From abb22b4eeb7756899871b7f4f23f6ae72be1da79 Mon Sep 17 00:00:00 2001 From: Sayak Paul Date: Mon, 27 Mar 2023 19:34:58 +0530 Subject: [PATCH 01/56] Update `examples` README.md to include the latest examples (#2839) --- examples/README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/examples/README.md b/examples/README.md index 4526d44e43d5..d09739768925 100644 --- a/examples/README.md +++ b/examples/README.md @@ -42,6 +42,8 @@ Training examples show how to pretrain or fine-tune diffusion models for a varie | [**Text-to-Image fine-tuning**](./text_to_image) | ✅ | ✅ | | [**Textual Inversion**](./textual_inversion) | ✅ | - | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb) | [**Dreambooth**](./dreambooth) | ✅ | - | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb) +| [**ControlNet**](./controlnet) | ✅ | ✅ | - +| [**InstructPix2Pix**](./instruct_pix2pix) | ✅ | ✅ | - | [**Reinforcement Learning for Control**](https://github.com/huggingface/diffusers/blob/main/examples/rl/run_diffusers_locomotion.py) | - | - | coming soon. ## Community From 1d7b4b60b7b7e19a0347da5a04ec76c045d8dbf0 Mon Sep 17 00:00:00 2001 From: Pedro Cuenca Date: Mon, 27 Mar 2023 16:18:57 +0200 Subject: [PATCH 02/56] Ruff: apply same rules as in transformers (#2827) * Apply same ruff settings as in transformers See https://github.com/huggingface/transformers/blob/main/pyproject.toml Co-authored-by: Aaron Gokaslan * Apply new style rules * Style Co-authored-by: Aaron Gokaslan * style * remove list, ruff wouldn't auto fix. --------- Co-authored-by: Aaron Gokaslan --- examples/community/checkpoint_merger.py | 20 ++--- examples/community/imagic_stable_diffusion.py | 2 +- examples/community/lpw_stable_diffusion.py | 4 +- .../community/lpw_stable_diffusion_onnx.py | 4 +- examples/community/stable_unclip.py | 2 +- .../train_instruct_pix2pix.py | 2 +- examples/rl/run_diffuser_locomotion.py | 22 ++--- pyproject.toml | 4 +- ...t_ddpm_original_checkpoint_to_diffusers.py | 2 +- .../convert_models_diffuser_to_diffusers.py | 80 +++++++++---------- .../convert_original_audioldm_to_diffusers.py | 68 ++++++++-------- ...onvert_versatile_diffusion_to_diffusers.py | 64 +++++++-------- src/diffusers/configuration_utils.py | 4 +- .../experimental/rl/value_guided_sampling.py | 4 +- src/diffusers/image_processor.py | 2 +- src/diffusers/loaders.py | 2 +- src/diffusers/models/modeling_utils.py | 2 +- .../pipeline_alt_diffusion_img2img.py | 2 +- .../pipeline_audio_diffusion.py | 6 +- ...peline_latent_diffusion_superresolution.py | 2 +- .../pipelines/pipeline_flax_utils.py | 2 +- src/diffusers/pipelines/pipeline_utils.py | 20 ++--- .../pipelines/repaint/pipeline_repaint.py | 4 +- .../spectrogram_diffusion/midi_utils.py | 2 +- .../stable_diffusion/convert_from_ckpt.py | 44 +++++----- .../pipeline_cycle_diffusion.py | 2 +- ...peline_flax_stable_diffusion_controlnet.py | 2 +- .../pipeline_flax_stable_diffusion_img2img.py | 2 +- .../pipeline_flax_stable_diffusion_inpaint.py | 4 +- .../pipeline_onnx_stable_diffusion_img2img.py | 2 +- ...ne_onnx_stable_diffusion_inpaint_legacy.py | 4 +- .../pipeline_onnx_stable_diffusion_upscale.py | 2 +- .../pipeline_stable_diffusion_depth2img.py | 4 +- .../pipeline_stable_diffusion_img2img.py | 2 +- ...ipeline_stable_diffusion_inpaint_legacy.py | 6 +- ...eline_stable_diffusion_instruct_pix2pix.py | 2 +- ...ipeline_stable_diffusion_latent_upscale.py | 2 +- .../pipeline_stable_diffusion_pix2pix_zero.py | 2 +- .../pipeline_stable_diffusion_upscale.py | 2 +- ...ine_versatile_diffusion_image_variation.py | 2 +- src/diffusers/utils/outputs.py | 2 +- utils/check_doc_toc.py | 2 +- utils/check_repo.py | 4 +- utils/overwrite_expected_slice.py | 2 +- utils/stale.py | 2 +- 45 files changed, 209 insertions(+), 213 deletions(-) diff --git a/examples/community/checkpoint_merger.py b/examples/community/checkpoint_merger.py index 24f187b41c07..3e29ae50078b 100644 --- a/examples/community/checkpoint_merger.py +++ b/examples/community/checkpoint_merger.py @@ -199,24 +199,20 @@ def merge(self, pretrained_model_name_or_path_list: List[Union[str, os.PathLike] if not attr.startswith("_"): checkpoint_path_1 = os.path.join(cached_folders[1], attr) if os.path.exists(checkpoint_path_1): - files = list( - ( - *glob.glob(os.path.join(checkpoint_path_1, "*.safetensors")), - *glob.glob(os.path.join(checkpoint_path_1, "*.bin")), - ) - ) + files = [ + *glob.glob(os.path.join(checkpoint_path_1, "*.safetensors")), + *glob.glob(os.path.join(checkpoint_path_1, "*.bin")), + ] checkpoint_path_1 = files[0] if len(files) > 0 else None if len(cached_folders) < 3: checkpoint_path_2 = None else: checkpoint_path_2 = os.path.join(cached_folders[2], attr) if os.path.exists(checkpoint_path_2): - files = list( - ( - *glob.glob(os.path.join(checkpoint_path_2, "*.safetensors")), - *glob.glob(os.path.join(checkpoint_path_2, "*.bin")), - ) - ) + files = [ + *glob.glob(os.path.join(checkpoint_path_2, "*.safetensors")), + *glob.glob(os.path.join(checkpoint_path_2, "*.bin")), + ] checkpoint_path_2 = files[0] if len(files) > 0 else None # For an attr if both checkpoint_path_1 and 2 are None, ignore. # If atleast one is present, deal with it according to interp method, of course only if the state_dict keys match. diff --git a/examples/community/imagic_stable_diffusion.py b/examples/community/imagic_stable_diffusion.py index 03917b187af7..dc8ce5f259dc 100644 --- a/examples/community/imagic_stable_diffusion.py +++ b/examples/community/imagic_stable_diffusion.py @@ -48,7 +48,7 @@ def preprocess(image): w, h = image.size - w, h = map(lambda x: x - x % 32, (w, h)) # resize to integer multiple of 32 + w, h = (x - x % 32 for x in (w, h)) # resize to integer multiple of 32 image = image.resize((w, h), resample=PIL_INTERPOLATION["lanczos"]) image = np.array(image).astype(np.float32) / 255.0 image = image[None].transpose(0, 3, 1, 2) diff --git a/examples/community/lpw_stable_diffusion.py b/examples/community/lpw_stable_diffusion.py index 80b7b90c8bbd..072f7cde17ad 100644 --- a/examples/community/lpw_stable_diffusion.py +++ b/examples/community/lpw_stable_diffusion.py @@ -376,7 +376,7 @@ def get_weighted_text_embeddings( def preprocess_image(image): w, h = image.size - w, h = map(lambda x: x - x % 32, (w, h)) # resize to integer multiple of 32 + w, h = (x - x % 32 for x in (w, h)) # resize to integer multiple of 32 image = image.resize((w, h), resample=PIL_INTERPOLATION["lanczos"]) image = np.array(image).astype(np.float32) / 255.0 image = image[None].transpose(0, 3, 1, 2) @@ -387,7 +387,7 @@ def preprocess_image(image): def preprocess_mask(mask, scale_factor=8): mask = mask.convert("L") w, h = mask.size - w, h = map(lambda x: x - x % 32, (w, h)) # resize to integer multiple of 32 + w, h = (x - x % 32 for x in (w, h)) # resize to integer multiple of 32 mask = mask.resize((w // scale_factor, h // scale_factor), resample=PIL_INTERPOLATION["nearest"]) mask = np.array(mask).astype(np.float32) / 255.0 mask = np.tile(mask, (4, 1, 1)) diff --git a/examples/community/lpw_stable_diffusion_onnx.py b/examples/community/lpw_stable_diffusion_onnx.py index 817bae262e94..1e0764de5d4d 100644 --- a/examples/community/lpw_stable_diffusion_onnx.py +++ b/examples/community/lpw_stable_diffusion_onnx.py @@ -403,7 +403,7 @@ def get_weighted_text_embeddings( def preprocess_image(image): w, h = image.size - w, h = map(lambda x: x - x % 32, (w, h)) # resize to integer multiple of 32 + w, h = (x - x % 32 for x in (w, h)) # resize to integer multiple of 32 image = image.resize((w, h), resample=PIL_INTERPOLATION["lanczos"]) image = np.array(image).astype(np.float32) / 255.0 image = image[None].transpose(0, 3, 1, 2) @@ -413,7 +413,7 @@ def preprocess_image(image): def preprocess_mask(mask, scale_factor=8): mask = mask.convert("L") w, h = mask.size - w, h = map(lambda x: x - x % 32, (w, h)) # resize to integer multiple of 32 + w, h = (x - x % 32 for x in (w, h)) # resize to integer multiple of 32 mask = mask.resize((w // scale_factor, h // scale_factor), resample=PIL_INTERPOLATION["nearest"]) mask = np.array(mask).astype(np.float32) / 255.0 mask = np.tile(mask, (4, 1, 1)) diff --git a/examples/community/stable_unclip.py b/examples/community/stable_unclip.py index 8ff9c44d19fd..1b438c8fcb3e 100644 --- a/examples/community/stable_unclip.py +++ b/examples/community/stable_unclip.py @@ -46,7 +46,7 @@ def __init__( ): super().__init__() - decoder_pipe_kwargs = dict(image_encoder=None) if decoder_pipe_kwargs is None else decoder_pipe_kwargs + decoder_pipe_kwargs = {"image_encoder": None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs decoder_pipe_kwargs["torch_dtype"] = decoder_pipe_kwargs.get("torch_dtype", None) or prior.dtype diff --git a/examples/instruct_pix2pix/train_instruct_pix2pix.py b/examples/instruct_pix2pix/train_instruct_pix2pix.py index 57430b7f150a..6e51e86a9f16 100644 --- a/examples/instruct_pix2pix/train_instruct_pix2pix.py +++ b/examples/instruct_pix2pix/train_instruct_pix2pix.py @@ -673,7 +673,7 @@ def preprocess_train(examples): examples["edited_pixel_values"] = edited_images # Preprocess the captions. - captions = [caption for caption in examples[edit_prompt_column]] + captions = list(examples[edit_prompt_column]) examples["input_ids"] = tokenize_captions(captions) return examples diff --git a/examples/rl/run_diffuser_locomotion.py b/examples/rl/run_diffuser_locomotion.py index e64a20500bea..adf6d1443d1c 100644 --- a/examples/rl/run_diffuser_locomotion.py +++ b/examples/rl/run_diffuser_locomotion.py @@ -4,17 +4,17 @@ from diffusers.experimental import ValueGuidedRLPipeline -config = dict( - n_samples=64, - horizon=32, - num_inference_steps=20, - n_guide_steps=2, # can set to 0 for faster sampling, does not use value network - scale_grad_by_std=True, - scale=0.1, - eta=0.0, - t_grad_cutoff=2, - device="cpu", -) +config = { + "n_samples": 64, + "horizon": 32, + "num_inference_steps": 20, + "n_guide_steps": 2, # can set to 0 for faster sampling, does not use value network + "scale_grad_by_std": True, + "scale": 0.1, + "eta": 0.0, + "t_grad_cutoff": 2, + "device": "cpu", +} if __name__ == "__main__": diff --git a/pyproject.toml b/pyproject.toml index 5ec7ae51be15..a5fe70af9ca7 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,8 +4,8 @@ target-version = ['py37'] [tool.ruff] # Never enforce `E501` (line length violations). -ignore = ["E501", "E741", "W605"] -select = ["E", "F", "I", "W"] +ignore = ["C901", "E501", "E741", "W605"] +select = ["C", "E", "F", "I", "W"] line-length = 119 # Ignore import violations in all `__init__.py` files. diff --git a/scripts/convert_ddpm_original_checkpoint_to_diffusers.py b/scripts/convert_ddpm_original_checkpoint_to_diffusers.py index 4222327c23de..46595784b0ba 100644 --- a/scripts/convert_ddpm_original_checkpoint_to_diffusers.py +++ b/scripts/convert_ddpm_original_checkpoint_to_diffusers.py @@ -404,7 +404,7 @@ def convert_vq_autoenc_checkpoint(checkpoint, config): config = json.loads(f.read()) # unet case - key_prefix_set = set(key.split(".")[0] for key in checkpoint.keys()) + key_prefix_set = {key.split(".")[0] for key in checkpoint.keys()} if "encoder" in key_prefix_set and "decoder" in key_prefix_set: converted_checkpoint = convert_vq_autoenc_checkpoint(checkpoint, config) else: diff --git a/scripts/convert_models_diffuser_to_diffusers.py b/scripts/convert_models_diffuser_to_diffusers.py index 9475f7da93fb..cc5321e33fe0 100644 --- a/scripts/convert_models_diffuser_to_diffusers.py +++ b/scripts/convert_models_diffuser_to_diffusers.py @@ -24,29 +24,29 @@ def unet(hor): up_block_types = ("UpResnetBlock1D", "UpResnetBlock1D", "UpResnetBlock1D") model = torch.load(f"/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch") state_dict = model.state_dict() - config = dict( - down_block_types=down_block_types, - block_out_channels=block_out_channels, - up_block_types=up_block_types, - layers_per_block=1, - use_timestep_embedding=True, - out_block_type="OutConv1DBlock", - norm_num_groups=8, - downsample_each_block=False, - in_channels=14, - out_channels=14, - extra_in_channels=0, - time_embedding_type="positional", - flip_sin_to_cos=False, - freq_shift=1, - sample_size=65536, - mid_block_type="MidResTemporalBlock1D", - act_fn="mish", - ) + config = { + "down_block_types": down_block_types, + "block_out_channels": block_out_channels, + "up_block_types": up_block_types, + "layers_per_block": 1, + "use_timestep_embedding": True, + "out_block_type": "OutConv1DBlock", + "norm_num_groups": 8, + "downsample_each_block": False, + "in_channels": 14, + "out_channels": 14, + "extra_in_channels": 0, + "time_embedding_type": "positional", + "flip_sin_to_cos": False, + "freq_shift": 1, + "sample_size": 65536, + "mid_block_type": "MidResTemporalBlock1D", + "act_fn": "mish", + } hf_value_function = UNet1DModel(**config) print(f"length of state dict: {len(state_dict.keys())}") print(f"length of value function dict: {len(hf_value_function.state_dict().keys())}") - mapping = dict((k, hfk) for k, hfk in zip(model.state_dict().keys(), hf_value_function.state_dict().keys())) + mapping = dict(zip(model.state_dict().keys(), hf_value_function.state_dict().keys())) for k, v in mapping.items(): state_dict[v] = state_dict.pop(k) hf_value_function.load_state_dict(state_dict) @@ -57,25 +57,25 @@ def unet(hor): def value_function(): - config = dict( - in_channels=14, - down_block_types=("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D"), - up_block_types=(), - out_block_type="ValueFunction", - mid_block_type="ValueFunctionMidBlock1D", - block_out_channels=(32, 64, 128, 256), - layers_per_block=1, - downsample_each_block=True, - sample_size=65536, - out_channels=14, - extra_in_channels=0, - time_embedding_type="positional", - use_timestep_embedding=True, - flip_sin_to_cos=False, - freq_shift=1, - norm_num_groups=8, - act_fn="mish", - ) + config = { + "in_channels": 14, + "down_block_types": ("DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D", "DownResnetBlock1D"), + "up_block_types": (), + "out_block_type": "ValueFunction", + "mid_block_type": "ValueFunctionMidBlock1D", + "block_out_channels": (32, 64, 128, 256), + "layers_per_block": 1, + "downsample_each_block": True, + "sample_size": 65536, + "out_channels": 14, + "extra_in_channels": 0, + "time_embedding_type": "positional", + "use_timestep_embedding": True, + "flip_sin_to_cos": False, + "freq_shift": 1, + "norm_num_groups": 8, + "act_fn": "mish", + } model = torch.load("/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch") state_dict = model @@ -83,7 +83,7 @@ def value_function(): print(f"length of state dict: {len(state_dict.keys())}") print(f"length of value function dict: {len(hf_value_function.state_dict().keys())}") - mapping = dict((k, hfk) for k, hfk in zip(state_dict.keys(), hf_value_function.state_dict().keys())) + mapping = dict(zip(state_dict.keys(), hf_value_function.state_dict().keys())) for k, v in mapping.items(): state_dict[v] = state_dict.pop(k) diff --git a/scripts/convert_original_audioldm_to_diffusers.py b/scripts/convert_original_audioldm_to_diffusers.py index bd671e3a7b70..189b165c0a01 100644 --- a/scripts/convert_original_audioldm_to_diffusers.py +++ b/scripts/convert_original_audioldm_to_diffusers.py @@ -246,19 +246,19 @@ def create_unet_diffusers_config(original_config, image_size: int): ) class_embeddings_concat = unet_params.extra_film_use_concat if "extra_film_use_concat" in unet_params else None - config = dict( - sample_size=image_size // vae_scale_factor, - in_channels=unet_params.in_channels, - out_channels=unet_params.out_channels, - down_block_types=tuple(down_block_types), - up_block_types=tuple(up_block_types), - block_out_channels=tuple(block_out_channels), - layers_per_block=unet_params.num_res_blocks, - cross_attention_dim=cross_attention_dim, - class_embed_type=class_embed_type, - projection_class_embeddings_input_dim=projection_class_embeddings_input_dim, - class_embeddings_concat=class_embeddings_concat, - ) + config = { + "sample_size": image_size // vae_scale_factor, + "in_channels": unet_params.in_channels, + "out_channels": unet_params.out_channels, + "down_block_types": tuple(down_block_types), + "up_block_types": tuple(up_block_types), + "block_out_channels": tuple(block_out_channels), + "layers_per_block": unet_params.num_res_blocks, + "cross_attention_dim": cross_attention_dim, + "class_embed_type": class_embed_type, + "projection_class_embeddings_input_dim": projection_class_embeddings_input_dim, + "class_embeddings_concat": class_embeddings_concat, + } return config @@ -278,17 +278,17 @@ def create_vae_diffusers_config(original_config, checkpoint, image_size: int): scaling_factor = checkpoint["scale_factor"] if "scale_by_std" in original_config.model.params else 0.18215 - config = dict( - sample_size=image_size, - in_channels=vae_params.in_channels, - out_channels=vae_params.out_ch, - down_block_types=tuple(down_block_types), - up_block_types=tuple(up_block_types), - block_out_channels=tuple(block_out_channels), - latent_channels=vae_params.z_channels, - layers_per_block=vae_params.num_res_blocks, - scaling_factor=float(scaling_factor), - ) + config = { + "sample_size": image_size, + "in_channels": vae_params.in_channels, + "out_channels": vae_params.out_ch, + "down_block_types": tuple(down_block_types), + "up_block_types": tuple(up_block_types), + "block_out_channels": tuple(block_out_channels), + "latent_channels": vae_params.z_channels, + "layers_per_block": vae_params.num_res_blocks, + "scaling_factor": float(scaling_factor), + } return config @@ -670,18 +670,18 @@ def create_transformers_vocoder_config(original_config): """ vocoder_params = original_config.model.params.vocoder_config.params - config = dict( - model_in_dim=vocoder_params.num_mels, - sampling_rate=vocoder_params.sampling_rate, - upsample_initial_channel=vocoder_params.upsample_initial_channel, - upsample_rates=list(vocoder_params.upsample_rates), - upsample_kernel_sizes=list(vocoder_params.upsample_kernel_sizes), - resblock_kernel_sizes=list(vocoder_params.resblock_kernel_sizes), - resblock_dilation_sizes=[ + config = { + "model_in_dim": vocoder_params.num_mels, + "sampling_rate": vocoder_params.sampling_rate, + "upsample_initial_channel": vocoder_params.upsample_initial_channel, + "upsample_rates": list(vocoder_params.upsample_rates), + "upsample_kernel_sizes": list(vocoder_params.upsample_kernel_sizes), + "resblock_kernel_sizes": list(vocoder_params.resblock_kernel_sizes), + "resblock_dilation_sizes": [ list(resblock_dilation) for resblock_dilation in vocoder_params.resblock_dilation_sizes ], - normalize_before=False, - ) + "normalize_before": False, + } return config diff --git a/scripts/convert_versatile_diffusion_to_diffusers.py b/scripts/convert_versatile_diffusion_to_diffusers.py index 06b0cec03448..b895e08e9de9 100644 --- a/scripts/convert_versatile_diffusion_to_diffusers.py +++ b/scripts/convert_versatile_diffusion_to_diffusers.py @@ -280,17 +280,17 @@ def create_image_unet_diffusers_config(unet_params): if not all(n == unet_params.num_noattn_blocks[0] for n in unet_params.num_noattn_blocks): raise ValueError("Not all num_res_blocks are equal, which is not supported in this script.") - config = dict( - sample_size=None, - in_channels=unet_params.input_channels, - out_channels=unet_params.output_channels, - down_block_types=tuple(down_block_types), - up_block_types=tuple(up_block_types), - block_out_channels=tuple(block_out_channels), - layers_per_block=unet_params.num_noattn_blocks[0], - cross_attention_dim=unet_params.context_dim, - attention_head_dim=unet_params.num_heads, - ) + config = { + "sample_size": None, + "in_channels": unet_params.input_channels, + "out_channels": unet_params.output_channels, + "down_block_types": tuple(down_block_types), + "up_block_types": tuple(up_block_types), + "block_out_channels": tuple(block_out_channels), + "layers_per_block": unet_params.num_noattn_blocks[0], + "cross_attention_dim": unet_params.context_dim, + "attention_head_dim": unet_params.num_heads, + } return config @@ -319,17 +319,17 @@ def create_text_unet_diffusers_config(unet_params): if not all(n == unet_params.num_noattn_blocks[0] for n in unet_params.num_noattn_blocks): raise ValueError("Not all num_res_blocks are equal, which is not supported in this script.") - config = dict( - sample_size=None, - in_channels=(unet_params.input_channels, 1, 1), - out_channels=(unet_params.output_channels, 1, 1), - down_block_types=tuple(down_block_types), - up_block_types=tuple(up_block_types), - block_out_channels=tuple(block_out_channels), - layers_per_block=unet_params.num_noattn_blocks[0], - cross_attention_dim=unet_params.context_dim, - attention_head_dim=unet_params.num_heads, - ) + config = { + "sample_size": None, + "in_channels": (unet_params.input_channels, 1, 1), + "out_channels": (unet_params.output_channels, 1, 1), + "down_block_types": tuple(down_block_types), + "up_block_types": tuple(up_block_types), + "block_out_channels": tuple(block_out_channels), + "layers_per_block": unet_params.num_noattn_blocks[0], + "cross_attention_dim": unet_params.context_dim, + "attention_head_dim": unet_params.num_heads, + } return config @@ -343,16 +343,16 @@ def create_vae_diffusers_config(vae_params): down_block_types = ["DownEncoderBlock2D"] * len(block_out_channels) up_block_types = ["UpDecoderBlock2D"] * len(block_out_channels) - config = dict( - sample_size=vae_params.resolution, - in_channels=vae_params.in_channels, - out_channels=vae_params.out_ch, - down_block_types=tuple(down_block_types), - up_block_types=tuple(up_block_types), - block_out_channels=tuple(block_out_channels), - latent_channels=vae_params.z_channels, - layers_per_block=vae_params.num_res_blocks, - ) + config = { + "sample_size": vae_params.resolution, + "in_channels": vae_params.in_channels, + "out_channels": vae_params.out_ch, + "down_block_types": tuple(down_block_types), + "up_block_types": tuple(up_block_types), + "block_out_channels": tuple(block_out_channels), + "latent_channels": vae_params.z_channels, + "layers_per_block": vae_params.num_res_blocks, + } return config diff --git a/src/diffusers/configuration_utils.py b/src/diffusers/configuration_utils.py index 20b7b273d5af..ce6e77b03f57 100644 --- a/src/diffusers/configuration_utils.py +++ b/src/diffusers/configuration_utils.py @@ -420,7 +420,7 @@ def _get_init_keys(cls): @classmethod def extract_init_dict(cls, config_dict, **kwargs): # 0. Copy origin config dict - original_dict = {k: v for k, v in config_dict.items()} + original_dict = dict(config_dict.items()) # 1. Retrieve expected config attributes from __init__ signature expected_keys = cls._get_init_keys(cls) @@ -610,7 +610,7 @@ def init(self, *args, **kwargs): ) # Ignore private kwargs in the init. Retrieve all passed attributes - init_kwargs = {k: v for k, v in kwargs.items()} + init_kwargs = dict(kwargs.items()) # Retrieve default values fields = dataclasses.fields(self) diff --git a/src/diffusers/experimental/rl/value_guided_sampling.py b/src/diffusers/experimental/rl/value_guided_sampling.py index 7de33a795c77..e4af4986faad 100644 --- a/src/diffusers/experimental/rl/value_guided_sampling.py +++ b/src/diffusers/experimental/rl/value_guided_sampling.py @@ -52,13 +52,13 @@ def __init__( self.scheduler = scheduler self.env = env self.data = env.get_dataset() - self.means = dict() + self.means = {} for key in self.data.keys(): try: self.means[key] = self.data[key].mean() except: # noqa: E722 pass - self.stds = dict() + self.stds = {} for key in self.data.keys(): try: self.stds[key] = self.data[key].std() diff --git a/src/diffusers/image_processor.py b/src/diffusers/image_processor.py index de6543800b2d..80e3412991cf 100644 --- a/src/diffusers/image_processor.py +++ b/src/diffusers/image_processor.py @@ -99,7 +99,7 @@ def resize(self, images: PIL.Image.Image) -> PIL.Image.Image: Resize a PIL image. Both height and width will be downscaled to the next integer multiple of `vae_scale_factor` """ w, h = images.size - w, h = map(lambda x: x - x % self.vae_scale_factor, (w, h)) # resize to integer multiple of vae_scale_factor + w, h = (x - x % self.vae_scale_factor for x in (w, h)) # resize to integer multiple of vae_scale_factor images = images.resize((w, h), resample=PIL_INTERPOLATION[self.resample]) return images diff --git a/src/diffusers/loaders.py b/src/diffusers/loaders.py index 31fdc46d9e1b..d6bb6fde6ac1 100644 --- a/src/diffusers/loaders.py +++ b/src/diffusers/loaders.py @@ -37,7 +37,7 @@ class AttnProcsLayers(torch.nn.Module): def __init__(self, state_dict: Dict[str, torch.Tensor]): super().__init__() self.layers = torch.nn.ModuleList(state_dict.values()) - self.mapping = {k: v for k, v in enumerate(state_dict.keys())} + self.mapping = dict(enumerate(state_dict.keys())) self.rev_mapping = {v: k for k, v in enumerate(state_dict.keys())} # we add a hook to state_dict() and load_state_dict() so that the diff --git a/src/diffusers/models/modeling_utils.py b/src/diffusers/models/modeling_utils.py index e51b40ce4509..aa4e2b0ea487 100644 --- a/src/diffusers/models/modeling_utils.py +++ b/src/diffusers/models/modeling_utils.py @@ -647,7 +647,7 @@ def _load_pretrained_model( ): # Retrieve missing & unexpected_keys model_state_dict = model.state_dict() - loaded_keys = [k for k in state_dict.keys()] + loaded_keys = list(state_dict.keys()) expected_keys = list(model_state_dict.keys()) diff --git a/src/diffusers/pipelines/alt_diffusion/pipeline_alt_diffusion_img2img.py b/src/diffusers/pipelines/alt_diffusion/pipeline_alt_diffusion_img2img.py index ab80072fa78f..23f4886f06c1 100644 --- a/src/diffusers/pipelines/alt_diffusion/pipeline_alt_diffusion_img2img.py +++ b/src/diffusers/pipelines/alt_diffusion/pipeline_alt_diffusion_img2img.py @@ -74,7 +74,7 @@ def preprocess(image): if isinstance(image[0], PIL.Image.Image): w, h = image[0].size - w, h = map(lambda x: x - x % 8, (w, h)) # resize to integer multiple of 8 + w, h = (x - x % 8 for x in (w, h)) # resize to integer multiple of 8 image = [np.array(i.resize((w, h), resample=PIL_INTERPOLATION["lanczos"]))[None, :] for i in image] image = np.concatenate(image, axis=0) diff --git a/src/diffusers/pipelines/audio_diffusion/pipeline_audio_diffusion.py b/src/diffusers/pipelines/audio_diffusion/pipeline_audio_diffusion.py index 8f0925ac4aaa..1b88270cbbe6 100644 --- a/src/diffusers/pipelines/audio_diffusion/pipeline_audio_diffusion.py +++ b/src/diffusers/pipelines/audio_diffusion/pipeline_audio_diffusion.py @@ -201,12 +201,12 @@ def __call__( images = images.cpu().permute(0, 2, 3, 1).numpy() images = (images * 255).round().astype("uint8") images = list( - map(lambda _: Image.fromarray(_[:, :, 0]), images) + (Image.fromarray(_[:, :, 0]) for _ in images) if images.shape[3] == 1 - else map(lambda _: Image.fromarray(_, mode="RGB").convert("L"), images) + else (Image.fromarray(_, mode="RGB").convert("L") for _ in images) ) - audios = list(map(lambda _: self.mel.image_to_audio(_), images)) + audios = [self.mel.image_to_audio(_) for _ in images] if not return_dict: return images, (self.mel.get_sample_rate(), audios) diff --git a/src/diffusers/pipelines/latent_diffusion/pipeline_latent_diffusion_superresolution.py b/src/diffusers/pipelines/latent_diffusion/pipeline_latent_diffusion_superresolution.py index 2ecf5f24a4a7..6887068f3443 100644 --- a/src/diffusers/pipelines/latent_diffusion/pipeline_latent_diffusion_superresolution.py +++ b/src/diffusers/pipelines/latent_diffusion/pipeline_latent_diffusion_superresolution.py @@ -21,7 +21,7 @@ def preprocess(image): w, h = image.size - w, h = map(lambda x: x - x % 32, (w, h)) # resize to integer multiple of 32 + w, h = (x - x % 32 for x in (w, h)) # resize to integer multiple of 32 image = image.resize((w, h), resample=PIL_INTERPOLATION["lanczos"]) image = np.array(image).astype(np.float32) / 255.0 image = image[None].transpose(0, 3, 1, 2) diff --git a/src/diffusers/pipelines/pipeline_flax_utils.py b/src/diffusers/pipelines/pipeline_flax_utils.py index d3fc415ab4d7..9d91ff757799 100644 --- a/src/diffusers/pipelines/pipeline_flax_utils.py +++ b/src/diffusers/pipelines/pipeline_flax_utils.py @@ -491,7 +491,7 @@ def _get_signature_keys(obj): parameters = inspect.signature(obj.__init__).parameters required_parameters = {k: v for k, v in parameters.items() if v.default == inspect._empty} optional_parameters = set({k for k, v in parameters.items() if v.default != inspect._empty}) - expected_modules = set(required_parameters.keys()) - set(["self"]) + expected_modules = set(required_parameters.keys()) - {"self"} return expected_modules, optional_parameters @property diff --git a/src/diffusers/pipelines/pipeline_utils.py b/src/diffusers/pipelines/pipeline_utils.py index 8f33b506827a..d3578745b8b3 100644 --- a/src/diffusers/pipelines/pipeline_utils.py +++ b/src/diffusers/pipelines/pipeline_utils.py @@ -204,11 +204,11 @@ def variant_compatible_siblings(filenames, variant=None) -> Union[List[os.PathLi non_variant_file_regex = re.compile(f"{'|'.join(weight_names)}") if variant is not None: - variant_filenames = set(f for f in filenames if variant_file_regex.match(f.split("/")[-1]) is not None) + variant_filenames = {f for f in filenames if variant_file_regex.match(f.split("/")[-1]) is not None} else: variant_filenames = set() - non_variant_filenames = set(f for f in filenames if non_variant_file_regex.match(f.split("/")[-1]) is not None) + non_variant_filenames = {f for f in filenames if non_variant_file_regex.match(f.split("/")[-1]) is not None} usable_filenames = set(variant_filenames) for f in non_variant_filenames: @@ -225,7 +225,7 @@ def warn_deprecated_model_variant(pretrained_model_name_or_path, use_auth_token, use_auth_token=use_auth_token, revision=None, ) - filenames = set(sibling.rfilename for sibling in info.siblings) + filenames = {sibling.rfilename for sibling in info.siblings} comp_model_filenames, _ = variant_compatible_siblings(filenames, variant=revision) comp_model_filenames = [".".join(f.split(".")[:1] + f.split(".")[2:]) for f in comp_model_filenames] @@ -1115,7 +1115,7 @@ def download(cls, pretrained_model_name, **kwargs) -> Union[str, os.PathLike]: # retrieve all folder_names that contain relevant files folder_names = [k for k, v in config_dict.items() if isinstance(v, list)] - filenames = set(sibling.rfilename for sibling in info.siblings) + filenames = {sibling.rfilename for sibling in info.siblings} model_filenames, variant_filenames = variant_compatible_siblings(filenames, variant=variant) # if the whole pipeline is cached we don't have to ping the Hub @@ -1126,7 +1126,7 @@ def download(cls, pretrained_model_name, **kwargs) -> Union[str, os.PathLike]: pretrained_model_name, use_auth_token, variant, revision, model_filenames ) - model_folder_names = set([os.path.split(f)[0] for f in model_filenames]) + model_folder_names = {os.path.split(f)[0] for f in model_filenames} # all filenames compatible with variant will be added allow_patterns = list(model_filenames) @@ -1157,8 +1157,8 @@ def download(cls, pretrained_model_name, **kwargs) -> Union[str, os.PathLike]: elif use_safetensors and is_safetensors_compatible(model_filenames, variant=variant): ignore_patterns = ["*.bin", "*.msgpack"] - safetensors_variant_filenames = set([f for f in variant_filenames if f.endswith(".safetensors")]) - safetensors_model_filenames = set([f for f in model_filenames if f.endswith(".safetensors")]) + safetensors_variant_filenames = {f for f in variant_filenames if f.endswith(".safetensors")} + safetensors_model_filenames = {f for f in model_filenames if f.endswith(".safetensors")} if ( len(safetensors_variant_filenames) > 0 and safetensors_model_filenames != safetensors_variant_filenames @@ -1169,8 +1169,8 @@ def download(cls, pretrained_model_name, **kwargs) -> Union[str, os.PathLike]: else: ignore_patterns = ["*.safetensors", "*.msgpack"] - bin_variant_filenames = set([f for f in variant_filenames if f.endswith(".bin")]) - bin_model_filenames = set([f for f in model_filenames if f.endswith(".bin")]) + bin_variant_filenames = {f for f in variant_filenames if f.endswith(".bin")} + bin_model_filenames = {f for f in model_filenames if f.endswith(".bin")} if len(bin_variant_filenames) > 0 and bin_model_filenames != bin_variant_filenames: logger.warn( f"\nA mixture of {variant} and non-{variant} filenames will be loaded.\nLoaded {variant} filenames:\n[{', '.join(bin_variant_filenames)}]\nLoaded non-{variant} filenames:\n[{', '.join(bin_model_filenames - bin_variant_filenames)}\nIf this behavior is not expected, please check your folder structure." @@ -1215,7 +1215,7 @@ def _get_signature_keys(obj): parameters = inspect.signature(obj.__init__).parameters required_parameters = {k: v for k, v in parameters.items() if v.default == inspect._empty} optional_parameters = set({k for k, v in parameters.items() if v.default != inspect._empty}) - expected_modules = set(required_parameters.keys()) - set(["self"]) + expected_modules = set(required_parameters.keys()) - {"self"} return expected_modules, optional_parameters @property diff --git a/src/diffusers/pipelines/repaint/pipeline_repaint.py b/src/diffusers/pipelines/repaint/pipeline_repaint.py index fabcd2610f43..f4914c46db51 100644 --- a/src/diffusers/pipelines/repaint/pipeline_repaint.py +++ b/src/diffusers/pipelines/repaint/pipeline_repaint.py @@ -37,7 +37,7 @@ def _preprocess_image(image: Union[List, PIL.Image.Image, torch.Tensor]): if isinstance(image[0], PIL.Image.Image): w, h = image[0].size - w, h = map(lambda x: x - x % 8, (w, h)) # resize to integer multiple of 8 + w, h = (x - x % 8 for x in (w, h)) # resize to integer multiple of 8 image = [np.array(i.resize((w, h), resample=PIL_INTERPOLATION["lanczos"]))[None, :] for i in image] image = np.concatenate(image, axis=0) @@ -58,7 +58,7 @@ def _preprocess_mask(mask: Union[List, PIL.Image.Image, torch.Tensor]): if isinstance(mask[0], PIL.Image.Image): w, h = mask[0].size - w, h = map(lambda x: x - x % 32, (w, h)) # resize to integer multiple of 32 + w, h = (x - x % 32 for x in (w, h)) # resize to integer multiple of 32 mask = [np.array(m.convert("L").resize((w, h), resample=PIL_INTERPOLATION["nearest"]))[None, :] for m in mask] mask = np.concatenate(mask, axis=0) mask = mask.astype(np.float32) / 255.0 diff --git a/src/diffusers/pipelines/spectrogram_diffusion/midi_utils.py b/src/diffusers/pipelines/spectrogram_diffusion/midi_utils.py index 00277adc7fbe..08d0878db588 100644 --- a/src/diffusers/pipelines/spectrogram_diffusion/midi_utils.py +++ b/src/diffusers/pipelines/spectrogram_diffusion/midi_utils.py @@ -166,7 +166,7 @@ def __init__(self, max_shift_steps: int, steps_per_second: float, event_ranges: self._shift_range = EventRange(type="shift", min_value=0, max_value=max_shift_steps) self._event_ranges = [self._shift_range] + event_ranges # Ensure all event types have unique names. - assert len(self._event_ranges) == len(set([er.type for er in self._event_ranges])) + assert len(self._event_ranges) == len({er.type for er in self._event_ranges}) @property def num_classes(self) -> int: diff --git a/src/diffusers/pipelines/stable_diffusion/convert_from_ckpt.py b/src/diffusers/pipelines/stable_diffusion/convert_from_ckpt.py index ef4598433f82..7fbdbdab1fa9 100644 --- a/src/diffusers/pipelines/stable_diffusion/convert_from_ckpt.py +++ b/src/diffusers/pipelines/stable_diffusion/convert_from_ckpt.py @@ -274,18 +274,18 @@ def create_unet_diffusers_config(original_config, image_size: int, controlnet=Fa else: raise NotImplementedError(f"Unknown conditional unet num_classes config: {unet_params.num_classes}") - config = dict( - sample_size=image_size // vae_scale_factor, - in_channels=unet_params.in_channels, - down_block_types=tuple(down_block_types), - block_out_channels=tuple(block_out_channels), - layers_per_block=unet_params.num_res_blocks, - cross_attention_dim=unet_params.context_dim, - attention_head_dim=head_dim, - use_linear_projection=use_linear_projection, - class_embed_type=class_embed_type, - projection_class_embeddings_input_dim=projection_class_embeddings_input_dim, - ) + config = { + "sample_size": image_size // vae_scale_factor, + "in_channels": unet_params.in_channels, + "down_block_types": tuple(down_block_types), + "block_out_channels": tuple(block_out_channels), + "layers_per_block": unet_params.num_res_blocks, + "cross_attention_dim": unet_params.context_dim, + "attention_head_dim": head_dim, + "use_linear_projection": use_linear_projection, + "class_embed_type": class_embed_type, + "projection_class_embeddings_input_dim": projection_class_embeddings_input_dim, + } if not controlnet: config["out_channels"] = unet_params.out_channels @@ -305,16 +305,16 @@ def create_vae_diffusers_config(original_config, image_size: int): down_block_types = ["DownEncoderBlock2D"] * len(block_out_channels) up_block_types = ["UpDecoderBlock2D"] * len(block_out_channels) - config = dict( - sample_size=image_size, - in_channels=vae_params.in_channels, - out_channels=vae_params.out_ch, - down_block_types=tuple(down_block_types), - up_block_types=tuple(up_block_types), - block_out_channels=tuple(block_out_channels), - latent_channels=vae_params.z_channels, - layers_per_block=vae_params.num_res_blocks, - ) + config = { + "sample_size": image_size, + "in_channels": vae_params.in_channels, + "out_channels": vae_params.out_ch, + "down_block_types": tuple(down_block_types), + "up_block_types": tuple(up_block_types), + "block_out_channels": tuple(block_out_channels), + "latent_channels": vae_params.z_channels, + "layers_per_block": vae_params.num_res_blocks, + } return config diff --git a/src/diffusers/pipelines/stable_diffusion/pipeline_cycle_diffusion.py b/src/diffusers/pipelines/stable_diffusion/pipeline_cycle_diffusion.py index 67cda0cfef32..4616dc1e4849 100644 --- a/src/diffusers/pipelines/stable_diffusion/pipeline_cycle_diffusion.py +++ b/src/diffusers/pipelines/stable_diffusion/pipeline_cycle_diffusion.py @@ -44,7 +44,7 @@ def preprocess(image): if isinstance(image[0], PIL.Image.Image): w, h = image[0].size - w, h = map(lambda x: x - x % 8, (w, h)) # resize to integer multiple of 8 + w, h = (x - x % 8 for x in (w, h)) # resize to integer multiple of 8 image = [np.array(i.resize((w, h), resample=PIL_INTERPOLATION["lanczos"]))[None, :] for i in image] image = np.concatenate(image, axis=0) diff --git a/src/diffusers/pipelines/stable_diffusion/pipeline_flax_stable_diffusion_controlnet.py b/src/diffusers/pipelines/stable_diffusion/pipeline_flax_stable_diffusion_controlnet.py index 4dc450cebc84..5af07ec8b9c4 100644 --- a/src/diffusers/pipelines/stable_diffusion/pipeline_flax_stable_diffusion_controlnet.py +++ b/src/diffusers/pipelines/stable_diffusion/pipeline_flax_stable_diffusion_controlnet.py @@ -530,7 +530,7 @@ def unshard(x: jnp.ndarray): def preprocess(image, dtype): image = image.convert("RGB") w, h = image.size - w, h = map(lambda x: x - x % 64, (w, h)) # resize to integer multiple of 64 + w, h = (x - x % 64 for x in (w, h)) # resize to integer multiple of 64 image = image.resize((w, h), resample=PIL_INTERPOLATION["lanczos"]) image = jnp.array(image).astype(dtype) / 255.0 image = image[None].transpose(0, 3, 1, 2) diff --git a/src/diffusers/pipelines/stable_diffusion/pipeline_flax_stable_diffusion_img2img.py b/src/diffusers/pipelines/stable_diffusion/pipeline_flax_stable_diffusion_img2img.py index 95cab9df61e8..2063238df27a 100644 --- a/src/diffusers/pipelines/stable_diffusion/pipeline_flax_stable_diffusion_img2img.py +++ b/src/diffusers/pipelines/stable_diffusion/pipeline_flax_stable_diffusion_img2img.py @@ -520,7 +520,7 @@ def unshard(x: jnp.ndarray): def preprocess(image, dtype): w, h = image.size - w, h = map(lambda x: x - x % 32, (w, h)) # resize to integer multiple of 32 + w, h = (x - x % 32 for x in (w, h)) # resize to integer multiple of 32 image = image.resize((w, h), resample=PIL_INTERPOLATION["lanczos"]) image = jnp.array(image).astype(dtype) / 255.0 image = image[None].transpose(0, 3, 1, 2) diff --git a/src/diffusers/pipelines/stable_diffusion/pipeline_flax_stable_diffusion_inpaint.py b/src/diffusers/pipelines/stable_diffusion/pipeline_flax_stable_diffusion_inpaint.py index 6e9b9ff6d00f..abb57f8b62e9 100644 --- a/src/diffusers/pipelines/stable_diffusion/pipeline_flax_stable_diffusion_inpaint.py +++ b/src/diffusers/pipelines/stable_diffusion/pipeline_flax_stable_diffusion_inpaint.py @@ -563,7 +563,7 @@ def unshard(x: jnp.ndarray): def preprocess_image(image, dtype): w, h = image.size - w, h = map(lambda x: x - x % 32, (w, h)) # resize to integer multiple of 32 + w, h = (x - x % 32 for x in (w, h)) # resize to integer multiple of 32 image = image.resize((w, h), resample=PIL_INTERPOLATION["lanczos"]) image = jnp.array(image).astype(dtype) / 255.0 image = image[None].transpose(0, 3, 1, 2) @@ -572,7 +572,7 @@ def preprocess_image(image, dtype): def preprocess_mask(mask, dtype): w, h = mask.size - w, h = map(lambda x: x - x % 32, (w, h)) # resize to integer multiple of 32 + w, h = (x - x % 32 for x in (w, h)) # resize to integer multiple of 32 mask = mask.resize((w, h)) mask = jnp.array(mask.convert("L")).astype(dtype) / 255.0 mask = jnp.expand_dims(mask, axis=(0, 1)) diff --git a/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_img2img.py b/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_img2img.py index 910fbaacfcca..80c4a8692a05 100644 --- a/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_img2img.py +++ b/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_img2img.py @@ -40,7 +40,7 @@ def preprocess(image): if isinstance(image[0], PIL.Image.Image): w, h = image[0].size - w, h = map(lambda x: x - x % 64, (w, h)) # resize to integer multiple of 64 + w, h = (x - x % 64 for x in (w, h)) # resize to integer multiple of 64 image = [np.array(i.resize((w, h), resample=PIL_INTERPOLATION["lanczos"]))[None, :] for i in image] image = np.concatenate(image, axis=0) diff --git a/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_inpaint_legacy.py b/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_inpaint_legacy.py index 987a343c718b..5cb3abb4f54e 100644 --- a/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_inpaint_legacy.py +++ b/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_inpaint_legacy.py @@ -19,7 +19,7 @@ def preprocess(image): w, h = image.size - w, h = map(lambda x: x - x % 32, (w, h)) # resize to integer multiple of 32 + w, h = (x - x % 32 for x in (w, h)) # resize to integer multiple of 32 image = image.resize((w, h), resample=PIL.Image.LANCZOS) image = np.array(image).astype(np.float32) / 255.0 image = image[None].transpose(0, 3, 1, 2) @@ -29,7 +29,7 @@ def preprocess(image): def preprocess_mask(mask, scale_factor=8): mask = mask.convert("L") w, h = mask.size - w, h = map(lambda x: x - x % 32, (w, h)) # resize to integer multiple of 32 + w, h = (x - x % 32 for x in (w, h)) # resize to integer multiple of 32 mask = mask.resize((w // scale_factor, h // scale_factor), resample=PIL.Image.NEAREST) mask = np.array(mask).astype(np.float32) / 255.0 mask = np.tile(mask, (4, 1, 1)) diff --git a/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_upscale.py b/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_upscale.py index 45b5a50467b0..b91262551b0f 100644 --- a/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_upscale.py +++ b/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_upscale.py @@ -31,7 +31,7 @@ def preprocess(image): if isinstance(image[0], PIL.Image.Image): w, h = image[0].size - w, h = map(lambda x: x - x % 64, (w, h)) # resize to integer multiple of 32 + w, h = (x - x % 64 for x in (w, h)) # resize to integer multiple of 32 image = [np.array(i.resize((w, h)))[None, :] for i in image] image = np.concatenate(image, axis=0) diff --git a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_depth2img.py b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_depth2img.py index b66cfe9b437e..96282771d777 100644 --- a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_depth2img.py +++ b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_depth2img.py @@ -41,7 +41,7 @@ def preprocess(image): if isinstance(image[0], PIL.Image.Image): w, h = image[0].size - w, h = map(lambda x: x - x % 8, (w, h)) # resize to integer multiple of 8 + w, h = (x - x % 8 for x in (w, h)) # resize to integer multiple of 8 image = [np.array(i.resize((w, h), resample=PIL_INTERPOLATION["lanczos"]))[None, :] for i in image] image = np.concatenate(image, axis=0) @@ -442,7 +442,7 @@ def prepare_depth_map(self, image, depth_map, batch_size, do_classifier_free_gui if isinstance(image, PIL.Image.Image): image = [image] else: - image = [img for img in image] + image = list(image) if isinstance(image[0], PIL.Image.Image): width, height = image[0].size diff --git a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py index 1c94c58450ab..ba124ffecbee 100644 --- a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py +++ b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py @@ -78,7 +78,7 @@ def preprocess(image): if isinstance(image[0], PIL.Image.Image): w, h = image[0].size - w, h = map(lambda x: x - x % 8, (w, h)) # resize to integer multiple of 8 + w, h = (x - x % 8 for x in (w, h)) # resize to integer multiple of 8 image = [np.array(i.resize((w, h), resample=PIL_INTERPOLATION["lanczos"]))[None, :] for i in image] image = np.concatenate(image, axis=0) diff --git a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint_legacy.py b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint_legacy.py index 6fafe08285ee..3d1615968369 100644 --- a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint_legacy.py +++ b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint_legacy.py @@ -42,7 +42,7 @@ def preprocess_image(image): w, h = image.size - w, h = map(lambda x: x - x % 8, (w, h)) # resize to integer multiple of 8 + w, h = (x - x % 8 for x in (w, h)) # resize to integer multiple of 8 image = image.resize((w, h), resample=PIL_INTERPOLATION["lanczos"]) image = np.array(image).astype(np.float32) / 255.0 image = image[None].transpose(0, 3, 1, 2) @@ -54,7 +54,7 @@ def preprocess_mask(mask, scale_factor=8): if not isinstance(mask, torch.FloatTensor): mask = mask.convert("L") w, h = mask.size - w, h = map(lambda x: x - x % 8, (w, h)) # resize to integer multiple of 8 + w, h = (x - x % 8 for x in (w, h)) # resize to integer multiple of 8 mask = mask.resize((w // scale_factor, h // scale_factor), resample=PIL_INTERPOLATION["nearest"]) mask = np.array(mask).astype(np.float32) / 255.0 mask = np.tile(mask, (4, 1, 1)) @@ -76,7 +76,7 @@ def preprocess_mask(mask, scale_factor=8): # (potentially) reduce mask channel dimension from 3 to 1 for broadcasting to latent shape mask = mask.mean(dim=1, keepdim=True) h, w = mask.shape[-2:] - h, w = map(lambda x: x - x % 8, (h, w)) # resize to integer multiple of 8 + h, w = (x - x % 8 for x in (h, w)) # resize to integer multiple of 8 mask = torch.nn.functional.interpolate(mask, (h // scale_factor, w // scale_factor)) return mask diff --git a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_instruct_pix2pix.py b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_instruct_pix2pix.py index a45937fd2045..5f4d6685185f 100644 --- a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_instruct_pix2pix.py +++ b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_instruct_pix2pix.py @@ -47,7 +47,7 @@ def preprocess(image): if isinstance(image[0], PIL.Image.Image): w, h = image[0].size - w, h = map(lambda x: x - x % 8, (w, h)) # resize to integer multiple of 8 + w, h = (x - x % 8 for x in (w, h)) # resize to integer multiple of 8 image = [np.array(i.resize((w, h), resample=PIL_INTERPOLATION["lanczos"]))[None, :] for i in image] image = np.concatenate(image, axis=0) diff --git a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_latent_upscale.py b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_latent_upscale.py index 624d0e625828..822bd49ce31c 100644 --- a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_latent_upscale.py +++ b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_latent_upscale.py @@ -38,7 +38,7 @@ def preprocess(image): if isinstance(image[0], PIL.Image.Image): w, h = image[0].size - w, h = map(lambda x: x - x % 64, (w, h)) # resize to integer multiple of 64 + w, h = (x - x % 64 for x in (w, h)) # resize to integer multiple of 64 image = [np.array(i.resize((w, h)))[None, :] for i in image] image = np.concatenate(image, axis=0) diff --git a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_pix2pix_zero.py b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_pix2pix_zero.py index 4c2dbe6ff85d..bc072d4c73e8 100644 --- a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_pix2pix_zero.py +++ b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_pix2pix_zero.py @@ -180,7 +180,7 @@ def preprocess(image): if isinstance(image[0], PIL.Image.Image): w, h = image[0].size - w, h = map(lambda x: x - x % 8, (w, h)) # resize to integer multiple of 8 + w, h = (x - x % 8 for x in (w, h)) # resize to integer multiple of 8 image = [np.array(i.resize((w, h), resample=PIL_INTERPOLATION["lanczos"]))[None, :] for i in image] image = np.concatenate(image, axis=0) diff --git a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_upscale.py b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_upscale.py index 9f8f44a12bb4..b25a91d4caef 100644 --- a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_upscale.py +++ b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_upscale.py @@ -37,7 +37,7 @@ def preprocess(image): if isinstance(image[0], PIL.Image.Image): w, h = image[0].size - w, h = map(lambda x: x - x % 64, (w, h)) # resize to integer multiple of 64 + w, h = (x - x % 64 for x in (w, h)) # resize to integer multiple of 64 image = [np.array(i.resize((w, h)))[None, :] for i in image] image = np.concatenate(image, axis=0) diff --git a/src/diffusers/pipelines/versatile_diffusion/pipeline_versatile_diffusion_image_variation.py b/src/diffusers/pipelines/versatile_diffusion/pipeline_versatile_diffusion_image_variation.py index f9ae82568e5c..2b47184d7773 100644 --- a/src/diffusers/pipelines/versatile_diffusion/pipeline_versatile_diffusion_image_variation.py +++ b/src/diffusers/pipelines/versatile_diffusion/pipeline_versatile_diffusion_image_variation.py @@ -134,7 +134,7 @@ def normalize_embeddings(encoder_output): return embeds if isinstance(prompt, torch.Tensor) and len(prompt.shape) == 4: - prompt = [p for p in prompt] + prompt = list(prompt) batch_size = len(prompt) if isinstance(prompt, list) else 1 diff --git a/src/diffusers/utils/outputs.py b/src/diffusers/utils/outputs.py index f91a49b7a8a7..b6e8a219e129 100644 --- a/src/diffusers/utils/outputs.py +++ b/src/diffusers/utils/outputs.py @@ -84,7 +84,7 @@ def update(self, *args, **kwargs): def __getitem__(self, k): if isinstance(k, str): - inner_dict = {k: v for (k, v) in self.items()} + inner_dict = dict(self.items()) return inner_dict[k] else: return self.to_tuple()[k] diff --git a/utils/check_doc_toc.py b/utils/check_doc_toc.py index c00feb9d8e3f..ff9285c63f16 100644 --- a/utils/check_doc_toc.py +++ b/utils/check_doc_toc.py @@ -43,7 +43,7 @@ def clean_doc_toc(doc_list): new_doc = [] for duplicate_key in duplicates: - titles = list(set(doc["title"] for doc in doc_list if doc["local"] == duplicate_key)) + titles = list({doc["title"] for doc in doc_list if doc["local"] == duplicate_key}) if len(titles) > 1: raise ValueError( f"{duplicate_key} is present several times in the documentation table of content at " diff --git a/utils/check_repo.py b/utils/check_repo.py index 2cdb9af62de9..cfd2964f9dcc 100644 --- a/utils/check_repo.py +++ b/utils/check_repo.py @@ -219,7 +219,7 @@ def check_model_list(): # Get the models from the directory structure of `src/transformers/models/` models = [model for model in dir(diffusers.models) if not model.startswith("__")] - missing_models = sorted(list(set(_models).difference(models))) + missing_models = sorted(set(_models).difference(models)) if missing_models: raise Exception( f"The following models should be included in {models_dir}/__init__.py: {','.join(missing_models)}." @@ -429,7 +429,7 @@ def get_all_auto_configured_models(): for attr_name in dir(diffusers.models.auto.modeling_flax_auto): if attr_name.startswith("FLAX_MODEL_") and attr_name.endswith("MAPPING_NAMES"): result = result | set(get_values(getattr(diffusers.models.auto.modeling_flax_auto, attr_name))) - return [cls for cls in result] + return list(result) def ignore_unautoclassed(model_name): diff --git a/utils/overwrite_expected_slice.py b/utils/overwrite_expected_slice.py index 95799f9ca625..7aa66727150a 100644 --- a/utils/overwrite_expected_slice.py +++ b/utils/overwrite_expected_slice.py @@ -67,7 +67,7 @@ def overwrite_file(file, class_name, test_name, correct_line, done_test): def main(correct, fail=None): if fail is not None: with open(fail, "r") as f: - test_failures = set([l.strip() for l in f.readlines()]) + test_failures = {l.strip() for l in f.readlines()} else: test_failures = None diff --git a/utils/stale.py b/utils/stale.py index 36631b65a3ba..12932f31c243 100644 --- a/utils/stale.py +++ b/utils/stale.py @@ -38,7 +38,7 @@ def main(): open_issues = repo.get_issues(state="open") for issue in open_issues: - comments = sorted([comment for comment in issue.get_comments()], key=lambda i: i.created_at, reverse=True) + comments = sorted(issue.get_comments(), key=lambda i: i.created_at, reverse=True) last_comment = comments[0] if len(comments) > 0 else None if ( last_comment is not None From 4c26cb9cc83b0ad0d750f7b4ac337e949cefedd7 Mon Sep 17 00:00:00 2001 From: Patrick von Platen Date: Mon, 27 Mar 2023 19:45:49 +0200 Subject: [PATCH 03/56] [Tests] Fix slow tests (#2846) --- tests/pipelines/stable_unclip/test_stable_unclip_img2img.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/tests/pipelines/stable_unclip/test_stable_unclip_img2img.py b/tests/pipelines/stable_unclip/test_stable_unclip_img2img.py index 5636815196ea..c7c0d2feeb54 100644 --- a/tests/pipelines/stable_unclip/test_stable_unclip_img2img.py +++ b/tests/pipelines/stable_unclip/test_stable_unclip_img2img.py @@ -197,7 +197,7 @@ def test_stable_unclip_l_img2img(self): pipe.enable_sequential_cpu_offload() generator = torch.Generator(device="cpu").manual_seed(0) - output = pipe("anime turle", image=input_image, generator=generator, output_type="np") + output = pipe(input_image, "anime turle", generator=generator, output_type="np") image = output.images[0] @@ -225,7 +225,7 @@ def test_stable_unclip_h_img2img(self): pipe.enable_sequential_cpu_offload() generator = torch.Generator(device="cpu").manual_seed(0) - output = pipe("anime turle", image=input_image, generator=generator, output_type="np") + output = pipe(input_image, "anime turle", generator=generator, output_type="np") image = output.images[0] @@ -251,8 +251,8 @@ def test_stable_unclip_img2img_pipeline_with_sequential_cpu_offloading(self): pipe.enable_sequential_cpu_offload() _ = pipe( + input_image, "anime turtle", - image=input_image, num_inference_steps=2, output_type="np", ) From 7bc2fff1a552ea16de1bdfccdf5d865613f6a63f Mon Sep 17 00:00:00 2001 From: Eugene Lyapustin <30509893+unishift@users.noreply.github.com> Date: Mon, 27 Mar 2023 22:03:59 +0400 Subject: [PATCH 04/56] Fix StableUnCLIPImg2ImgPipeline handling of explicitly passed image embeddings (#2845) --- .../stable_diffusion/pipeline_stable_unclip_img2img.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_unclip_img2img.py b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_unclip_img2img.py index bdebb507a7b5..c7758850f750 100644 --- a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_unclip_img2img.py +++ b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_unclip_img2img.py @@ -388,7 +388,7 @@ def _encode_image( # what the expected dimensions of inputs should be and how we handle the encoding. repeat_by = num_images_per_prompt - if not image_embeds: + if image_embeds is None: if not isinstance(image, torch.Tensor): image = self.feature_extractor(images=image, return_tensors="pt").pixel_values From b10f527577c10dd9de78876c01c6c9fc0af91fc1 Mon Sep 17 00:00:00 2001 From: Pedro Cuenca Date: Mon, 27 Mar 2023 20:31:19 +0200 Subject: [PATCH 05/56] Helper function to disable custom attention processors (#2791) * Helper function to disable custom attention processors. * Restore code deleted by mistake. * Format * Fix modeling_text_unet copy. --- src/diffusers/models/controlnet.py | 9 ++++++++- src/diffusers/models/unet_2d_condition.py | 8 +++++++- src/diffusers/models/unet_3d_condition.py | 9 ++++++++- .../versatile_diffusion/modeling_text_unet.py | 8 +++++++- tests/models/test_models_unet_2d_condition.py | 4 ++-- .../stable_diffusion/test_stable_diffusion.py | 5 ++--- .../stable_diffusion_2/test_stable_diffusion.py | 5 ++--- tests/test_modeling_common.py | 17 ++++++++--------- 8 files changed, 44 insertions(+), 21 deletions(-) diff --git a/src/diffusers/models/controlnet.py b/src/diffusers/models/controlnet.py index ac6e64e4c779..bb608ad82a7a 100644 --- a/src/diffusers/models/controlnet.py +++ b/src/diffusers/models/controlnet.py @@ -20,7 +20,7 @@ from ..configuration_utils import ConfigMixin, register_to_config from ..utils import BaseOutput, logging -from .attention_processor import AttentionProcessor +from .attention_processor import AttentionProcessor, AttnProcessor from .embeddings import TimestepEmbedding, Timesteps from .modeling_utils import ModelMixin from .unet_2d_blocks import ( @@ -368,6 +368,13 @@ def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor): for name, module in self.named_children(): fn_recursive_attn_processor(name, module, processor) + # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.set_default_attn_processor + def set_default_attn_processor(self): + """ + Disables custom attention processors and sets the default attention implementation. + """ + self.set_attn_processor(AttnProcessor()) + # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.set_attention_slice def set_attention_slice(self, slice_size): r""" diff --git a/src/diffusers/models/unet_2d_condition.py b/src/diffusers/models/unet_2d_condition.py index eaf3e48ef6c9..4d237286fb32 100644 --- a/src/diffusers/models/unet_2d_condition.py +++ b/src/diffusers/models/unet_2d_condition.py @@ -21,7 +21,7 @@ from ..configuration_utils import ConfigMixin, register_to_config from ..loaders import UNet2DConditionLoadersMixin from ..utils import BaseOutput, logging -from .attention_processor import AttentionProcessor +from .attention_processor import AttentionProcessor, AttnProcessor from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps from .modeling_utils import ModelMixin from .unet_2d_blocks import ( @@ -442,6 +442,12 @@ def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor): for name, module in self.named_children(): fn_recursive_attn_processor(name, module, processor) + def set_default_attn_processor(self): + """ + Disables custom attention processors and sets the default attention implementation. + """ + self.set_attn_processor(AttnProcessor()) + def set_attention_slice(self, slice_size): r""" Enable sliced attention computation. diff --git a/src/diffusers/models/unet_3d_condition.py b/src/diffusers/models/unet_3d_condition.py index 8006d0e1c127..ec8865f31031 100644 --- a/src/diffusers/models/unet_3d_condition.py +++ b/src/diffusers/models/unet_3d_condition.py @@ -21,7 +21,7 @@ from ..configuration_utils import ConfigMixin, register_to_config from ..utils import BaseOutput, logging -from .attention_processor import AttentionProcessor +from .attention_processor import AttentionProcessor, AttnProcessor from .embeddings import TimestepEmbedding, Timesteps from .modeling_utils import ModelMixin from .transformer_temporal import TransformerTemporalModel @@ -372,6 +372,13 @@ def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor): for name, module in self.named_children(): fn_recursive_attn_processor(name, module, processor) + # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.set_default_attn_processor + def set_default_attn_processor(self): + """ + Disables custom attention processors and sets the default attention implementation. + """ + self.set_attn_processor(AttnProcessor()) + def _set_gradient_checkpointing(self, module, value=False): if isinstance(module, (CrossAttnDownBlock3D, DownBlock3D, CrossAttnUpBlock3D, UpBlock3D)): module.gradient_checkpointing = value diff --git a/src/diffusers/pipelines/versatile_diffusion/modeling_text_unet.py b/src/diffusers/pipelines/versatile_diffusion/modeling_text_unet.py index 0b2308f409dd..deaa709ab319 100644 --- a/src/diffusers/pipelines/versatile_diffusion/modeling_text_unet.py +++ b/src/diffusers/pipelines/versatile_diffusion/modeling_text_unet.py @@ -7,7 +7,7 @@ from ...configuration_utils import ConfigMixin, register_to_config from ...models import ModelMixin from ...models.attention import Attention -from ...models.attention_processor import AttentionProcessor, AttnAddedKVProcessor +from ...models.attention_processor import AttentionProcessor, AttnAddedKVProcessor, AttnProcessor from ...models.dual_transformer_2d import DualTransformer2DModel from ...models.embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps from ...models.transformer_2d import Transformer2DModel @@ -533,6 +533,12 @@ def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor): for name, module in self.named_children(): fn_recursive_attn_processor(name, module, processor) + def set_default_attn_processor(self): + """ + Disables custom attention processors and sets the default attention implementation. + """ + self.set_attn_processor(AttnProcessor()) + def set_attention_slice(self, slice_size): r""" Enable sliced attention computation. diff --git a/tests/models/test_models_unet_2d_condition.py b/tests/models/test_models_unet_2d_condition.py index 08e960dcd1da..c0cb9d3d8ebd 100644 --- a/tests/models/test_models_unet_2d_condition.py +++ b/tests/models/test_models_unet_2d_condition.py @@ -22,7 +22,7 @@ from parameterized import parameterized from diffusers import UNet2DConditionModel -from diffusers.models.attention_processor import AttnProcessor, LoRAAttnProcessor +from diffusers.models.attention_processor import LoRAAttnProcessor from diffusers.utils import ( floats_tensor, load_hf_numpy, @@ -599,7 +599,7 @@ def test_lora_on_off(self): with torch.no_grad(): sample = model(**inputs_dict, cross_attention_kwargs={"scale": 0.0}).sample - model.set_attn_processor(AttnProcessor()) + model.set_default_attn_processor() with torch.no_grad(): new_sample = model(**inputs_dict).sample diff --git a/tests/pipelines/stable_diffusion/test_stable_diffusion.py b/tests/pipelines/stable_diffusion/test_stable_diffusion.py index 33ef9368586e..f4e8113a298f 100644 --- a/tests/pipelines/stable_diffusion/test_stable_diffusion.py +++ b/tests/pipelines/stable_diffusion/test_stable_diffusion.py @@ -35,7 +35,6 @@ UNet2DConditionModel, logging, ) -from diffusers.models.attention_processor import AttnProcessor from diffusers.utils import load_numpy, nightly, slow, torch_device from diffusers.utils.testing_utils import CaptureLogger, require_torch_gpu @@ -843,7 +842,7 @@ def test_stable_diffusion_pipeline_with_model_offloading(self): "CompVis/stable-diffusion-v1-4", torch_dtype=torch.float16, ) - pipe.unet.set_attn_processor(AttnProcessor()) + pipe.unet.set_default_attn_processor() pipe.to(torch_device) pipe.set_progress_bar_config(disable=None) outputs = pipe(**inputs) @@ -856,7 +855,7 @@ def test_stable_diffusion_pipeline_with_model_offloading(self): "CompVis/stable-diffusion-v1-4", torch_dtype=torch.float16, ) - pipe.unet.set_attn_processor(AttnProcessor()) + pipe.unet.set_default_attn_processor() torch.cuda.empty_cache() torch.cuda.reset_max_memory_allocated() diff --git a/tests/pipelines/stable_diffusion_2/test_stable_diffusion.py b/tests/pipelines/stable_diffusion_2/test_stable_diffusion.py index 481c265cbee4..fa3c3d628e4f 100644 --- a/tests/pipelines/stable_diffusion_2/test_stable_diffusion.py +++ b/tests/pipelines/stable_diffusion_2/test_stable_diffusion.py @@ -32,7 +32,6 @@ UNet2DConditionModel, logging, ) -from diffusers.models.attention_processor import AttnProcessor from diffusers.utils import load_numpy, nightly, slow, torch_device from diffusers.utils.testing_utils import CaptureLogger, require_torch_gpu @@ -410,7 +409,7 @@ def test_stable_diffusion_pipeline_with_model_offloading(self): "stabilityai/stable-diffusion-2-base", torch_dtype=torch.float16, ) - pipe.unet.set_attn_processor(AttnProcessor()) + pipe.unet.set_default_attn_processor() pipe.to(torch_device) pipe.set_progress_bar_config(disable=None) outputs = pipe(**inputs) @@ -423,7 +422,7 @@ def test_stable_diffusion_pipeline_with_model_offloading(self): "stabilityai/stable-diffusion-2-base", torch_dtype=torch.float16, ) - pipe.unet.set_attn_processor(AttnProcessor()) + pipe.unet.set_default_attn_processor() torch.cuda.empty_cache() torch.cuda.reset_max_memory_allocated() diff --git a/tests/test_modeling_common.py b/tests/test_modeling_common.py index e880950a7914..932c147027d3 100644 --- a/tests/test_modeling_common.py +++ b/tests/test_modeling_common.py @@ -25,7 +25,6 @@ from requests.exceptions import HTTPError from diffusers.models import UNet2DConditionModel -from diffusers.models.attention_processor import AttnProcessor from diffusers.training_utils import EMAModel from diffusers.utils import torch_device @@ -106,16 +105,16 @@ def test_from_save_pretrained(self): init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common() model = self.model_class(**init_dict) - if hasattr(model, "set_attn_processor"): - model.set_attn_processor(AttnProcessor()) + if hasattr(model, "set_default_attn_processor"): + model.set_default_attn_processor() model.to(torch_device) model.eval() with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname) new_model = self.model_class.from_pretrained(tmpdirname) - if hasattr(new_model, "set_attn_processor"): - new_model.set_attn_processor(AttnProcessor()) + if hasattr(new_model, "set_default_attn_processor"): + new_model.set_default_attn_processor() new_model.to(torch_device) with torch.no_grad(): @@ -135,16 +134,16 @@ def test_from_save_pretrained_variant(self): init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common() model = self.model_class(**init_dict) - if hasattr(model, "set_attn_processor"): - model.set_attn_processor(AttnProcessor()) + if hasattr(model, "set_default_attn_processor"): + model.set_default_attn_processor() model.to(torch_device) model.eval() with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname, variant="fp16") new_model = self.model_class.from_pretrained(tmpdirname, variant="fp16") - if hasattr(new_model, "set_attn_processor"): - new_model.set_attn_processor(AttnProcessor()) + if hasattr(new_model, "set_default_attn_processor"): + new_model.set_default_attn_processor() # non-variant cannot be loaded with self.assertRaises(OSError) as error_context: From fab4f3d6e4bf055555e8fc492b1e6a2307cfa9c8 Mon Sep 17 00:00:00 2001 From: Sayak Paul Date: Tue, 28 Mar 2023 08:18:29 +0530 Subject: [PATCH 06/56] improve stable unclip doc. (#2823) --- .../source/en/api/pipelines/stable_unclip.mdx | 58 +++++++++++++++---- 1 file changed, 48 insertions(+), 10 deletions(-) diff --git a/docs/source/en/api/pipelines/stable_unclip.mdx b/docs/source/en/api/pipelines/stable_unclip.mdx index c8b5d58705ba..372242ae2dff 100644 --- a/docs/source/en/api/pipelines/stable_unclip.mdx +++ b/docs/source/en/api/pipelines/stable_unclip.mdx @@ -42,12 +42,9 @@ Coming soon! ### Text guided Image-to-Image Variation ```python -import requests -import torch -from PIL import Image -from io import BytesIO - from diffusers import StableUnCLIPImg2ImgPipeline +from diffusers.utils import load_image +import torch pipe = StableUnCLIPImg2ImgPipeline.from_pretrained( "stabilityai/stable-diffusion-2-1-unclip", torch_dtype=torch.float16, variation="fp16" @@ -55,12 +52,10 @@ pipe = StableUnCLIPImg2ImgPipeline.from_pretrained( pipe = pipe.to("cuda") url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/tarsila_do_amaral.png" - -response = requests.get(url) -init_image = Image.open(BytesIO(response.content)).convert("RGB") +init_image = load_image(url) images = pipe(init_image).images -images[0].save("fantasy_landscape.png") +images[0].save("variation_image.png") ``` Optionally, you can also pass a prompt to `pipe` such as: @@ -69,7 +64,50 @@ Optionally, you can also pass a prompt to `pipe` such as: prompt = "A fantasy landscape, trending on artstation" images = pipe(init_image, prompt=prompt).images -images[0].save("fantasy_landscape.png") +images[0].save("variation_image_two.png") +``` + +### Memory optimization + +If you are short on GPU memory, you can enable smart CPU offloading so that models that are not needed +immediately for a computation can be offloaded to CPU: + +```python +from diffusers import StableUnCLIPImg2ImgPipeline +from diffusers.utils import load_image +import torch + +pipe = StableUnCLIPImg2ImgPipeline.from_pretrained( + "stabilityai/stable-diffusion-2-1-unclip", torch_dtype=torch.float16, variation="fp16" +) +# Offload to CPU. +pipe.enable_model_cpu_offload() + +url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/tarsila_do_amaral.png" +init_image = load_image(url) + +images = pipe(init_image).images +images[0] +``` + +Further memory optimizations are possible by enabling VAE slicing on the pipeline: + +```python +from diffusers import StableUnCLIPImg2ImgPipeline +from diffusers.utils import load_image +import torch + +pipe = StableUnCLIPImg2ImgPipeline.from_pretrained( + "stabilityai/stable-diffusion-2-1-unclip", torch_dtype=torch.float16, variation="fp16" +) +pipe.enable_model_cpu_offload() +pipe.enable_vae_slicing() + +url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/tarsila_do_amaral.png" +init_image = load_image(url) + +images = pipe(init_image).images +images[0] ``` ### StableUnCLIPPipeline From 58fc8244881f2225803c85fa3179ac32b310cb9d Mon Sep 17 00:00:00 2001 From: Sayak Paul Date: Tue, 28 Mar 2023 08:19:39 +0530 Subject: [PATCH 07/56] add: better warning messages when handling multiple conditionings. (#2804) * add: better warning messages when handling multiple conditioning. * fix: handling of controlnet_conditioning_scale --- .../pipeline_stable_diffusion_controlnet.py | 22 +++++++++++++++---- 1 file changed, 18 insertions(+), 4 deletions(-) diff --git a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_controlnet.py b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_controlnet.py index cbfdfb07bdf0..aea424366346 100644 --- a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_controlnet.py +++ b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_controlnet.py @@ -537,15 +537,27 @@ def check_inputs( f" {negative_prompt_embeds.shape}." ) - # Check `image` + # `prompt` needs more sophisticated handling when there are multiple + # conditionings. + if isinstance(self.controlnet, MultiControlNetModel): + if isinstance(prompt, list): + logger.warning( + f"You have {len(self.controlnet.nets)} ControlNets and you have passed {len(prompt)}" + " prompts. The conditionings will be fixed across the prompts." + ) + # Check `image` if isinstance(self.controlnet, ControlNetModel): self.check_image(image, prompt, prompt_embeds) elif isinstance(self.controlnet, MultiControlNetModel): if not isinstance(image, list): raise TypeError("For multiple controlnets: `image` must be type `list`") - if len(image) != len(self.controlnet.nets): + # When `image` is a nested list: + # (e.g. [[canny_image_1, pose_image_1], [canny_image_2, pose_image_2]]) + elif any(isinstance(i, list) for i in image): + raise ValueError("A single batch of multiple conditionings are supported at the moment.") + elif len(image) != len(self.controlnet.nets): raise ValueError( "For multiple controlnets: `image` must have the same length as the number of controlnets." ) @@ -556,12 +568,14 @@ def check_inputs( assert False # Check `controlnet_conditioning_scale` - if isinstance(self.controlnet, ControlNetModel): if not isinstance(controlnet_conditioning_scale, float): raise TypeError("For single controlnet: `controlnet_conditioning_scale` must be type `float`.") elif isinstance(self.controlnet, MultiControlNetModel): - if isinstance(controlnet_conditioning_scale, list) and len(controlnet_conditioning_scale) != len( + if isinstance(controlnet_conditioning_scale, list): + if any(isinstance(i, list) for i in controlnet_conditioning_scale): + raise ValueError("A single batch of multiple conditionings are supported at the moment.") + elif isinstance(controlnet_conditioning_scale, list) and len(controlnet_conditioning_scale) != len( self.controlnet.nets ): raise ValueError( From d4f846fa74cb713509bab6e3a24d4333eb38a080 Mon Sep 17 00:00:00 2001 From: YiYi Xu Date: Mon, 27 Mar 2023 19:13:35 -1000 Subject: [PATCH 08/56] [WIP]Flax training script for controlnet (#2818) * add train_controlnet_flax --------- Co-authored-by: Patrick von Platen --- examples/controlnet/README.md | 96 ++ examples/controlnet/train_controlnet_flax.py | 1001 ++++++++++++++++++ 2 files changed, 1097 insertions(+) create mode 100644 examples/controlnet/train_controlnet_flax.py diff --git a/examples/controlnet/README.md b/examples/controlnet/README.md index 32de31e14bbd..0650c2230b71 100644 --- a/examples/controlnet/README.md +++ b/examples/controlnet/README.md @@ -267,3 +267,99 @@ image = pipe( image.save("./output.png") ``` + +## Training with Flax/JAX + +For faster training on TPUs and GPUs you can leverage the flax training example. Follow the instructions above to get the model and dataset before running the script. + +### Running on Google Cloud TPU + +See below for commands to set up a TPU VM(`--accelerator-type v4-8`). For more details about how to set up and use TPUs, refer to [Cloud docs for single VM setup](https://cloud.google.com/tpu/docs/run-calculation-jax). + +First create a single TPUv4-8 VM and connect to it: + +``` +ZONE=us-central2-b +TPU_TYPE=v4-8 +VM_NAME=hg_flax + +gcloud alpha compute tpus tpu-vm create $VM_NAME \ + --zone $ZONE \ + --accelerator-type $TPU_TYPE \ + --version tpu-vm-v4-base + +gcloud alpha compute tpus tpu-vm ssh $VM_NAME --zone $ZONE -- \ +``` + +When connected install JAX `0.4.5`: + +``` +pip install "jax[tpu]==0.4.5" -f https://storage.googleapis.com/jax-releases/libtpu_releases.html +``` + +To verify that JAX was correctly installed, you can run the following command: + +``` +import jax +jax.device_count() +``` + +This should display the number of TPU cores, which should be 4 on a TPUv4-8 VM. + +Then install Diffusers and the library's training dependencies: + +```bash +git clone https://github.com/huggingface/diffusers +cd diffusers +pip install . +``` + +Then cd in the example folder and run + +```bash +pip install -U -r requirements_flax.txt +``` + +Now let's downloading two conditioning images that we will use to run validation during the training in order to track our progress + +``` +wget https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet_training/conditioning_image_1.png +wget https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet_training/conditioning_image_2.png +``` + +We encourage you to store or share your model with the community. To use huggingface hub, please login to your Hugging Face account, or ([create one](https://huggingface.co/docs/diffusers/main/en/training/hf.co/join) if you don’t have one already): + +``` +huggingface-cli login +``` + +Make sure you have the `MODEL_DIR`,`OUTPUT_DIR` and `HUB_MODEL_ID` environment variables set. The `OUTPUT_DIR` and `HUB_MODEL_ID` variables specify where to save the model to on the Hub: + +``` +export MODEL_DIR="runwayml/stable-diffusion-v1-5" +export OUTPUT_DIR="control_out" +export HUB_MODEL_ID="fill-circle-controlnet" +``` + +And finally start the training + +``` +python3 train_controlnet_flax.py \ + --pretrained_model_name_or_path=$MODEL_DIR \ + --output_dir=$OUTPUT_DIR \ + --dataset_name=fusing/fill50k \ + --resolution=512 \ + --learning_rate=1e-5 \ + --validation_image "./conditioning_image_1.png" "./conditioning_image_2.png" \ + --validation_prompt "red circle with blue background" "cyan circle with brown floral background" \ + --validation_steps=1000 \ + --train_batch_size=2 \ + --revision="non-ema" \ + --from_pt \ + --report_to="wandb" \ + --max_train_steps=10000 \ + --push_to_hub \ + --hub_model_id=$HUB_MODEL_ID + ``` + +Since we passed the `--push_to_hub` flag, it will automatically create a model repo under your huggingface account based on `$HUB_MODEL_ID`. By the end of training, the final checkpoint will be automatically stored on the hub. You can find an example model repo [here](https://huggingface.co/YiYiXu/fill-circle-controlnet). diff --git a/examples/controlnet/train_controlnet_flax.py b/examples/controlnet/train_controlnet_flax.py new file mode 100644 index 000000000000..c6c95170da2d --- /dev/null +++ b/examples/controlnet/train_controlnet_flax.py @@ -0,0 +1,1001 @@ +#!/usr/bin/env python +# coding=utf-8 +# Copyright 2023 The HuggingFace Inc. 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 + +import argparse +import logging +import math +import os +import random +from pathlib import Path +from typing import Optional + +import jax +import jax.numpy as jnp +import numpy as np +import optax +import torch +import torch.utils.checkpoint +import transformers +from datasets import load_dataset +from flax import jax_utils +from flax.core.frozen_dict import unfreeze +from flax.training import train_state +from flax.training.common_utils import shard +from huggingface_hub import HfFolder, Repository, create_repo, whoami +from PIL import Image +from torchvision import transforms +from tqdm.auto import tqdm +from transformers import CLIPTokenizer, FlaxCLIPTextModel, set_seed + +from diffusers import ( + FlaxAutoencoderKL, + FlaxControlNetModel, + FlaxDDPMScheduler, + FlaxStableDiffusionControlNetPipeline, + FlaxUNet2DConditionModel, +) +from diffusers.utils import check_min_version, is_wandb_available + + +if is_wandb_available(): + import wandb + +# Will error if the minimal version of diffusers is not installed. Remove at your own risks. +check_min_version("0.15.0.dev0") + +logger = logging.getLogger(__name__) + + +def image_grid(imgs, rows, cols): + assert len(imgs) == rows * cols + + w, h = imgs[0].size + grid = Image.new("RGB", size=(cols * w, rows * h)) + grid_w, grid_h = grid.size + + for i, img in enumerate(imgs): + grid.paste(img, box=(i % cols * w, i // cols * h)) + return grid + + +def log_validation(controlnet, controlnet_params, tokenizer, args, rng, weight_dtype): + logger.info("Running validation... ") + + pipeline, params = FlaxStableDiffusionControlNetPipeline.from_pretrained( + args.pretrained_model_name_or_path, + tokenizer=tokenizer, + controlnet=controlnet, + safety_checker=None, + dtype=weight_dtype, + revision=args.revision, + from_pt=args.from_pt, + ) + params = jax_utils.replicate(params) + params["controlnet"] = controlnet_params + + num_samples = jax.device_count() + prng_seed = jax.random.split(rng, jax.device_count()) + + if len(args.validation_image) == len(args.validation_prompt): + validation_images = args.validation_image + validation_prompts = args.validation_prompt + elif len(args.validation_image) == 1: + validation_images = args.validation_image * len(args.validation_prompt) + validation_prompts = args.validation_prompt + elif len(args.validation_prompt) == 1: + validation_images = args.validation_image + validation_prompts = args.validation_prompt * len(args.validation_image) + else: + raise ValueError( + "number of `args.validation_image` and `args.validation_prompt` should be checked in `parse_args`" + ) + + image_logs = [] + + for validation_prompt, validation_image in zip(validation_prompts, validation_images): + prompts = num_samples * [validation_prompt] + prompt_ids = pipeline.prepare_text_inputs(prompts) + prompt_ids = shard(prompt_ids) + + validation_image = Image.open(validation_image) + processed_image = pipeline.prepare_image_inputs(num_samples * [validation_image]) + processed_image = shard(processed_image) + images = pipeline( + prompt_ids=prompt_ids, + image=processed_image, + params=params, + prng_seed=prng_seed, + num_inference_steps=50, + jit=True, + ).images + + images = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]) + images = pipeline.numpy_to_pil(images) + + image_logs.append( + {"validation_image": validation_image, "images": images, "validation_prompt": validation_prompt} + ) + + if args.report_to == "wandb": + formatted_images = [] + for log in image_logs: + images = log["images"] + validation_prompt = log["validation_prompt"] + validation_image = log["validation_image"] + + formatted_images.append(wandb.Image(validation_image, caption="Controlnet conditioning")) + for image in images: + image = wandb.Image(image, caption=validation_prompt) + formatted_images.append(image) + + wandb.log({"validation": formatted_images}) + else: + logger.warn(f"image logging not implemented for {args.report_to}") + + return image_logs + + +def save_model_card(repo_name, image_logs=None, base_model=str, repo_folder=None): + img_str = "" + for i, log in enumerate(image_logs): + images = log["images"] + validation_prompt = log["validation_prompt"] + validation_image = log["validation_image"] + validation_image.save(os.path.join(repo_folder, "image_control.png")) + img_str += f"prompt: {validation_prompt}\n" + images = [validation_image] + images + image_grid(images, 1, len(images)).save(os.path.join(repo_folder, f"images_{i}.png")) + img_str += f"![images_{i})](./images_{i}.png)\n" + + yaml = f""" +--- +license: creativeml-openrail-m +base_model: {base_model} +tags: +- stable-diffusion +- stable-diffusion-diffusers +- text-to-image +- diffusers +- controlnet +inference: true +--- + """ + model_card = f""" +# controlnet- {repo_name} + +These are controlnet weights trained on {base_model} with new type of conditioning. You can find some example images in the following. \n +{img_str} +""" + with open(os.path.join(repo_folder, "README.md"), "w") as f: + f.write(yaml + model_card) + + +def parse_args(): + parser = argparse.ArgumentParser(description="Simple example of a training script.") + parser.add_argument( + "--pretrained_model_name_or_path", + type=str, + required=True, + help="Path to pretrained model or model identifier from huggingface.co/models.", + ) + parser.add_argument( + "--controlnet_model_name_or_path", + type=str, + default=None, + help="Path to pretrained controlnet model or model identifier from huggingface.co/models." + " If not specified controlnet weights are initialized from unet.", + ) + parser.add_argument( + "--revision", + type=str, + default=None, + help="Revision of pretrained model identifier from huggingface.co/models.", + ) + parser.add_argument( + "--from_pt", + action="store_true", + help="Load the pretrained model from a pytorch checkpoint.", + ) + parser.add_argument( + "--tokenizer_name", + type=str, + default=None, + help="Pretrained tokenizer name or path if not the same as model_name", + ) + parser.add_argument( + "--output_dir", + type=str, + default="controlnet-model", + help="The output directory where the model predictions and checkpoints will be written.", + ) + parser.add_argument( + "--cache_dir", + type=str, + default=None, + help="The directory where the downloaded models and datasets will be stored.", + ) + parser.add_argument("--seed", type=int, default=0, help="A seed for reproducible training.") + parser.add_argument( + "--resolution", + type=int, + default=512, + help=( + "The resolution for input images, all the images in the train/validation dataset will be resized to this" + " resolution" + ), + ) + parser.add_argument( + "--train_batch_size", type=int, default=1, help="Batch size (per device) for the training dataloader." + ) + parser.add_argument("--num_train_epochs", type=int, default=100) + parser.add_argument( + "--max_train_steps", + type=int, + default=None, + help="Total number of training steps to perform.", + ) + parser.add_argument( + "--learning_rate", + type=float, + default=1e-4, + help="Initial learning rate (after the potential warmup period) to use.", + ) + parser.add_argument( + "--scale_lr", + action="store_true", + help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.", + ) + parser.add_argument( + "--lr_scheduler", + type=str, + default="constant", + help=( + 'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",' + ' "constant", "constant_with_warmup"]' + ), + ) + parser.add_argument( + "--dataloader_num_workers", + type=int, + default=0, + help=( + "Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process." + ), + ) + parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.") + parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.") + parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.") + parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer") + parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.") + parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.") + parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.") + parser.add_argument( + "--hub_model_id", + type=str, + default=None, + help="The name of the repository to keep in sync with the local `output_dir`.", + ) + parser.add_argument( + "--logging_dir", + type=str, + default="logs", + help=( + "[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to" + " *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***." + ), + ) + parser.add_argument( + "--logging_steps", + type=int, + default=100, + help=("log training metric every X steps to `--report_t`"), + ) + parser.add_argument( + "--report_to", + type=str, + default="tensorboard", + help=( + 'The integration to report the results and logs to. Supported platforms are `"tensorboard"`' + ' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.' + ), + ) + parser.add_argument( + "--mixed_precision", + type=str, + default="no", + choices=["no", "fp16", "bf16"], + help=( + "Whether to use mixed precision. Choose" + "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10." + "and an Nvidia Ampere GPU." + ), + ) + parser.add_argument( + "--dataset_name", + type=str, + default=None, + help=( + "The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private," + " dataset). It can also be a path pointing to a local copy of a dataset in your filesystem," + " or to a folder containing files that 🤗 Datasets can understand." + ), + ) + parser.add_argument( + "--dataset_config_name", + type=str, + default=None, + help="The config of the Dataset, leave as None if there's only one config.", + ) + parser.add_argument( + "--train_data_dir", + type=str, + default=None, + help=( + "A folder containing the training data. Folder contents must follow the structure described in" + " https://huggingface.co/docs/datasets/image_dataset#imagefolder. In particular, a `metadata.jsonl` file" + " must exist to provide the captions for the images. Ignored if `dataset_name` is specified." + ), + ) + parser.add_argument( + "--image_column", type=str, default="image", help="The column of the dataset containing the target image." + ) + parser.add_argument( + "--conditioning_image_column", + type=str, + default="conditioning_image", + help="The column of the dataset containing the controlnet conditioning image.", + ) + parser.add_argument( + "--caption_column", + type=str, + default="text", + help="The column of the dataset containing a caption or a list of captions.", + ) + parser.add_argument( + "--max_train_samples", + type=int, + default=None, + help=( + "For debugging purposes or quicker training, truncate the number of training examples to this " + "value if set." + ), + ) + parser.add_argument( + "--proportion_empty_prompts", + type=float, + default=0, + help="Proportion of image prompts to be replaced with empty strings. Defaults to 0 (no prompt