumina/test_lumina_nextdit.py index 9f028eb3bbc9..3be8c7828c12 100644 --- a/tests/pipelines/lumina/test_lumina_nextdit.py +++ b/tests/pipelines/lumina/test_lumina_nextdit.py @@ -160,16 +160,16 @@ def test_lumina_inference(self): image_slice = image[0, :10, :10] expected_slice = np.array( [ - [0.36132812, 0.30004883, 0.25830078], - [0.36669922, 0.31103516, 0.23754883], - [0.34814453, 0.29248047, 0.23583984], - [0.35791016, 0.30981445, 0.23999023], - [0.36328125, 0.31274414, 0.2607422], - [0.37304688, 0.32177734, 0.26171875], - [0.3671875, 0.31933594, 0.25756836], - [0.36035156, 0.31103516, 0.2578125], - [0.3857422, 0.33789062, 0.27563477], - [0.3701172, 0.31982422, 0.265625], + [0.17773438, 0.18554688, 0.22070312], + [0.046875 , 0.06640625, 0.10351562], + [0. , 0. , 0.02148438], + [0. , 0. , 0. ], + [0. , 0. , 0. ], + [0. , 0. , 0. ], + [0. , 0. , 0. ], + [0. , 0. , 0. ], + [0. , 0. , 0. ], + [0. , 0. , 0. ], ], dtype=np.float32, ) From 44daf8c58e4d43645d50075316ba4052fa147fe6 Mon Sep 17 00:00:00 2001 From: yiyixuxu Date: Mon, 8 Jul 2024 04:58:45 +0200 Subject: [PATCH 92/92] style --- tests/pipelines/lumina/test_lumina_nextdit.py | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/tests/pipelines/lumina/test_lumina_nextdit.py b/tests/pipelines/lumina/test_lumina_nextdit.py index 3be8c7828c12..a53758ce2808 100644 --- a/tests/pipelines/lumina/test_lumina_nextdit.py +++ b/tests/pipelines/lumina/test_lumina_nextdit.py @@ -161,15 +161,15 @@ def test_lumina_inference(self): expected_slice = np.array( [ [0.17773438, 0.18554688, 0.22070312], - [0.046875 , 0.06640625, 0.10351562], - [0. , 0. , 0.02148438], - [0. , 0. , 0. ], - [0. , 0. , 0. ], - [0. , 0. , 0. ], - [0. , 0. , 0. ], - [0. , 0. , 0. ], - [0. , 0. , 0. ], - [0. , 0. , 0. ], + [0.046875, 0.06640625, 0.10351562], + [0.0, 0.0, 0.02148438], + [0.0, 0.0, 0.0], + [0.0, 0.0, 0.0], + [0.0, 0.0, 0.0], + [0.0, 0.0, 0.0], + [0.0, 0.0, 0.0], + [0.0, 0.0, 0.0], + [0.0, 0.0, 0.0], ], dtype=np.float32, )

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