replacement).", + ) + parser.add_argument( + "--validation_prompt", + type=str, + default=None, + nargs="+", + help=( + "A set of prompts evaluated every `--validation_steps` and logged to `--report_to`." + " Provide either a matching number of `--validation_image`s, a single `--validation_image`" + " to be used with all prompts, or a single prompt that will be used with all `--validation_image`s." + ), + ) + parser.add_argument( + "--validation_image", + type=str, + default=None, + nargs="+", + help=( + "A set of paths to the controlnet conditioning image be evaluated every `--validation_steps`" + " and logged to `--report_to`. Provide either a matching number of `--validation_prompt`s, a" + " a single `--validation_prompt` to be used with all `--validation_image`s, or a single" + " `--validation_image` that will be used with all `--validation_prompt`s." + ), + ) + parser.add_argument( + "--validation_steps", + type=int, + default=100, + help=( + "Run validation every X steps. Validation consists of running the prompt" + " `args.validation_prompt` and logging the images." + ), + ) + parser.add_argument( + "--tracker_project_name", + type=str, + default="train_controlnet_flax", + help=("The `project` argument passed to wandb"), + ) + parser.add_argument( + "--gradient_accumulation_steps", type=int, default=1, help="Number of steps to accumulate gradients over" + ) + parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank") + + args = parser.parse_args() + env_local_rank = int(os.environ.get("LOCAL_RANK", -1)) + if env_local_rank != -1 and env_local_rank != args.local_rank: + args.local_rank = env_local_rank + + # Sanity checks + if args.dataset_name is None and args.train_data_dir is None: + raise ValueError("Need either a dataset name or a training folder.") + if args.dataset_name is not None and args.train_data_dir is not None: + raise ValueError("Specify only one of `--dataset_name` or `--train_data_dir`") + + if args.proportion_empty_prompts < 0 or args.proportion_empty_prompts > 1: + raise ValueError("`--proportion_empty_prompts` must be in the range [0, 1].") + + if args.validation_prompt is not None and args.validation_image is None: + raise ValueError("`--validation_image` must be set if `--validation_prompt` is set") + + if args.validation_prompt is None and args.validation_image is not None: + raise ValueError("`--validation_prompt` must be set if `--validation_image` is set") + + if ( + args.validation_image is not None + and args.validation_prompt is not None + and len(args.validation_image) != 1 + and len(args.validation_prompt) != 1 + and len(args.validation_image) != len(args.validation_prompt) + ): + raise ValueError( + "Must provide either 1 `--validation_image`, 1 `--validation_prompt`," + " or the same number of `--validation_prompt`s and `--validation_image`s" + ) + + return args + + +def make_train_dataset(args, tokenizer): + # Get the datasets: you can either provide your own training and evaluation files (see below) + # or specify a Dataset from the hub (the dataset will be downloaded automatically from the datasets Hub). + + # In distributed training, the load_dataset function guarantees that only one local process can concurrently + # download the dataset. + if args.dataset_name is not None: + # Downloading and loading a dataset from the hub. + dataset = load_dataset( + args.dataset_name, + args.dataset_config_name, + cache_dir=args.cache_dir, + ) + else: + data_files = {} + if args.train_data_dir is not None: + data_files["train"] = os.path.join(args.train_data_dir, "**") + dataset = load_dataset( + "imagefolder", + data_files=data_files, + cache_dir=args.cache_dir, + ) + # See more about loading custom images at + # https://huggingface.co/docs/datasets/v2.4.0/en/image_load#imagefolder + + # Preprocessing the datasets. + # We need to tokenize inputs and targets. + column_names = dataset["train"].column_names + + # 6. Get the column names for input/target. + if args.image_column is None: + image_column = column_names[0] + logger.info(f"image column defaulting to {image_column}") + else: + image_column = args.image_column + if image_column not in column_names: + raise ValueError( + f"`--image_column` value '{args.image_column}' not found in dataset columns. Dataset columns are: {', '.join(column_names)}" + ) + + if args.caption_column is None: + caption_column = column_names[1] + logger.info(f"caption column defaulting to {caption_column}") + else: + caption_column = args.caption_column + if caption_column not in column_names: + raise ValueError( + f"`--caption_column` value '{args.caption_column}' not found in dataset columns. Dataset columns are: {', '.join(column_names)}" + ) + + if args.conditioning_image_column is None: + conditioning_image_column = column_names[2] + logger.info(f"conditioning image column defaulting to {caption_column}") + else: + conditioning_image_column = args.conditioning_image_column + if conditioning_image_column not in column_names: + raise ValueError( + f"`--conditioning_image_column` value '{args.conditioning_image_column}' not found in dataset columns. Dataset columns are: {', '.join(column_names)}" + ) + + def tokenize_captions(examples, is_train=True): + captions = [] + for caption in examples[caption_column]: + if random.random() < args.proportion_empty_prompts: + captions.append("") + elif isinstance(caption, str): + captions.append(caption) + elif isinstance(caption, (list, np.ndarray)): + # take a random caption if there are multiple + captions.append(random.choice(caption) if is_train else caption[0]) + else: + raise ValueError( + f"Caption column `{caption_column}` should contain either strings or lists of strings." + ) + inputs = tokenizer( + captions, max_length=tokenizer.model_max_length, padding="max_length", truncation=True, return_tensors="pt" + ) + return inputs.input_ids + + image_transforms = transforms.Compose( + [ + transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR), + transforms.ToTensor(), + transforms.Normalize([0.5], [0.5]), + ] + ) + + conditioning_image_transforms = transforms.Compose( + [ + transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR), + transforms.ToTensor(), + ] + ) + + def preprocess_train(examples): + images = [image.convert("RGB") for image in examples[image_column]] + images = [image_transforms(image) for image in images] + + conditioning_images = [image.convert("RGB") for image in examples[conditioning_image_column]] + conditioning_images = [conditioning_image_transforms(image) for image in conditioning_images] + + examples["pixel_values"] = images + examples["conditioning_pixel_values"] = conditioning_images + examples["input_ids"] = tokenize_captions(examples) + + return examples + + if jax.process_index() == 0: + if args.max_train_samples is not None: + dataset["train"] = dataset["train"].shuffle(seed=args.seed).select(range(args.max_train_samples)) + # Set the training transforms + train_dataset = dataset["train"].with_transform(preprocess_train) + + return train_dataset + + +def collate_fn(examples): + pixel_values = torch.stack([example["pixel_values"] for example in examples]) + pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float() + + conditioning_pixel_values = torch.stack([example["conditioning_pixel_values"] for example in examples]) + conditioning_pixel_values = conditioning_pixel_values.to(memory_format=torch.contiguous_format).float() + + input_ids = torch.stack([example["input_ids"] for example in examples]) + + batch = { + "pixel_values": pixel_values, + "conditioning_pixel_values": conditioning_pixel_values, + "input_ids": input_ids, + } + batch = {k: v.numpy() for k, v in batch.items()} + return batch + + +def get_full_repo_name(model_id: str, organization: Optional[str] = None, token: Optional[str] = None): + if token is None: + token = HfFolder.get_token() + if organization is None: + username = whoami(token)["name"] + return f"{username}/{model_id}" + else: + return f"{organization}/{model_id}" + + +def get_params_to_save(params): + return jax.device_get(jax.tree_util.tree_map(lambda x: x[0], params)) + + +def main(): + args = parse_args() + + logging.basicConfig( + format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", + datefmt="%m/%d/%Y %H:%M:%S", + level=logging.INFO, + ) + # Setup logging, we only want one process per machine to log things on the screen. + logger.setLevel(logging.INFO if jax.process_index() == 0 else logging.ERROR) + if jax.process_index() == 0: + transformers.utils.logging.set_verbosity_info() + else: + transformers.utils.logging.set_verbosity_error() + + # wandb init + if jax.process_index() == 0 and args.report_to == "wandb": + wandb.init( + project=args.tracker_project_name, + job_type="train", + config=args, + ) + + if args.seed is not None: + set_seed(args.seed) + + rng = jax.random.PRNGKey(0) + + # Handle the repository creation + if jax.process_index() == 0: + if args.push_to_hub: + if args.hub_model_id is None: + repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token) + else: + repo_name = args.hub_model_id + repo_url = create_repo(repo_name, exist_ok=True, token=args.hub_token) + repo = Repository(args.output_dir, clone_from=repo_url, token=args.hub_token) + + with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore: + if "step_*" not in gitignore: + gitignore.write("step_*\n") + if "epoch_*" not in gitignore: + gitignore.write("epoch_*\n") + elif args.output_dir is not None: + os.makedirs(args.output_dir, exist_ok=True) + + # Load the tokenizer and add the placeholder token as a additional special token + if args.tokenizer_name: + tokenizer = CLIPTokenizer.from_pretrained(args.tokenizer_name) + elif args.pretrained_model_name_or_path: + tokenizer = CLIPTokenizer.from_pretrained( + args.pretrained_model_name_or_path, subfolder="tokenizer", revision=args.revision + ) + else: + raise NotImplementedError("No tokenizer specified!") + + # Get the datasets: you can either provide your own training and evaluation files (see below) + train_dataset = make_train_dataset(args, tokenizer) + total_train_batch_size = args.train_batch_size * jax.local_device_count() * args.gradient_accumulation_steps + + train_dataloader = torch.utils.data.DataLoader( + train_dataset, + shuffle=True, + collate_fn=collate_fn, + batch_size=total_train_batch_size, + num_workers=args.dataloader_num_workers, + drop_last=True, + ) + + weight_dtype = jnp.float32 + if args.mixed_precision == "fp16": + weight_dtype = jnp.float16 + elif args.mixed_precision == "bf16": + weight_dtype = jnp.bfloat16 + + # Load models and create wrapper for stable diffusion + text_encoder = FlaxCLIPTextModel.from_pretrained( + args.pretrained_model_name_or_path, + subfolder="text_encoder", + dtype=weight_dtype, + revision=args.revision, + from_pt=args.from_pt, + ) + vae, vae_params = FlaxAutoencoderKL.from_pretrained( + args.pretrained_model_name_or_path, + revision=args.revision, + subfolder="vae", + dtype=weight_dtype, + from_pt=args.from_pt, + ) + unet, unet_params = FlaxUNet2DConditionModel.from_pretrained( + args.pretrained_model_name_or_path, + subfolder="unet", + dtype=weight_dtype, + revision=args.revision, + from_pt=args.from_pt, + ) + + if args.controlnet_model_name_or_path: + logger.info("Loading existing controlnet weights") + controlnet, controlnet_params = FlaxControlNetModel.from_pretrained( + args.controlnet_model_name_or_path, from_pt=True, dtype=jnp.float32 + ) + else: + logger.info("Initializing controlnet weights from unet") + rng, rng_params = jax.random.split(rng) + + controlnet = FlaxControlNetModel( + in_channels=unet.config.in_channels, + down_block_types=unet.config.down_block_types, + only_cross_attention=unet.config.only_cross_attention, + block_out_channels=unet.config.block_out_channels, + layers_per_block=unet.config.layers_per_block, + attention_head_dim=unet.config.attention_head_dim, + cross_attention_dim=unet.config.cross_attention_dim, + use_linear_projection=unet.config.use_linear_projection, + flip_sin_to_cos=unet.config.flip_sin_to_cos, + freq_shift=unet.config.freq_shift, + ) + controlnet_params = controlnet.init_weights(rng=rng_params) + controlnet_params = unfreeze(controlnet_params) + for key in [ + "conv_in", + "time_embedding", + "down_blocks_0", + "down_blocks_1", + "down_blocks_2", + "down_blocks_3", + "mid_block", + ]: + controlnet_params[key] = unet_params[key] + + # Optimization + if args.scale_lr: + args.learning_rate = args.learning_rate * total_train_batch_size + + constant_scheduler = optax.constant_schedule(args.learning_rate) + + adamw = optax.adamw( + learning_rate=constant_scheduler, + b1=args.adam_beta1, + b2=args.adam_beta2, + eps=args.adam_epsilon, + weight_decay=args.adam_weight_decay, + ) + + optimizer = optax.chain( + optax.clip_by_global_norm(args.max_grad_norm), + adamw, + ) + + state = train_state.TrainState.create(apply_fn=controlnet.__call__, params=controlnet_params, tx=optimizer) + + noise_scheduler, noise_scheduler_state = FlaxDDPMScheduler.from_pretrained( + args.pretrained_model_name_or_path, subfolder="scheduler" + ) + + # Initialize our training + validation_rng, train_rngs = jax.random.split(rng) + train_rngs = jax.random.split(train_rngs, jax.local_device_count()) + + def train_step(state, unet_params, text_encoder_params, vae_params, batch, train_rng): + # reshape batch, add grad_step_dim if gradient_accumulation_steps > 1 + if args.gradient_accumulation_steps > 1: + grad_steps = args.gradient_accumulation_steps + batch = jax.tree_map(lambda x: x.reshape((grad_steps, x.shape[0] // grad_steps) + x.shape[1:]), batch) + + def compute_loss(params, minibatch, sample_rng): + # Convert images to latent space + vae_outputs = vae.apply( + {"params": vae_params}, minibatch["pixel_values"], deterministic=True, method=vae.encode + ) + latents = vae_outputs.latent_dist.sample(sample_rng) + # (NHWC) -> (NCHW) + latents = jnp.transpose(latents, (0, 3, 1, 2)) + latents = latents * vae.config.scaling_factor + + # Sample noise that we'll add to the latents + noise_rng, timestep_rng = jax.random.split(sample_rng) + noise = jax.random.normal(noise_rng, latents.shape) + # Sample a random timestep for each image + bsz = latents.shape[0] + timesteps = jax.random.randint( + timestep_rng, + (bsz,), + 0, + noise_scheduler.config.num_train_timesteps, + ) + + # Add noise to the latents according to the noise magnitude at each timestep + # (this is the forward diffusion process) + noisy_latents = noise_scheduler.add_noise(noise_scheduler_state, latents, noise, timesteps) + + # Get the text embedding for conditioning + encoder_hidden_states = text_encoder( + minibatch["input_ids"], + params=text_encoder_params, + train=False, + )[0] + + controlnet_cond = minibatch["conditioning_pixel_values"] + + # Predict the noise residual and compute loss + down_block_res_samples, mid_block_res_sample = controlnet.apply( + {"params": params}, + noisy_latents, + timesteps, + encoder_hidden_states, + controlnet_cond, + train=True, + return_dict=False, + ) + + model_pred = unet.apply( + {"params": unet_params}, + noisy_latents, + timesteps, + encoder_hidden_states, + down_block_additional_residuals=down_block_res_samples, + mid_block_additional_residual=mid_block_res_sample, + ).sample + + # Get the target for loss depending on the prediction type + if noise_scheduler.config.prediction_type == "epsilon": + target = noise + elif noise_scheduler.config.prediction_type == "v_prediction": + target = noise_scheduler.get_velocity(noise_scheduler_state, latents, noise, timesteps) + else: + raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}") + + loss = (target - model_pred) ** 2 + loss = loss.mean() + + return loss + + grad_fn = jax.value_and_grad(compute_loss) + + # get a minibatch (one gradient accumulation slice) + def get_minibatch(batch, grad_idx): + return jax.tree_util.tree_map( + lambda x: jax.lax.dynamic_index_in_dim(x, grad_idx, keepdims=False), + batch, + ) + + def loss_and_grad(grad_idx, train_rng): + # create minibatch for the grad step + minibatch = get_minibatch(batch, grad_idx) if grad_idx is not None else batch + sample_rng, train_rng = jax.random.split(train_rng, 2) + loss, grad = grad_fn(state.params, minibatch, sample_rng) + return loss, grad, train_rng + + if args.gradient_accumulation_steps == 1: + loss, grad, new_train_rng = loss_and_grad(None, train_rng) + else: + init_loss_grad_rng = ( + 0.0, # initial value for cumul_loss + jax.tree_map(jnp.zeros_like, state.params), # initial value for cumul_grad + train_rng, # initial value for train_rng + ) + + def cumul_grad_step(grad_idx, loss_grad_rng): + cumul_loss, cumul_grad, train_rng = loss_grad_rng + loss, grad, new_train_rng = loss_and_grad(grad_idx, train_rng) + cumul_loss, cumul_grad = jax.tree_map(jnp.add, (cumul_loss, cumul_grad), (loss, grad)) + return cumul_loss, cumul_grad, new_train_rng + + loss, grad, new_train_rng = jax.lax.fori_loop( + 0, + args.gradient_accumulation_steps, + cumul_grad_step, + init_loss_grad_rng, + ) + loss, grad = jax.tree_map(lambda x: x / args.gradient_accumulation_steps, (loss, grad)) + + grad = jax.lax.pmean(grad, "batch") + + new_state = state.apply_gradients(grads=grad) + + metrics = {"loss": loss} + metrics = jax.lax.pmean(metrics, axis_name="batch") + + return new_state, metrics, new_train_rng + + # Create parallel version of the train step + p_train_step = jax.pmap(train_step, "batch", donate_argnums=(0,)) + + # Replicate the train state on each device + state = jax_utils.replicate(state) + unet_params = jax_utils.replicate(unet_params) + text_encoder_params = jax_utils.replicate(text_encoder.params) + vae_params = jax_utils.replicate(vae_params) + + # Train! + num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) + + # Scheduler and math around the number of training steps. + if args.max_train_steps is None: + args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch + + args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch) + + logger.info("***** Running training *****") + logger.info(f" Num examples = {len(train_dataset)}") + logger.info(f" Num Epochs = {args.num_train_epochs}") + logger.info(f" Instantaneous batch size per device = {args.train_batch_size}") + logger.info(f" Total train batch size (w. parallel & distributed) = {total_train_batch_size}") + logger.info(f" Total optimization steps = {args.num_train_epochs * num_update_steps_per_epoch}") + + if jax.process_index() == 0: + wandb.define_metric("*", step_metric="train/step") + wandb.config.update( + { + "num_train_examples": len(train_dataset), + "total_train_batch_size": total_train_batch_size, + "total_optimization_step": args.num_train_epochs * num_update_steps_per_epoch, + "num_devices": jax.device_count(), + } + ) + + global_step = 0 + epochs = tqdm( + range(args.num_train_epochs), + desc="Epoch ... ", + position=0, + disable=jax.process_index() > 0, + ) + for epoch in epochs: + # ======================== Training ================================ + + train_metrics = [] + + steps_per_epoch = len(train_dataset) // total_train_batch_size + train_step_progress_bar = tqdm( + total=steps_per_epoch, + desc="Training...", + position=1, + leave=False, + disable=jax.process_index() > 0, + ) + # train + for batch in train_dataloader: + batch = shard(batch) + state, train_metric, train_rngs = p_train_step( + state, unet_params, text_encoder_params, vae_params, batch, train_rngs + ) + train_metrics.append(train_metric) + + train_step_progress_bar.update(1) + + global_step += 1 + if global_step >= args.max_train_steps: + break + + if ( + args.validation_prompt is not None + and global_step % args.validation_steps == 0 + and jax.process_index() == 0 + ): + _ = log_validation(controlnet, state.params, tokenizer, args, validation_rng, weight_dtype) + + if global_step % args.logging_steps == 0 and jax.process_index() == 0: + if args.report_to == "wandb": + wandb.log( + { + "train/step": global_step, + "train/epoch": epoch, + "train/loss": jax_utils.unreplicate(train_metric)["loss"], + } + ) + + train_metric = jax_utils.unreplicate(train_metric) + train_step_progress_bar.close() + epochs.write(f"Epoch... ({epoch + 1}/{args.num_train_epochs} | Loss: {train_metric['loss']})") + + # Create the pipeline using using the trained modules and save it. + if jax.process_index() == 0: + image_logs = log_validation(controlnet, state.params, tokenizer, args, validation_rng, weight_dtype) + + controlnet.save_pretrained( + args.output_dir, + params=get_params_to_save(state.params), + ) + + if args.push_to_hub: + save_model_card( + repo_name, + image_logs=image_logs, + base_model=args.pretrained_model_name_or_path, + repo_folder=args.output_dir, + ) + repo.push_to_hub(commit_message="End of training", blocking=False, auto_lfs_prune=True) + + +if __name__ == "__main__": + main() From 81125d8499b82da80e997c45c72ea54ebd8b8abb Mon Sep 17 00:00:00 2001 From: Pedro Cuenca Date: Tue, 28 Mar 2023 09:03:21 +0200 Subject: [PATCH 09/56] Make dynamo wrapped modules work with save_pretrained (#2726) * Workaround for saving dynamo-wrapped models. * Accept suggestion from code review Co-authored-by: Patrick von Platen * Apply workaround when overriding pipeline components. * Ensure the correct config.json is saved to disk. Instead of the dynamo class. * Save correct module (not compiled one) * Add test * style * fix docstrings * Go back to using string comparisons. PyTorch CPU does not have _dynamo. * Simple test for save_pretrained of compiled models. * Helper function to test whether module is compiled. --------- Co-authored-by: Patrick von Platen --- src/diffusers/pipelines/pipeline_utils.py | 20 +++++++++- src/diffusers/utils/__init__.py | 3 +- src/diffusers/utils/testing_utils.py | 10 +++++ src/diffusers/utils/torch_utils.py | 9 ++++- tests/test_modeling_common.py | 16 ++++++++ tests/test_pipelines.py | 47 +++++++++++++++++++++-- 6 files changed, 99 insertions(+), 6 deletions(-) diff --git a/src/diffusers/pipelines/pipeline_utils.py b/src/diffusers/pipelines/pipeline_utils.py index d3578745b8b3..a03c454e9244 100644 --- a/src/diffusers/pipelines/pipeline_utils.py +++ b/src/diffusers/pipelines/pipeline_utils.py @@ -50,6 +50,7 @@ get_class_from_dynamic_module, is_accelerate_available, is_accelerate_version, + is_compiled_module, is_safetensors_available, is_torch_version, is_transformers_available, @@ -255,7 +256,14 @@ def maybe_raise_or_warn( if class_candidate is not None and issubclass(class_obj, class_candidate): expected_class_obj = class_candidate - if not issubclass(passed_class_obj[name].__class__, expected_class_obj): + # Dynamo wraps the original model in a private class. + # I didn't find a public API to get the original class. + sub_model = passed_class_obj[name] + model_cls = sub_model.__class__ + if is_compiled_module(sub_model): + model_cls = sub_model._orig_mod.__class__ + + if not issubclass(model_cls, expected_class_obj): raise ValueError( f"{passed_class_obj[name]} is of type: {type(passed_class_obj[name])}, but should be" f" {expected_class_obj}" @@ -419,6 +427,10 @@ def register_modules(self, **kwargs): if module is None: register_dict = {name: (None, None)} else: + # register the original module, not the dynamo compiled one + if is_compiled_module(module): + module = module._orig_mod + library = module.__module__.split(".")[0] # check if the module is a pipeline module @@ -484,6 +496,12 @@ def is_saveable_module(name, value): sub_model = getattr(self, pipeline_component_name) model_cls = sub_model.__class__ + # Dynamo wraps the original model in a private class. + # I didn't find a public API to get the original class. + if is_compiled_module(sub_model): + sub_model = sub_model._orig_mod + model_cls = sub_model.__class__ + save_method_name = None # search for the model's base class in LOADABLE_CLASSES for library_name, library_classes in LOADABLE_CLASSES.items(): diff --git a/src/diffusers/utils/__init__.py b/src/diffusers/utils/__init__.py index 14e975c48726..615804c91a19 100644 --- a/src/diffusers/utils/__init__.py +++ b/src/diffusers/utils/__init__.py @@ -74,7 +74,7 @@ from .logging import get_logger from .outputs import BaseOutput from .pil_utils import PIL_INTERPOLATION -from .torch_utils import randn_tensor +from .torch_utils import is_compiled_module, randn_tensor if is_torch_available(): @@ -86,6 +86,7 @@ nightly, parse_flag_from_env, print_tensor_test, + require_torch_2, require_torch_gpu, skip_mps, slow, diff --git a/src/diffusers/utils/testing_utils.py b/src/diffusers/utils/testing_utils.py index bf8109ae5cc1..afea0540b765 100644 --- a/src/diffusers/utils/testing_utils.py +++ b/src/diffusers/utils/testing_utils.py @@ -25,6 +25,7 @@ is_onnx_available, is_opencv_available, is_torch_available, + is_torch_version, ) from .logging import get_logger @@ -165,6 +166,15 @@ def require_torch(test_case): return unittest.skipUnless(is_torch_available(), "test requires PyTorch")(test_case) +def require_torch_2(test_case): + """ + Decorator marking a test that requires PyTorch 2. These tests are skipped when it isn't installed. + """ + return unittest.skipUnless(is_torch_available() and is_torch_version(">=", "2.0.0"), "test requires PyTorch 2")( + test_case + ) + + def require_torch_gpu(test_case): """Decorator marking a test that requires CUDA and PyTorch.""" return unittest.skipUnless(is_torch_available() and torch_device == "cuda", "test requires PyTorch+CUDA")( diff --git a/src/diffusers/utils/torch_utils.py b/src/diffusers/utils/torch_utils.py index 113e64c16bac..b9815cbceede 100644 --- a/src/diffusers/utils/torch_utils.py +++ b/src/diffusers/utils/torch_utils.py @@ -17,7 +17,7 @@ from typing import List, Optional, Tuple, Union from . import logging -from .import_utils import is_torch_available +from .import_utils import is_torch_available, is_torch_version if is_torch_available(): @@ -68,3 +68,10 @@ def randn_tensor( latents = torch.randn(shape, generator=generator, device=rand_device, dtype=dtype, layout=layout).to(device) return latents + + +def is_compiled_module(module): + """Check whether the module was compiled with torch.compile()""" + if is_torch_version("<", "2.0.0") or not hasattr(torch, "_dynamo"): + return False + return isinstance(module, torch._dynamo.eval_frame.OptimizedModule) diff --git a/tests/test_modeling_common.py b/tests/test_modeling_common.py index 932c147027d3..1c45ce11b8d2 100644 --- a/tests/test_modeling_common.py +++ b/tests/test_modeling_common.py @@ -27,6 +27,7 @@ from diffusers.models import UNet2DConditionModel from diffusers.training_utils import EMAModel from diffusers.utils import torch_device +from diffusers.utils.testing_utils import require_torch_gpu class ModelUtilsTest(unittest.TestCase): @@ -167,6 +168,21 @@ def test_from_save_pretrained_variant(self): max_diff = (image - new_image).abs().sum().item() self.assertLessEqual(max_diff, 5e-5, "Models give different forward passes") + @require_torch_gpu + def test_from_save_pretrained_dynamo(self): + init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common() + + model = self.model_class(**init_dict) + model.to(torch_device) + model = torch.compile(model) + + with tempfile.TemporaryDirectory() as tmpdirname: + model.save_pretrained(tmpdirname) + new_model = self.model_class.from_pretrained(tmpdirname) + new_model.to(torch_device) + + assert new_model.__class__ == self.model_class + def test_from_save_pretrained_dtype(self): init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common() diff --git a/tests/test_pipelines.py b/tests/test_pipelines.py index cb5984885cea..2616223c5447 100644 --- a/tests/test_pipelines.py +++ b/tests/test_pipelines.py @@ -54,7 +54,16 @@ logging, ) from diffusers.schedulers.scheduling_utils import SCHEDULER_CONFIG_NAME -from diffusers.utils import CONFIG_NAME, WEIGHTS_NAME, floats_tensor, is_flax_available, nightly, slow, torch_device +from diffusers.utils import ( + CONFIG_NAME, + WEIGHTS_NAME, + floats_tensor, + is_flax_available, + nightly, + require_torch_2, + slow, + torch_device, +) from diffusers.utils.testing_utils import CaptureLogger, get_tests_dir, load_numpy, require_compel, require_torch_gpu @@ -966,9 +975,41 @@ def test_from_save_pretrained(self): down_block_types=("DownBlock2D", "AttnDownBlock2D"), up_block_types=("AttnUpBlock2D", "UpBlock2D"), ) - schedular = DDPMScheduler(num_train_timesteps=10) + scheduler = DDPMScheduler(num_train_timesteps=10) + + ddpm = DDPMPipeline(model, scheduler) + ddpm.to(torch_device) + ddpm.set_progress_bar_config(disable=None) + + with tempfile.TemporaryDirectory() as tmpdirname: + ddpm.save_pretrained(tmpdirname) + new_ddpm = DDPMPipeline.from_pretrained(tmpdirname) + new_ddpm.to(torch_device) + + generator = torch.Generator(device=torch_device).manual_seed(0) + image = ddpm(generator=generator, num_inference_steps=5, output_type="numpy").images + + generator = torch.Generator(device=torch_device).manual_seed(0) + new_image = new_ddpm(generator=generator, num_inference_steps=5, output_type="numpy").images + + assert np.abs(image - new_image).sum() < 1e-5, "Models don't give the same forward pass" + + @require_torch_2 + def test_from_save_pretrained_dynamo(self): + # 1. Load models + model = UNet2DModel( + block_out_channels=(32, 64), + layers_per_block=2, + sample_size=32, + in_channels=3, + out_channels=3, + down_block_types=("DownBlock2D", "AttnDownBlock2D"), + up_block_types=("AttnUpBlock2D", "UpBlock2D"), + ) + model = torch.compile(model) + scheduler = DDPMScheduler(num_train_timesteps=10) - ddpm = DDPMPipeline(model, schedular) + ddpm = DDPMPipeline(model, scheduler) ddpm.to(torch_device) ddpm.set_progress_bar_config(disable=None) From 42d950174f5da973d3d35e55d3e1e49edf87a35b Mon Sep 17 00:00:00 2001 From: Patrick von Platen Date: Tue, 28 Mar 2023 10:08:28 +0200 Subject: [PATCH 10/56] [Init] Make sure shape mismatches are caught early (#2847) Improve init --- src/diffusers/models/modeling_utils.py | 7 +++++++ tests/test_modeling_common.py | 24 ++++++++++++++++++++++++ 2 files changed, 31 insertions(+) diff --git a/src/diffusers/models/modeling_utils.py b/src/diffusers/models/modeling_utils.py index aa4e2b0ea487..5a5d233fbb4e 100644 --- a/src/diffusers/models/modeling_utils.py +++ b/src/diffusers/models/modeling_utils.py @@ -579,10 +579,17 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P " those weights or else make sure your checkpoint file is correct." ) + empty_state_dict = model.state_dict() for param_name, param in state_dict.items(): accepts_dtype = "dtype" in set( inspect.signature(set_module_tensor_to_device).parameters.keys() ) + + if empty_state_dict[param_name].shape != param.shape: + raise ValueError( + f"Cannot load {pretrained_model_name_or_path} because {param_name} expected shape {empty_state_dict[param_name]}, but got {param.shape}. If you want to instead overwrite randomly initialized weights, please make sure to pass both `low_cpu_mem_usage=False` and `ignore_mismatched_sizes=True`. For more information, see also: https://github.com/huggingface/diffusers/issues/1619#issuecomment-1345604389 as an example." + ) + if accepts_dtype: set_module_tensor_to_device( model, param_name, param_device, value=param, dtype=torch_dtype diff --git a/tests/test_modeling_common.py b/tests/test_modeling_common.py index 1c45ce11b8d2..40aba3b24967 100644 --- a/tests/test_modeling_common.py +++ b/tests/test_modeling_common.py @@ -100,6 +100,30 @@ def test_one_request_upon_cached(self): diffusers.utils.import_utils._safetensors_available = True + def test_weight_overwrite(self): + with tempfile.TemporaryDirectory() as tmpdirname, self.assertRaises(ValueError) as error_context: + UNet2DConditionModel.from_pretrained( + "hf-internal-testing/tiny-stable-diffusion-torch", + subfolder="unet", + cache_dir=tmpdirname, + in_channels=9, + ) + + # make sure that error message states what keys are missing + assert "Cannot load" in str(error_context.exception) + + with tempfile.TemporaryDirectory() as tmpdirname: + model = UNet2DConditionModel.from_pretrained( + "hf-internal-testing/tiny-stable-diffusion-torch", + subfolder="unet", + cache_dir=tmpdirname, + in_channels=9, + low_cpu_mem_usage=False, + ignore_mismatched_sizes=True, + ) + + assert model.config.in_channels == 9 + class ModelTesterMixin: def test_from_save_pretrained(self): From c0afca2d12bd18b8237603feb832c6b453fe9ed4 Mon Sep 17 00:00:00 2001 From: Kashif Rasul Date: Tue, 28 Mar 2023 14:43:24 +0200 Subject: [PATCH 11/56] updated onnx pndm test (#2811) --- .../stable_diffusion/test_onnx_stable_diffusion_img2img.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/pipelines/stable_diffusion/test_onnx_stable_diffusion_img2img.py b/tests/pipelines/stable_diffusion/test_onnx_stable_diffusion_img2img.py index 06e75d035d04..e1aa2f6dc0a1 100644 --- a/tests/pipelines/stable_diffusion/test_onnx_stable_diffusion_img2img.py +++ b/tests/pipelines/stable_diffusion/test_onnx_stable_diffusion_img2img.py @@ -81,7 +81,7 @@ def test_pipeline_pndm(self): image_slice = image[0, -3:, -3:, -1] assert image.shape == (1, 128, 128, 3) - expected_slice = np.array([0.61710, 0.53390, 0.49310, 0.55622, 0.50982, 0.58240, 0.50716, 0.38629, 0.46856]) + expected_slice = np.array([0.61737, 0.54642, 0.53183, 0.54465, 0.52742, 0.60525, 0.49969, 0.40655, 0.48154]) assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-1 From 585f621af2bfc171887e1864b447178df3123241 Mon Sep 17 00:00:00 2001 From: Stax124 <60222162+Stax124@users.noreply.github.com> Date: Tue, 28 Mar 2023 16:06:48 +0200 Subject: [PATCH 12/56] [Stable Diffusion] Allow users to disable Safety checker if loading model from checkpoint (#2768) * Allow user to disable SafetyChecker and enable dtypes if loading models from .ckpt or .safetensors * Fix Import sorting (Ruff error) * Get rid of the dtype convert method as it was implemented all along * Fix the docstring * Fix ruff formatting --------- Co-authored-by: Patrick von Platen --- .../pipelines/stable_diffusion/convert_from_ckpt.py | 12 ++++++++++-- 1 file changed, 10 insertions(+), 2 deletions(-) diff --git a/src/diffusers/pipelines/stable_diffusion/convert_from_ckpt.py b/src/diffusers/pipelines/stable_diffusion/convert_from_ckpt.py index 7fbdbdab1fa9..a16213639526 100644 --- a/src/diffusers/pipelines/stable_diffusion/convert_from_ckpt.py +++ b/src/diffusers/pipelines/stable_diffusion/convert_from_ckpt.py @@ -989,6 +989,7 @@ def download_from_original_stable_diffusion_ckpt( stable_unclip_prior: Optional[str] = None, clip_stats_path: Optional[str] = None, controlnet: Optional[bool] = None, + load_safety_checker: bool = True, ) -> StableDiffusionPipeline: """ Load a Stable Diffusion pipeline object from a CompVis-style `.ckpt`/`.safetensors` file and (ideally) a `.yaml` @@ -1028,6 +1029,8 @@ def download_from_original_stable_diffusion_ckpt( The device to use. Pass `None` to determine automatically. :param from_safetensors: If `checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch. :return: A StableDiffusionPipeline object representing the passed-in `.ckpt`/`.safetensors` file. + load_safety_checker (`bool`, *optional*, defaults to `True`): + Whether to load the safety checker or not. Defaults to `True`. """ if prediction_type == "v-prediction": prediction_type = "v_prediction" @@ -1270,8 +1273,13 @@ def download_from_original_stable_diffusion_ckpt( elif model_type == "FrozenCLIPEmbedder": text_model = convert_ldm_clip_checkpoint(checkpoint) tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14") - safety_checker = StableDiffusionSafetyChecker.from_pretrained("CompVis/stable-diffusion-safety-checker") - feature_extractor = AutoFeatureExtractor.from_pretrained("CompVis/stable-diffusion-safety-checker") + + if load_safety_checker: + safety_checker = StableDiffusionSafetyChecker.from_pretrained("CompVis/stable-diffusion-safety-checker") + feature_extractor = AutoFeatureExtractor.from_pretrained("CompVis/stable-diffusion-safety-checker") + else: + safety_checker = None + feature_extractor = None if controlnet: pipe = StableDiffusionControlNetPipeline( From 8bdf423645b80da612d821f9a0fb2977b96fe448 Mon Sep 17 00:00:00 2001 From: junhsss Date: Tue, 28 Mar 2023 23:58:19 +0900 Subject: [PATCH 13/56] fix KarrasVePipeline bug (#2828) --- .../stochastic_karras_ve/pipeline_stochastic_karras_ve.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/diffusers/pipelines/stochastic_karras_ve/pipeline_stochastic_karras_ve.py b/src/diffusers/pipelines/stochastic_karras_ve/pipeline_stochastic_karras_ve.py index 4535500e2592..2e0ab15eb975 100644 --- a/src/diffusers/pipelines/stochastic_karras_ve/pipeline_stochastic_karras_ve.py +++ b/src/diffusers/pipelines/stochastic_karras_ve/pipeline_stochastic_karras_ve.py @@ -120,7 +120,7 @@ def __call__( sample = (sample / 2 + 0.5).clamp(0, 1) image = sample.cpu().permute(0, 2, 3, 1).numpy() if output_type == "pil": - image = self.numpy_to_pil(sample) + image = self.numpy_to_pil(image) if not return_dict: return (image,) From 0f14335af3a86f40b419b9cadffc801e193f9666 Mon Sep 17 00:00:00 2001 From: Aki Sakurai <75532970+AkiSakurai@users.noreply.github.com> Date: Tue, 28 Mar 2023 23:00:56 +0800 Subject: [PATCH 14/56] StableDiffusionLongPromptWeightingPipeline: Do not hardcode pad token (#2832) --- examples/community/lpw_stable_diffusion.py | 7 +++++-- examples/community/lpw_stable_diffusion_onnx.py | 7 +++++-- 2 files changed, 10 insertions(+), 4 deletions(-) diff --git a/examples/community/lpw_stable_diffusion.py b/examples/community/lpw_stable_diffusion.py index 072f7cde17ad..b4863f65abf7 100644 --- a/examples/community/lpw_stable_diffusion.py +++ b/examples/community/lpw_stable_diffusion.py @@ -179,14 +179,14 @@ def get_prompts_with_weights(pipe: StableDiffusionPipeline, prompt: List[str], m return tokens, weights -def pad_tokens_and_weights(tokens, weights, max_length, bos, eos, no_boseos_middle=True, chunk_length=77): +def pad_tokens_and_weights(tokens, weights, max_length, bos, eos, pad, no_boseos_middle=True, chunk_length=77): r""" Pad the tokens (with starting and ending tokens) and weights (with 1.0) to max_length. """ max_embeddings_multiples = (max_length - 2) // (chunk_length - 2) weights_length = max_length if no_boseos_middle else max_embeddings_multiples * chunk_length for i in range(len(tokens)): - tokens[i] = [bos] + tokens[i] + [eos] * (max_length - 1 - len(tokens[i])) + tokens[i] = [bos] + tokens[i] + [pad] * (max_length - 1 - len(tokens[i]) - 1) + [eos] if no_boseos_middle: weights[i] = [1.0] + weights[i] + [1.0] * (max_length - 1 - len(weights[i])) else: @@ -317,12 +317,14 @@ def get_weighted_text_embeddings( # pad the length of tokens and weights bos = pipe.tokenizer.bos_token_id eos = pipe.tokenizer.eos_token_id + pad = getattr(pipe.tokenizer, "pad_token_id", eos) prompt_tokens, prompt_weights = pad_tokens_and_weights( prompt_tokens, prompt_weights, max_length, bos, eos, + pad, no_boseos_middle=no_boseos_middle, chunk_length=pipe.tokenizer.model_max_length, ) @@ -334,6 +336,7 @@ def get_weighted_text_embeddings( max_length, bos, eos, + pad, no_boseos_middle=no_boseos_middle, chunk_length=pipe.tokenizer.model_max_length, ) diff --git a/examples/community/lpw_stable_diffusion_onnx.py b/examples/community/lpw_stable_diffusion_onnx.py index 1e0764de5d4d..9aa7d47eeab0 100644 --- a/examples/community/lpw_stable_diffusion_onnx.py +++ b/examples/community/lpw_stable_diffusion_onnx.py @@ -196,14 +196,14 @@ def get_prompts_with_weights(pipe, prompt: List[str], max_length: int): return tokens, weights -def pad_tokens_and_weights(tokens, weights, max_length, bos, eos, no_boseos_middle=True, chunk_length=77): +def pad_tokens_and_weights(tokens, weights, max_length, bos, eos, pad, no_boseos_middle=True, chunk_length=77): r""" Pad the tokens (with starting and ending tokens) and weights (with 1.0) to max_length. """ max_embeddings_multiples = (max_length - 2) // (chunk_length - 2) weights_length = max_length if no_boseos_middle else max_embeddings_multiples * chunk_length for i in range(len(tokens)): - tokens[i] = [bos] + tokens[i] + [eos] * (max_length - 1 - len(tokens[i])) + tokens[i] = [bos] + tokens[i] + [pad] * (max_length - 1 - len(tokens[i]) - 1) + [eos] if no_boseos_middle: weights[i] = [1.0] + weights[i] + [1.0] * (max_length - 1 - len(weights[i])) else: @@ -342,12 +342,14 @@ def get_weighted_text_embeddings( # pad the length of tokens and weights bos = pipe.tokenizer.bos_token_id eos = pipe.tokenizer.eos_token_id + pad = getattr(pipe.tokenizer, "pad_token_id", eos) prompt_tokens, prompt_weights = pad_tokens_and_weights( prompt_tokens, prompt_weights, max_length, bos, eos, + pad, no_boseos_middle=no_boseos_middle, chunk_length=pipe.tokenizer.model_max_length, ) @@ -359,6 +361,7 @@ def get_weighted_text_embeddings( max_length, bos, eos, + pad, no_boseos_middle=no_boseos_middle, chunk_length=pipe.tokenizer.model_max_length, ) From b76d9fde8de381a50d64c401b5d12864a28c5556 Mon Sep 17 00:00:00 2001 From: Sandeep Date: Tue, 28 Mar 2023 20:31:30 +0530 Subject: [PATCH 15/56] Remove suggestion to use cuDNN benchmark in docs (#2793) * Remove suggestion to use cuDNN benchmark in docs * removing the wrong line --- docs/source/en/optimization/fp16.mdx | 13 ------------- 1 file changed, 13 deletions(-) diff --git a/docs/source/en/optimization/fp16.mdx b/docs/source/en/optimization/fp16.mdx index c18cefbde6a9..9d7c0234cdda 100644 --- a/docs/source/en/optimization/fp16.mdx +++ b/docs/source/en/optimization/fp16.mdx @@ -19,7 +19,6 @@ We'll discuss how the following settings impact performance and memory. | | Latency | Speedup | | ---------------- | ------- | ------- | | original | 9.50s | x1 | -| cuDNN auto-tuner | 9.37s | x1.01 | | fp16 | 3.61s | x2.63 | | channels last | 3.30s | x2.88 | | traced UNet | 3.21s | x2.96 | @@ -31,18 +30,6 @@ We'll discuss how the following settings impact performance and memory. steps. -## Enable cuDNN auto-tuner - -[NVIDIA cuDNN](https://developer.nvidia.com/cudnn) supports many algorithms to compute a convolution. Autotuner runs a short benchmark and selects the kernel with the best performance on a given hardware for a given input size. - -Since we’re using **convolutional networks** (other types currently not supported), we can enable cuDNN autotuner before launching the inference by setting: - -```python -import torch - -torch.backends.cudnn.benchmark = True -``` - ### Use tf32 instead of fp32 (on Ampere and later CUDA devices) On Ampere and later CUDA devices matrix multiplications and convolutions can use the TensorFloat32 (TF32) mode for faster but slightly less accurate computations. By default PyTorch enables TF32 mode for convolutions but not matrix multiplications, and unless a network requires full float32 precision we recommend enabling this setting for matrix multiplications, too. It can significantly speed up computations with typically negligible loss of numerical accuracy. You can read more about it [here](https://huggingface.co/docs/transformers/v4.18.0/en/performance#tf32). All you need to do is to add this before your inference: From 159a0bff3461dfdf37372f249e0fcc907cd0a81b Mon Sep 17 00:00:00 2001 From: "Li-Huai (Allan) Lin" Date: Tue, 28 Mar 2023 23:27:51 +0800 Subject: [PATCH 16/56] Remove duplicate sentence in docstrings (#2834) * Remove duplicate sentence * format --- examples/community/stable_diffusion_controlnet_img2img.py | 6 ++---- examples/community/stable_diffusion_controlnet_inpaint.py | 6 ++---- .../stable_diffusion_controlnet_inpaint_img2img.py | 6 ++---- .../pipelines/alt_diffusion/pipeline_alt_diffusion.py | 8 ++++---- .../alt_diffusion/pipeline_alt_diffusion_img2img.py | 4 ++-- src/diffusers/pipelines/audioldm/pipeline_audioldm.py | 8 ++++---- .../stable_diffusion/pipeline_cycle_diffusion.py | 4 ++-- .../stable_diffusion/pipeline_stable_diffusion.py | 8 ++++---- .../pipeline_stable_diffusion_attend_and_excite.py | 8 ++++---- .../pipeline_stable_diffusion_controlnet.py | 8 ++++---- .../pipeline_stable_diffusion_depth2img.py | 4 ++-- .../stable_diffusion/pipeline_stable_diffusion_img2img.py | 4 ++-- .../stable_diffusion/pipeline_stable_diffusion_inpaint.py | 4 ++-- .../pipeline_stable_diffusion_inpaint_legacy.py | 4 ++-- .../pipeline_stable_diffusion_instruct_pix2pix.py | 4 ++-- .../pipeline_stable_diffusion_k_diffusion.py | 4 ++-- .../pipeline_stable_diffusion_model_editing.py | 8 ++++---- .../pipeline_stable_diffusion_panorama.py | 8 ++++---- .../pipeline_stable_diffusion_pix2pix_zero.py | 8 ++++---- .../stable_diffusion/pipeline_stable_diffusion_sag.py | 8 ++++---- .../stable_diffusion/pipeline_stable_diffusion_upscale.py | 4 ++-- .../pipelines/stable_diffusion/pipeline_stable_unclip.py | 8 ++++---- .../stable_diffusion/pipeline_stable_unclip_img2img.py | 8 ++++---- .../pipeline_text_to_video_synth.py | 8 ++++---- 24 files changed, 72 insertions(+), 78 deletions(-) diff --git a/examples/community/stable_diffusion_controlnet_img2img.py b/examples/community/stable_diffusion_controlnet_img2img.py index 95e5fe7db061..1c7ef8aa230a 100644 --- a/examples/community/stable_diffusion_controlnet_img2img.py +++ b/examples/community/stable_diffusion_controlnet_img2img.py @@ -276,8 +276,7 @@ def _encode_prompt( whether to use classifier free guidance or not 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. 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`). + `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). prompt_embeds (`torch.FloatTensor`, *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. @@ -699,8 +698,7 @@ def __call__( usually at the expense of lower image quality. 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. 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`). + `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). num_images_per_prompt (`int`, *optional*, defaults to 1): The number of images to generate per prompt. eta (`float`, *optional*, defaults to 0.0): diff --git a/examples/community/stable_diffusion_controlnet_inpaint.py b/examples/community/stable_diffusion_controlnet_inpaint.py index 0121b2b26fc2..c47f4c3194e8 100644 --- a/examples/community/stable_diffusion_controlnet_inpaint.py +++ b/examples/community/stable_diffusion_controlnet_inpaint.py @@ -373,8 +373,7 @@ def _encode_prompt( do_classifier_free_guidance (`bool`): whether to use classifier free guidance or not 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. If not defined, one has to pass `negative_prompt_embeds`. instead. + 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`). prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not @@ -833,8 +832,7 @@ def __call__( 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, usually at the expense of lower image quality. 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. If not defined, one has to pass `negative_prompt_embeds`. instead. + 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_images_per_prompt (`int`, *optional*, defaults to 1): The number of images to generate per prompt. diff --git a/examples/community/stable_diffusion_controlnet_inpaint_img2img.py b/examples/community/stable_diffusion_controlnet_inpaint_img2img.py index 5df9cc10afab..bad1df0e13fb 100644 --- a/examples/community/stable_diffusion_controlnet_inpaint_img2img.py +++ b/examples/community/stable_diffusion_controlnet_inpaint_img2img.py @@ -373,8 +373,7 @@ def _encode_prompt( do_classifier_free_guidance (`bool`): whether to use classifier free guidance or not 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. If not defined, one has to pass `negative_prompt_embeds`. instead. + 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`). prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not @@ -876,8 +875,7 @@ def __call__( 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, usually at the expense of lower image quality. 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. If not defined, one has to pass `negative_prompt_embeds`. instead. + 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_images_per_prompt (`int`, *optional*, defaults to 1): The number of images to generate per prompt. diff --git a/src/diffusers/pipelines/alt_diffusion/pipeline_alt_diffusion.py b/src/diffusers/pipelines/alt_diffusion/pipeline_alt_diffusion.py index 71ae1e93a5ea..68ad20c1598a 100644 --- a/src/diffusers/pipelines/alt_diffusion/pipeline_alt_diffusion.py +++ b/src/diffusers/pipelines/alt_diffusion/pipeline_alt_diffusion.py @@ -294,8 +294,8 @@ def _encode_prompt( whether to use classifier free guidance or not 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. 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`). + `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is + less than `1`). prompt_embeds (`torch.FloatTensor`, *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. @@ -551,8 +551,8 @@ def __call__( usually at the expense of lower image quality. 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. 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`). + `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is + less than `1`). num_images_per_prompt (`int`, *optional*, defaults to 1): The number of images to generate per prompt. eta (`float`, *optional*, defaults to 0.0): diff --git a/src/diffusers/pipelines/alt_diffusion/pipeline_alt_diffusion_img2img.py b/src/diffusers/pipelines/alt_diffusion/pipeline_alt_diffusion_img2img.py index 23f4886f06c1..3521867f2b9f 100644 --- a/src/diffusers/pipelines/alt_diffusion/pipeline_alt_diffusion_img2img.py +++ b/src/diffusers/pipelines/alt_diffusion/pipeline_alt_diffusion_img2img.py @@ -304,8 +304,8 @@ def _encode_prompt( whether to use classifier free guidance or not 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. 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`). + `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is + less than `1`). prompt_embeds (`torch.FloatTensor`, *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. diff --git a/src/diffusers/pipelines/audioldm/pipeline_audioldm.py b/src/diffusers/pipelines/audioldm/pipeline_audioldm.py index 2086cb0c8a8d..b392cd4cc246 100644 --- a/src/diffusers/pipelines/audioldm/pipeline_audioldm.py +++ b/src/diffusers/pipelines/audioldm/pipeline_audioldm.py @@ -167,8 +167,8 @@ def _encode_prompt( whether to use classifier free guidance or not negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the audio generation. If not defined, one has to pass - `negative_prompt_embeds`. instead. 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`). + `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is + less than `1`). prompt_embeds (`torch.FloatTensor`, *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. @@ -436,8 +436,8 @@ def __call__( usually at the expense of lower sound quality. negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the audio generation. If not defined, one has to pass - `negative_prompt_embeds`. instead. 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`). + `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is + less than `1`). num_waveforms_per_prompt (`int`, *optional*, defaults to 1): The number of waveforms to generate per prompt. eta (`float`, *optional*, defaults to 0.0): diff --git a/src/diffusers/pipelines/stable_diffusion/pipeline_cycle_diffusion.py b/src/diffusers/pipelines/stable_diffusion/pipeline_cycle_diffusion.py index 4616dc1e4849..08dad43784f8 100644 --- a/src/diffusers/pipelines/stable_diffusion/pipeline_cycle_diffusion.py +++ b/src/diffusers/pipelines/stable_diffusion/pipeline_cycle_diffusion.py @@ -320,8 +320,8 @@ def _encode_prompt( whether to use classifier free guidance or not 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. 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`). + `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is + less than `1`). prompt_embeds (`torch.FloatTensor`, *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. diff --git a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py index b927e7553399..b428b4341849 100644 --- a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py +++ b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py @@ -297,8 +297,8 @@ def _encode_prompt( whether to use classifier free guidance or not 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. 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`). + `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is + less than `1`). prompt_embeds (`torch.FloatTensor`, *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. @@ -554,8 +554,8 @@ def __call__( usually at the expense of lower image quality. 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. 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`). + `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is + less than `1`). num_images_per_prompt (`int`, *optional*, defaults to 1): The number of images to generate per prompt. eta (`float`, *optional*, defaults to 0.0): diff --git a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_attend_and_excite.py b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_attend_and_excite.py index c239664edebe..ae92ba5526a8 100644 --- a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_attend_and_excite.py +++ b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_attend_and_excite.py @@ -317,8 +317,8 @@ def _encode_prompt( whether to use classifier free guidance or not 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. 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`). + `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is + less than `1`). prompt_embeds (`torch.FloatTensor`, *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. @@ -741,8 +741,8 @@ def __call__( usually at the expense of lower image quality. 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. 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`). + `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is + less than `1`). num_images_per_prompt (`int`, *optional*, defaults to 1): The number of images to generate per prompt. eta (`float`, *optional*, defaults to 0.0): diff --git a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_controlnet.py b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_controlnet.py index aea424366346..d7f84d2e697b 100644 --- a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_controlnet.py +++ b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_controlnet.py @@ -336,8 +336,8 @@ def _encode_prompt( whether to use classifier free guidance or not 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. 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`). + `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is + less than `1`). prompt_embeds (`torch.FloatTensor`, *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. @@ -769,8 +769,8 @@ def __call__( usually at the expense of lower image quality. 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. 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`). + `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is + less than `1`). num_images_per_prompt (`int`, *optional*, defaults to 1): The number of images to generate per prompt. eta (`float`, *optional*, defaults to 0.0): diff --git a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_depth2img.py b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_depth2img.py index 96282771d777..876b1b8305f2 100644 --- a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_depth2img.py +++ b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_depth2img.py @@ -182,8 +182,8 @@ def _encode_prompt( whether to use classifier free guidance or not 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. 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`). + `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is + less than `1`). prompt_embeds (`torch.FloatTensor`, *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. diff --git a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py index ba124ffecbee..14512e180992 100644 --- a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py +++ b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py @@ -311,8 +311,8 @@ def _encode_prompt( whether to use classifier free guidance or not 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. 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`). + `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is + less than `1`). prompt_embeds (`torch.FloatTensor`, *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. diff --git a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint.py b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint.py index 8f36e675987a..199325236c67 100644 --- a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint.py +++ b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint.py @@ -355,8 +355,8 @@ def _encode_prompt( whether to use classifier free guidance or not 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. 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`). + `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is + less than `1`). prompt_embeds (`torch.FloatTensor`, *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. diff --git a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint_legacy.py b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint_legacy.py index 3d1615968369..accbf9674ec8 100644 --- a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint_legacy.py +++ b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint_legacy.py @@ -299,8 +299,8 @@ def _encode_prompt( whether to use classifier free guidance or not 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. 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`). + `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is + less than `1`). prompt_embeds (`torch.FloatTensor`, *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. diff --git a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_instruct_pix2pix.py b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_instruct_pix2pix.py index 5f4d6685185f..40cde74a0596 100644 --- a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_instruct_pix2pix.py +++ b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_instruct_pix2pix.py @@ -493,8 +493,8 @@ def _encode_prompt( whether to use classifier free guidance or not 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. 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`). + `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is + less than `1`). prompt_embeds (`torch.FloatTensor`, *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. diff --git a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_k_diffusion.py b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_k_diffusion.py index 3bd1e865b90b..6a895a6d0f29 100755 --- a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_k_diffusion.py +++ b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_k_diffusion.py @@ -220,8 +220,8 @@ def _encode_prompt( whether to use classifier free guidance or not 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. 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`). + `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is + less than `1`). prompt_embeds (`torch.FloatTensor`, *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. diff --git a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_model_editing.py b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_model_editing.py index b5c253ca56cf..0e850b43bd7c 100644 --- a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_model_editing.py +++ b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_model_editing.py @@ -248,8 +248,8 @@ def _encode_prompt( whether to use classifier free guidance or not 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. 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`). + `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is + less than `1`). prompt_embeds (`torch.FloatTensor`, *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. @@ -627,8 +627,8 @@ def __call__( usually at the expense of lower image quality. 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. 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`). + `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is + less than `1`). num_images_per_prompt (`int`, *optional*, defaults to 1): The number of images to generate per prompt. eta (`float`, *optional*, defaults to 0.0): diff --git a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_panorama.py b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_panorama.py index c7f47666c3f9..fdae1ed3679b 100644 --- a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_panorama.py +++ b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_panorama.py @@ -212,8 +212,8 @@ def _encode_prompt( whether to use classifier free guidance or not 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. 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`). + `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is + less than `1`). prompt_embeds (`torch.FloatTensor`, *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. @@ -491,8 +491,8 @@ def __call__( usually at the expense of lower image quality. 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. 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`). + `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is + less than `1`). num_images_per_prompt (`int`, *optional*, defaults to 1): The number of images to generate per prompt. eta (`float`, *optional*, defaults to 0.0): diff --git a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_pix2pix_zero.py b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_pix2pix_zero.py index bc072d4c73e8..89cf823a1f7e 100644 --- a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_pix2pix_zero.py +++ b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_pix2pix_zero.py @@ -452,8 +452,8 @@ def _encode_prompt( whether to use classifier free guidance or not 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. 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`). + `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is + less than `1`). prompt_embeds (`torch.FloatTensor`, *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. @@ -828,8 +828,8 @@ def __call__( usually at the expense of lower image quality. 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. 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`). + `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is + less than `1`). num_images_per_prompt (`int`, *optional*, defaults to 1): The number of images to generate per prompt. eta (`float`, *optional*, defaults to 0.0): diff --git a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_sag.py b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_sag.py index 5ad0c9fe94b8..d77e3550fc75 100644 --- a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_sag.py +++ b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_sag.py @@ -229,8 +229,8 @@ def _encode_prompt( whether to use classifier free guidance or not 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. 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`). + `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is + less than `1`). prompt_embeds (`torch.FloatTensor`, *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. @@ -496,8 +496,8 @@ def __call__( https://arxiv.org/pdf/2210.00939.pdf. Typically chosen between [0, 1.0] for better quality. 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. 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`). + `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is + less than `1`). num_images_per_prompt (`int`, *optional*, defaults to 1): The number of images to generate per prompt. eta (`float`, *optional*, defaults to 0.0): diff --git a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_upscale.py b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_upscale.py index b25a91d4caef..e21b41ccac6d 100644 --- a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_upscale.py +++ b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_upscale.py @@ -176,8 +176,8 @@ def _encode_prompt( whether to use classifier free guidance or not 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. 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`). + `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is + less than `1`). prompt_embeds (`torch.FloatTensor`, *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. diff --git a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_unclip.py b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_unclip.py index 1341ec2b284b..9c3d39564f6e 100644 --- a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_unclip.py +++ b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_unclip.py @@ -349,8 +349,8 @@ def _encode_prompt( whether to use classifier free guidance or not 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. 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`). + `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is + less than `1`). prompt_embeds (`torch.FloatTensor`, *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. @@ -676,8 +676,8 @@ def __call__( usually at the expense of lower image quality. 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. 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`). + `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is + less than `1`). num_images_per_prompt (`int`, *optional*, defaults to 1): The number of images to generate per prompt. eta (`float`, *optional*, defaults to 0.0): diff --git a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_unclip_img2img.py b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_unclip_img2img.py index c7758850f750..c8fb3f8021b9 100644 --- a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_unclip_img2img.py +++ b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_unclip_img2img.py @@ -249,8 +249,8 @@ def _encode_prompt( whether to use classifier free guidance or not 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. 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`). + `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is + less than `1`). prompt_embeds (`torch.FloatTensor`, *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. @@ -645,8 +645,8 @@ def __call__( usually at the expense of lower image quality. 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. 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`). + `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is + less than `1`). num_images_per_prompt (`int`, *optional*, defaults to 1): The number of images to generate per prompt. eta (`float`, *optional*, defaults to 0.0): diff --git a/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth.py b/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth.py index 453809ef6df7..9129ae0118b8 100644 --- a/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth.py +++ b/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth.py @@ -238,8 +238,8 @@ def _encode_prompt( whether to use classifier free guidance or not 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. 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`). + `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is + less than `1`). prompt_embeds (`torch.FloatTensor`, *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. @@ -513,8 +513,8 @@ def __call__( usually at the expense of lower video quality. negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the video generation. If not defined, one has to pass - `negative_prompt_embeds`. instead. 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`). + `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is + less than `1`). 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. From 7d756813d4b094b1c19d3890d2afdc4ff54f4f25 Mon Sep 17 00:00:00 2001 From: cmdr2 Date: Tue, 28 Mar 2023 21:00:49 +0530 Subject: [PATCH 17/56] Update the legacy inpainting SD pipeline, to allow calling it with only prompt_embeds (instead of always requiring a prompt) (#2842) Fix error 'required positional argument: prompt' when Legacy Inpaint is called only with prompt_embeds --- .../pipeline_stable_diffusion_inpaint_legacy.py | 12 +++++++++--- 1 file changed, 9 insertions(+), 3 deletions(-) diff --git a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint_legacy.py b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint_legacy.py index accbf9674ec8..feb13d100089 100644 --- a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint_legacy.py +++ b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint_legacy.py @@ -521,7 +521,7 @@ def prepare_latents(self, image, timestep, batch_size, num_images_per_prompt, dt @torch.no_grad() def __call__( self, - prompt: Union[str, List[str]], + prompt: Union[str, List[str]] = None, image: Union[torch.FloatTensor, PIL.Image.Image] = None, mask_image: Union[torch.FloatTensor, PIL.Image.Image] = None, strength: float = 0.8, @@ -611,10 +611,16 @@ def __call__( (nsfw) content, according to the `safety_checker`. """ # 1. Check inputs - self.check_inputs(prompt, strength, callback_steps) + self.check_inputs(prompt, strength, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds) # 2. Define call parameters - batch_size = 1 if isinstance(prompt, str) else len(prompt) + 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` From 920a15cf70c9c540bdc56fcfd52f7e8f2c02e33a Mon Sep 17 00:00:00 2001 From: John HU Date: Tue, 28 Mar 2023 08:35:41 -0700 Subject: [PATCH 18/56] Fix link to LoRA training guide in DreamBooth training guide (#2836) Fix link to LoRA training guide --- docs/source/en/training/dreambooth.mdx | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/en/training/dreambooth.mdx b/docs/source/en/training/dreambooth.mdx index 623b9124f303..51e4e498d5bc 100644 --- a/docs/source/en/training/dreambooth.mdx +++ b/docs/source/en/training/dreambooth.mdx @@ -237,7 +237,7 @@ python train_dreambooth_flax.py \ ## Finetuning with LoRA -You can also use Low-Rank Adaptation of Large Language Models (LoRA), a fine-tuning technique for accelerating training large models, on DreamBooth. For more details, take a look at the [LoRA training](training/lora#dreambooth) guide. +You can also use Low-Rank Adaptation of Large Language Models (LoRA), a fine-tuning technique for accelerating training large models, on DreamBooth. For more details, take a look at the [LoRA training](./lora#dreambooth) guide. ## Saving checkpoints while training From 663c6545779b889c7ce51d2aaf8c43309aba6652 Mon Sep 17 00:00:00 2001 From: dg845 <58458699+dg845@users.noreply.github.com> Date: Tue, 28 Mar 2023 08:44:34 -0700 Subject: [PATCH 19/56] [WIP][Docs] Use DiffusionPipeline Instead of Child Classes when Loading Pipeline (#2809) * Change the docs to use the parent DiffusionPipeline class when loading a checkpoint using from_pretrained() instead of a child class (e.g. StableDiffusionPipeline) where possible. * Run make style to fix style issues. * Change more docs to use DiffusionPipeline rather than a subclass. --------- Co-authored-by: Patrick von Platen --- docs/source/en/optimization/fp16.mdx | 15 +++++++++------ docs/source/en/optimization/mps.mdx | 4 ++-- docs/source/en/optimization/torch2.0.mdx | 14 ++++++-------- docs/source/en/quicktour.mdx | 2 +- docs/source/en/stable_diffusion.mdx | 6 +++--- docs/source/en/training/dreambooth.mdx | 4 ++-- .../en/using-diffusers/using_safetensors.mdx | 4 ++-- 7 files changed, 25 insertions(+), 24 deletions(-) diff --git a/docs/source/en/optimization/fp16.mdx b/docs/source/en/optimization/fp16.mdx index 9d7c0234cdda..d05c5aabea2b 100644 --- a/docs/source/en/optimization/fp16.mdx +++ b/docs/source/en/optimization/fp16.mdx @@ -45,7 +45,10 @@ torch.backends.cuda.matmul.allow_tf32 = True To save more GPU memory and get more speed, you can load and run the model weights directly in half precision. This involves loading the float16 version of the weights, which was saved to a branch named `fp16`, and telling PyTorch to use the `float16` type when loading them: ```Python -pipe = StableDiffusionPipeline.from_pretrained( +import torch +from diffusers import DiffusionPipeline + +pipe = DiffusionPipeline.from_pretrained( "runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16, @@ -72,13 +75,13 @@ For even additional memory savings, you can use a sliced version of attention th each head which can save a significant amount of memory. -To perform the attention computation sequentially over each head, you only need to invoke [`~StableDiffusionPipeline.enable_attention_slicing`] in your pipeline before inference, like here: +To perform the attention computation sequentially over each head, you only need to invoke [`~DiffusionPipeline.enable_attention_slicing`] in your pipeline before inference, like here: ```Python import torch -from diffusers import StableDiffusionPipeline +from diffusers import DiffusionPipeline -pipe = StableDiffusionPipeline.from_pretrained( +pipe = DiffusionPipeline.from_pretrained( "runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16, @@ -402,10 +405,10 @@ To leverage it just make sure you have: - Cuda available - [Installed the xformers library](xformers). ```python -from diffusers import StableDiffusionPipeline +from diffusers import DiffusionPipeline import torch -pipe = StableDiffusionPipeline.from_pretrained( +pipe = DiffusionPipeline.from_pretrained( "runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16, ).to("cuda") diff --git a/docs/source/en/optimization/mps.mdx b/docs/source/en/optimization/mps.mdx index 3750724bce57..3be8c621ee3e 100644 --- a/docs/source/en/optimization/mps.mdx +++ b/docs/source/en/optimization/mps.mdx @@ -35,9 +35,9 @@ The snippet below demonstrates how to use the `mps` backend using the familiar ` We strongly recommend you use PyTorch 2 or better, as it solves a number of problems like the one described in the previous tip. ```python -from diffusers import StableDiffusionPipeline +from diffusers import DiffusionPipeline -pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5") +pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5") pipe = pipe.to("mps") # Recommended if your computer has < 64 GB of RAM diff --git a/docs/source/en/optimization/torch2.0.mdx b/docs/source/en/optimization/torch2.0.mdx index a6a40469e97b..206ac4e447cc 100644 --- a/docs/source/en/optimization/torch2.0.mdx +++ b/docs/source/en/optimization/torch2.0.mdx @@ -35,9 +35,9 @@ pip install --upgrade torch torchvision diffusers ```Python import torch - from diffusers import StableDiffusionPipeline + from diffusers import DiffusionPipeline - pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16) + pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16) pipe = pipe.to("cuda") prompt = "a photo of an astronaut riding a horse on mars" @@ -48,10 +48,10 @@ pip install --upgrade torch torchvision diffusers ```Python import torch - from diffusers import StableDiffusionPipeline + from diffusers import DiffusionPipeline from diffusers.models.attention_processor import AttnProcessor2_0 - pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16).to("cuda") + pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16).to("cuda") pipe.unet.set_attn_processor(AttnProcessor2_0()) prompt = "a photo of an astronaut riding a horse on mars" @@ -68,11 +68,9 @@ pip install --upgrade torch torchvision diffusers ```python import torch - from diffusers import StableDiffusionPipeline + from diffusers import DiffusionPipeline - pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16).to( - "cuda" - ) + pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16).to("cuda") pipe.unet = torch.compile(pipe.unet) batch_size = 10 diff --git a/docs/source/en/quicktour.mdx b/docs/source/en/quicktour.mdx index 3aecb422af2a..d494b79dccd5 100644 --- a/docs/source/en/quicktour.mdx +++ b/docs/source/en/quicktour.mdx @@ -141,7 +141,7 @@ Different schedulers come with different denoising speeds and quality trade-offs ```py >>> from diffusers import EulerDiscreteScheduler ->>> pipeline = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5") +>>> pipeline = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5") >>> pipeline.scheduler = EulerDiscreteScheduler.from_config(pipeline.scheduler.config) ``` diff --git a/docs/source/en/stable_diffusion.mdx b/docs/source/en/stable_diffusion.mdx index 8190813e488a..c1eef6fa3c5c 100644 --- a/docs/source/en/stable_diffusion.mdx +++ b/docs/source/en/stable_diffusion.mdx @@ -47,9 +47,9 @@ Let's load the pipeline. ## Speed Optimization ``` python -from diffusers import StableDiffusionPipeline +from diffusers import DiffusionPipeline -pipe = StableDiffusionPipeline.from_pretrained(model_id) +pipe = DiffusionPipeline.from_pretrained(model_id) ``` We aim at generating a beautiful photograph of an *old warrior chief* and will later try to find the best prompt to generate such a photograph. For now, let's keep the prompt simple: @@ -88,7 +88,7 @@ The default run we did above used full float32 precision and ran the default num ``` python import torch -pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16) +pipe = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16) pipe = pipe.to("cuda") ``` diff --git a/docs/source/en/training/dreambooth.mdx b/docs/source/en/training/dreambooth.mdx index 51e4e498d5bc..908355e496dc 100644 --- a/docs/source/en/training/dreambooth.mdx +++ b/docs/source/en/training/dreambooth.mdx @@ -457,11 +457,11 @@ If you have **`"accelerate>=0.16.0"`** installed, you can use the following code inference from an intermediate checkpoint: ```python -from diffusers import StableDiffusionPipeline +from diffusers import DiffusionPipeline import torch model_id = "path_to_saved_model" -pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to("cuda") +pipe = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to("cuda") prompt = "A photo of sks dog in a bucket" image = pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0] diff --git a/docs/source/en/using-diffusers/using_safetensors.mdx b/docs/source/en/using-diffusers/using_safetensors.mdx index 50bcb6b9933b..b522f3236fbb 100644 --- a/docs/source/en/using-diffusers/using_safetensors.mdx +++ b/docs/source/en/using-diffusers/using_safetensors.mdx @@ -75,9 +75,9 @@ And we're equipped with dealing with it. Then in order to use the model, even before the branch gets accepted by the original author you can do: ```python -from diffusers import StableDiffusionPipeline +from diffusers import DiffusionPipeline -pipe = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1", revision="refs/pr/22") +pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1", revision="refs/pr/22") ``` or you can test it directly online with this [space](https://huggingface.co/spaces/diffusers/check_pr). From 25d927aa519f80882654926a153bc95f09f38319 Mon Sep 17 00:00:00 2001 From: Felix Blanke <45953206+felixblanke@users.noreply.github.com> Date: Tue, 28 Mar 2023 17:46:41 +0200 Subject: [PATCH 20/56] Add `last_epoch` argument to `optimization.get_scheduler` (#2850) Add last_epoch arg to optimization.get_scheduler. Allows the specification of the index of the last epoch when resuming training. --- src/diffusers/optimization.py | 21 ++++++++++++++++----- 1 file changed, 16 insertions(+), 5 deletions(-) diff --git a/src/diffusers/optimization.py b/src/diffusers/optimization.py index d7f923b49690..657e085062e0 100644 --- a/src/diffusers/optimization.py +++ b/src/diffusers/optimization.py @@ -242,6 +242,7 @@ def get_scheduler( num_training_steps: Optional[int] = None, num_cycles: int = 1, power: float = 1.0, + last_epoch: int = -1, ): """ Unified API to get any scheduler from its name. @@ -267,14 +268,14 @@ def get_scheduler( name = SchedulerType(name) schedule_func = TYPE_TO_SCHEDULER_FUNCTION[name] if name == SchedulerType.CONSTANT: - return schedule_func(optimizer) + return schedule_func(optimizer, last_epoch=last_epoch) # All other schedulers require `num_warmup_steps` if num_warmup_steps is None: raise ValueError(f"{name} requires `num_warmup_steps`, please provide that argument.") if name == SchedulerType.CONSTANT_WITH_WARMUP: - return schedule_func(optimizer, num_warmup_steps=num_warmup_steps) + return schedule_func(optimizer, num_warmup_steps=num_warmup_steps, last_epoch=last_epoch) # All other schedulers require `num_training_steps` if num_training_steps is None: @@ -282,12 +283,22 @@ def get_scheduler( if name == SchedulerType.COSINE_WITH_RESTARTS: return schedule_func( - optimizer, num_warmup_steps=num_warmup_steps, num_training_steps=num_training_steps, num_cycles=num_cycles + optimizer, + num_warmup_steps=num_warmup_steps, + num_training_steps=num_training_steps, + num_cycles=num_cycles, + last_epoch=last_epoch, ) if name == SchedulerType.POLYNOMIAL: return schedule_func( - optimizer, num_warmup_steps=num_warmup_steps, num_training_steps=num_training_steps, power=power + optimizer, + num_warmup_steps=num_warmup_steps, + num_training_steps=num_training_steps, + power=power, + last_epoch=last_epoch, ) - return schedule_func(optimizer, num_warmup_steps=num_warmup_steps, num_training_steps=num_training_steps) + return schedule_func( + optimizer, num_warmup_steps=num_warmup_steps, num_training_steps=num_training_steps, last_epoch=last_epoch + ) From 4d0f412d0d5a55d7d653cecfe6cf770a7f1af277 Mon Sep 17 00:00:00 2001 From: dg845 <58458699+dg845@users.noreply.github.com> Date: Tue, 28 Mar 2023 08:53:52 -0700 Subject: [PATCH 21/56] [WIP] Check UNet shapes in StableDiffusionInpaintPipeline __init__ (#2853) Add warning in __init__ if user loads a checkpoint with pipeline.unet.config.in_channels other than 9. --- .../stable_diffusion/pipeline_stable_diffusion_inpaint.py | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint.py b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint.py index 199325236c67..a934f639a508 100644 --- a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint.py +++ b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint.py @@ -243,6 +243,14 @@ def __init__( new_config = dict(unet.config) new_config["sample_size"] = 64 unet._internal_dict = FrozenDict(new_config) + # Check shapes, assume num_channels_latents == 4, num_channels_mask == 1, num_channels_masked == 4 + if unet.config.in_channels != 9: + logger.warning( + f"You have loaded a UNet with {unet.config.in_channels} input channels, whereas by default," + f" {self.__class__} assumes that `pipeline.unet` has 9 input channels: 4 for `num_channels_latents`," + " 1 for `num_channels_mask`, and 4 for `num_channels_masked_image`. If you did not intend to modify" + " this behavior, please check whether you have loaded the right checkpoint." + ) self.register_modules( vae=vae, From 53377ef83c6446033f3ee506e3ef718db817b293 Mon Sep 17 00:00:00 2001 From: Nipun Jindal Date: Tue, 28 Mar 2023 21:26:45 +0530 Subject: [PATCH 22/56] [2761]: Add documentation for extra_in_channels UNet1DModel (#2817) Co-authored-by: njindal --- src/diffusers/models/unet_1d.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/src/diffusers/models/unet_1d.py b/src/diffusers/models/unet_1d.py index 5062295fc668..34a1d2b5160e 100644 --- a/src/diffusers/models/unet_1d.py +++ b/src/diffusers/models/unet_1d.py @@ -47,6 +47,9 @@ class UNet1DModel(ModelMixin, ConfigMixin): sample_size (`int`, *optional*): Default length of sample. Should be adaptable at runtime. in_channels (`int`, *optional*, defaults to 2): Number of channels in the input sample. out_channels (`int`, *optional*, defaults to 2): Number of channels in the output. + extra_in_channels (`int`, *optional*, defaults to 0): + Number of additional channels to be added to the input of the first down block. Useful for cases where the + input data has more channels than what the model is initially designed for. time_embedding_type (`str`, *optional*, defaults to `"fourier"`): Type of time embedding to use. freq_shift (`float`, *optional*, defaults to 0.0): Frequency shift for fourier time embedding. flip_sin_to_cos (`bool`, *optional*, defaults to : From 13845462db124789b20327567129a6a5903776a1 Mon Sep 17 00:00:00 2001 From: Sayak Paul Date: Tue, 28 Mar 2023 21:44:08 +0530 Subject: [PATCH 23/56] [Tests] Adds a test to check if `image_embeds` None case is handled properly in `StableUnCLIPImg2ImgPipeline` (#2861) * improve stable unclip doc. * add: test to check if image_emebds None case is handled. * apply formatting/ --- .../test_stable_unclip_img2img.py | 20 +++++++++++++++++++ 1 file changed, 20 insertions(+) diff --git a/tests/pipelines/stable_unclip/test_stable_unclip_img2img.py b/tests/pipelines/stable_unclip/test_stable_unclip_img2img.py index c7c0d2feeb54..f93fa3a59014 100644 --- a/tests/pipelines/stable_unclip/test_stable_unclip_img2img.py +++ b/tests/pipelines/stable_unclip/test_stable_unclip_img2img.py @@ -2,6 +2,7 @@ import random import unittest +import numpy as np import torch from transformers import ( CLIPImageProcessor, @@ -146,6 +147,25 @@ def get_dummy_inputs(self, device, seed=0, pil_image=True): "output_type": "np", } + def test_image_embeds_none(self): + device = "cpu" # ensure determinism for the device-dependent torch.Generator + components = self.get_dummy_components() + sd_pipe = StableUnCLIPImg2ImgPipeline(**components) + sd_pipe = sd_pipe.to(device) + sd_pipe.set_progress_bar_config(disable=None) + + inputs = self.get_dummy_inputs(device) + inputs.update({"image_embeds": None}) + image = sd_pipe(**inputs).images + image_slice = image[0, -3:, -3:, -1] + + assert image.shape == (1, 32, 32, 3) + expected_slice = np.array( + [0.34588397, 0.7747054, 0.5453714, 0.5227859, 0.57656777, 0.6532228, 0.5177634, 0.49932978, 0.56626225] + ) + + assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3 + # Overriding PipelineTesterMixin::test_attention_slicing_forward_pass # because GPU undeterminism requires a looser check. def test_attention_slicing_forward_pass(self): From 37c82480bbd7c97a9f2d9796eb368a54e666334d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?M=2E=20Tolga=20Cang=C3=B6z?= <46008593+standardAI@users.noreply.github.com> Date: Tue, 28 Mar 2023 19:15:37 +0300 Subject: [PATCH 24/56] Update evaluation.mdx (#2862) Fix typos --- docs/source/en/conceptual/evaluation.mdx | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/docs/source/en/conceptual/evaluation.mdx b/docs/source/en/conceptual/evaluation.mdx index 98821010e203..2721adea0c16 100644 --- a/docs/source/en/conceptual/evaluation.mdx +++ b/docs/source/en/conceptual/evaluation.mdx @@ -310,7 +310,7 @@ for idx in range(len(dataset)): edited_images.append(edited_image) ``` -To measure the directional similarity, we first load CLIP's image and text encoders. +To measure the directional similarity, we first load CLIP's image and text encoders: ```python from transformers import ( @@ -329,7 +329,7 @@ image_encoder = CLIPVisionModelWithProjection.from_pretrained(clip_id).to(device Notice that we are using a particular CLIP checkpoint, i.e., `openai/clip-vit-large-patch14`. This is because the Stable Diffusion pre-training was performed with this CLIP variant. For more details, refer to the [documentation](https://huggingface.co/docs/diffusers/main/en/api/pipelines/stable_diffusion/pix2pix#diffusers.StableDiffusionInstructPix2PixPipeline.text_encoder). -Next, we prepare a PyTorch `nn.module` to compute directional similarity: +Next, we prepare a PyTorch `nn.Module` to compute directional similarity: ```python import torch.nn as nn @@ -410,7 +410,7 @@ It should be noted that the `StableDiffusionInstructPix2PixPipeline` exposes t We can extend the idea of this metric to measure how similar the original image and edited version are. To do that, we can just do `F.cosine_similarity(img_feat_two, img_feat_one)`. For these kinds of edits, we would still want the primary semantics of the images to be preserved as much as possible, i.e., a high similarity score. -We can use these metrics for similar pipelines such as the[`StableDiffusionPix2PixZeroPipeline`](https://huggingface.co/docs/diffusers/main/en/api/pipelines/stable_diffusion/pix2pix_zero#diffusers.StableDiffusionPix2PixZeroPipeline)`. +We can use these metrics for similar pipelines such as the [`StableDiffusionPix2PixZeroPipeline`](https://huggingface.co/docs/diffusers/main/en/api/pipelines/stable_diffusion/pix2pix_zero#diffusers.StableDiffusionPix2PixZeroPipeline). @@ -550,7 +550,7 @@ FID results tend to be fragile as they depend on a lot of factors: * The image format (not the same if we start from PNGs vs JPGs). Keeping that in mind, FID is often most useful when comparing similar runs, but it is -hard to to reproduce paper results unless the authors carefully disclose the FID +hard to reproduce paper results unless the authors carefully disclose the FID measurement code. These points apply to other related metrics too, such as KID and IS. From 3980858ad40d46d0d0b52b09d9667344b91ab783 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?M=2E=20Tolga=20Cang=C3=B6z?= <46008593+standardAI@users.noreply.github.com> Date: Tue, 28 Mar 2023 19:17:33 +0300 Subject: [PATCH 25/56] Update overview.mdx (#2864) Fix typos --- docs/source/en/api/pipelines/overview.mdx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/source/en/api/pipelines/overview.mdx b/docs/source/en/api/pipelines/overview.mdx index bb8115223fab..3b0e7c66152f 100644 --- a/docs/source/en/api/pipelines/overview.mdx +++ b/docs/source/en/api/pipelines/overview.mdx @@ -108,7 +108,7 @@ from the local path. each pipeline, one should look directly into the respective pipeline. **Note**: All pipelines have PyTorch's autograd disabled by decorating the `__call__` method with a [`torch.no_grad`](https://pytorch.org/docs/stable/generated/torch.no_grad.html) decorator because pipelines should -not be used for training. If you want to store the gradients during the forward pass, we recommend writing your own pipeline, see also our [community-examples](https://github.com/huggingface/diffusers/tree/main/examples/community) +not be used for training. If you want to store the gradients during the forward pass, we recommend writing your own pipeline, see also our [community-examples](https://github.com/huggingface/diffusers/tree/main/examples/community). ## Contribution @@ -173,7 +173,7 @@ You can also run this example on colab [![Open In Colab](https://colab.research. ### Tweak prompts reusing seeds and latents -You can generate your own latents to reproduce results, or tweak your prompt on a specific result you liked. [This notebook](https://github.com/pcuenca/diffusers-examples/blob/main/notebooks/stable-diffusion-seeds.ipynb) shows how to do it step by step. You can also run it in Google Colab [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pcuenca/diffusers-examples/blob/main/notebooks/stable-diffusion-seeds.ipynb). +You can generate your own latents to reproduce results, or tweak your prompt on a specific result you liked. [This notebook](https://github.com/pcuenca/diffusers-examples/blob/main/notebooks/stable-diffusion-seeds.ipynb) shows how to do it step by step. You can also run it in Google Colab [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pcuenca/diffusers-examples/blob/main/notebooks/stable-diffusion-seeds.ipynb) ### In-painting using Stable Diffusion From ef4c2fa4f1cfaebab186d8007923ab129d219bd3 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?M=2E=20Tolga=20Cang=C3=B6z?= <46008593+standardAI@users.noreply.github.com> Date: Tue, 28 Mar 2023 19:17:53 +0300 Subject: [PATCH 26/56] Update alt_diffusion.mdx (#2865) Fix typos --- docs/source/en/api/pipelines/alt_diffusion.mdx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/source/en/api/pipelines/alt_diffusion.mdx b/docs/source/en/api/pipelines/alt_diffusion.mdx index cb86208ddbe1..dbe3b079a201 100644 --- a/docs/source/en/api/pipelines/alt_diffusion.mdx +++ b/docs/source/en/api/pipelines/alt_diffusion.mdx @@ -12,7 +12,7 @@ specific language governing permissions and limitations under the License. # AltDiffusion -AltDiffusion was proposed in [AltCLIP: Altering the Language Encoder in CLIP for Extended Language Capabilities](https://arxiv.org/abs/2211.06679) by Zhongzhi Chen, Guang Liu, Bo-Wen Zhang, Fulong Ye, Qinghong Yang, Ledell Wu +AltDiffusion was proposed in [AltCLIP: Altering the Language Encoder in CLIP for Extended Language Capabilities](https://arxiv.org/abs/2211.06679) by Zhongzhi Chen, Guang Liu, Bo-Wen Zhang, Fulong Ye, Qinghong Yang, Ledell Wu. The abstract of the paper is the following: @@ -28,7 +28,7 @@ The abstract of the paper is the following: ## Tips -- AltDiffusion is conceptually exaclty the same as [Stable Diffusion](./api/pipelines/stable_diffusion/overview). +- AltDiffusion is conceptually exactly the same as [Stable Diffusion](./api/pipelines/stable_diffusion/overview). - *Run AltDiffusion* From 03fe36f183ccefdcaddba7e7c2d4c93764326696 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?M=2E=20Tolga=20Cang=C3=B6z?= <46008593+standardAI@users.noreply.github.com> Date: Tue, 28 Mar 2023 19:23:39 +0300 Subject: [PATCH 27/56] Update paint_by_example.mdx (#2869) . --- docs/source/en/api/pipelines/paint_by_example.mdx | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/en/api/pipelines/paint_by_example.mdx b/docs/source/en/api/pipelines/paint_by_example.mdx index 04390a14b758..5abb3406db44 100644 --- a/docs/source/en/api/pipelines/paint_by_example.mdx +++ b/docs/source/en/api/pipelines/paint_by_example.mdx @@ -14,7 +14,7 @@ specific language governing permissions and limitations under the License. ## Overview -[Paint by Example: Exemplar-based Image Editing with Diffusion Models](https://arxiv.org/abs/2211.13227) by Binxin Yang, Shuyang Gu, Bo Zhang, Ting Zhang, Xuejin Chen, Xiaoyan Sun, Dong Chen, Fang Wen +[Paint by Example: Exemplar-based Image Editing with Diffusion Models](https://arxiv.org/abs/2211.13227) by Binxin Yang, Shuyang Gu, Bo Zhang, Ting Zhang, Xuejin Chen, Xiaoyan Sun, Dong Chen, Fang Wen. The abstract of the paper is the following: From 628fefb232f0695e6360ea94a0b3977d11738643 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?M=2E=20Tolga=20Cang=C3=B6z?= <46008593+standardAI@users.noreply.github.com> Date: Tue, 28 Mar 2023 19:23:54 +0300 Subject: [PATCH 28/56] Update stable_diffusion_safe.mdx (#2870) Fix typos --- docs/source/en/api/pipelines/stable_diffusion_safe.mdx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/source/en/api/pipelines/stable_diffusion_safe.mdx b/docs/source/en/api/pipelines/stable_diffusion_safe.mdx index 900f22badf6f..688eb5013c6a 100644 --- a/docs/source/en/api/pipelines/stable_diffusion_safe.mdx +++ b/docs/source/en/api/pipelines/stable_diffusion_safe.mdx @@ -36,7 +36,7 @@ Safe Stable Diffusion can be tested very easily with the [`StableDiffusionPipeli ### Interacting with the Safety Concept -To check and edit the currently used safety concept, use the `safety_concept` property of [`StableDiffusionPipelineSafe`] +To check and edit the currently used safety concept, use the `safety_concept` property of [`StableDiffusionPipelineSafe`]: ```python >>> from diffusers import StableDiffusionPipelineSafe @@ -60,7 +60,7 @@ You may use the 4 configurations defined in the [Safe Latent Diffusion paper](ht The following configurations are available: `SafetyConfig.WEAK`, `SafetyConfig.MEDIUM`, `SafetyConfig.STRONG`, and `SafetyConfig.MAX`. -### How to load and use different schedulers. +### How to load and use different schedulers The safe stable diffusion pipeline uses [`PNDMScheduler`] scheduler by default. But `diffusers` provides many other schedulers that can be used with the stable diffusion pipeline such as [`DDIMScheduler`], [`LMSDiscreteScheduler`], [`EulerDiscreteScheduler`], [`EulerAncestralDiscreteScheduler`] etc. To use a different scheduler, you can either change it via the [`ConfigMixin.from_config`] method or pass the `scheduler` argument to the `from_pretrained` method of the pipeline. For example, to use the [`EulerDiscreteScheduler`], you can do the following: From 40a7b8629e6a0470b7baac2b7843ecc7e923ca93 Mon Sep 17 00:00:00 2001 From: Patrick von Platen Date: Tue, 28 Mar 2023 18:32:18 +0200 Subject: [PATCH 29/56] [Docs] Correct phrasing (#2873) --- CONTRIBUTING.md | 2 +- docs/source/en/conceptual/contribution.mdx | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index e0e873892ca2..e9aa10a871d3 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -439,7 +439,7 @@ Push the changes to your account using: $ git push -u origin a-descriptive-name-for-my-changes ``` -6. Once you are satisfied (**and the checklist below is happy too**), go to the +6. Once you are satisfied, go to the webpage of your fork on GitHub. Click on 'Pull request' to send your changes to the project maintainers for review. diff --git a/docs/source/en/conceptual/contribution.mdx b/docs/source/en/conceptual/contribution.mdx index e0e873892ca2..e9aa10a871d3 100644 --- a/docs/source/en/conceptual/contribution.mdx +++ b/docs/source/en/conceptual/contribution.mdx @@ -439,7 +439,7 @@ Push the changes to your account using: $ git push -u origin a-descriptive-name-for-my-changes ``` -6. Once you are satisfied (**and the checklist below is happy too**), go to the +6. Once you are satisfied, go to the webpage of your fork on GitHub. Click on 'Pull request' to send your changes to the project maintainers for review. From d82b032319984bad3bf3a897f4826773dd422144 Mon Sep 17 00:00:00 2001 From: Sayak Paul Date: Wed, 29 Mar 2023 06:42:08 +0530 Subject: [PATCH 30/56] [Examples] Add streaming support to the ControlNet training example in JAX (#2859) * improve stable unclip doc. * feat: add streaming support to controlnet flax training script. * fix: CLI arg. * fix: torch dataloader shuffle setting. * fix: dataset length. * fix: wandb config. * fix: steps_per_epoch in the training loop. * add: entry about streaming in the readme * get column names from iterable dataset + fix final logging --------- Co-authored-by: yiyixuxu --- examples/controlnet/README.md | 31 ++++++++++- examples/controlnet/train_controlnet_flax.py | 57 +++++++++++++++----- 2 files changed, 73 insertions(+), 15 deletions(-) diff --git a/examples/controlnet/README.md b/examples/controlnet/README.md index 0650c2230b71..4e6856560bde 100644 --- a/examples/controlnet/README.md +++ b/examples/controlnet/README.md @@ -335,7 +335,7 @@ huggingface-cli login Make sure you have the `MODEL_DIR`,`OUTPUT_DIR` and `HUB_MODEL_ID` environment variables set. The `OUTPUT_DIR` and `HUB_MODEL_ID` variables specify where to save the model to on the Hub: -``` +```bash export MODEL_DIR="runwayml/stable-diffusion-v1-5" export OUTPUT_DIR="control_out" export HUB_MODEL_ID="fill-circle-controlnet" @@ -343,7 +343,7 @@ export HUB_MODEL_ID="fill-circle-controlnet" And finally start the training -``` +```bash python3 train_controlnet_flax.py \ --pretrained_model_name_or_path=$MODEL_DIR \ --output_dir=$OUTPUT_DIR \ @@ -363,3 +363,30 @@ python3 train_controlnet_flax.py \ ``` Since we passed the `--push_to_hub` flag, it will automatically create a model repo under your huggingface account based on `$HUB_MODEL_ID`. By the end of training, the final checkpoint will be automatically stored on the hub. You can find an example model repo [here](https://huggingface.co/YiYiXu/fill-circle-controlnet). + +Our training script also provides limited support for streaming large datasets from the Hugging Face Hub. In order to enable streaming, one must also set `--max_train_samples`. Here is an example command: + +```bash +python3 train_controlnet_flax.py \ + --pretrained_model_name_or_path=$MODEL_DIR \ + --output_dir=$OUTPUT_DIR \ + --dataset_name=multimodalart/facesyntheticsspigacaptioned \ + --streaming \ + --conditioning_image_column=spiga_seg \ + --image_column=image \ + --caption_column=image_caption \ + --resolution=512 \ + --max_train_samples 50 \ + --max_train_steps 5 \ + --learning_rate=1e-5 \ + --validation_steps=2 \ + --train_batch_size=1 \ + --revision="flax" \ + --report_to="wandb" +``` + +Note, however, that the performance of the TPUs might get bottlenecked as streaming with `datasets` is not optimized for images. For ensuring maximum throughput, we encourage you to explore the following options: + +* [Webdataset](https://webdataset.github.io/webdataset/) +* [TorchData](https://github.com/pytorch/data) +* [TensorFlow Datasets](https://www.tensorflow.org/datasets/tfless_tfds) \ No newline at end of file diff --git a/examples/controlnet/train_controlnet_flax.py b/examples/controlnet/train_controlnet_flax.py index c6c95170da2d..f409a539667c 100644 --- a/examples/controlnet/train_controlnet_flax.py +++ b/examples/controlnet/train_controlnet_flax.py @@ -35,6 +35,7 @@ from flax.training.common_utils import shard from huggingface_hub import HfFolder, Repository, create_repo, whoami from PIL import Image +from torch.utils.data import IterableDataset from torchvision import transforms from tqdm.auto import tqdm from transformers import CLIPTokenizer, FlaxCLIPTextModel, set_seed @@ -206,7 +207,7 @@ def parse_args(): parser.add_argument( "--from_pt", action="store_true", - help="Load the pretrained model from a pytorch checkpoint.", + help="Load the pretrained model from a PyTorch checkpoint.", ) parser.add_argument( "--tokenizer_name", @@ -332,6 +333,7 @@ def parse_args(): " or to a folder containing files that 🤗 Datasets can understand." ), ) + parser.add_argument("--streaming", action="store_true", help="To stream a large dataset from Hub.") parser.add_argument( "--dataset_config_name", type=str, @@ -369,7 +371,7 @@ def parse_args(): default=None, help=( "For debugging purposes or quicker training, truncate the number of training examples to this " - "value if set." + "value if set. Needed if `streaming` is set to True." ), ) parser.add_argument( @@ -453,10 +455,15 @@ def parse_args(): " or the same number of `--validation_prompt`s and `--validation_image`s" ) + # This idea comes from + # https://github.com/borisdayma/dalle-mini/blob/d2be512d4a6a9cda2d63ba04afc33038f98f705f/src/dalle_mini/data.py#L370 + if args.streaming and args.max_train_samples is None: + raise ValueError("You must specify `max_train_samples` when using dataset streaming.") + return args -def make_train_dataset(args, tokenizer): +def make_train_dataset(args, tokenizer, batch_size=None): # Get the datasets: you can either provide your own training and evaluation files (see below) # or specify a Dataset from the hub (the dataset will be downloaded automatically from the datasets Hub). @@ -468,6 +475,7 @@ def make_train_dataset(args, tokenizer): args.dataset_name, args.dataset_config_name, cache_dir=args.cache_dir, + streaming=args.streaming, ) else: data_files = {} @@ -483,7 +491,10 @@ def make_train_dataset(args, tokenizer): # Preprocessing the datasets. # We need to tokenize inputs and targets. - column_names = dataset["train"].column_names + if isinstance(dataset["train"], IterableDataset): + column_names = next(iter(dataset["train"])).keys() + else: + column_names = dataset["train"].column_names # 6. Get the column names for input/target. if args.image_column is None: @@ -565,9 +576,20 @@ def preprocess_train(examples): if jax.process_index() == 0: if args.max_train_samples is not None: - dataset["train"] = dataset["train"].shuffle(seed=args.seed).select(range(args.max_train_samples)) + if args.streaming: + dataset["train"] = dataset["train"].shuffle(seed=args.seed).take(args.max_train_samples) + else: + dataset["train"] = dataset["train"].shuffle(seed=args.seed).select(range(args.max_train_samples)) # Set the training transforms - train_dataset = dataset["train"].with_transform(preprocess_train) + if args.streaming: + train_dataset = dataset["train"].map( + preprocess_train, + batched=True, + batch_size=batch_size, + remove_columns=list(dataset["train"].features.keys()), + ) + else: + train_dataset = dataset["train"].with_transform(preprocess_train) return train_dataset @@ -661,12 +683,12 @@ def main(): raise NotImplementedError("No tokenizer specified!") # Get the datasets: you can either provide your own training and evaluation files (see below) - train_dataset = make_train_dataset(args, tokenizer) total_train_batch_size = args.train_batch_size * jax.local_device_count() * args.gradient_accumulation_steps + train_dataset = make_train_dataset(args, tokenizer, batch_size=total_train_batch_size) train_dataloader = torch.utils.data.DataLoader( train_dataset, - shuffle=True, + shuffle=not args.streaming, collate_fn=collate_fn, batch_size=total_train_batch_size, num_workers=args.dataloader_num_workers, @@ -897,7 +919,11 @@ def cumul_grad_step(grad_idx, loss_grad_rng): vae_params = jax_utils.replicate(vae_params) # Train! - num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) + if args.streaming: + dataset_length = args.max_train_samples + else: + dataset_length = len(train_dataloader) + num_update_steps_per_epoch = math.ceil(dataset_length / args.gradient_accumulation_steps) # Scheduler and math around the number of training steps. if args.max_train_steps is None: @@ -906,7 +932,7 @@ def cumul_grad_step(grad_idx, loss_grad_rng): args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch) logger.info("***** Running training *****") - logger.info(f" Num examples = {len(train_dataset)}") + logger.info(f" Num examples = {args.max_train_samples if args.streaming else len(train_dataset)}") logger.info(f" Num Epochs = {args.num_train_epochs}") logger.info(f" Instantaneous batch size per device = {args.train_batch_size}") logger.info(f" Total train batch size (w. parallel & distributed) = {total_train_batch_size}") @@ -916,7 +942,7 @@ def cumul_grad_step(grad_idx, loss_grad_rng): wandb.define_metric("*", step_metric="train/step") wandb.config.update( { - "num_train_examples": len(train_dataset), + "num_train_examples": args.max_train_samples if args.streaming else len(train_dataset), "total_train_batch_size": total_train_batch_size, "total_optimization_step": args.num_train_epochs * num_update_steps_per_epoch, "num_devices": jax.device_count(), @@ -935,7 +961,11 @@ def cumul_grad_step(grad_idx, loss_grad_rng): train_metrics = [] - steps_per_epoch = len(train_dataset) // total_train_batch_size + steps_per_epoch = ( + args.max_train_samples // total_train_batch_size + if args.streaming + else len(train_dataset) // total_train_batch_size + ) train_step_progress_bar = tqdm( total=steps_per_epoch, desc="Training...", @@ -980,7 +1010,8 @@ def cumul_grad_step(grad_idx, loss_grad_rng): # Create the pipeline using using the trained modules and save it. if jax.process_index() == 0: - image_logs = log_validation(controlnet, state.params, tokenizer, args, validation_rng, weight_dtype) + if args.validation_prompt is not None: + image_logs = log_validation(controlnet, state.params, tokenizer, args, validation_rng, weight_dtype) controlnet.save_pretrained( args.output_dir, From 3be489182ef2a1170d425327a7cb69dff460b461 Mon Sep 17 00:00:00 2001 From: Yaman Ahlawat Date: Wed, 29 Mar 2023 16:01:02 +0530 Subject: [PATCH 31/56] feat: allow offset_noise in dreambooth training example (#2826) --- examples/dreambooth/train_dreambooth.py | 17 ++++++++++++++++- 1 file changed, 16 insertions(+), 1 deletion(-) diff --git a/examples/dreambooth/train_dreambooth.py b/examples/dreambooth/train_dreambooth.py index 414ecdeb1fb7..3d2e694a1015 100644 --- a/examples/dreambooth/train_dreambooth.py +++ b/examples/dreambooth/train_dreambooth.py @@ -417,6 +417,16 @@ def parse_args(input_args=None): ), ) + parser.add_argument( + "--offset_noise", + action="store_true", + default=False, + help=( + "Fine-tuning against a modified noise" + " See: https://www.crosslabs.org//blog/diffusion-with-offset-noise for more information." + ), + ) + if input_args is not None: args = parser.parse_args(input_args) else: @@ -943,7 +953,12 @@ def load_model_hook(models, input_dir): latents = latents * vae.config.scaling_factor # Sample noise that we'll add to the latents - noise = torch.randn_like(latents) + if args.offset_noise: + noise = torch.randn_like(latents) + 0.1 * torch.randn( + latents.shape[0], latents.shape[1], 1, 1, device=latents.device + ) + else: + noise = torch.randn_like(latents) bsz = latents.shape[0] # Sample a random timestep for each image timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device) From e47459c80f6f6a5a1c19d32c3fd74edf94f47aa2 Mon Sep 17 00:00:00 2001 From: Steven Liu <59462357+stevhliu@users.noreply.github.com> Date: Wed, 29 Mar 2023 12:48:14 -0700 Subject: [PATCH 32/56] [docs] Performance tutorial (#2773) * update performance tutorial * fix divs * oops forgot to close tag * apply feedback * apply feedback * apply feedback * align doc title --- docs/source/en/_toctree.yml | 2 +- docs/source/en/stable_diffusion.mdx | 480 ++++++++++++---------------- 2 files changed, 210 insertions(+), 272 deletions(-) diff --git a/docs/source/en/_toctree.yml b/docs/source/en/_toctree.yml index 2381791a241b..1a0d8f5cd6c8 100644 --- a/docs/source/en/_toctree.yml +++ b/docs/source/en/_toctree.yml @@ -4,7 +4,7 @@ - local: quicktour title: Quicktour - local: stable_diffusion - title: Stable Diffusion + title: Effective and efficient diffusion - local: installation title: Installation title: Get started diff --git a/docs/source/en/stable_diffusion.mdx b/docs/source/en/stable_diffusion.mdx index c1eef6fa3c5c..eebe0ec660f2 100644 --- a/docs/source/en/stable_diffusion.mdx +++ b/docs/source/en/stable_diffusion.mdx @@ -1,333 +1,271 @@ - - -# The Stable Diffusion Guide 🎨 - - Open In Colab - - -## Intro - -Stable Diffusion is a [Latent Diffusion model](https://github.com/CompVis/latent-diffusion) developed by researchers from the Machine Vision and Learning group at LMU Munich, *a.k.a* CompVis. -Model checkpoints were publicly released at the end of August 2022 by a collaboration of Stability AI, CompVis, and Runway with support from EleutherAI and LAION. For more information, you can check out [the official blog post](https://stability.ai/blog/stable-diffusion-public-release). - -Since its public release the community has done an incredible job at working together to make the stable diffusion checkpoints **faster**, **more memory efficient**, and **more performant**. - -🧨 Diffusers offers a simple API to run stable diffusion with all memory, computing, and quality improvements. - -This notebook walks you through the improvements one-by-one so you can best leverage [`StableDiffusionPipeline`] for **inference**. - -## Prompt Engineering 🎨 - -When running *Stable Diffusion* in inference, we usually want to generate a certain type, or style of image and then improve upon it. Improving upon a previously generated image means running inference over and over again with a different prompt and potentially a different seed until we are happy with our generation. - -So to begin with, it is most important to speed up stable diffusion as much as possible to generate as many pictures as possible in a given amount of time. - -This can be done by both improving the **computational efficiency** (speed) and the **memory efficiency** (GPU RAM). - -Let's start by looking into computational efficiency first. - -Throughout the notebook, we will focus on [runwayml/stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5): - -``` python -model_id = "runwayml/stable-diffusion-v1-5" -``` - -Let's load the pipeline. - -## Speed Optimization - -``` python -from diffusers import DiffusionPipeline - -pipe = DiffusionPipeline.from_pretrained(model_id) -``` - -We aim at generating a beautiful photograph of an *old warrior chief* and will later try to find the best prompt to generate such a photograph. For now, let's keep the prompt simple: - -``` python -prompt = "portrait photo of a old warrior chief" -``` - -To begin with, we should make sure we run inference on GPU, so let's move the pipeline to GPU, just like you would with any PyTorch module. - -``` python -pipe = pipe.to("cuda") -``` - -To generate an image, you should use the [~`StableDiffusionPipeline.__call__`] method. - -To make sure we can reproduce more or less the same image in every call, let's make use of the generator. See the documentation on reproducibility [here](./conceptual/reproducibility) for more information. - -``` python -generator = torch.Generator("cuda").manual_seed(0) -``` - -Now, let's take a spin on it. - -``` python -image = pipe(prompt, generator=generator).images[0] -image -``` - -![img](https://huggingface.co/datasets/diffusers/docs-images/resolve/main/stable_diffusion_101/sd_101_1.png) - -Cool, this now took roughly 30 seconds on a T4 GPU (you might see faster inference if your allocated GPU is better than a T4). - -The default run we did above used full float32 precision and ran the default number of inference steps (50). The easiest speed-ups come from switching to float16 (or half) precision and simply running fewer inference steps. Let's load the model now in float16 instead. - -``` python -import torch - -pipe = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16) -pipe = pipe.to("cuda") -``` - -And we can again call the pipeline to generate an image. - -``` python -generator = torch.Generator("cuda").manual_seed(0) - -image = pipe(prompt, generator=generator).images[0] -image -``` -![img](https://huggingface.co/datasets/diffusers/docs-images/resolve/main/stable_diffusion_101/sd_101_2.png) - -Cool, this is almost three times as fast for arguably the same image quality. - -We strongly suggest always running your pipelines in float16 as so far we have very rarely seen degradations in quality because of it. - -Next, let's see if we need to use 50 inference steps or whether we could use significantly fewer. The number of inference steps is associated with the denoising scheduler we use. Choosing a more efficient scheduler could help us decrease the number of steps. - -Let's have a look at all the schedulers the stable diffusion pipeline is compatible with. - -``` python -pipe.scheduler.compatibles -``` - -``` - [diffusers.schedulers.scheduling_dpmsolver_singlestep.DPMSolverSinglestepScheduler, - diffusers.schedulers.scheduling_lms_discrete.LMSDiscreteScheduler, - diffusers.schedulers.scheduling_heun_discrete.HeunDiscreteScheduler, - diffusers.schedulers.scheduling_pndm.PNDMScheduler, - diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler, - diffusers.schedulers.scheduling_euler_ancestral_discrete.EulerAncestralDiscreteScheduler, - diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler, - diffusers.schedulers.scheduling_ddpm.DDPMScheduler, - diffusers.schedulers.scheduling_ddim.DDIMScheduler] -``` - -Cool, that's a lot of schedulers. - -🧨 Diffusers is constantly adding a bunch of novel schedulers/samplers that can be used with Stable Diffusion. For more information, we recommend taking a look at the official documentation [here](https://huggingface.co/docs/diffusers/main/en/api/schedulers/overview). - -Alright, right now Stable Diffusion is using the `PNDMScheduler` which usually requires around 50 inference steps. However, other schedulers such as `DPMSolverMultistepScheduler` or `DPMSolverSinglestepScheduler` seem to get away with just 20 to 25 inference steps. Let's try them out. - -You can set a new scheduler by making use of the [from_config](https://huggingface.co/docs/diffusers/main/en/api/configuration#diffusers.ConfigMixin.from_config) function. - -``` python -from diffusers import DPMSolverMultistepScheduler - -pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config) -``` - -Now, let's try to reduce the number of inference steps to just 20. - -``` python -generator = torch.Generator("cuda").manual_seed(0) - -image = pipe(prompt, generator=generator, num_inference_steps=20).images[0] -image -``` - -![img](https://huggingface.co/datasets/diffusers/docs-images/resolve/main/stable_diffusion_101/sd_101_3.png) - -The image now does look a little different, but it's arguably still of equally high quality. We now cut inference time to just 4 seconds though 😍. - -## Memory Optimization + + +# Effective and efficient diffusion -``` python -def get_inputs(batch_size=1): - generator = [torch.Generator("cuda").manual_seed(i) for i in range(batch_size)] - prompts = batch_size * [prompt] - num_inference_steps = 20 +[[open-in-colab]] - return {"prompt": prompts, "generator": generator, "num_inference_steps": num_inference_steps} -``` -This function returns a list of prompts and a list of generators, so we can reuse the generator that produced a result we like. +Getting the [`DiffusionPipeline`] to generate images in a certain style or include what you want can be tricky. Often times, you have to run the [`DiffusionPipeline`] several times before you end up with an image you're happy with. But generating something out of nothing is a computationally intensive process, especially if you're running inference over and over again. -We also need a method that allows us to easily display a batch of images. +This is why it's important to get the most *computational* (speed) and *memory* (GPU RAM) efficiency from the pipeline to reduce the time between inference cycles so you can iterate faster. -``` python -from PIL import Image +This tutorial walks you through how to generate faster and better with the [`DiffusionPipeline`]. -def image_grid(imgs, rows=2, cols=2): - w, h = imgs[0].size - grid = Image.new('RGB', size=(cols*w, rows*h)) - - for i, img in enumerate(imgs): - grid.paste(img, box=(i%cols*w, i//cols*h)) - return grid -``` +Begin by loading the [`runwayml/stable-diffusion-v1-5`](https://huggingface.co/runwayml/stable-diffusion-v1-5) model: -Cool, let's see how much memory we can use starting with `batch_size=4`. +```python +from diffusers import DiffusionPipeline -``` python -images = pipe(**get_inputs(batch_size=4)).images -image_grid(images) -``` +model_id = "runwayml/stable-diffusion-v1-5" +pipeline = DiffusionPipeline.from_pretrained(model_id) +``` -![img](https://huggingface.co/datasets/diffusers/docs-images/resolve/main/stable_diffusion_101/sd_101_4.png) +The example prompt you'll use is a portrait of an old warrior chief, but feel free to use your own prompt: -Going over a batch_size of 4 will error out in this notebook (assuming we are running it on a T4 GPU). Also, we can see we only generate slightly more images per second (3.75s/image) compared to 4s/image previously. +```python +prompt = "portrait photo of a old warrior chief" +``` -However, the community has found some nice tricks to improve the memory constraints further. After stable diffusion was released, the community found improvements within days and shared them freely over GitHub - open-source at its finest! I believe the original idea came from [this](https://github.com/basujindal/stable-diffusion/pull/117) GitHub thread. +## Speed -By far most of the memory is taken up by the cross-attention layers. Instead of running this operation in batch, one can run it sequentially to save a significant amount of memory. + -It can easily be enabled by calling `enable_attention_slicing` as is documented [here](https://huggingface.co/docs/diffusers/main/en/api/pipelines/stable_diffusion/text2img#diffusers.StableDiffusionPipeline.enable_attention_slicing). +💡 If you don't have access to a GPU, you can use one for free from a GPU provider like [Colab](https://colab.research.google.com/)! -``` python -pipe.enable_attention_slicing() -``` + -Great, now that attention slicing is enabled, let's try to double the batch size again, going for `batch_size=8`. +One of the simplest ways to speed up inference is to place the pipeline on a GPU the same way you would with any PyTorch module: -``` python -images = pipe(**get_inputs(batch_size=8)).images -image_grid(images, rows=2, cols=4) -``` +```python +pipeline = pipeline.to("cuda") +``` -![img](https://huggingface.co/datasets/diffusers/docs-images/resolve/main/stable_diffusion_101/sd_101_5.png) +To make sure you can use the same image and improve on it, use a [`Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) and set a seed for [reproducibility](./using-diffusers/reproducibility): -Nice, it works. However, the speed gain is again not very big (it might however be much more significant on other GPUs). +```python +generator = torch.Generator("cuda").manual_seed(0) +``` -We're at roughly 3.5 seconds per image 🔥 which is probably the fastest we can be with a simple T4 without sacrificing quality. +Now you can generate an image: -Next, let's look into how to improve the quality! +```python +image = pipeline(prompt, generator=generator).images[0] +image +``` -## Quality Improvements +
+ +
-Now that our image generation pipeline is blazing fast, let's try to get maximum image quality. +This process took ~30 seconds on a T4 GPU (it might be faster if your allocated GPU is better than a T4). By default, the [`DiffusionPipeline`] runs inference with full `float32` precision for 50 inference steps. You can speed this up by switching to a lower precision like `float16` or running fewer inference steps. -First of all, image quality is extremely subjective, so it's difficult to make general claims here. +Let's start by loading the model in `float16` and generate an image: -The most obvious step to take to improve quality is to use *better checkpoints*. Since the release of Stable Diffusion, many improved versions have been released, which are summarized here: +```python +import torch -- *Official Release - 22 Aug 2022*: [Stable-Diffusion 1.4](https://huggingface.co/CompVis/stable-diffusion-v1-4) -- *20 October 2022*: [Stable-Diffusion 1.5](https://huggingface.co/runwayml/stable-diffusion-v1-5) -- *24 Nov 2022*: [Stable-Diffusion 2.0](https://huggingface.co/stabilityai/stable-diffusion-2-0) -- *7 Dec 2022*: [Stable-Diffusion 2.1](https://huggingface.co/stabilityai/stable-diffusion-2-1) +pipeline = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16) +pipeline = pipeline.to("cuda") +generator = torch.Generator("cuda").manual_seed(0) +image = pipeline(prompt, generator=generator).images[0] +image +``` -Newer versions don't necessarily mean better image quality with the same parameters. People mentioned that *2.0* is slightly worse than *1.5* for certain prompts, but given the right prompt engineering *2.0* and *2.1* seem to be better. +
+ +
-Overall, we strongly recommend just trying the models out and reading up on advice online (e.g. it has been shown that using negative prompts is very important for 2.0 and 2.1 to get the highest possible quality. See for example [this nice blog post](https://minimaxir.com/2022/11/stable-diffusion-negative-prompt/). +This time, it only took ~11 seconds to generate the image, which is almost 3x faster than before! -Additionally, the community has started fine-tuning many of the above versions on certain styles with some of them having an extremely high quality and gaining a lot of traction. + -We recommend having a look at all [diffusers checkpoints sorted by downloads and trying out the different checkpoints](https://huggingface.co/models?library=diffusers). +💡 We strongly suggest always running your pipelines in `float16`, and so far, we've rarely seen any degradation in output quality. -For the following, we will stick to v1.5 for simplicity. + -Next, we can also try to optimize single components of the pipeline, e.g. switching out the latent decoder. For more details on how the whole Stable Diffusion pipeline works, please have a look at [this blog post](https://huggingface.co/blog/stable_diffusion). +Another option is to reduce the number of inference steps. Choosing a more efficient scheduler could help decrease the number of steps without sacrificing output quality. You can find which schedulers are compatible with the current model in the [`DiffusionPipeline`] by calling the `compatibles` method: -Let's load [stabilityai's newest auto-decoder](https://huggingface.co/stabilityai/stable-diffusion-2-1). +```python +pipeline.scheduler.compatibles +[ + diffusers.schedulers.scheduling_lms_discrete.LMSDiscreteScheduler, + diffusers.schedulers.scheduling_unipc_multistep.UniPCMultistepScheduler, + diffusers.schedulers.scheduling_k_dpm_2_discrete.KDPM2DiscreteScheduler, + diffusers.schedulers.scheduling_deis_multistep.DEISMultistepScheduler, + diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler, + diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler, + diffusers.schedulers.scheduling_ddpm.DDPMScheduler, + diffusers.schedulers.scheduling_dpmsolver_singlestep.DPMSolverSinglestepScheduler, + diffusers.schedulers.scheduling_k_dpm_2_ancestral_discrete.KDPM2AncestralDiscreteScheduler, + diffusers.schedulers.scheduling_heun_discrete.HeunDiscreteScheduler, + diffusers.schedulers.scheduling_pndm.PNDMScheduler, + diffusers.schedulers.scheduling_euler_ancestral_discrete.EulerAncestralDiscreteScheduler, + diffusers.schedulers.scheduling_ddim.DDIMScheduler, +] +``` -``` python -from diffusers import AutoencoderKL +The Stable Diffusion model uses the [`PNDMScheduler`] by default which usually requires ~50 inference steps, but more performant schedulers like [`DPMSolverMultistepScheduler`], require only ~20 or 25 inference steps. Use the [`ConfigMixin.from_config`] method to load a new scheduler: -vae = AutoencoderKL.from_pretrained("stabilityai/sd-vae-ft-mse", torch_dtype=torch.float16).to("cuda") -``` +```python +from diffusers import DPMSolverMultistepScheduler -Now we can set it to the vae of the pipeline to use it. +pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config) +``` -``` python -pipe.vae = vae -``` +Now set the `num_inference_steps` to 20: -Let's run the same prompt as before to compare quality. +```python +generator = torch.Generator("cuda").manual_seed(0) +image = pipeline(prompt, generator=generator, num_inference_steps=20).images[0] +image +``` -``` python -images = pipe(**get_inputs(batch_size=8)).images -image_grid(images, rows=2, cols=4) -``` +
+ +
-![img](https://huggingface.co/datasets/diffusers/docs-images/resolve/main/stable_diffusion_101/sd_101_6.png) +Great, you've managed to cut the inference time to just 4 seconds! ⚡️ -Seems like the difference is only very minor, but the new generations are arguably a bit *sharper*. +## Memory -Cool, finally, let's look a bit into prompt engineering. +The other key to improving pipeline performance is consuming less memory, which indirectly implies more speed, since you're often trying to maximize the number of images generated per second. The easiest way to see how many images you can generate at once is to try out different batch sizes until you get an `OutOfMemoryError` (OOM). -Our goal was to generate a photo of an old warrior chief. Let's now try to bring a bit more color into the photos and make the look more impressive. +Create a function that'll generate a batch of images from a list of prompts and `Generators`. Make sure to assign each `Generator` a seed so you can reuse it if it produces a good result. -Originally our prompt was "*portrait photo of an old warrior chief*". +```python +def get_inputs(batch_size=1): + generator = [torch.Generator("cuda").manual_seed(i) for i in range(batch_size)] + prompts = batch_size * [prompt] + num_inference_steps = 20 -To improve the prompt, it often helps to add cues that could have been used online to save high-quality photos, as well as add more details. -Essentially, when doing prompt engineering, one has to think: + return {"prompt": prompts, "generator": generator, "num_inference_steps": num_inference_steps} +``` -- How was the photo or similar photos of the one I want probably stored on the internet? -- What additional detail can I give that steers the models into the style that I want? +You'll also need a function that'll display each batch of images: -Cool, let's add more details. +```python +from PIL import image -``` python -prompt += ", tribal panther make up, blue on red, side profile, looking away, serious eyes" -``` -and let's also add some cues that usually help to generate higher quality images. +def image_grid(imgs, rows=2, cols=2): + w, h = imgs[0].size + grid = Image.new("RGB", size=(cols * w, rows * h)) -``` python -prompt += " 50mm portrait photography, hard rim lighting photography--beta --ar 2:3 --beta --upbeta" -prompt -``` + for i, img in enumerate(imgs): + grid.paste(img, box=(i % cols * w, i // cols * h)) + return grid +``` -Cool, let's now try this prompt. +Start with `batch_size=4` and see how much memory you've consumed: -``` python -images = pipe(**get_inputs(batch_size=8)).images -image_grid(images, rows=2, cols=4) -``` +```python +images = pipeline(**get_inputs(batch_size=4)).images +image_grid(images) +``` -![img](https://huggingface.co/datasets/diffusers/docs-images/resolve/main/stable_diffusion_101/sd_101_7.png) +Unless you have a GPU with more RAM, the code above probably returned an `OOM` error! Most of the memory is taken up by the cross-attention layers. Instead of running this operation in a batch, you can run it sequentially to save a significant amount of memory. All you have to do is configure the pipeline to use the [`~DiffusionPipeline.enable_attention_slicing`] function: -Pretty impressive! We got some very high-quality image generations there. The 2nd image is my personal favorite, so I'll re-use this seed and see whether I can tweak the prompts slightly by using "oldest warrior", "old", "", and "young" instead of "old". +```python +pipeline.enable_attention_slicing() +``` -``` python -prompts = [ - "portrait photo of the oldest warrior chief, tribal panther make up, blue on red, side profile, looking away, serious eyes 50mm portrait photography, hard rim lighting photography--beta --ar 2:3 --beta --upbeta", - "portrait photo of a old warrior chief, tribal panther make up, blue on red, side profile, looking away, serious eyes 50mm portrait photography, hard rim lighting photography--beta --ar 2:3 --beta --upbeta", - "portrait photo of a warrior chief, tribal panther make up, blue on red, side profile, looking away, serious eyes 50mm portrait photography, hard rim lighting photography--beta --ar 2:3 --beta --upbeta", - "portrait photo of a young warrior chief, tribal panther make up, blue on red, side profile, looking away, serious eyes 50mm portrait photography, hard rim lighting photography--beta --ar 2:3 --beta --upbeta", -] +Now try increasing the `batch_size` to 8! -generator = [torch.Generator("cuda").manual_seed(1) for _ in range(len(prompts))] # 1 because we want the 2nd image +```python +images = pipeline(**get_inputs(batch_size=8)).images +image_grid(images, rows=2, cols=4) +``` -images = pipe(prompt=prompts, generator=generator, num_inference_steps=25).images -image_grid(images) -``` +
+ +
-![img](https://huggingface.co/datasets/diffusers/docs-images/resolve/main/stable_diffusion_101/sd_101_8.png) +Whereas before you couldn't even generate a batch of 4 images, now you can generate a batch of 8 images at ~3.5 seconds per image! This is probably the fastest you can go on a T4 GPU without sacrificing quality. -The first picture looks nice! The eye movement slightly changed and looks nice. This finished up our 101-guide on how to use Stable Diffusion 🤗. +## Quality -For more information on optimization or other guides, I recommend taking a look at the following: +In the last two sections, you learned how to optimize the speed of your pipeline by using `fp16`, reducing the number of inference steps by using a more performant scheduler, and enabling attention slicing to reduce memory consumption. Now you're going to focus on how to improve the quality of generated images. -- [Blog post about Stable Diffusion](https://huggingface.co/blog/stable_diffusion): In-detail blog post explaining Stable Diffusion. -- [FlashAttention](https://huggingface.co/docs/diffusers/optimization/xformers): XFormers flash attention can optimize your model even further with more speed and memory improvements. -- [Dreambooth](https://huggingface.co/docs/diffusers/training/dreambooth) - Quickly customize the model by fine-tuning it. -- [General info on Stable Diffusion](https://huggingface.co/docs/diffusers/main/en/api/pipelines/stable_diffusion/overview) - Info on other tasks that are powered by Stable Diffusion. +### Better checkpoints + +The most obvious step is to use better checkpoints. The Stable Diffusion model is a good starting point, and since its official launch, several improved versions have also been released. However, using a newer version doesn't automatically mean you'll get better results. You'll still have to experiment with different checkpoints yourself, and do a little research (such as using [negative prompts](https://minimaxir.com/2022/11/stable-diffusion-negative-prompt/)) to get the best results. + +As the field grows, there are more and more high-quality checkpoints finetuned to produce certain styles. Try exploring the [Hub](https://huggingface.co/models?library=diffusers&sort=downloads) and [Diffusers Gallery](https://huggingface.co/spaces/huggingface-projects/diffusers-gallery) to find one you're interested in! + +### Better pipeline components + +You can also try replacing the current pipeline components with a newer version. Let's try loading the latest [autodecoder](https://huggingface.co/stabilityai/stable-diffusion-2-1/tree/main/vae) from Stability AI into the pipeline, and generate some images: + +```python +from diffusers import AutoencoderKL + +vae = AutoencoderKL.from_pretrained("stabilityai/sd-vae-ft-mse", torch_dtype=torch.float16).to("cuda") +pipeline.vae = vae +images = pipeline(**get_inputs(batch_size=8)).images +image_grid(images, rows=2, cols=4) +``` + +
+ +
+ +### Better prompt engineering + +The text prompt you use to generate an image is super important, so much so that it is called *prompt engineering*. Some considerations to keep during prompt engineering are: + +- How is the image or similar images of the one I want to generate stored on the internet? +- What additional detail can I give that steers the model towards the style I want? + +With this in mind, let's improve the prompt to include color and higher quality details: + +```python +prompt += ", tribal panther make up, blue on red, side profile, looking away, serious eyes" +prompt += " 50mm portrait photography, hard rim lighting photography--beta --ar 2:3 --beta --upbeta" +``` + +Generate a batch of images with the new prompt: + +```python +images = pipeline(**get_inputs(batch_size=8)).images +image_grid(images, rows=2, cols=4) +``` + +
+ +
+ +Pretty impressive! Let's tweak the second image - corresponding to the `Generator` with a seed of `1` - a bit more by adding some text about the age of the subject: + +```python +prommpts = [ + "portrait photo of the oldest warrior chief, tribal panther make up, blue on red, side profile, looking away, serious eyes 50mm portrait photography, hard rim lighting photography--beta --ar 2:3 --beta --upbeta", + "portrait photo of a old warrior chief, tribal panther make up, blue on red, side profile, looking away, serious eyes 50mm portrait photography, hard rim lighting photography--beta --ar 2:3 --beta --upbeta", + "portrait photo of a warrior chief, tribal panther make up, blue on red, side profile, looking away, serious eyes 50mm portrait photography, hard rim lighting photography--beta --ar 2:3 --beta --upbeta", + "portrait photo of a young warrior chief, tribal panther make up, blue on red, side profile, looking away, serious eyes 50mm portrait photography, hard rim lighting photography--beta --ar 2:3 --beta --upbeta", +] + +generator = [torch.Generator("cuda").manual_seed(1) for _ in range(len(prompts))] +images = pipeline(prompt=prompts, generator=generator, num_inference_steps=25).images +image_grid(images) +``` + +
+ +
+ +## Next steps + +In this tutorial, you learned how to optimize a [`DiffusionPipeline`] for computational and memory efficiency as well as improving the quality of generated outputs. If you're interested in making your pipeline even faster, take a look at the following resources: + +- Enable [xFormers](./optimization/xformers) memory efficient attention mechanism for faster speed and reduced memory consumption. +- Learn how in [PyTorch 2.0](./optimization/torch2.0), [`torch.compile`](https://pytorch.org/docs/stable/generated/torch.compile.html) can yield 2-9% faster inference speed. +- Many optimization techniques for inference are also included in this memory and speed [guide](./optimization/fp16), such as memory offloading. \ No newline at end of file From b2021273eb54a39ab16cd4a8178dddc6c3cf05f5 Mon Sep 17 00:00:00 2001 From: Sayak Paul Date: Thu, 30 Mar 2023 17:14:04 +0530 Subject: [PATCH 33/56] [Docs] add an example use for `StableUnCLIPPipeline` in the pipeline docs (#2897) * improve stable unclip doc. * add: entry of StableUnCLIPPipeline to the docs * Apply suggestions from code review Co-authored-by: apolinario --------- Co-authored-by: apolinario --- .../source/en/api/pipelines/stable_unclip.mdx | 42 ++++++++++++++++++- 1 file changed, 40 insertions(+), 2 deletions(-) diff --git a/docs/source/en/api/pipelines/stable_unclip.mdx b/docs/source/en/api/pipelines/stable_unclip.mdx index 372242ae2dff..ee359d0ba486 100644 --- a/docs/source/en/api/pipelines/stable_unclip.mdx +++ b/docs/source/en/api/pipelines/stable_unclip.mdx @@ -32,12 +32,50 @@ we do not add any additional noise to the image embeddings i.e. `noise_level = 0 * [stabilityai/stable-diffusion-2-1-unclip](https://hf.co/stabilityai/stable-diffusion-2-1-unclip) * [stabilityai/stable-diffusion-2-1-unclip-small](https://hf.co/stabilityai/stable-diffusion-2-1-unclip-small) * Text-to-image - * Coming soon! + * [stabilityai/stable-diffusion-2-1-unclip-small](https://hf.co/stabilityai/stable-diffusion-2-1-unclip-small) ### Text-to-Image Generation +Stable unCLIP can be leveraged for text-to-image generation by pipelining it with the prior model of KakaoBrain's open source DALL-E 2 replication [Karlo](https://huggingface.co/kakaobrain/karlo-v1-alpha) + +```python +import torch +from diffusers import UnCLIPScheduler, DDPMScheduler, StableUnCLIPPipeline +from diffusers.models import PriorTransformer +from transformers import CLIPTokenizer, CLIPTextModelWithProjection + +prior_model_id = "kakaobrain/karlo-v1-alpha" +data_type = torch.float16 +prior = PriorTransformer.from_pretrained(prior_model_id, subfolder="prior", torch_dtype=data_type) + +prior_text_model_id = "openai/clip-vit-large-patch14" +prior_tokenizer = CLIPTokenizer.from_pretrained(prior_text_model_id) +prior_text_model = CLIPTextModelWithProjection.from_pretrained(prior_text_model_id, torch_dtype=data_type) +prior_scheduler = UnCLIPScheduler.from_pretrained(prior_model_id, subfolder="prior_scheduler") +prior_scheduler = DDPMScheduler.from_config(prior_scheduler.config) + +stable_unclip_model_id = "stabilityai/stable-diffusion-2-1-unclip-small" + +pipe = StableUnCLIPPipeline.from_pretrained( + stable_unclip_model_id, + torch_dtype=data_type, + variant="fp16", + prior_tokenizer=prior_tokenizer, + prior_text_encoder=prior_text_model, + prior=prior, + prior_scheduler=prior_scheduler, +) + +pipe = pipe.to("cuda") +wave_prompt = "dramatic wave, the Oceans roar, Strong wave spiral across the oceans as the waves unfurl into roaring crests; perfect wave form; perfect wave shape; dramatic wave shape; wave shape unbelievable; wave; wave shape spectacular" + +images = pipe(prompt=wave_prompt).images +images[0].save("waves.png") +``` + -Coming soon! +For text-to-image we use `stabilityai/stable-diffusion-2-1-unclip-small` as it was trained on CLIP ViT-L/14 embedding, the same as the Karlo model prior. [stabilityai/stable-diffusion-2-1-unclip](https://hf.co/stabilityai/stable-diffusion-2-1-unclip) was trained on OpenCLIP ViT-H, so we don't recommend its use. + ### Text guided Image-to-Image Variation From b3d5cc4a3622d91f387c7c732b3a9bdcea2a1e8f Mon Sep 17 00:00:00 2001 From: YiYi Xu Date: Thu, 30 Mar 2023 06:10:26 -1000 Subject: [PATCH 34/56] add flax requirement (#2894) Co-authored-by: yiyixuxu --- examples/controlnet/requirements_flax.txt | 9 +++++++++ 1 file changed, 9 insertions(+) create mode 100644 examples/controlnet/requirements_flax.txt diff --git a/examples/controlnet/requirements_flax.txt b/examples/controlnet/requirements_flax.txt new file mode 100644 index 000000000000..b6eb64e25462 --- /dev/null +++ b/examples/controlnet/requirements_flax.txt @@ -0,0 +1,9 @@ +transformers>=4.25.1 +datasets +flax +optax +torch +torchvision +ftfy +tensorboard +Jinja2 From 9062b2847d0ab412ed12b9bd5590779dda28d6b2 Mon Sep 17 00:00:00 2001 From: Alon Burg Date: Thu, 30 Mar 2023 19:26:18 +0300 Subject: [PATCH 35/56] Support fp16 in conversion from original ckpt (#2733) add --half to convert_original_stable_diffusion_to_diffusers.py --- scripts/convert_original_stable_diffusion_to_diffusers.py | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/scripts/convert_original_stable_diffusion_to_diffusers.py b/scripts/convert_original_stable_diffusion_to_diffusers.py index b90737892815..20228582e9e2 100644 --- a/scripts/convert_original_stable_diffusion_to_diffusers.py +++ b/scripts/convert_original_stable_diffusion_to_diffusers.py @@ -14,6 +14,7 @@ # limitations under the License. """ Conversion script for the LDM checkpoints. """ +import torch import argparse from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt @@ -123,6 +124,7 @@ parser.add_argument( "--controlnet", action="store_true", default=None, help="Set flag if this is a controlnet checkpoint." ) + parser.add_argument("--half", action="store_true", help="Save weights in half precision.") args = parser.parse_args() pipe = download_from_original_stable_diffusion_ckpt( @@ -143,6 +145,9 @@ controlnet=args.controlnet, ) + if args.half: + pipe.to(torch_dtype=torch.float16) + if args.controlnet: # only save the controlnet model pipe.controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors) From 49609768b47f08c810e6d92846685c72bd64ca81 Mon Sep 17 00:00:00 2001 From: Patrick von Platen Date: Thu, 30 Mar 2023 18:26:41 +0200 Subject: [PATCH 36/56] make style --- scripts/convert_original_stable_diffusion_to_diffusers.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/scripts/convert_original_stable_diffusion_to_diffusers.py b/scripts/convert_original_stable_diffusion_to_diffusers.py index 20228582e9e2..de64095523b6 100644 --- a/scripts/convert_original_stable_diffusion_to_diffusers.py +++ b/scripts/convert_original_stable_diffusion_to_diffusers.py @@ -14,9 +14,10 @@ # limitations under the License. """ Conversion script for the LDM checkpoints. """ -import torch import argparse +import torch + from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt From 1d033a95f62ccf2cdbb31795f69798ff1870241d Mon Sep 17 00:00:00 2001 From: Michael Gartsbein Date: Thu, 30 Mar 2023 20:00:12 +0300 Subject: [PATCH 37/56] img2img.multiple.controlnets.pipeline (#2833) * img2img.multiple.controlnets.pipeline * remove comments --------- Co-authored-by: mishka --- .../stable_diffusion_controlnet_img2img.py | 186 ++++++++++++------ 1 file changed, 122 insertions(+), 64 deletions(-) diff --git a/examples/community/stable_diffusion_controlnet_img2img.py b/examples/community/stable_diffusion_controlnet_img2img.py index 1c7ef8aa230a..a8a51b5489a3 100644 --- a/examples/community/stable_diffusion_controlnet_img2img.py +++ b/examples/community/stable_diffusion_controlnet_img2img.py @@ -1,7 +1,7 @@ # Inspired by: https://github.com/haofanwang/ControlNet-for-Diffusers/ import inspect -from typing import Any, Callable, Dict, List, Optional, Union +from typing import Any, Callable, Dict, List, Optional, Tuple, Union import numpy as np import PIL.Image @@ -10,6 +10,7 @@ from diffusers import AutoencoderKL, ControlNetModel, DiffusionPipeline, UNet2DConditionModel, logging from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput, StableDiffusionSafetyChecker +from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel from diffusers.schedulers import KarrasDiffusionSchedulers from diffusers.utils import ( PIL_INTERPOLATION, @@ -86,7 +87,14 @@ def prepare_image(image): def prepare_controlnet_conditioning_image( - controlnet_conditioning_image, width, height, batch_size, num_images_per_prompt, device, dtype + controlnet_conditioning_image, + width, + height, + batch_size, + num_images_per_prompt, + device, + dtype, + do_classifier_free_guidance, ): if not isinstance(controlnet_conditioning_image, torch.Tensor): if isinstance(controlnet_conditioning_image, PIL.Image.Image): @@ -116,6 +124,9 @@ def prepare_controlnet_conditioning_image( controlnet_conditioning_image = controlnet_conditioning_image.to(device=device, dtype=dtype) + if do_classifier_free_guidance: + controlnet_conditioning_image = torch.cat([controlnet_conditioning_image] * 2) + return controlnet_conditioning_image @@ -132,7 +143,7 @@ def __init__( text_encoder: CLIPTextModel, tokenizer: CLIPTokenizer, unet: UNet2DConditionModel, - controlnet: ControlNetModel, + controlnet: Union[ControlNetModel, List[ControlNetModel], Tuple[ControlNetModel], MultiControlNetModel], scheduler: KarrasDiffusionSchedulers, safety_checker: StableDiffusionSafetyChecker, feature_extractor: CLIPImageProcessor, @@ -156,6 +167,9 @@ def __init__( " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead." ) + if isinstance(controlnet, (list, tuple)): + controlnet = MultiControlNetModel(controlnet) + self.register_modules( vae=vae, text_encoder=text_encoder, @@ -424,6 +438,42 @@ def prepare_extra_step_kwargs(self, generator, eta): extra_step_kwargs["generator"] = generator return extra_step_kwargs + def check_controlnet_conditioning_image(self, image, prompt, prompt_embeds): + image_is_pil = isinstance(image, PIL.Image.Image) + image_is_tensor = isinstance(image, torch.Tensor) + image_is_pil_list = isinstance(image, list) and isinstance(image[0], PIL.Image.Image) + image_is_tensor_list = isinstance(image, list) and isinstance(image[0], torch.Tensor) + + if not image_is_pil and not image_is_tensor and not image_is_pil_list and not image_is_tensor_list: + raise TypeError( + "image must be passed and be one of PIL image, torch tensor, list of PIL images, or list of torch tensors" + ) + + if image_is_pil: + image_batch_size = 1 + elif image_is_tensor: + image_batch_size = image.shape[0] + elif image_is_pil_list: + image_batch_size = len(image) + elif image_is_tensor_list: + image_batch_size = len(image) + else: + raise ValueError("controlnet condition image is not valid") + + if prompt is not None and isinstance(prompt, str): + prompt_batch_size = 1 + elif prompt is not None and isinstance(prompt, list): + prompt_batch_size = len(prompt) + elif prompt_embeds is not None: + prompt_batch_size = prompt_embeds.shape[0] + else: + raise ValueError("prompt or prompt_embeds are not valid") + + if image_batch_size != 1 and image_batch_size != prompt_batch_size: + raise ValueError( + f"If image batch size is not 1, image batch size must be same as prompt batch size. image batch size: {image_batch_size}, prompt batch size: {prompt_batch_size}" + ) + def check_inputs( self, prompt, @@ -438,6 +488,7 @@ def check_inputs( strength=None, controlnet_guidance_start=None, controlnet_guidance_end=None, + controlnet_conditioning_scale=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}.") @@ -476,58 +527,51 @@ def check_inputs( f" {negative_prompt_embeds.shape}." ) - controlnet_cond_image_is_pil = isinstance(controlnet_conditioning_image, PIL.Image.Image) - controlnet_cond_image_is_tensor = isinstance(controlnet_conditioning_image, torch.Tensor) - controlnet_cond_image_is_pil_list = isinstance(controlnet_conditioning_image, list) and isinstance( - controlnet_conditioning_image[0], PIL.Image.Image - ) - controlnet_cond_image_is_tensor_list = isinstance(controlnet_conditioning_image, list) and isinstance( - controlnet_conditioning_image[0], torch.Tensor - ) + # check controlnet condition image - if ( - not controlnet_cond_image_is_pil - and not controlnet_cond_image_is_tensor - and not controlnet_cond_image_is_pil_list - and not controlnet_cond_image_is_tensor_list - ): - raise TypeError( - "image must be passed and be one of PIL image, torch tensor, list of PIL images, or list of torch tensors" - ) + if isinstance(self.controlnet, ControlNetModel): + self.check_controlnet_conditioning_image(controlnet_conditioning_image, prompt, prompt_embeds) + elif isinstance(self.controlnet, MultiControlNetModel): + if not isinstance(controlnet_conditioning_image, list): + raise TypeError("For multiple controlnets: `image` must be type `list`") - if controlnet_cond_image_is_pil: - controlnet_cond_image_batch_size = 1 - elif controlnet_cond_image_is_tensor: - controlnet_cond_image_batch_size = controlnet_conditioning_image.shape[0] - elif controlnet_cond_image_is_pil_list: - controlnet_cond_image_batch_size = len(controlnet_conditioning_image) - elif controlnet_cond_image_is_tensor_list: - controlnet_cond_image_batch_size = len(controlnet_conditioning_image) + if len(controlnet_conditioning_image) != len(self.controlnet.nets): + raise ValueError( + "For multiple controlnets: `image` must have the same length as the number of controlnets." + ) - if prompt is not None and isinstance(prompt, str): - prompt_batch_size = 1 - elif prompt is not None and isinstance(prompt, list): - prompt_batch_size = len(prompt) - elif prompt_embeds is not None: - prompt_batch_size = prompt_embeds.shape[0] + for image_ in controlnet_conditioning_image: + self.check_controlnet_conditioning_image(image_, prompt, prompt_embeds) + else: + assert False - if controlnet_cond_image_batch_size != 1 and controlnet_cond_image_batch_size != prompt_batch_size: - raise ValueError( - f"If image batch size is not 1, image batch size must be same as prompt batch size. image batch size: {controlnet_cond_image_batch_size}, prompt batch size: {prompt_batch_size}" - ) + # Check `controlnet_conditioning_scale` + + if isinstance(self.controlnet, ControlNetModel): + if not isinstance(controlnet_conditioning_scale, float): + raise TypeError("For single controlnet: `controlnet_conditioning_scale` must be type `float`.") + elif isinstance(self.controlnet, MultiControlNetModel): + if isinstance(controlnet_conditioning_scale, list) and len(controlnet_conditioning_scale) != len( + self.controlnet.nets + ): + raise ValueError( + "For multiple controlnets: When `controlnet_conditioning_scale` is specified as `list`, it must have" + " the same length as the number of controlnets" + ) + else: + assert False if isinstance(image, torch.Tensor): if image.ndim != 3 and image.ndim != 4: raise ValueError("`image` must have 3 or 4 dimensions") - # if mask_image.ndim != 2 and mask_image.ndim != 3 and mask_image.ndim != 4: - # raise ValueError("`mask_image` must have 2, 3, or 4 dimensions") - if image.ndim == 3: image_batch_size = 1 image_channels, image_height, image_width = image.shape elif image.ndim == 4: image_batch_size, image_channels, image_height, image_width = image.shape + else: + assert False if image_channels != 3: raise ValueError("`image` must have 3 channels") @@ -659,7 +703,7 @@ def __call__( callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, callback_steps: int = 1, cross_attention_kwargs: Optional[Dict[str, Any]] = None, - controlnet_conditioning_scale: float = 1.0, + controlnet_conditioning_scale: Union[float, List[float]] = 1.0, controlnet_guidance_start: float = 0.0, controlnet_guidance_end: float = 1.0, ): @@ -759,7 +803,6 @@ def __call__( self.check_inputs( prompt, image, - # mask_image, controlnet_conditioning_image, height, width, @@ -770,6 +813,7 @@ def __call__( strength, controlnet_guidance_start, controlnet_guidance_end, + controlnet_conditioning_scale, ) # 2. Define call parameters @@ -786,6 +830,9 @@ def __call__( # corresponds to doing no classifier free guidance. do_classifier_free_guidance = guidance_scale > 1.0 + if isinstance(self.controlnet, MultiControlNetModel) and isinstance(controlnet_conditioning_scale, float): + controlnet_conditioning_scale = [controlnet_conditioning_scale] * len(self.controlnet.nets) + # 3. Encode input prompt prompt_embeds = self._encode_prompt( prompt, @@ -797,22 +844,41 @@ def __call__( negative_prompt_embeds=negative_prompt_embeds, ) - # 4. Prepare mask, image, and controlnet_conditioning_image + # 4. Prepare image, and controlnet_conditioning_image image = prepare_image(image) - # mask_image = prepare_mask_image(mask_image) + # condition image(s) + if isinstance(self.controlnet, ControlNetModel): + controlnet_conditioning_image = prepare_controlnet_conditioning_image( + controlnet_conditioning_image=controlnet_conditioning_image, + width=width, + height=height, + batch_size=batch_size * num_images_per_prompt, + num_images_per_prompt=num_images_per_prompt, + device=device, + dtype=self.controlnet.dtype, + do_classifier_free_guidance=do_classifier_free_guidance, + ) + elif isinstance(self.controlnet, MultiControlNetModel): + controlnet_conditioning_images = [] + + for image_ in controlnet_conditioning_image: + image_ = prepare_controlnet_conditioning_image( + controlnet_conditioning_image=image_, + width=width, + height=height, + batch_size=batch_size * num_images_per_prompt, + num_images_per_prompt=num_images_per_prompt, + device=device, + dtype=self.controlnet.dtype, + do_classifier_free_guidance=do_classifier_free_guidance, + ) - controlnet_conditioning_image = prepare_controlnet_conditioning_image( - controlnet_conditioning_image, - width, - height, - batch_size * num_images_per_prompt, - num_images_per_prompt, - device, - self.controlnet.dtype, - ) + controlnet_conditioning_images.append(image_) - # masked_image = image * (mask_image < 0.5) + controlnet_conditioning_image = controlnet_conditioning_images + else: + assert False # 5. Prepare timesteps self.scheduler.set_timesteps(num_inference_steps, device=device) @@ -830,9 +896,6 @@ def __call__( generator, ) - if do_classifier_free_guidance: - controlnet_conditioning_image = torch.cat([controlnet_conditioning_image] * 2) - # 7. 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) @@ -862,15 +925,10 @@ def __call__( t, encoder_hidden_states=prompt_embeds, controlnet_cond=controlnet_conditioning_image, + conditioning_scale=controlnet_conditioning_scale, return_dict=False, ) - down_block_res_samples = [ - down_block_res_sample * controlnet_conditioning_scale - for down_block_res_sample in down_block_res_samples - ] - mid_block_res_sample *= controlnet_conditioning_scale - # predict the noise residual noise_pred = self.unet( latent_model_input, From a937e1b594da34b35ea9a090dc3ada57df12df49 Mon Sep 17 00:00:00 2001 From: Pi Esposito Date: Thu, 30 Mar 2023 14:08:39 -0300 Subject: [PATCH 38/56] add load textual inversion embeddings to stable diffusion (#2009) * add load textual inversion embeddings draft * fix quality * fix typo * make fix copies * move to textual inversion mixin * make it accept from sd-concept library * accept list of paths to embeddings * fix styling of stable diffusion pipeline * add dummy TextualInversionMixin * add docstring to textualinversionmixin * add load textual inversion embeddings draft * fix quality * fix typo * make fix copies * move to textual inversion mixin * make it accept from sd-concept library * accept list of paths to embeddings * fix styling of stable diffusion pipeline * add dummy TextualInversionMixin * add docstring to textualinversionmixin * add case for parsing embedding from auto1111 UI format Co-authored-by: Evan Jones Co-authored-by: Ana Tamais * fix style after rebase * move textual inversion mixin to loaders * move mixin inheritance to DiffusionPipeline from StableDiffusionPipeline) * update dummy class name * addressed allo comments * fix old dangling import * fix style * proposal * remove bogus * Apply suggestions from code review Co-authored-by: Sayak Paul Co-authored-by: Will Berman * finish * make style * up * fix code quality * fix code quality - again * fix code quality - 3 * fix alt diffusion code quality * fix model editing pipeline * Apply suggestions from code review Co-authored-by: Pedro Cuenca * Finish --------- Co-authored-by: Evan Jones Co-authored-by: Ana Tamais Co-authored-by: Patrick von Platen Co-authored-by: Sayak Paul Co-authored-by: Will Berman Co-authored-by: Pedro Cuenca --- src/diffusers/__init__.py | 1 + src/diffusers/loaders.py | 295 +++++++++++++++++- src/diffusers/models/modeling_utils.py | 136 +------- .../alt_diffusion/pipeline_alt_diffusion.py | 11 +- .../pipeline_alt_diffusion_img2img.py | 11 +- .../pipeline_cycle_diffusion.py | 11 +- .../pipeline_stable_diffusion.py | 11 +- ...line_stable_diffusion_attend_and_excite.py | 11 +- .../pipeline_stable_diffusion_controlnet.py | 11 +- .../pipeline_stable_diffusion_depth2img.py | 11 +- .../pipeline_stable_diffusion_img2img.py | 11 +- .../pipeline_stable_diffusion_inpaint.py | 11 +- ...ipeline_stable_diffusion_inpaint_legacy.py | 11 +- ...eline_stable_diffusion_instruct_pix2pix.py | 11 +- .../pipeline_stable_diffusion_k_diffusion.py | 11 +- ...pipeline_stable_diffusion_model_editing.py | 11 +- .../pipeline_stable_diffusion_panorama.py | 11 +- .../pipeline_stable_diffusion_pix2pix_zero.py | 11 +- .../pipeline_stable_diffusion_sag.py | 11 +- .../pipeline_stable_diffusion_upscale.py | 11 +- .../pipeline_stable_unclip.py | 11 +- .../pipeline_stable_unclip_img2img.py | 11 +- .../pipeline_text_to_video_synth.py | 11 +- src/diffusers/utils/__init__.py | 2 + .../dummy_torch_and_transformers_objects.py | 15 + src/diffusers/utils/hub_utils.py | 147 ++++++++- .../stable_diffusion/test_stable_diffusion.py | 27 ++ tests/test_pipelines.py | 91 ++++++ 28 files changed, 766 insertions(+), 168 deletions(-) diff --git a/src/diffusers/__init__.py b/src/diffusers/__init__.py index 25ca322351d3..bba8d4084636 100644 --- a/src/diffusers/__init__.py +++ b/src/diffusers/__init__.py @@ -109,6 +109,7 @@ except OptionalDependencyNotAvailable: from .utils.dummy_torch_and_transformers_objects import * # noqa F403 else: + from .loaders import TextualInversionLoaderMixin from .pipelines import ( AltDiffusionImg2ImgPipeline, AltDiffusionPipeline, diff --git a/src/diffusers/loaders.py b/src/diffusers/loaders.py index d6bb6fde6ac1..265ea92625f5 100644 --- a/src/diffusers/loaders.py +++ b/src/diffusers/loaders.py @@ -13,18 +13,28 @@ # limitations under the License. import os from collections import defaultdict -from typing import Callable, Dict, Union +from typing import Callable, Dict, List, Optional, Union import torch from .models.attention_processor import LoRAAttnProcessor -from .models.modeling_utils import _get_model_file -from .utils import DIFFUSERS_CACHE, HF_HUB_OFFLINE, deprecate, is_safetensors_available, logging +from .utils import ( + DIFFUSERS_CACHE, + HF_HUB_OFFLINE, + _get_model_file, + deprecate, + is_safetensors_available, + is_transformers_available, + logging, +) if is_safetensors_available(): import safetensors +if is_transformers_available(): + from transformers import PreTrainedModel, PreTrainedTokenizer + logger = logging.get_logger(__name__) @@ -32,6 +42,9 @@ LORA_WEIGHT_NAME = "pytorch_lora_weights.bin" LORA_WEIGHT_NAME_SAFE = "pytorch_lora_weights.safetensors" +TEXT_INVERSION_NAME = "learned_embeds.bin" +TEXT_INVERSION_NAME_SAFE = "learned_embeds.safetensors" + class AttnProcsLayers(torch.nn.Module): def __init__(self, state_dict: Dict[str, torch.Tensor]): @@ -123,13 +136,6 @@ def load_attn_procs(self, pretrained_model_name_or_path_or_dict: Union[str, Dict It is required to be logged in (`huggingface-cli login`) when you want to use private or [gated models](https://huggingface.co/docs/hub/models-gated#gated-models). - - - - - Activate the special ["offline-mode"](https://huggingface.co/diffusers/installation.html#offline-mode) to use - this method in a firewalled environment. - """ @@ -292,5 +298,272 @@ def save_function(weights, filename): # Save the model save_function(state_dict, os.path.join(save_directory, weight_name)) - logger.info(f"Model weights saved in {os.path.join(save_directory, weight_name)}") + + +class TextualInversionLoaderMixin: + r""" + Mixin class for loading textual inversion tokens and embeddings to the tokenizer and text encoder. + """ + + def maybe_convert_prompt(self, prompt: Union[str, List[str]], tokenizer: PreTrainedTokenizer): + r""" + Maybe convert a prompt into a "multi vector"-compatible prompt. If the prompt includes a token that corresponds + to a multi-vector textual inversion embedding, this function will process the prompt so that the special token + is replaced with multiple special tokens each corresponding to one of the vectors. If the prompt has no textual + inversion token or a textual inversion token that is a single vector, the input prompt is simply returned. + + Parameters: + prompt (`str` or list of `str`): + The prompt or prompts to guide the image generation. + tokenizer (`PreTrainedTokenizer`): + The tokenizer responsible for encoding the prompt into input tokens. + + Returns: + `str` or list of `str`: The converted prompt + """ + if not isinstance(prompt, List): + prompts = [prompt] + else: + prompts = prompt + + prompts = [self._maybe_convert_prompt(p, tokenizer) for p in prompts] + + if not isinstance(prompt, List): + return prompts[0] + + return prompts + + def _maybe_convert_prompt(self, prompt: str, tokenizer: PreTrainedTokenizer): + r""" + Maybe convert a prompt into a "multi vector"-compatible prompt. If the prompt includes a token that corresponds + to a multi-vector textual inversion embedding, this function will process the prompt so that the special token + is replaced with multiple special tokens each corresponding to one of the vectors. If the prompt has no textual + inversion token or a textual inversion token that is a single vector, the input prompt is simply returned. + + Parameters: + prompt (`str`): + The prompt to guide the image generation. + tokenizer (`PreTrainedTokenizer`): + The tokenizer responsible for encoding the prompt into input tokens. + + Returns: + `str`: The converted prompt + """ + tokens = tokenizer.tokenize(prompt) + for token in tokens: + if token in tokenizer.added_tokens_encoder: + replacement = token + i = 1 + while f"{token}_{i}" in tokenizer.added_tokens_encoder: + replacement += f"{token}_{i}" + i += 1 + + prompt = prompt.replace(token, replacement) + + return prompt + + def load_textual_inversion( + self, pretrained_model_name_or_path: Union[str, Dict[str, torch.Tensor]], token: Optional[str] = None, **kwargs + ): + r""" + Load textual inversion embeddings into the text encoder of stable diffusion pipelines. Both `diffusers` and + `Automatic1111` formats are supported. + + + + This function is experimental and might change in the future. + + + + Parameters: + pretrained_model_name_or_path (`str` or `os.PathLike`): + Can be either: + + - A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co. + Valid model ids should have an organization name, like + `"sd-concepts-library/low-poly-hd-logos-icons"`. + - A path to a *directory* containing textual inversion weights, e.g. + `./my_text_inversion_directory/`. + weight_name (`str`, *optional*): + Name of a custom weight file. This should be used in two cases: + + - The saved textual inversion file is in `diffusers` format, but was saved under a specific weight + name, such as `text_inv.bin`. + - The saved textual inversion file is in the "Automatic1111" form. + cache_dir (`Union[str, os.PathLike]`, *optional*): + Path to a directory in which a downloaded pretrained model configuration should be cached if the + standard cache should not be used. + force_download (`bool`, *optional*, defaults to `False`): + Whether or not to force the (re-)download of the model weights and configuration files, overriding the + cached versions if they exist. + resume_download (`bool`, *optional*, defaults to `False`): + Whether or not to delete incompletely received files. Will attempt to resume the download if such a + file exists. + proxies (`Dict[str, str]`, *optional*): + A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128', + 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request. + local_files_only(`bool`, *optional*, defaults to `False`): + Whether or not to only look at local files (i.e., do not try to download the model). + use_auth_token (`str` or *bool*, *optional*): + The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated + when running `diffusers-cli login` (stored in `~/.huggingface`). + revision (`str`, *optional*, defaults to `"main"`): + The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a + git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any + identifier allowed by git. + subfolder (`str`, *optional*, defaults to `""`): + In case the relevant files are located inside a subfolder of the model repo (either remote in + huggingface.co or downloaded locally), you can specify the folder name here. + + mirror (`str`, *optional*): + Mirror source to accelerate downloads in China. If you are from China and have an accessibility + problem, you can set this option to resolve it. Note that we do not guarantee the timeliness or safety. + Please refer to the mirror site for more information. + + + + It is required to be logged in (`huggingface-cli login`) when you want to use private or [gated + models](https://huggingface.co/docs/hub/models-gated#gated-models). + + + """ + if not hasattr(self, "tokenizer") or not isinstance(self.tokenizer, PreTrainedTokenizer): + raise ValueError( + f"{self.__class__.__name__} requires `self.tokenizer` of type `PreTrainedTokenizer` for calling" + f" `{self.load_textual_inversion.__name__}`" + ) + + if not hasattr(self, "text_encoder") or not isinstance(self.text_encoder, PreTrainedModel): + raise ValueError( + f"{self.__class__.__name__} requires `self.text_encoder` of type `PreTrainedModel` for calling" + f" `{self.load_textual_inversion.__name__}`" + ) + + cache_dir = kwargs.pop("cache_dir", DIFFUSERS_CACHE) + force_download = kwargs.pop("force_download", False) + resume_download = kwargs.pop("resume_download", False) + proxies = kwargs.pop("proxies", None) + local_files_only = kwargs.pop("local_files_only", HF_HUB_OFFLINE) + use_auth_token = kwargs.pop("use_auth_token", None) + revision = kwargs.pop("revision", None) + subfolder = kwargs.pop("subfolder", None) + weight_name = kwargs.pop("weight_name", None) + use_safetensors = kwargs.pop("use_safetensors", None) + + if use_safetensors and not is_safetensors_available(): + raise ValueError( + "`use_safetensors`=True but safetensors is not installed. Please install safetensors with `pip install safetenstors" + ) + + allow_pickle = False + if use_safetensors is None: + use_safetensors = is_safetensors_available() + allow_pickle = True + + user_agent = { + "file_type": "text_inversion", + "framework": "pytorch", + } + + # 1. Load textual inversion file + model_file = None + # Let's first try to load .safetensors weights + if (use_safetensors and weight_name is None) or ( + weight_name is not None and weight_name.endswith(".safetensors") + ): + try: + model_file = _get_model_file( + pretrained_model_name_or_path, + weights_name=weight_name or TEXT_INVERSION_NAME_SAFE, + cache_dir=cache_dir, + force_download=force_download, + resume_download=resume_download, + proxies=proxies, + local_files_only=local_files_only, + use_auth_token=use_auth_token, + revision=revision, + subfolder=subfolder, + user_agent=user_agent, + ) + state_dict = safetensors.torch.load_file(model_file, device="cpu") + except Exception as e: + if not allow_pickle: + raise e + + model_file = None + + if model_file is None: + model_file = _get_model_file( + pretrained_model_name_or_path, + weights_name=weight_name or TEXT_INVERSION_NAME, + cache_dir=cache_dir, + force_download=force_download, + resume_download=resume_download, + proxies=proxies, + local_files_only=local_files_only, + use_auth_token=use_auth_token, + revision=revision, + subfolder=subfolder, + user_agent=user_agent, + ) + state_dict = torch.load(model_file, map_location="cpu") + + # 2. Load token and embedding correcly from file + if isinstance(state_dict, torch.Tensor): + if token is None: + raise ValueError( + "You are trying to load a textual inversion embedding that has been saved as a PyTorch tensor. Make sure to pass the name of the corresponding token in this case: `token=...`." + ) + embedding = state_dict + elif len(state_dict) == 1: + # diffusers + loaded_token, embedding = next(iter(state_dict.items())) + elif "string_to_param" in state_dict: + # A1111 + loaded_token = state_dict["name"] + embedding = state_dict["string_to_param"]["*"] + + if token is not None and loaded_token != token: + logger.warn(f"The loaded token: {loaded_token} is overwritten by the passed token {token}.") + else: + token = loaded_token + + embedding = embedding.to(dtype=self.text_encoder.dtype, device=self.text_encoder.device) + + # 3. Make sure we don't mess up the tokenizer or text encoder + vocab = self.tokenizer.get_vocab() + if token in vocab: + raise ValueError( + f"Token {token} already in tokenizer vocabulary. Please choose a different token name or remove {token} and embedding from the tokenizer and text encoder." + ) + elif f"{token}_1" in vocab: + multi_vector_tokens = [token] + i = 1 + while f"{token}_{i}" in self.tokenizer.added_tokens_encoder: + multi_vector_tokens.append(f"{token}_{i}") + i += 1 + + raise ValueError( + f"Multi-vector Token {multi_vector_tokens} already in tokenizer vocabulary. Please choose a different token name or remove the {multi_vector_tokens} and embedding from the tokenizer and text encoder." + ) + + is_multi_vector = len(embedding.shape) > 1 and embedding.shape[0] > 1 + + if is_multi_vector: + tokens = [token] + [f"{token}_{i}" for i in range(1, embedding.shape[0])] + embeddings = [e for e in embedding] # noqa: C416 + else: + tokens = [token] + embeddings = [embedding] if len(embedding.shape) > 1 else [embedding[0]] + + # add tokens and get ids + self.tokenizer.add_tokens(tokens) + token_ids = self.tokenizer.convert_tokens_to_ids(tokens) + + # resize token embeddings and set new embeddings + self.text_encoder.resize_token_embeddings(len(self.tokenizer)) + for token_id, embedding in zip(token_ids, embeddings): + self.text_encoder.get_input_embeddings().weight.data[token_id] = embedding + + logger.info("Loaded textual inversion embedding for {token}.") diff --git a/src/diffusers/models/modeling_utils.py b/src/diffusers/models/modeling_utils.py index 5a5d233fbb4e..6a849f6f0e45 100644 --- a/src/diffusers/models/modeling_utils.py +++ b/src/diffusers/models/modeling_utils.py @@ -16,27 +16,22 @@ import inspect import os -import warnings from functools import partial from typing import Callable, List, Optional, Tuple, Union import torch -from huggingface_hub import hf_hub_download -from huggingface_hub.utils import EntryNotFoundError, RepositoryNotFoundError, RevisionNotFoundError -from packaging import version -from requests import HTTPError from torch import Tensor, device from .. import __version__ from ..utils import ( CONFIG_NAME, - DEPRECATED_REVISION_ARGS, DIFFUSERS_CACHE, FLAX_WEIGHTS_NAME, HF_HUB_OFFLINE, - HUGGINGFACE_CO_RESOLVE_ENDPOINT, SAFETENSORS_WEIGHTS_NAME, WEIGHTS_NAME, + _add_variant, + _get_model_file, is_accelerate_available, is_safetensors_available, is_torch_version, @@ -144,15 +139,6 @@ def load(module: torch.nn.Module, prefix=""): return error_msgs -def _add_variant(weights_name: str, variant: Optional[str] = None) -> str: - if variant is not None: - splits = weights_name.split(".") - splits = splits[:-1] + [variant] + splits[-1:] - weights_name = ".".join(splits) - - return weights_name - - class ModelMixin(torch.nn.Module): r""" Base class for all models. @@ -789,121 +775,3 @@ def num_parameters(self, only_trainable: bool = False, exclude_embeddings: bool return sum(p.numel() for p in non_embedding_parameters if p.requires_grad or not only_trainable) else: return sum(p.numel() for p in self.parameters() if p.requires_grad or not only_trainable) - - -def _get_model_file( - pretrained_model_name_or_path, - *, - weights_name, - subfolder, - cache_dir, - force_download, - proxies, - resume_download, - local_files_only, - use_auth_token, - user_agent, - revision, - commit_hash=None, -): - pretrained_model_name_or_path = str(pretrained_model_name_or_path) - if os.path.isfile(pretrained_model_name_or_path): - return pretrained_model_name_or_path - elif os.path.isdir(pretrained_model_name_or_path): - if os.path.isfile(os.path.join(pretrained_model_name_or_path, weights_name)): - # Load from a PyTorch checkpoint - model_file = os.path.join(pretrained_model_name_or_path, weights_name) - return model_file - elif subfolder is not None and os.path.isfile( - os.path.join(pretrained_model_name_or_path, subfolder, weights_name) - ): - model_file = os.path.join(pretrained_model_name_or_path, subfolder, weights_name) - return model_file - else: - raise EnvironmentError( - f"Error no file named {weights_name} found in directory {pretrained_model_name_or_path}." - ) - else: - # 1. First check if deprecated way of loading from branches is used - if ( - revision in DEPRECATED_REVISION_ARGS - and (weights_name == WEIGHTS_NAME or weights_name == SAFETENSORS_WEIGHTS_NAME) - and version.parse(version.parse(__version__).base_version) >= version.parse("0.17.0") - ): - try: - model_file = hf_hub_download( - pretrained_model_name_or_path, - filename=_add_variant(weights_name, revision), - cache_dir=cache_dir, - force_download=force_download, - proxies=proxies, - resume_download=resume_download, - local_files_only=local_files_only, - use_auth_token=use_auth_token, - user_agent=user_agent, - subfolder=subfolder, - revision=revision or commit_hash, - ) - warnings.warn( - f"Loading the variant {revision} from {pretrained_model_name_or_path} via `revision='{revision}'` is deprecated. Loading instead from `revision='main'` with `variant={revision}`. Loading model variants via `revision='{revision}'` will be removed in diffusers v1. Please use `variant='{revision}'` instead.", - FutureWarning, - ) - return model_file - except: # noqa: E722 - warnings.warn( - f"You are loading the variant {revision} from {pretrained_model_name_or_path} via `revision='{revision}'`. This behavior is deprecated and will be removed in diffusers v1. One should use `variant='{revision}'` instead. However, it appears that {pretrained_model_name_or_path} currently does not have a {_add_variant(weights_name, revision)} file in the 'main' branch of {pretrained_model_name_or_path}. \n The Diffusers team and community would be very grateful if you could open an issue: https://github.com/huggingface/diffusers/issues/new with the title '{pretrained_model_name_or_path} is missing {_add_variant(weights_name, revision)}' so that the correct variant file can be added.", - FutureWarning, - ) - try: - # 2. Load model file as usual - model_file = hf_hub_download( - pretrained_model_name_or_path, - filename=weights_name, - cache_dir=cache_dir, - force_download=force_download, - proxies=proxies, - resume_download=resume_download, - local_files_only=local_files_only, - use_auth_token=use_auth_token, - user_agent=user_agent, - subfolder=subfolder, - revision=revision or commit_hash, - ) - return model_file - - except RepositoryNotFoundError: - raise EnvironmentError( - f"{pretrained_model_name_or_path} is not a local folder and is not a valid model identifier " - "listed on 'https://huggingface.co/models'\nIf this is a private repository, make sure to pass a " - "token having permission to this repo with `use_auth_token` or log in with `huggingface-cli " - "login`." - ) - except RevisionNotFoundError: - raise EnvironmentError( - f"{revision} is not a valid git identifier (branch name, tag name or commit id) that exists for " - "this model name. Check the model page at " - f"'https://huggingface.co/{pretrained_model_name_or_path}' for available revisions." - ) - except EntryNotFoundError: - raise EnvironmentError( - f"{pretrained_model_name_or_path} does not appear to have a file named {weights_name}." - ) - except HTTPError as err: - raise EnvironmentError( - f"There was a specific connection error when trying to load {pretrained_model_name_or_path}:\n{err}" - ) - except ValueError: - raise EnvironmentError( - f"We couldn't connect to '{HUGGINGFACE_CO_RESOLVE_ENDPOINT}' to load this model, couldn't find it" - f" in the cached files and it looks like {pretrained_model_name_or_path} is not the path to a" - f" directory containing a file named {weights_name} or" - " \nCheckout your internet connection or see how to run the library in" - " offline mode at 'https://huggingface.co/docs/diffusers/installation#offline-mode'." - ) - except EnvironmentError: - raise EnvironmentError( - f"Can't load the model for '{pretrained_model_name_or_path}'. If you were trying to load it from " - "'https://huggingface.co/models', make sure you don't have a local directory with the same name. " - f"Otherwise, make sure '{pretrained_model_name_or_path}' is the correct path to a directory " - f"containing a file named {weights_name}" - ) diff --git a/src/diffusers/pipelines/alt_diffusion/pipeline_alt_diffusion.py b/src/diffusers/pipelines/alt_diffusion/pipeline_alt_diffusion.py index 68ad20c1598a..c5bb8f9ac7b1 100644 --- a/src/diffusers/pipelines/alt_diffusion/pipeline_alt_diffusion.py +++ b/src/diffusers/pipelines/alt_diffusion/pipeline_alt_diffusion.py @@ -22,6 +22,7 @@ from diffusers.utils import is_accelerate_available, is_accelerate_version from ...configuration_utils import FrozenDict +from ...loaders import TextualInversionLoaderMixin from ...models import AutoencoderKL, UNet2DConditionModel from ...schedulers import KarrasDiffusionSchedulers from ...utils import deprecate, logging, randn_tensor, replace_example_docstring @@ -49,7 +50,7 @@ # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline with Stable->Alt, CLIPTextModel->RobertaSeriesModelWithTransformation, CLIPTokenizer->XLMRobertaTokenizer, AltDiffusionSafetyChecker->StableDiffusionSafetyChecker -class AltDiffusionPipeline(DiffusionPipeline): +class AltDiffusionPipeline(DiffusionPipeline, TextualInversionLoaderMixin): r""" Pipeline for text-to-image generation using Alt Diffusion. @@ -312,6 +313,10 @@ def _encode_prompt( batch_size = prompt_embeds.shape[0] if prompt_embeds is None: + # textual inversion: procecss multi-vector tokens if necessary + if isinstance(self, TextualInversionLoaderMixin): + prompt = self.maybe_convert_prompt(prompt, self.tokenizer) + text_inputs = self.tokenizer( prompt, padding="max_length", @@ -372,6 +377,10 @@ def _encode_prompt( else: uncond_tokens = negative_prompt + # textual inversion: procecss multi-vector tokens if necessary + if isinstance(self, TextualInversionLoaderMixin): + uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer) + max_length = prompt_embeds.shape[1] uncond_input = self.tokenizer( uncond_tokens, diff --git a/src/diffusers/pipelines/alt_diffusion/pipeline_alt_diffusion_img2img.py b/src/diffusers/pipelines/alt_diffusion/pipeline_alt_diffusion_img2img.py index 3521867f2b9f..9af55d1d018a 100644 --- a/src/diffusers/pipelines/alt_diffusion/pipeline_alt_diffusion_img2img.py +++ b/src/diffusers/pipelines/alt_diffusion/pipeline_alt_diffusion_img2img.py @@ -25,6 +25,7 @@ from ...configuration_utils import FrozenDict from ...image_processor import VaeImageProcessor +from ...loaders import TextualInversionLoaderMixin from ...models import AutoencoderKL, UNet2DConditionModel from ...schedulers import KarrasDiffusionSchedulers from ...utils import PIL_INTERPOLATION, deprecate, logging, randn_tensor, replace_example_docstring @@ -88,7 +89,7 @@ def preprocess(image): # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline with Stable->Alt, CLIPTextModel->RobertaSeriesModelWithTransformation, CLIPTokenizer->XLMRobertaTokenizer, AltDiffusionSafetyChecker->StableDiffusionSafetyChecker -class AltDiffusionImg2ImgPipeline(DiffusionPipeline): +class AltDiffusionImg2ImgPipeline(DiffusionPipeline, TextualInversionLoaderMixin): r""" Pipeline for text-guided image to image generation using Alt Diffusion. @@ -322,6 +323,10 @@ def _encode_prompt( batch_size = prompt_embeds.shape[0] if prompt_embeds is None: + # textual inversion: procecss multi-vector tokens if necessary + if isinstance(self, TextualInversionLoaderMixin): + prompt = self.maybe_convert_prompt(prompt, self.tokenizer) + text_inputs = self.tokenizer( prompt, padding="max_length", @@ -382,6 +387,10 @@ def _encode_prompt( else: uncond_tokens = negative_prompt + # textual inversion: procecss multi-vector tokens if necessary + if isinstance(self, TextualInversionLoaderMixin): + uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer) + max_length = prompt_embeds.shape[1] uncond_input = self.tokenizer( uncond_tokens, diff --git a/src/diffusers/pipelines/stable_diffusion/pipeline_cycle_diffusion.py b/src/diffusers/pipelines/stable_diffusion/pipeline_cycle_diffusion.py index 08dad43784f8..dd8e4f16dfc0 100644 --- a/src/diffusers/pipelines/stable_diffusion/pipeline_cycle_diffusion.py +++ b/src/diffusers/pipelines/stable_diffusion/pipeline_cycle_diffusion.py @@ -24,6 +24,7 @@ from diffusers.utils import is_accelerate_available, is_accelerate_version from ...configuration_utils import FrozenDict +from ...loaders import TextualInversionLoaderMixin from ...models import AutoencoderKL, UNet2DConditionModel from ...schedulers import DDIMScheduler from ...utils import PIL_INTERPOLATION, deprecate, logging, randn_tensor @@ -118,7 +119,7 @@ def compute_noise(scheduler, prev_latents, latents, timestep, noise_pred, eta): return noise -class CycleDiffusionPipeline(DiffusionPipeline): +class CycleDiffusionPipeline(DiffusionPipeline, TextualInversionLoaderMixin): r""" Pipeline for text-guided image to image generation using Stable Diffusion. @@ -338,6 +339,10 @@ def _encode_prompt( batch_size = prompt_embeds.shape[0] if prompt_embeds is None: + # textual inversion: procecss multi-vector tokens if necessary + if isinstance(self, TextualInversionLoaderMixin): + prompt = self.maybe_convert_prompt(prompt, self.tokenizer) + text_inputs = self.tokenizer( prompt, padding="max_length", @@ -398,6 +403,10 @@ def _encode_prompt( else: uncond_tokens = negative_prompt + # textual inversion: procecss multi-vector tokens if necessary + if isinstance(self, TextualInversionLoaderMixin): + uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer) + max_length = prompt_embeds.shape[1] uncond_input = self.tokenizer( uncond_tokens, diff --git a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py index b428b4341849..73b9178e3ab1 100644 --- a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py +++ b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py @@ -20,6 +20,7 @@ from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer from ...configuration_utils import FrozenDict +from ...loaders import TextualInversionLoaderMixin from ...models import AutoencoderKL, UNet2DConditionModel from ...schedulers import KarrasDiffusionSchedulers from ...utils import ( @@ -52,7 +53,7 @@ """ -class StableDiffusionPipeline(DiffusionPipeline): +class StableDiffusionPipeline(DiffusionPipeline, TextualInversionLoaderMixin): r""" Pipeline for text-to-image generation using Stable Diffusion. @@ -315,6 +316,10 @@ def _encode_prompt( batch_size = prompt_embeds.shape[0] if prompt_embeds is None: + # textual inversion: procecss multi-vector tokens if necessary + if isinstance(self, TextualInversionLoaderMixin): + prompt = self.maybe_convert_prompt(prompt, self.tokenizer) + text_inputs = self.tokenizer( prompt, padding="max_length", @@ -375,6 +380,10 @@ def _encode_prompt( else: uncond_tokens = negative_prompt + # textual inversion: procecss multi-vector tokens if necessary + if isinstance(self, TextualInversionLoaderMixin): + uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer) + max_length = prompt_embeds.shape[1] uncond_input = self.tokenizer( uncond_tokens, diff --git a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_attend_and_excite.py b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_attend_and_excite.py index ae92ba5526a8..46adb6967140 100644 --- a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_attend_and_excite.py +++ b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_attend_and_excite.py @@ -21,6 +21,7 @@ from torch.nn import functional as F from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer +from ...loaders import TextualInversionLoaderMixin from ...models import AutoencoderKL, UNet2DConditionModel from ...models.attention_processor import Attention from ...schedulers import KarrasDiffusionSchedulers @@ -159,7 +160,7 @@ def __call__(self, attn: Attention, hidden_states, encoder_hidden_states=None, a return hidden_states -class StableDiffusionAttendAndExcitePipeline(DiffusionPipeline): +class StableDiffusionAttendAndExcitePipeline(DiffusionPipeline, TextualInversionLoaderMixin): r""" Pipeline for text-to-image generation using Stable Diffusion and Attend and Excite. @@ -335,6 +336,10 @@ def _encode_prompt( batch_size = prompt_embeds.shape[0] if prompt_embeds is None: + # textual inversion: procecss multi-vector tokens if necessary + if isinstance(self, TextualInversionLoaderMixin): + prompt = self.maybe_convert_prompt(prompt, self.tokenizer) + text_inputs = self.tokenizer( prompt, padding="max_length", @@ -395,6 +400,10 @@ def _encode_prompt( else: uncond_tokens = negative_prompt + # textual inversion: procecss multi-vector tokens if necessary + if isinstance(self, TextualInversionLoaderMixin): + uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer) + max_length = prompt_embeds.shape[1] uncond_input = self.tokenizer( uncond_tokens, diff --git a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_controlnet.py b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_controlnet.py index d7f84d2e697b..93cbc03b12ed 100644 --- a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_controlnet.py +++ b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_controlnet.py @@ -23,6 +23,7 @@ from torch import nn from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer +from ...loaders import TextualInversionLoaderMixin from ...models import AutoencoderKL, ControlNetModel, UNet2DConditionModel from ...models.controlnet import ControlNetOutput from ...models.modeling_utils import ModelMixin @@ -146,7 +147,7 @@ def forward( return down_block_res_samples, mid_block_res_sample -class StableDiffusionControlNetPipeline(DiffusionPipeline): +class StableDiffusionControlNetPipeline(DiffusionPipeline, TextualInversionLoaderMixin): r""" Pipeline for text-to-image generation using Stable Diffusion with ControlNet guidance. @@ -354,6 +355,10 @@ def _encode_prompt( batch_size = prompt_embeds.shape[0] if prompt_embeds is None: + # textual inversion: procecss multi-vector tokens if necessary + if isinstance(self, TextualInversionLoaderMixin): + prompt = self.maybe_convert_prompt(prompt, self.tokenizer) + text_inputs = self.tokenizer( prompt, padding="max_length", @@ -414,6 +419,10 @@ def _encode_prompt( else: uncond_tokens = negative_prompt + # textual inversion: procecss multi-vector tokens if necessary + if isinstance(self, TextualInversionLoaderMixin): + uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer) + max_length = prompt_embeds.shape[1] uncond_input = self.tokenizer( uncond_tokens, diff --git a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_depth2img.py b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_depth2img.py index 876b1b8305f2..54f00ebc23f2 100644 --- a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_depth2img.py +++ b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_depth2img.py @@ -23,6 +23,7 @@ from transformers import CLIPTextModel, CLIPTokenizer, DPTFeatureExtractor, DPTForDepthEstimation from ...configuration_utils import FrozenDict +from ...loaders import TextualInversionLoaderMixin from ...models import AutoencoderKL, UNet2DConditionModel from ...schedulers import KarrasDiffusionSchedulers from ...utils import PIL_INTERPOLATION, deprecate, is_accelerate_available, logging, randn_tensor @@ -54,7 +55,7 @@ def preprocess(image): return image -class StableDiffusionDepth2ImgPipeline(DiffusionPipeline): +class StableDiffusionDepth2ImgPipeline(DiffusionPipeline, TextualInversionLoaderMixin): r""" Pipeline for text-guided image to image generation using Stable Diffusion. @@ -200,6 +201,10 @@ def _encode_prompt( batch_size = prompt_embeds.shape[0] if prompt_embeds is None: + # textual inversion: procecss multi-vector tokens if necessary + if isinstance(self, TextualInversionLoaderMixin): + prompt = self.maybe_convert_prompt(prompt, self.tokenizer) + text_inputs = self.tokenizer( prompt, padding="max_length", @@ -260,6 +265,10 @@ def _encode_prompt( else: uncond_tokens = negative_prompt + # textual inversion: procecss multi-vector tokens if necessary + if isinstance(self, TextualInversionLoaderMixin): + uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer) + max_length = prompt_embeds.shape[1] uncond_input = self.tokenizer( uncond_tokens, diff --git a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py index 14512e180992..e47fae663de3 100644 --- a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py +++ b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py @@ -23,6 +23,7 @@ from ...configuration_utils import FrozenDict from ...image_processor import VaeImageProcessor +from ...loaders import TextualInversionLoaderMixin from ...models import AutoencoderKL, UNet2DConditionModel from ...schedulers import KarrasDiffusionSchedulers from ...utils import ( @@ -91,7 +92,7 @@ def preprocess(image): return image -class StableDiffusionImg2ImgPipeline(DiffusionPipeline): +class StableDiffusionImg2ImgPipeline(DiffusionPipeline, TextualInversionLoaderMixin): r""" Pipeline for text-guided image to image generation using Stable Diffusion. @@ -329,6 +330,10 @@ def _encode_prompt( batch_size = prompt_embeds.shape[0] if prompt_embeds is None: + # textual inversion: procecss multi-vector tokens if necessary + if isinstance(self, TextualInversionLoaderMixin): + prompt = self.maybe_convert_prompt(prompt, self.tokenizer) + text_inputs = self.tokenizer( prompt, padding="max_length", @@ -389,6 +394,10 @@ def _encode_prompt( else: uncond_tokens = negative_prompt + # textual inversion: procecss multi-vector tokens if necessary + if isinstance(self, TextualInversionLoaderMixin): + uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer) + max_length = prompt_embeds.shape[1] uncond_input = self.tokenizer( uncond_tokens, diff --git a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint.py b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint.py index a934f639a508..8e0ea5a8d079 100644 --- a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint.py +++ b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint.py @@ -22,6 +22,7 @@ from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer from ...configuration_utils import FrozenDict +from ...loaders import TextualInversionLoaderMixin from ...models import AutoencoderKL, UNet2DConditionModel from ...schedulers import KarrasDiffusionSchedulers from ...utils import deprecate, is_accelerate_available, is_accelerate_version, logging, randn_tensor @@ -137,7 +138,7 @@ def prepare_mask_and_masked_image(image, mask): return mask, masked_image -class StableDiffusionInpaintPipeline(DiffusionPipeline): +class StableDiffusionInpaintPipeline(DiffusionPipeline, TextualInversionLoaderMixin): r""" Pipeline for text-guided image inpainting using Stable Diffusion. *This is an experimental feature*. @@ -381,6 +382,10 @@ def _encode_prompt( batch_size = prompt_embeds.shape[0] if prompt_embeds is None: + # textual inversion: procecss multi-vector tokens if necessary + if isinstance(self, TextualInversionLoaderMixin): + prompt = self.maybe_convert_prompt(prompt, self.tokenizer) + text_inputs = self.tokenizer( prompt, padding="max_length", @@ -441,6 +446,10 @@ def _encode_prompt( else: uncond_tokens = negative_prompt + # textual inversion: procecss multi-vector tokens if necessary + if isinstance(self, TextualInversionLoaderMixin): + uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer) + max_length = prompt_embeds.shape[1] uncond_input = self.tokenizer( uncond_tokens, diff --git a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint_legacy.py b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint_legacy.py index feb13d100089..b7a0c942bbe2 100644 --- a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint_legacy.py +++ b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint_legacy.py @@ -22,6 +22,7 @@ from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer from ...configuration_utils import FrozenDict +from ...loaders import TextualInversionLoaderMixin from ...models import AutoencoderKL, UNet2DConditionModel from ...schedulers import KarrasDiffusionSchedulers from ...utils import ( @@ -81,7 +82,7 @@ def preprocess_mask(mask, scale_factor=8): return mask -class StableDiffusionInpaintPipelineLegacy(DiffusionPipeline): +class StableDiffusionInpaintPipelineLegacy(DiffusionPipeline, TextualInversionLoaderMixin): r""" Pipeline for text-guided image inpainting using Stable Diffusion. *This is an experimental feature*. @@ -317,6 +318,10 @@ def _encode_prompt( batch_size = prompt_embeds.shape[0] if prompt_embeds is None: + # textual inversion: procecss multi-vector tokens if necessary + if isinstance(self, TextualInversionLoaderMixin): + prompt = self.maybe_convert_prompt(prompt, self.tokenizer) + text_inputs = self.tokenizer( prompt, padding="max_length", @@ -377,6 +382,10 @@ def _encode_prompt( else: uncond_tokens = negative_prompt + # textual inversion: procecss multi-vector tokens if necessary + if isinstance(self, TextualInversionLoaderMixin): + uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer) + max_length = prompt_embeds.shape[1] uncond_input = self.tokenizer( uncond_tokens, diff --git a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_instruct_pix2pix.py b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_instruct_pix2pix.py index 40cde74a0596..f7999a08dc9b 100644 --- a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_instruct_pix2pix.py +++ b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_instruct_pix2pix.py @@ -20,6 +20,7 @@ import torch from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer +from ...loaders import TextualInversionLoaderMixin from ...models import AutoencoderKL, UNet2DConditionModel from ...schedulers import KarrasDiffusionSchedulers from ...utils import ( @@ -60,7 +61,7 @@ def preprocess(image): return image -class StableDiffusionInstructPix2PixPipeline(DiffusionPipeline): +class StableDiffusionInstructPix2PixPipeline(DiffusionPipeline, TextualInversionLoaderMixin): r""" Pipeline for pixel-level image editing by following text instructions. Based on Stable Diffusion. @@ -511,6 +512,10 @@ def _encode_prompt( batch_size = prompt_embeds.shape[0] if prompt_embeds is None: + # textual inversion: procecss multi-vector tokens if necessary + if isinstance(self, TextualInversionLoaderMixin): + prompt = self.maybe_convert_prompt(prompt, self.tokenizer) + text_inputs = self.tokenizer( prompt, padding="max_length", @@ -571,6 +576,10 @@ def _encode_prompt( else: uncond_tokens = negative_prompt + # textual inversion: procecss multi-vector tokens if necessary + if isinstance(self, TextualInversionLoaderMixin): + uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer) + max_length = prompt_embeds.shape[1] uncond_input = self.tokenizer( uncond_tokens, diff --git a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_k_diffusion.py b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_k_diffusion.py index 6a895a6d0f29..3d10c7d4e8e8 100755 --- a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_k_diffusion.py +++ b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_k_diffusion.py @@ -18,6 +18,7 @@ import torch from k_diffusion.external import CompVisDenoiser, CompVisVDenoiser +from ...loaders import TextualInversionLoaderMixin from ...pipelines import DiffusionPipeline from ...schedulers import LMSDiscreteScheduler from ...utils import is_accelerate_available, is_accelerate_version, logging, randn_tensor @@ -41,7 +42,7 @@ def apply_model(self, *args, **kwargs): return self.model(*args, encoder_hidden_states=encoder_hidden_states, **kwargs).sample -class StableDiffusionKDiffusionPipeline(DiffusionPipeline): +class StableDiffusionKDiffusionPipeline(DiffusionPipeline, TextualInversionLoaderMixin): r""" Pipeline for text-to-image generation using Stable Diffusion. @@ -238,6 +239,10 @@ def _encode_prompt( batch_size = prompt_embeds.shape[0] if prompt_embeds is None: + # textual inversion: procecss multi-vector tokens if necessary + if isinstance(self, TextualInversionLoaderMixin): + prompt = self.maybe_convert_prompt(prompt, self.tokenizer) + text_inputs = self.tokenizer( prompt, padding="max_length", @@ -298,6 +303,10 @@ def _encode_prompt( else: uncond_tokens = negative_prompt + # textual inversion: procecss multi-vector tokens if necessary + if isinstance(self, TextualInversionLoaderMixin): + uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer) + max_length = prompt_embeds.shape[1] uncond_input = self.tokenizer( uncond_tokens, diff --git a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_model_editing.py b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_model_editing.py index 0e850b43bd7c..d841bd8a2d26 100644 --- a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_model_editing.py +++ b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_model_editing.py @@ -18,6 +18,7 @@ import torch from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer +from ...loaders import TextualInversionLoaderMixin from ...models import AutoencoderKL, UNet2DConditionModel from ...schedulers import PNDMScheduler from ...schedulers.scheduling_utils import SchedulerMixin @@ -52,7 +53,7 @@ """ -class StableDiffusionModelEditingPipeline(DiffusionPipeline): +class StableDiffusionModelEditingPipeline(DiffusionPipeline, TextualInversionLoaderMixin): r""" Pipeline for text-to-image model editing using "Editing Implicit Assumptions in Text-to-Image Diffusion Models". @@ -266,6 +267,10 @@ def _encode_prompt( batch_size = prompt_embeds.shape[0] if prompt_embeds is None: + # textual inversion: procecss multi-vector tokens if necessary + if isinstance(self, TextualInversionLoaderMixin): + prompt = self.maybe_convert_prompt(prompt, self.tokenizer) + text_inputs = self.tokenizer( prompt, padding="max_length", @@ -326,6 +331,10 @@ def _encode_prompt( else: uncond_tokens = negative_prompt + # textual inversion: procecss multi-vector tokens if necessary + if isinstance(self, TextualInversionLoaderMixin): + uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer) + max_length = prompt_embeds.shape[1] uncond_input = self.tokenizer( uncond_tokens, diff --git a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_panorama.py b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_panorama.py index fdae1ed3679b..c47423bdee5b 100644 --- a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_panorama.py +++ b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_panorama.py @@ -17,6 +17,7 @@ import torch from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer +from ...loaders import TextualInversionLoaderMixin from ...models import AutoencoderKL, UNet2DConditionModel from ...schedulers import DDIMScheduler, PNDMScheduler from ...utils import is_accelerate_available, is_accelerate_version, logging, randn_tensor, replace_example_docstring @@ -47,7 +48,7 @@ """ -class StableDiffusionPanoramaPipeline(DiffusionPipeline): +class StableDiffusionPanoramaPipeline(DiffusionPipeline, TextualInversionLoaderMixin): r""" Pipeline for text-to-image generation using "MultiDiffusion: Fusing Diffusion Paths for Controlled Image Generation". @@ -230,6 +231,10 @@ def _encode_prompt( batch_size = prompt_embeds.shape[0] if prompt_embeds is None: + # textual inversion: procecss multi-vector tokens if necessary + if isinstance(self, TextualInversionLoaderMixin): + prompt = self.maybe_convert_prompt(prompt, self.tokenizer) + text_inputs = self.tokenizer( prompt, padding="max_length", @@ -290,6 +295,10 @@ def _encode_prompt( else: uncond_tokens = negative_prompt + # textual inversion: procecss multi-vector tokens if necessary + if isinstance(self, TextualInversionLoaderMixin): + uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer) + max_length = prompt_embeds.shape[1] uncond_input = self.tokenizer( uncond_tokens, diff --git a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_pix2pix_zero.py b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_pix2pix_zero.py index 89cf823a1f7e..6af923cb7743 100644 --- a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_pix2pix_zero.py +++ b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_pix2pix_zero.py @@ -28,6 +28,7 @@ CLIPTokenizer, ) +from ...loaders import TextualInversionLoaderMixin from ...models import AutoencoderKL, UNet2DConditionModel from ...models.attention_processor import Attention from ...schedulers import DDIMScheduler, DDPMScheduler, EulerAncestralDiscreteScheduler, LMSDiscreteScheduler @@ -50,7 +51,7 @@ @dataclass -class Pix2PixInversionPipelineOutput(BaseOutput): +class Pix2PixInversionPipelineOutput(BaseOutput, TextualInversionLoaderMixin): """ Output class for Stable Diffusion pipelines. @@ -470,6 +471,10 @@ def _encode_prompt( batch_size = prompt_embeds.shape[0] if prompt_embeds is None: + # textual inversion: procecss multi-vector tokens if necessary + if isinstance(self, TextualInversionLoaderMixin): + prompt = self.maybe_convert_prompt(prompt, self.tokenizer) + text_inputs = self.tokenizer( prompt, padding="max_length", @@ -530,6 +535,10 @@ def _encode_prompt( else: uncond_tokens = negative_prompt + # textual inversion: procecss multi-vector tokens if necessary + if isinstance(self, TextualInversionLoaderMixin): + uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer) + max_length = prompt_embeds.shape[1] uncond_input = self.tokenizer( uncond_tokens, diff --git a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_sag.py b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_sag.py index d77e3550fc75..2b08cf662bb4 100644 --- a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_sag.py +++ b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_sag.py @@ -19,6 +19,7 @@ import torch.nn.functional as F from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer +from ...loaders import TextualInversionLoaderMixin from ...models import AutoencoderKL, UNet2DConditionModel from ...schedulers import KarrasDiffusionSchedulers from ...utils import is_accelerate_available, is_accelerate_version, logging, randn_tensor, replace_example_docstring @@ -87,7 +88,7 @@ def __call__( # Modified to get self-attention guidance scale in this paper (https://arxiv.org/pdf/2210.00939.pdf) as an input -class StableDiffusionSAGPipeline(DiffusionPipeline): +class StableDiffusionSAGPipeline(DiffusionPipeline, TextualInversionLoaderMixin): r""" Pipeline for text-to-image generation using Stable Diffusion. @@ -247,6 +248,10 @@ def _encode_prompt( batch_size = prompt_embeds.shape[0] if prompt_embeds is None: + # textual inversion: procecss multi-vector tokens if necessary + if isinstance(self, TextualInversionLoaderMixin): + prompt = self.maybe_convert_prompt(prompt, self.tokenizer) + text_inputs = self.tokenizer( prompt, padding="max_length", @@ -307,6 +312,10 @@ def _encode_prompt( else: uncond_tokens = negative_prompt + # textual inversion: procecss multi-vector tokens if necessary + if isinstance(self, TextualInversionLoaderMixin): + uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer) + max_length = prompt_embeds.shape[1] uncond_input = self.tokenizer( uncond_tokens, diff --git a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_upscale.py b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_upscale.py index e21b41ccac6d..606202bd3911 100644 --- a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_upscale.py +++ b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_upscale.py @@ -20,6 +20,7 @@ import torch from transformers import CLIPTextModel, CLIPTokenizer +from ...loaders import TextualInversionLoaderMixin from ...models import AutoencoderKL, UNet2DConditionModel from ...schedulers import DDPMScheduler, KarrasDiffusionSchedulers from ...utils import deprecate, is_accelerate_available, logging, randn_tensor @@ -50,7 +51,7 @@ def preprocess(image): return image -class StableDiffusionUpscalePipeline(DiffusionPipeline): +class StableDiffusionUpscalePipeline(DiffusionPipeline, TextualInversionLoaderMixin): r""" Pipeline for text-guided image super-resolution using Stable Diffusion 2. @@ -194,6 +195,10 @@ def _encode_prompt( batch_size = prompt_embeds.shape[0] if prompt_embeds is None: + # textual inversion: procecss multi-vector tokens if necessary + if isinstance(self, TextualInversionLoaderMixin): + prompt = self.maybe_convert_prompt(prompt, self.tokenizer) + text_inputs = self.tokenizer( prompt, padding="max_length", @@ -254,6 +259,10 @@ def _encode_prompt( else: uncond_tokens = negative_prompt + # textual inversion: procecss multi-vector tokens if necessary + if isinstance(self, TextualInversionLoaderMixin): + uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer) + max_length = prompt_embeds.shape[1] uncond_input = self.tokenizer( uncond_tokens, diff --git a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_unclip.py b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_unclip.py index 9c3d39564f6e..ce41572e683c 100644 --- a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_unclip.py +++ b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_unclip.py @@ -19,6 +19,7 @@ from transformers import CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer from transformers.models.clip.modeling_clip import CLIPTextModelOutput +from ...loaders import TextualInversionLoaderMixin from ...models import AutoencoderKL, PriorTransformer, UNet2DConditionModel from ...models.embeddings import get_timestep_embedding from ...schedulers import KarrasDiffusionSchedulers @@ -47,7 +48,7 @@ """ -class StableUnCLIPPipeline(DiffusionPipeline): +class StableUnCLIPPipeline(DiffusionPipeline, TextualInversionLoaderMixin): """ Pipeline for text-to-image generation using stable unCLIP. @@ -367,6 +368,10 @@ def _encode_prompt( batch_size = prompt_embeds.shape[0] if prompt_embeds is None: + # textual inversion: procecss multi-vector tokens if necessary + if isinstance(self, TextualInversionLoaderMixin): + prompt = self.maybe_convert_prompt(prompt, self.tokenizer) + text_inputs = self.tokenizer( prompt, padding="max_length", @@ -427,6 +432,10 @@ def _encode_prompt( else: uncond_tokens = negative_prompt + # textual inversion: procecss multi-vector tokens if necessary + if isinstance(self, TextualInversionLoaderMixin): + uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer) + max_length = prompt_embeds.shape[1] uncond_input = self.tokenizer( uncond_tokens, diff --git a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_unclip_img2img.py b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_unclip_img2img.py index c8fb3f8021b9..b9bf00bc7835 100644 --- a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_unclip_img2img.py +++ b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_unclip_img2img.py @@ -21,6 +21,7 @@ from diffusers.utils.import_utils import is_accelerate_available +from ...loaders import TextualInversionLoaderMixin from ...models import AutoencoderKL, UNet2DConditionModel from ...models.embeddings import get_timestep_embedding from ...schedulers import KarrasDiffusionSchedulers @@ -60,7 +61,7 @@ """ -class StableUnCLIPImg2ImgPipeline(DiffusionPipeline): +class StableUnCLIPImg2ImgPipeline(DiffusionPipeline, TextualInversionLoaderMixin): """ Pipeline for text-guided image to image generation using stable unCLIP. @@ -267,6 +268,10 @@ def _encode_prompt( batch_size = prompt_embeds.shape[0] if prompt_embeds is None: + # textual inversion: procecss multi-vector tokens if necessary + if isinstance(self, TextualInversionLoaderMixin): + prompt = self.maybe_convert_prompt(prompt, self.tokenizer) + text_inputs = self.tokenizer( prompt, padding="max_length", @@ -327,6 +332,10 @@ def _encode_prompt( else: uncond_tokens = negative_prompt + # textual inversion: procecss multi-vector tokens if necessary + if isinstance(self, TextualInversionLoaderMixin): + uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer) + max_length = prompt_embeds.shape[1] uncond_input = self.tokenizer( uncond_tokens, diff --git a/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth.py b/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth.py index 9129ae0118b8..1cbe78f0c964 100644 --- a/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth.py +++ b/src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth.py @@ -19,6 +19,7 @@ import torch from transformers import CLIPTextModel, CLIPTokenizer +from ...loaders import TextualInversionLoaderMixin from ...models import AutoencoderKL, UNet3DConditionModel from ...schedulers import KarrasDiffusionSchedulers from ...utils import ( @@ -72,7 +73,7 @@ def tensor2vid(video: torch.Tensor, mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]) - return images -class TextToVideoSDPipeline(DiffusionPipeline): +class TextToVideoSDPipeline(DiffusionPipeline, TextualInversionLoaderMixin): r""" Pipeline for text-to-video generation. @@ -256,6 +257,10 @@ def _encode_prompt( batch_size = prompt_embeds.shape[0] if prompt_embeds is None: + # textual inversion: procecss multi-vector tokens if necessary + if isinstance(self, TextualInversionLoaderMixin): + prompt = self.maybe_convert_prompt(prompt, self.tokenizer) + text_inputs = self.tokenizer( prompt, padding="max_length", @@ -316,6 +321,10 @@ def _encode_prompt( else: uncond_tokens = negative_prompt + # textual inversion: procecss multi-vector tokens if necessary + if isinstance(self, TextualInversionLoaderMixin): + uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer) + max_length = prompt_embeds.shape[1] uncond_input = self.tokenizer( uncond_tokens, diff --git a/src/diffusers/utils/__init__.py b/src/diffusers/utils/__init__.py index 615804c91a19..3a1103ac1adf 100644 --- a/src/diffusers/utils/__init__.py +++ b/src/diffusers/utils/__init__.py @@ -37,6 +37,8 @@ from .dynamic_modules_utils import get_class_from_dynamic_module from .hub_utils import ( HF_HUB_OFFLINE, + _add_variant, + _get_model_file, extract_commit_hash, http_user_agent, ) diff --git a/src/diffusers/utils/dummy_torch_and_transformers_objects.py b/src/diffusers/utils/dummy_torch_and_transformers_objects.py index ab85566049d8..cf85ff157f57 100644 --- a/src/diffusers/utils/dummy_torch_and_transformers_objects.py +++ b/src/diffusers/utils/dummy_torch_and_transformers_objects.py @@ -2,6 +2,21 @@ from ..utils import DummyObject, requires_backends +class TextualInversionLoaderMixin(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 AltDiffusionImg2ImgPipeline(metaclass=DummyObject): _backends = ["torch", "transformers"] diff --git a/src/diffusers/utils/hub_utils.py b/src/diffusers/utils/hub_utils.py index 916b18d35e7e..511763ec6687 100644 --- a/src/diffusers/utils/hub_utils.py +++ b/src/diffusers/utils/hub_utils.py @@ -18,16 +18,30 @@ import re import sys import traceback +import warnings from pathlib import Path from typing import Dict, Optional, Union from uuid import uuid4 -from huggingface_hub import HfFolder, ModelCard, ModelCardData, whoami +from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami from huggingface_hub.file_download import REGEX_COMMIT_HASH -from huggingface_hub.utils import is_jinja_available +from huggingface_hub.utils import ( + EntryNotFoundError, + RepositoryNotFoundError, + RevisionNotFoundError, + is_jinja_available, +) +from packaging import version +from requests import HTTPError from .. import __version__ -from .constants import DIFFUSERS_CACHE, HUGGINGFACE_CO_RESOLVE_ENDPOINT +from .constants import ( + DEPRECATED_REVISION_ARGS, + DIFFUSERS_CACHE, + HUGGINGFACE_CO_RESOLVE_ENDPOINT, + SAFETENSORS_WEIGHTS_NAME, + WEIGHTS_NAME, +) from .import_utils import ( ENV_VARS_TRUE_VALUES, _flax_version, @@ -215,3 +229,130 @@ def move_cache(old_cache_dir: Optional[str] = None, new_cache_dir: Optional[str] f"There was a problem when trying to write in your cache folder ({DIFFUSERS_CACHE}). Please, ensure " "the directory exists and can be written to." ) + + +def _add_variant(weights_name: str, variant: Optional[str] = None) -> str: + if variant is not None: + splits = weights_name.split(".") + splits = splits[:-1] + [variant] + splits[-1:] + weights_name = ".".join(splits) + + return weights_name + + +def _get_model_file( + pretrained_model_name_or_path, + *, + weights_name, + subfolder, + cache_dir, + force_download, + proxies, + resume_download, + local_files_only, + use_auth_token, + user_agent, + revision, + commit_hash=None, +): + pretrained_model_name_or_path = str(pretrained_model_name_or_path) + if os.path.isfile(pretrained_model_name_or_path): + return pretrained_model_name_or_path + elif os.path.isdir(pretrained_model_name_or_path): + if os.path.isfile(os.path.join(pretrained_model_name_or_path, weights_name)): + # Load from a PyTorch checkpoint + model_file = os.path.join(pretrained_model_name_or_path, weights_name) + return model_file + elif subfolder is not None and os.path.isfile( + os.path.join(pretrained_model_name_or_path, subfolder, weights_name) + ): + model_file = os.path.join(pretrained_model_name_or_path, subfolder, weights_name) + return model_file + else: + raise EnvironmentError( + f"Error no file named {weights_name} found in directory {pretrained_model_name_or_path}." + ) + else: + # 1. First check if deprecated way of loading from branches is used + if ( + revision in DEPRECATED_REVISION_ARGS + and (weights_name == WEIGHTS_NAME or weights_name == SAFETENSORS_WEIGHTS_NAME) + and version.parse(version.parse(__version__).base_version) >= version.parse("0.17.0") + ): + try: + model_file = hf_hub_download( + pretrained_model_name_or_path, + filename=_add_variant(weights_name, revision), + cache_dir=cache_dir, + force_download=force_download, + proxies=proxies, + resume_download=resume_download, + local_files_only=local_files_only, + use_auth_token=use_auth_token, + user_agent=user_agent, + subfolder=subfolder, + revision=revision or commit_hash, + ) + warnings.warn( + f"Loading the variant {revision} from {pretrained_model_name_or_path} via `revision='{revision}'` is deprecated. Loading instead from `revision='main'` with `variant={revision}`. Loading model variants via `revision='{revision}'` will be removed in diffusers v1. Please use `variant='{revision}'` instead.", + FutureWarning, + ) + return model_file + except: # noqa: E722 + warnings.warn( + f"You are loading the variant {revision} from {pretrained_model_name_or_path} via `revision='{revision}'`. This behavior is deprecated and will be removed in diffusers v1. One should use `variant='{revision}'` instead. However, it appears that {pretrained_model_name_or_path} currently does not have a {_add_variant(weights_name, revision)} file in the 'main' branch of {pretrained_model_name_or_path}. \n The Diffusers team and community would be very grateful if you could open an issue: https://github.com/huggingface/diffusers/issues/new with the title '{pretrained_model_name_or_path} is missing {_add_variant(weights_name, revision)}' so that the correct variant file can be added.", + FutureWarning, + ) + try: + # 2. Load model file as usual + model_file = hf_hub_download( + pretrained_model_name_or_path, + filename=weights_name, + cache_dir=cache_dir, + force_download=force_download, + proxies=proxies, + resume_download=resume_download, + local_files_only=local_files_only, + use_auth_token=use_auth_token, + user_agent=user_agent, + subfolder=subfolder, + revision=revision or commit_hash, + ) + return model_file + + except RepositoryNotFoundError: + raise EnvironmentError( + f"{pretrained_model_name_or_path} is not a local folder and is not a valid model identifier " + "listed on 'https://huggingface.co/models'\nIf this is a private repository, make sure to pass a " + "token having permission to this repo with `use_auth_token` or log in with `huggingface-cli " + "login`." + ) + except RevisionNotFoundError: + raise EnvironmentError( + f"{revision} is not a valid git identifier (branch name, tag name or commit id) that exists for " + "this model name. Check the model page at " + f"'https://huggingface.co/{pretrained_model_name_or_path}' for available revisions." + ) + except EntryNotFoundError: + raise EnvironmentError( + f"{pretrained_model_name_or_path} does not appear to have a file named {weights_name}." + ) + except HTTPError as err: + raise EnvironmentError( + f"There was a specific connection error when trying to load {pretrained_model_name_or_path}:\n{err}" + ) + except ValueError: + raise EnvironmentError( + f"We couldn't connect to '{HUGGINGFACE_CO_RESOLVE_ENDPOINT}' to load this model, couldn't find it" + f" in the cached files and it looks like {pretrained_model_name_or_path} is not the path to a" + f" directory containing a file named {weights_name} or" + " \nCheckout your internet connection or see how to run the library in" + " offline mode at 'https://huggingface.co/docs/diffusers/installation#offline-mode'." + ) + except EnvironmentError: + raise EnvironmentError( + f"Can't load the model for '{pretrained_model_name_or_path}'. If you were trying to load it from " + "'https://huggingface.co/models', make sure you don't have a local directory with the same name. " + f"Otherwise, make sure '{pretrained_model_name_or_path}' is the correct path to a directory " + f"containing a file named {weights_name}" + ) diff --git a/tests/pipelines/stable_diffusion/test_stable_diffusion.py b/tests/pipelines/stable_diffusion/test_stable_diffusion.py index f4e8113a298f..c3ad88b34acb 100644 --- a/tests/pipelines/stable_diffusion/test_stable_diffusion.py +++ b/tests/pipelines/stable_diffusion/test_stable_diffusion.py @@ -21,6 +21,7 @@ import numpy as np import torch +from huggingface_hub import hf_hub_download from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer from diffusers import ( @@ -886,6 +887,32 @@ def test_stable_diffusion_pipeline_with_model_offloading(self): assert mem_bytes_slicing < mem_bytes_offloaded assert mem_bytes_slicing < 3 * 10**9 + def test_stable_diffusion_textual_inversion(self): + pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4") + pipe.load_textual_inversion("sd-concepts-library/low-poly-hd-logos-icons") + + a111_file = hf_hub_download("hf-internal-testing/text_inv_embedding_a1111_format", "winter_style.pt") + a111_file_neg = hf_hub_download( + "hf-internal-testing/text_inv_embedding_a1111_format", "winter_style_negative.pt" + ) + pipe.load_textual_inversion(a111_file) + pipe.load_textual_inversion(a111_file_neg) + pipe.to("cuda") + + generator = torch.Generator(device="cpu").manual_seed(1) + + prompt = "An logo of a turtle in strong Style-Winter with " + neg_prompt = "Style-Winter-neg" + + image = pipe(prompt=prompt, negative_prompt=neg_prompt, generator=generator, output_type="np").images[0] + + expected_image = load_numpy( + "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_inv/winter_logo_style.npy" + ) + + max_diff = np.abs(expected_image - image).max() + assert max_diff < 5e-3 + @nightly @require_torch_gpu diff --git a/tests/test_pipelines.py b/tests/test_pipelines.py index 2616223c5447..0525eaca50da 100644 --- a/tests/test_pipelines.py +++ b/tests/test_pipelines.py @@ -362,6 +362,97 @@ def test_download_broken_variant(self): diffusers.utils.import_utils._safetensors_available = True + def test_text_inversion_download(self): + pipe = StableDiffusionPipeline.from_pretrained( + "hf-internal-testing/tiny-stable-diffusion-torch", safety_checker=None + ) + pipe = pipe.to(torch_device) + + num_tokens = len(pipe.tokenizer) + + # single token load local + with tempfile.TemporaryDirectory() as tmpdirname: + ten = {"<*>": torch.ones((32,))} + torch.save(ten, os.path.join(tmpdirname, "learned_embeds.bin")) + + pipe.load_textual_inversion(tmpdirname) + + token = pipe.tokenizer.convert_tokens_to_ids("<*>") + assert token == num_tokens, "Added token must be at spot `num_tokens`" + assert pipe.text_encoder.get_input_embeddings().weight[-1].sum().item() == 32 + assert pipe._maybe_convert_prompt("<*>", pipe.tokenizer) == "<*>" + + prompt = "hey <*>" + out = pipe(prompt, num_inference_steps=1, output_type="numpy").images + assert out.shape == (1, 128, 128, 3) + + # single token load local with weight name + with tempfile.TemporaryDirectory() as tmpdirname: + ten = {"<**>": 2 * torch.ones((1, 32))} + torch.save(ten, os.path.join(tmpdirname, "learned_embeds.bin")) + + pipe.load_textual_inversion(tmpdirname, weight_name="learned_embeds.bin") + + token = pipe.tokenizer.convert_tokens_to_ids("<**>") + assert token == num_tokens + 1, "Added token must be at spot `num_tokens`" + assert pipe.text_encoder.get_input_embeddings().weight[-1].sum().item() == 64 + assert pipe._maybe_convert_prompt("<**>", pipe.tokenizer) == "<**>" + + prompt = "hey <**>" + out = pipe(prompt, num_inference_steps=1, output_type="numpy").images + assert out.shape == (1, 128, 128, 3) + + # multi token load + with tempfile.TemporaryDirectory() as tmpdirname: + ten = {"<***>": torch.cat([3 * torch.ones((1, 32)), 4 * torch.ones((1, 32)), 5 * torch.ones((1, 32))])} + torch.save(ten, os.path.join(tmpdirname, "learned_embeds.bin")) + + pipe.load_textual_inversion(tmpdirname) + + token = pipe.tokenizer.convert_tokens_to_ids("<***>") + token_1 = pipe.tokenizer.convert_tokens_to_ids("<***>_1") + token_2 = pipe.tokenizer.convert_tokens_to_ids("<***>_2") + + assert token == num_tokens + 2, "Added token must be at spot `num_tokens`" + assert token_1 == num_tokens + 3, "Added token must be at spot `num_tokens`" + assert token_2 == num_tokens + 4, "Added token must be at spot `num_tokens`" + assert pipe.text_encoder.get_input_embeddings().weight[-3].sum().item() == 96 + assert pipe.text_encoder.get_input_embeddings().weight[-2].sum().item() == 128 + assert pipe.text_encoder.get_input_embeddings().weight[-1].sum().item() == 160 + assert pipe._maybe_convert_prompt("<***>", pipe.tokenizer) == "<***><***>_1<***>_2" + + prompt = "hey <***>" + out = pipe(prompt, num_inference_steps=1, output_type="numpy").images + assert out.shape == (1, 128, 128, 3) + + # multi token load a1111 + with tempfile.TemporaryDirectory() as tmpdirname: + ten = { + "string_to_param": { + "*": torch.cat([3 * torch.ones((1, 32)), 4 * torch.ones((1, 32)), 5 * torch.ones((1, 32))]) + }, + "name": "<****>", + } + torch.save(ten, os.path.join(tmpdirname, "a1111.bin")) + + pipe.load_textual_inversion(tmpdirname, weight_name="a1111.bin") + + token = pipe.tokenizer.convert_tokens_to_ids("<****>") + token_1 = pipe.tokenizer.convert_tokens_to_ids("<****>_1") + token_2 = pipe.tokenizer.convert_tokens_to_ids("<****>_2") + + assert token == num_tokens + 5, "Added token must be at spot `num_tokens`" + assert token_1 == num_tokens + 6, "Added token must be at spot `num_tokens`" + assert token_2 == num_tokens + 7, "Added token must be at spot `num_tokens`" + assert pipe.text_encoder.get_input_embeddings().weight[-3].sum().item() == 96 + assert pipe.text_encoder.get_input_embeddings().weight[-2].sum().item() == 128 + assert pipe.text_encoder.get_input_embeddings().weight[-1].sum().item() == 160 + assert pipe._maybe_convert_prompt("<****>", pipe.tokenizer) == "<****><****>_1<****>_2" + + prompt = "hey <****>" + out = pipe(prompt, num_inference_steps=1, output_type="numpy").images + assert out.shape == (1, 128, 128, 3) + class CustomPipelineTests(unittest.TestCase): def test_load_custom_pipeline(self): From 51d970d60da858d6ff0ddbf5007e648443108c58 Mon Sep 17 00:00:00 2001 From: YiYi Xu Date: Thu, 30 Mar 2023 16:22:40 -1000 Subject: [PATCH 39/56] [docs] add the Stable diffusion with Jax/Flax Guide into the docs (#2487) * add stable diffusion jax guide --------- Co-authored-by: Patrick von Platen --- docs/source/en/_toctree.yml | 2 + .../stable_diffusion_jax_how_to.mdx | 250 ++++++++++++++++++ 2 files changed, 252 insertions(+) create mode 100644 docs/source/en/using-diffusers/stable_diffusion_jax_how_to.mdx diff --git a/docs/source/en/_toctree.yml b/docs/source/en/_toctree.yml index 1a0d8f5cd6c8..dc40d9b142ba 100644 --- a/docs/source/en/_toctree.yml +++ b/docs/source/en/_toctree.yml @@ -52,6 +52,8 @@ title: How to contribute a Pipeline - local: using-diffusers/using_safetensors title: Using safetensors + - local: using-diffusers/stable_diffusion_jax_how_to + title: Stable Diffusion in JAX/Flax - local: using-diffusers/weighted_prompts title: Weighting Prompts title: Pipelines for Inference diff --git a/docs/source/en/using-diffusers/stable_diffusion_jax_how_to.mdx b/docs/source/en/using-diffusers/stable_diffusion_jax_how_to.mdx new file mode 100644 index 000000000000..e0332fdc6496 --- /dev/null +++ b/docs/source/en/using-diffusers/stable_diffusion_jax_how_to.mdx @@ -0,0 +1,250 @@ +# 🧨 Stable Diffusion in JAX / Flax ! + +[[open-in-colab]] + +🤗 Hugging Face [Diffusers](https://github.com/huggingface/diffusers) supports Flax since version `0.5.1`! This allows for super fast inference on Google TPUs, such as those available in Colab, Kaggle or Google Cloud Platform. + +This notebook shows how to run inference using JAX / Flax. If you want more details about how Stable Diffusion works or want to run it in GPU, please refer to [this notebook](https://huggingface.co/docs/diffusers/stable_diffusion). + +First, make sure you are using a TPU backend. If you are running this notebook in Colab, select `Runtime` in the menu above, then select the option "Change runtime type" and then select `TPU` under the `Hardware accelerator` setting. + +Note that JAX is not exclusive to TPUs, but it shines on that hardware because each TPU server has 8 TPU accelerators working in parallel. + +## Setup + +First make sure diffusers is installed. + +```bash +!pip install jax==0.3.25 jaxlib==0.3.25 flax transformers ftfy +!pip install diffusers +``` + +```python +import jax.tools.colab_tpu + +jax.tools.colab_tpu.setup_tpu() +import jax +``` + +```python +num_devices = jax.device_count() +device_type = jax.devices()[0].device_kind + +print(f"Found {num_devices} JAX devices of type {device_type}.") +assert ( + "TPU" in device_type +), "Available device is not a TPU, please select TPU from Edit > Notebook settings > Hardware accelerator" +``` + +```python out +Found 8 JAX devices of type Cloud TPU. +``` + +Then we import all the dependencies. + +```python +import numpy as np +import jax +import jax.numpy as jnp + +from pathlib import Path +from jax import pmap +from flax.jax_utils import replicate +from flax.training.common_utils import shard +from PIL import Image + +from huggingface_hub import notebook_login +from diffusers import FlaxStableDiffusionPipeline +``` + +## Model Loading + +TPU devices support `bfloat16`, an efficient half-float type. We'll use it for our tests, but you can also use `float32` to use full precision instead. + +```python +dtype = jnp.bfloat16 +``` + +Flax is a functional framework, so models are stateless and parameters are stored outside them. Loading the pre-trained Flax pipeline will return both the pipeline itself and the model weights (or parameters). We are using a `bf16` version of the weights, which leads to type warnings that you can safely ignore. + +```python +pipeline, params = FlaxStableDiffusionPipeline.from_pretrained( + "CompVis/stable-diffusion-v1-4", + revision="bf16", + dtype=dtype, +) +``` + +## Inference + +Since TPUs usually have 8 devices working in parallel, we'll replicate our prompt as many times as devices we have. Then we'll perform inference on the 8 devices at once, each responsible for generating one image. Thus, we'll get 8 images in the same amount of time it takes for one chip to generate a single one. + +After replicating the prompt, we obtain the tokenized text ids by invoking the `prepare_inputs` function of the pipeline. The length of the tokenized text is set to 77 tokens, as required by the configuration of the underlying CLIP Text model. + +```python +prompt = "A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of field, close up, split lighting, cinematic" +prompt = [prompt] * jax.device_count() +prompt_ids = pipeline.prepare_inputs(prompt) +prompt_ids.shape +``` + +```python out +(8, 77) +``` + +### Replication and parallelization + +Model parameters and inputs have to be replicated across the 8 parallel devices we have. The parameters dictionary is replicated using `flax.jax_utils.replicate`, which traverses the dictionary and changes the shape of the weights so they are repeated 8 times. Arrays are replicated using `shard`. + +```python +p_params = replicate(params) +``` + +```python +prompt_ids = shard(prompt_ids) +prompt_ids.shape +``` + +```python out +(8, 1, 77) +``` + +That shape means that each one of the `8` devices will receive as an input a `jnp` array with shape `(1, 77)`. `1` is therefore the batch size per device. In TPUs with sufficient memory, it could be larger than `1` if we wanted to generate multiple images (per chip) at once. + +We are almost ready to generate images! We just need to create a random number generator to pass to the generation function. This is the standard procedure in Flax, which is very serious and opinionated about random numbers – all functions that deal with random numbers are expected to receive a generator. This ensures reproducibility, even when we are training across multiple distributed devices. + +The helper function below uses a seed to initialize a random number generator. As long as we use the same seed, we'll get the exact same results. Feel free to use different seeds when exploring results later in the notebook. + +```python +def create_key(seed=0): + return jax.random.PRNGKey(seed) +``` + +We obtain a rng and then "split" it 8 times so each device receives a different generator. Therefore, each device will create a different image, and the full process is reproducible. + +```python +rng = create_key(0) +rng = jax.random.split(rng, jax.device_count()) +``` + +JAX code can be compiled to an efficient representation that runs very fast. However, we need to ensure that all inputs have the same shape in subsequent calls; otherwise, JAX will have to recompile the code, and we wouldn't be able to take advantage of the optimized speed. + +The Flax pipeline can compile the code for us if we pass `jit = True` as an argument. It will also ensure that the model runs in parallel in the 8 available devices. + +The first time we run the following cell it will take a long time to compile, but subequent calls (even with different inputs) will be much faster. For example, it took more than a minute to compile in a TPU v2-8 when I tested, but then it takes about **`7s`** for future inference runs. + +``` +%%time +images = pipeline(prompt_ids, p_params, rng, jit=True)[0] +``` + +```python out +CPU times: user 56.2 s, sys: 42.5 s, total: 1min 38s +Wall time: 1min 29s +``` + +The returned array has shape `(8, 1, 512, 512, 3)`. We reshape it to get rid of the second dimension and obtain 8 images of `512 × 512 × 3` and then convert them to PIL. + +```python +images = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]) +images = pipeline.numpy_to_pil(images) +``` + +### Visualization + +Let's create a helper function to display images in a grid. + +```python +def image_grid(imgs, rows, cols): + w, h = imgs[0].size + grid = Image.new("RGB", size=(cols * w, rows * h)) + for i, img in enumerate(imgs): + grid.paste(img, box=(i % cols * w, i // cols * h)) + return grid +``` + +```python +image_grid(images, 2, 4) +``` + +![img](https://huggingface.co/datasets/YiYiXu/test-doc-assets/resolve/main/stable_diffusion_jax_how_to_cell_38_output_0.jpeg) + + +## Using different prompts + +We don't have to replicate the _same_ prompt in all the devices. We can do whatever we want: generate 2 prompts 4 times each, or even generate 8 different prompts at once. Let's do that! + +First, we'll refactor the input preparation code into a handy function: + +```python +prompts = [ + "Labrador in the style of Hokusai", + "Painting of a squirrel skating in New York", + "HAL-9000 in the style of Van Gogh", + "Times Square under water, with fish and a dolphin swimming around", + "Ancient Roman fresco showing a man working on his laptop", + "Close-up photograph of young black woman against urban background, high quality, bokeh", + "Armchair in the shape of an avocado", + "Clown astronaut in space, with Earth in the background", +] +``` + +```python +prompt_ids = pipeline.prepare_inputs(prompts) +prompt_ids = shard(prompt_ids) + +images = pipeline(prompt_ids, p_params, rng, jit=True).images +images = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]) +images = pipeline.numpy_to_pil(images) + +image_grid(images, 2, 4) +``` + +![img](https://huggingface.co/datasets/YiYiXu/test-doc-assets/resolve/main/stable_diffusion_jax_how_to_cell_43_output_0.jpeg) + + +## How does parallelization work? + +We said before that the `diffusers` Flax pipeline automatically compiles the model and runs it in parallel on all available devices. We'll now briefly look inside that process to show how it works. + +JAX parallelization can be done in multiple ways. The easiest one revolves around using the `jax.pmap` function to achieve single-program, multiple-data (SPMD) parallelization. It means we'll run several copies of the same code, each on different data inputs. More sophisticated approaches are possible, we invite you to go over the [JAX documentation](https://jax.readthedocs.io/en/latest/index.html) and the [`pjit` pages](https://jax.readthedocs.io/en/latest/jax-101/08-pjit.html?highlight=pjit) to explore this topic if you are interested! + +`jax.pmap` does two things for us: +- Compiles (or `jit`s) the code, as if we had invoked `jax.jit()`. This does not happen when we call `pmap`, but the first time the pmapped function is invoked. +- Ensures the compiled code runs in parallel in all the available devices. + +To show how it works we `pmap` the `_generate` method of the pipeline, which is the private method that runs generates images. Please, note that this method may be renamed or removed in future releases of `diffusers`. + +```python +p_generate = pmap(pipeline._generate) +``` + +After we use `pmap`, the prepared function `p_generate` will conceptually do the following: +* Invoke a copy of the underlying function `pipeline._generate` in each device. +* Send each device a different portion of the input arguments. That's what sharding is used for. In our case, `prompt_ids` has shape `(8, 1, 77, 768)`. This array will be split in `8` and each copy of `_generate` will receive an input with shape `(1, 77, 768)`. + +We can code `_generate` completely ignoring the fact that it will be invoked in parallel. We just care about our batch size (`1` in this example) and the dimensions that make sense for our code, and don't have to change anything to make it work in parallel. + +The same way as when we used the pipeline call, the first time we run the following cell it will take a while, but then it will be much faster. + +``` +%%time +images = p_generate(prompt_ids, p_params, rng) +images = images.block_until_ready() +images.shape +``` + +```python out +CPU times: user 1min 15s, sys: 18.2 s, total: 1min 34s +Wall time: 1min 15s +``` + +```python +images.shape +``` + +```python out +(8, 1, 512, 512, 3) +``` + +We use `block_until_ready()` to correctly measure inference time, because JAX uses asynchronous dispatch and returns control to the Python loop as soon as it can. You don't need to use that in your code; blocking will occur automatically when you want to use the result of a computation that has not yet been materialized. \ No newline at end of file From 0df4ad541f317a6c4d4f4dc6bd4a3e0324a5658d Mon Sep 17 00:00:00 2001 From: Takuma Mori Date: Fri, 31 Mar 2023 12:42:11 +0900 Subject: [PATCH 40/56] Add support `Karras sigmas` for StableDiffusionKDiffusionPipeline (#2874) * add use_karras_sigmas option thanks @Stax124 * fix sigma_min/max from scheduler.sigmas * add docstring * revert to use k_diffusion_model.sigma, to(device) * add integration test * make style --- .../pipeline_stable_diffusion_k_diffusion.py | 29 ++++++++++++++----- .../test_stable_diffusion_k_diffusion.py | 29 +++++++++++++++++++ 2 files changed, 50 insertions(+), 8 deletions(-) diff --git a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_k_diffusion.py b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_k_diffusion.py index 3d10c7d4e8e8..a02eb42750f7 100755 --- a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_k_diffusion.py +++ b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_k_diffusion.py @@ -17,6 +17,7 @@ import torch from k_diffusion.external import CompVisDenoiser, CompVisVDenoiser +from k_diffusion.sampling import get_sigmas_karras from ...loaders import TextualInversionLoaderMixin from ...pipelines import DiffusionPipeline @@ -409,6 +410,7 @@ def __call__( return_dict: bool = True, callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, callback_steps: int = 1, + use_karras_sigmas: Optional[bool] = False, ): r""" Function invoked when calling the pipeline for generation. @@ -465,7 +467,10 @@ def __call__( 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. - + use_karras_sigmas (`bool`, *optional*, defaults to `False`): + Use karras sigmas. For example, specifying `sample_dpmpp_2m` to `set_scheduler` will be equivalent to + `DPM++2M` in stable-diffusion-webui. On top of that, setting this option to True will make it `DPM++2M + Karras`. Returns: [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`: [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple. @@ -503,10 +508,18 @@ def __call__( # 4. Prepare timesteps self.scheduler.set_timesteps(num_inference_steps, device=prompt_embeds.device) - sigmas = self.scheduler.sigmas + + # 5. Prepare sigmas + if use_karras_sigmas: + sigma_min: float = self.k_diffusion_model.sigmas[0].item() + sigma_max: float = self.k_diffusion_model.sigmas[-1].item() + sigmas = get_sigmas_karras(n=num_inference_steps, sigma_min=sigma_min, sigma_max=sigma_max) + sigmas = sigmas.to(device) + else: + sigmas = self.scheduler.sigmas sigmas = sigmas.to(prompt_embeds.dtype) - # 5. Prepare latent variables + # 6. Prepare latent variables num_channels_latents = self.unet.in_channels latents = self.prepare_latents( batch_size * num_images_per_prompt, @@ -522,7 +535,7 @@ def __call__( self.k_diffusion_model.sigmas = self.k_diffusion_model.sigmas.to(latents.device) self.k_diffusion_model.log_sigmas = self.k_diffusion_model.log_sigmas.to(latents.device) - # 6. Define model function + # 7. Define model function def model_fn(x, t): latent_model_input = torch.cat([x] * 2) t = torch.cat([t] * 2) @@ -533,16 +546,16 @@ def model_fn(x, t): noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond) return noise_pred - # 7. Run k-diffusion solver + # 8. Run k-diffusion solver latents = self.sampler(model_fn, latents, sigmas) - # 8. Post-processing + # 9. Post-processing image = self.decode_latents(latents) - # 9. Run safety checker + # 10. Run safety checker image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype) - # 10. Convert to PIL + # 11. Convert to PIL if output_type == "pil": image = self.numpy_to_pil(image) diff --git a/tests/pipelines/stable_diffusion/test_stable_diffusion_k_diffusion.py b/tests/pipelines/stable_diffusion/test_stable_diffusion_k_diffusion.py index 7869790c6218..546b1d21252c 100644 --- a/tests/pipelines/stable_diffusion/test_stable_diffusion_k_diffusion.py +++ b/tests/pipelines/stable_diffusion/test_stable_diffusion_k_diffusion.py @@ -75,3 +75,32 @@ def test_stable_diffusion_2(self): expected_slice = np.array([0.1237, 0.1320, 0.1438, 0.1359, 0.1390, 0.1132, 0.1277, 0.1175, 0.1112]) assert np.abs(image_slice.flatten() - expected_slice).max() < 5e-1 + + def test_stable_diffusion_karras_sigmas(self): + sd_pipe = StableDiffusionKDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base") + sd_pipe = sd_pipe.to(torch_device) + sd_pipe.set_progress_bar_config(disable=None) + + sd_pipe.set_scheduler("sample_dpmpp_2m") + + prompt = "A painting of a squirrel eating a burger" + generator = torch.manual_seed(0) + output = sd_pipe( + [prompt], + generator=generator, + guidance_scale=7.5, + num_inference_steps=15, + output_type="np", + use_karras_sigmas=True, + ) + + image = output.images + + image_slice = image[0, -3:, -3:, -1] + + assert image.shape == (1, 512, 512, 3) + expected_slice = np.array( + [0.11381689, 0.12112921, 0.1389457, 0.12549606, 0.1244964, 0.10831517, 0.11562866, 0.10867816, 0.10499048] + ) + + assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 From 1055175a1896fc7592bd506b5d8d562fafd61a01 Mon Sep 17 00:00:00 2001 From: Guillermo Cique Date: Fri, 31 Mar 2023 10:52:48 +0200 Subject: [PATCH 41/56] Fix textual inversion loading (#2914) --- src/diffusers/loaders.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/diffusers/loaders.py b/src/diffusers/loaders.py index 265ea92625f5..5e85341221a2 100644 --- a/src/diffusers/loaders.py +++ b/src/diffusers/loaders.py @@ -555,7 +555,7 @@ def load_textual_inversion( embeddings = [e for e in embedding] # noqa: C416 else: tokens = [token] - embeddings = [embedding] if len(embedding.shape) > 1 else [embedding[0]] + embeddings = [embedding[0]] if len(embedding.shape) > 1 else [embedding] # add tokens and get ids self.tokenizer.add_tokens(tokens) From e1144ac20c4c96f09f1c1e203715969bf80feb65 Mon Sep 17 00:00:00 2001 From: Patrick von Platen Date: Fri, 31 Mar 2023 11:03:32 +0200 Subject: [PATCH 42/56] Fix slow tests text inv (#2915) * fix slow tests * uP --- tests/pipelines/stable_diffusion/test_stable_diffusion.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/tests/pipelines/stable_diffusion/test_stable_diffusion.py b/tests/pipelines/stable_diffusion/test_stable_diffusion.py index c3ad88b34acb..857122782d35 100644 --- a/tests/pipelines/stable_diffusion/test_stable_diffusion.py +++ b/tests/pipelines/stable_diffusion/test_stable_diffusion.py @@ -905,13 +905,12 @@ def test_stable_diffusion_textual_inversion(self): neg_prompt = "Style-Winter-neg" image = pipe(prompt=prompt, negative_prompt=neg_prompt, generator=generator, output_type="np").images[0] - expected_image = load_numpy( "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_inv/winter_logo_style.npy" ) max_diff = np.abs(expected_image - image).max() - assert max_diff < 5e-3 + assert max_diff < 5e-2 @nightly From f3fbf9bfc0c4613e93faa4500629f77fae32c3e6 Mon Sep 17 00:00:00 2001 From: Sandeep Date: Fri, 31 Mar 2023 17:16:20 +0530 Subject: [PATCH 43/56] Fix check_inputs in upscaler pipeline to allow embeds (#2892) * Remove suggestion to use cuDNN benchmark in docs * removing the wrong line * add support for embeds * fix line length --- .../pipeline_stable_diffusion_upscale.py | 62 +++++++++++++++++-- 1 file changed, 58 insertions(+), 4 deletions(-) diff --git a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_upscale.py b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_upscale.py index 606202bd3911..c0086b32d6fd 100644 --- a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_upscale.py +++ b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_upscale.py @@ -326,10 +326,50 @@ def decode_latents(self, latents): image = image.cpu().permute(0, 2, 3, 1).float().numpy() return image - def check_inputs(self, prompt, image, noise_level, callback_steps): - if not isinstance(prompt, str) and not isinstance(prompt, list): + def check_inputs( + self, + prompt, + image, + noise_level, + callback_steps, + negative_prompt=None, + prompt_embeds=None, + negative_prompt_embeds=None, + ): + 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 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 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 ( not isinstance(image, torch.Tensor) and not isinstance(image, PIL.Image.Image) @@ -489,13 +529,27 @@ def __call__( """ # 1. Check inputs - self.check_inputs(prompt, image, noise_level, callback_steps) + self.check_inputs( + prompt, + image, + noise_level, + callback_steps, + negative_prompt, + prompt_embeds, + negative_prompt_embeds, + ) if image is None: raise ValueError("`image` input cannot be undefined.") # 2. Define call parameters - batch_size = 1 if isinstance(prompt, str) else len(prompt) + 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` From 7b6caca9eb08d3b545f11521c4242c379181fd1f Mon Sep 17 00:00:00 2001 From: mengfei25 Date: Fri, 31 Mar 2023 20:07:20 +0800 Subject: [PATCH 44/56] Modify example with intel optimization (#2896) * modify intel opts inference script * modify readme * modify doc * fix some issues * reformat * reformat script * format issue * format issue --- .../research_projects/intel_opts/README.md | 20 ++++++ .../intel_opts/inference_bf16.py | 67 ++++++++++--------- 2 files changed, 57 insertions(+), 30 deletions(-) diff --git a/examples/research_projects/intel_opts/README.md b/examples/research_projects/intel_opts/README.md index fc606df7d170..6b25679efbe9 100644 --- a/examples/research_projects/intel_opts/README.md +++ b/examples/research_projects/intel_opts/README.md @@ -11,6 +11,26 @@ We accelereate the fine-tuning for textual inversion with Intel Extension for Py ## Accelerating the inference for Stable Diffusion using Bfloat16 We start the inference acceleration with Bfloat16 using Intel Extension for PyTorch. The [script](inference_bf16.py) is generally designed to support standard Stable Diffusion models with Bfloat16 support. +```bash +pip install diffusers transformers accelerate scipy safetensors + +export KMP_BLOCKTIME=1 +export KMP_SETTINGS=1 +export KMP_AFFINITY=granularity=fine,compact,1,0 + +# Intel OpenMP +export OMP_NUM_THREADS=< Cores to use > +export LD_PRELOAD=${LD_PRELOAD}:/path/to/lib/libiomp5.so +# Jemalloc is a recommended malloc implementation that emphasizes fragmentation avoidance and scalable concurrency support. +export LD_PRELOAD=${LD_PRELOAD}:/path/to/lib/libjemalloc.so +export MALLOC_CONF="oversize_threshold:1,background_thread:true,metadata_thp:auto,dirty_decay_ms:-1,muzzy_decay_ms:9000000000" + +# Launch with default DDIM +numactl --membind -C python python inference_bf16.py +# Launch with DPMSolverMultistepScheduler +numactl --membind -C python python inference_bf16.py --dpm + +``` ## Accelerating the inference for Stable Diffusion using INT8 diff --git a/examples/research_projects/intel_opts/inference_bf16.py b/examples/research_projects/intel_opts/inference_bf16.py index 8431693a45c8..96ec709f433c 100644 --- a/examples/research_projects/intel_opts/inference_bf16.py +++ b/examples/research_projects/intel_opts/inference_bf16.py @@ -1,49 +1,56 @@ +import argparse + import intel_extension_for_pytorch as ipex import torch -from PIL import Image - -from diffusers import StableDiffusionPipeline - -def image_grid(imgs, rows, cols): - assert len(imgs) == rows * cols +from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline - w, h = imgs[0].size - grid = Image.new("RGB", size=(cols * w, rows * h)) - grid_w, grid_h = grid.size - for i, img in enumerate(imgs): - grid.paste(img, box=(i % cols * w, i // cols * h)) - return grid +parser = argparse.ArgumentParser("Stable Diffusion script with intel optimization", add_help=False) +parser.add_argument("--dpm", action="store_true", help="Enable DPMSolver or not") +parser.add_argument("--steps", default=None, type=int, help="Num inference steps") +args = parser.parse_args() -prompt = ["a lovely in red dress and hat, in the snowly and brightly night, with many brighly buildings"] -batch_size = 8 -prompt = prompt * batch_size - device = "cpu" +prompt = "a lovely in red dress and hat, in the snowly and brightly night, with many brighly buildings" + model_id = "path-to-your-trained-model" -model = StableDiffusionPipeline.from_pretrained(model_id) -model = model.to(device) +pipe = StableDiffusionPipeline.from_pretrained(model_id) +if args.dpm: + pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config) +pipe = pipe.to(device) # to channels last -model.unet = model.unet.to(memory_format=torch.channels_last) -model.vae = model.vae.to(memory_format=torch.channels_last) -model.text_encoder = model.text_encoder.to(memory_format=torch.channels_last) -model.safety_checker = model.safety_checker.to(memory_format=torch.channels_last) +pipe.unet = pipe.unet.to(memory_format=torch.channels_last) +pipe.vae = pipe.vae.to(memory_format=torch.channels_last) +pipe.text_encoder = pipe.text_encoder.to(memory_format=torch.channels_last) +if pipe.requires_safety_checker: + pipe.safety_checker = pipe.safety_checker.to(memory_format=torch.channels_last) # optimize with ipex -model.unet = ipex.optimize(model.unet.eval(), dtype=torch.bfloat16, inplace=True) -model.vae = ipex.optimize(model.vae.eval(), dtype=torch.bfloat16, inplace=True) -model.text_encoder = ipex.optimize(model.text_encoder.eval(), dtype=torch.bfloat16, inplace=True) -model.safety_checker = ipex.optimize(model.safety_checker.eval(), dtype=torch.bfloat16, inplace=True) +sample = torch.randn(2, 4, 64, 64) +timestep = torch.rand(1) * 999 +encoder_hidden_status = torch.randn(2, 77, 768) +input_example = (sample, timestep, encoder_hidden_status) +try: + pipe.unet = ipex.optimize(pipe.unet.eval(), dtype=torch.bfloat16, inplace=True, sample_input=input_example) +except Exception: + pipe.unet = ipex.optimize(pipe.unet.eval(), dtype=torch.bfloat16, inplace=True) +pipe.vae = ipex.optimize(pipe.vae.eval(), dtype=torch.bfloat16, inplace=True) +pipe.text_encoder = ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloat16, inplace=True) +if pipe.requires_safety_checker: + pipe.safety_checker = ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloat16, inplace=True) # compute seed = 666 generator = torch.Generator(device).manual_seed(seed) +generate_kwargs = {"generator": generator} +if args.steps is not None: + generate_kwargs["num_inference_steps"] = args.steps + with torch.cpu.amp.autocast(enabled=True, dtype=torch.bfloat16): - images = model(prompt, guidance_scale=7.5, num_inference_steps=50, generator=generator).images + image = pipe(prompt, **generate_kwargs).images[0] - # save image - grid = image_grid(images, rows=2, cols=4) - grid.save(model_id + ".png") +# save image +image.save("generated.png") From b3c437e009b1efc44c56f52e62c298ff9acd727c Mon Sep 17 00:00:00 2001 From: Nipun Jindal Date: Fri, 31 Mar 2023 17:56:04 +0530 Subject: [PATCH 45/56] [2884]: Fix cross_attention_kwargs in StableDiffusionImg2ImgPipeline (#2902) * [2884]: Fix cross_attention_kwargs in StableDiffusionImg2ImgPipeline * [Build Fix] * [Build Fix] --------- Co-authored-by: njindal --- .../pipeline_alt_diffusion_img2img.py | 14 ++++++++++++-- .../pipeline_stable_diffusion_img2img.py | 14 ++++++++++++-- 2 files changed, 24 insertions(+), 4 deletions(-) diff --git a/src/diffusers/pipelines/alt_diffusion/pipeline_alt_diffusion_img2img.py b/src/diffusers/pipelines/alt_diffusion/pipeline_alt_diffusion_img2img.py index 9af55d1d018a..f9dfe3f38f2e 100644 --- a/src/diffusers/pipelines/alt_diffusion/pipeline_alt_diffusion_img2img.py +++ b/src/diffusers/pipelines/alt_diffusion/pipeline_alt_diffusion_img2img.py @@ -13,7 +13,7 @@ # limitations under the License. import inspect -from typing import Callable, List, Optional, Union +from typing import Any, Callable, Dict, List, Optional, Union import numpy as np import PIL @@ -578,6 +578,7 @@ def __call__( return_dict: bool = True, callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, callback_steps: int = 1, + cross_attention_kwargs: Optional[Dict[str, Any]] = None, ): r""" Function invoked when calling the pipeline for generation. @@ -635,6 +636,10 @@ def __call__( 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. + cross_attention_kwargs (`dict`, *optional*): + A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under + `self.processor` in + [diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py). Examples: Returns: @@ -696,7 +701,12 @@ def __call__( latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) # predict the noise residual - noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=prompt_embeds).sample + noise_pred = self.unet( + latent_model_input, + t, + encoder_hidden_states=prompt_embeds, + cross_attention_kwargs=cross_attention_kwargs, + ).sample # perform guidance if do_classifier_free_guidance: diff --git a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py index e47fae663de3..a91431f71973 100644 --- a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py +++ b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py @@ -13,7 +13,7 @@ # limitations under the License. import inspect -from typing import Callable, List, Optional, Union +from typing import Any, Callable, Dict, List, Optional, Union import numpy as np import PIL @@ -586,6 +586,7 @@ def __call__( return_dict: bool = True, callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, callback_steps: int = 1, + cross_attention_kwargs: Optional[Dict[str, Any]] = None, ): r""" Function invoked when calling the pipeline for generation. @@ -643,6 +644,10 @@ def __call__( 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. + cross_attention_kwargs (`dict`, *optional*): + A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under + `self.processor` in + [diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py). Examples: Returns: @@ -704,7 +709,12 @@ def __call__( latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) # predict the noise residual - noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=prompt_embeds).sample + noise_pred = self.unet( + latent_model_input, + t, + encoder_hidden_states=prompt_embeds, + cross_attention_kwargs=cross_attention_kwargs, + ).sample # perform guidance if do_classifier_free_guidance: From d36103a0897248ff288c3dff84991e18c6cc34a5 Mon Sep 17 00:00:00 2001 From: Patrick von Platen Date: Fri, 31 Mar 2023 15:20:46 +0200 Subject: [PATCH 46/56] [Tests] Speed up test (#2919) speed up test --- tests/models/test_models_unet_3d_condition.py | 10 ++++------ 1 file changed, 4 insertions(+), 6 deletions(-) diff --git a/tests/models/test_models_unet_3d_condition.py b/tests/models/test_models_unet_3d_condition.py index ea71ae4af26c..729367a0c164 100644 --- a/tests/models/test_models_unet_3d_condition.py +++ b/tests/models/test_models_unet_3d_condition.py @@ -88,19 +88,17 @@ def output_shape(self): def prepare_init_args_and_inputs_for_common(self): init_dict = { - "block_out_channels": (32, 64, 64, 64), + "block_out_channels": (32, 64), "down_block_types": ( - "CrossAttnDownBlock3D", - "CrossAttnDownBlock3D", "CrossAttnDownBlock3D", "DownBlock3D", ), - "up_block_types": ("UpBlock3D", "CrossAttnUpBlock3D", "CrossAttnUpBlock3D", "CrossAttnUpBlock3D"), + "up_block_types": ("UpBlock3D", "CrossAttnUpBlock3D"), "cross_attention_dim": 32, - "attention_head_dim": 4, + "attention_head_dim": 8, "out_channels": 4, "in_channels": 4, - "layers_per_block": 2, + "layers_per_block": 1, "sample_size": 32, } inputs_dict = self.dummy_input From 419660c99b40e5ac0e26f11a5430d892025aa14c Mon Sep 17 00:00:00 2001 From: Guspan Tanadi <36249910+guspan-tanadi@users.noreply.github.com> Date: Fri, 31 Mar 2023 20:31:14 +0700 Subject: [PATCH 47/56] Have fix current pipeline link (#2910) Also capitalization notebook provider name --- docs/source/en/api/pipelines/semantic_stable_diffusion.mdx | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/docs/source/en/api/pipelines/semantic_stable_diffusion.mdx b/docs/source/en/api/pipelines/semantic_stable_diffusion.mdx index f1b2cc3892dd..44644860800a 100644 --- a/docs/source/en/api/pipelines/semantic_stable_diffusion.mdx +++ b/docs/source/en/api/pipelines/semantic_stable_diffusion.mdx @@ -24,11 +24,11 @@ The abstract of the paper is the following: | Pipeline | Tasks | Colab | Demo |---|---|:---:|:---:| -| [pipeline_semantic_stable_diffusion.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/semantic_stable_diffusion/pipeline_semantic_stable_diffusion) | *Text-to-Image Generation* | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ml-research/semantic-image-editing/blob/main/examples/SemanticGuidance.ipynb) | [Coming Soon](https://huggingface.co/AIML-TUDA) +| [pipeline_semantic_stable_diffusion.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/semantic_stable_diffusion/pipeline_semantic_stable_diffusion.py) | *Text-to-Image Generation* | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ml-research/semantic-image-editing/blob/main/examples/SemanticGuidance.ipynb) | [Coming Soon](https://huggingface.co/AIML-TUDA) ## Tips -- The Semantic Guidance pipeline can be used with any [Stable Diffusion](./api/pipelines/stable_diffusion/text2img) checkpoint. +- The Semantic Guidance pipeline can be used with any [Stable Diffusion](./stable_diffusion/text2img.mdx) checkpoint. ### Run Semantic Guidance @@ -67,7 +67,7 @@ out = pipe( ) ``` -For more examples check the colab notebook. +For more examples check the Colab notebook. ## StableDiffusionSafePipelineOutput [[autodoc]] pipelines.semantic_stable_diffusion.SemanticStableDiffusionPipelineOutput From 89b23d986958bd9597a5c397bde0bdaca6b9134f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?M=2E=20Tolga=20Cang=C3=B6z?= <46008593+standardAI@users.noreply.github.com> Date: Fri, 31 Mar 2023 16:31:43 +0300 Subject: [PATCH 48/56] Update image_variation.mdx (#2911) . --- .../en/api/pipelines/stable_diffusion/image_variation.mdx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/source/en/api/pipelines/stable_diffusion/image_variation.mdx b/docs/source/en/api/pipelines/stable_diffusion/image_variation.mdx index 939732f4c274..8ca69ff69aec 100644 --- a/docs/source/en/api/pipelines/stable_diffusion/image_variation.mdx +++ b/docs/source/en/api/pipelines/stable_diffusion/image_variation.mdx @@ -14,7 +14,7 @@ specific language governing permissions and limitations under the License. ## StableDiffusionImageVariationPipeline -[`StableDiffusionImageVariationPipeline`] lets you generate variations from an input image using Stable Diffusion. It uses a fine-tuned version of Stable Diffusion model, trained by [Justin Pinkney](https://www.justinpinkney.com/) (@Buntworthy) at [Lambda](https://lambdalabs.com/) +[`StableDiffusionImageVariationPipeline`] lets you generate variations from an input image using Stable Diffusion. It uses a fine-tuned version of Stable Diffusion model, trained by [Justin Pinkney](https://www.justinpinkney.com/) (@Buntworthy) at [Lambda](https://lambdalabs.com/). The original codebase can be found here: [Stable Diffusion Image Variations](https://github.com/LambdaLabsML/lambda-diffusers#stable-diffusion-image-variations) @@ -28,4 +28,4 @@ Available Checkpoints are: - enable_attention_slicing - disable_attention_slicing - enable_xformers_memory_efficient_attention - - disable_xformers_memory_efficient_attention \ No newline at end of file + - disable_xformers_memory_efficient_attention From c43356267b6e74a80e7b76ac3d680a0c2aca3a80 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?M=2E=20Tolga=20Cang=C3=B6z?= <46008593+standardAI@users.noreply.github.com> Date: Fri, 31 Mar 2023 16:32:36 +0300 Subject: [PATCH 49/56] Update controlnet.mdx (#2912) . --- docs/source/en/api/pipelines/stable_diffusion/controlnet.mdx | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/en/api/pipelines/stable_diffusion/controlnet.mdx b/docs/source/en/api/pipelines/stable_diffusion/controlnet.mdx index 4c93bbf23f83..5a4cfa41ca43 100644 --- a/docs/source/en/api/pipelines/stable_diffusion/controlnet.mdx +++ b/docs/source/en/api/pipelines/stable_diffusion/controlnet.mdx @@ -131,7 +131,7 @@ This should take only around 3-4 seconds on GPU (depending on hardware). The out ![img](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/vermeer_disco_dancing.png) -**Note**: To see how to run all other ControlNet checkpoints, please have a look at [ControlNet with Stable Diffusion 1.5](#controlnet-with-stable-diffusion-1.5) +**Note**: To see how to run all other ControlNet checkpoints, please have a look at [ControlNet with Stable Diffusion 1.5](#controlnet-with-stable-diffusion-1.5). From a5bdb678c07f3875020c56a7d3001ac7e64c72b2 Mon Sep 17 00:00:00 2001 From: Patrick von Platen Date: Fri, 31 Mar 2023 13:56:38 +0000 Subject: [PATCH 50/56] fix importing diffusers without transformers installed --- src/diffusers/__init__.py | 4 +-- src/diffusers/loaders.py | 4 +-- src/diffusers/pipelines/__init__.py | 7 ++--- .../spectrogram_diffusion/__init__.py | 31 +++++++++++++------ ...formers_and_torch_and_note_seq_objects.py} | 8 ++--- 5 files changed, 33 insertions(+), 21 deletions(-) rename src/diffusers/utils/{dummy_torch_and_note_seq_objects.py => dummy_transformers_and_torch_and_note_seq_objects.py} (57%) diff --git a/src/diffusers/__init__.py b/src/diffusers/__init__.py index bba8d4084636..f8ac91c0eb95 100644 --- a/src/diffusers/__init__.py +++ b/src/diffusers/__init__.py @@ -178,10 +178,10 @@ from .pipelines import AudioDiffusionPipeline, Mel try: - if not (is_torch_available() and is_note_seq_available()): + if not (is_transformers_available() and is_torch_available() and is_note_seq_available()): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: - from .utils.dummy_torch_and_note_seq_objects import * # noqa F403 + from .utils.dummy_transformers_and_torch_and_note_seq_objects import * # noqa F403 else: from .pipelines import SpectrogramDiffusionPipeline diff --git a/src/diffusers/loaders.py b/src/diffusers/loaders.py index 5e85341221a2..a262833938e7 100644 --- a/src/diffusers/loaders.py +++ b/src/diffusers/loaders.py @@ -306,7 +306,7 @@ class TextualInversionLoaderMixin: Mixin class for loading textual inversion tokens and embeddings to the tokenizer and text encoder. """ - def maybe_convert_prompt(self, prompt: Union[str, List[str]], tokenizer: PreTrainedTokenizer): + def maybe_convert_prompt(self, prompt: Union[str, List[str]], tokenizer: "PreTrainedTokenizer"): r""" Maybe convert a prompt into a "multi vector"-compatible prompt. If the prompt includes a token that corresponds to a multi-vector textual inversion embedding, this function will process the prompt so that the special token @@ -334,7 +334,7 @@ def maybe_convert_prompt(self, prompt: Union[str, List[str]], tokenizer: PreTrai return prompts - def _maybe_convert_prompt(self, prompt: str, tokenizer: PreTrainedTokenizer): + def _maybe_convert_prompt(self, prompt: str, tokenizer: "PreTrainedTokenizer"): r""" Maybe convert a prompt into a "multi vector"-compatible prompt. If the prompt includes a token that corresponds to a multi-vector textual inversion embedding, this function will process the prompt so that the special token diff --git a/src/diffusers/pipelines/__init__.py b/src/diffusers/pipelines/__init__.py index 240cd21cd248..f73eb8383f79 100644 --- a/src/diffusers/pipelines/__init__.py +++ b/src/diffusers/pipelines/__init__.py @@ -26,7 +26,6 @@ from .pndm import PNDMPipeline from .repaint import RePaintPipeline from .score_sde_ve import ScoreSdeVePipeline - from .spectrogram_diffusion import SpectrogramDiffusionPipeline from .stochastic_karras_ve import KarrasVePipeline try: @@ -132,9 +131,9 @@ FlaxStableDiffusionPipeline, ) try: - if not (is_note_seq_available()): + if not (is_transformers_available() and is_torch_available() and is_note_seq_available()): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: - from ..utils.dummy_note_seq_objects import * # noqa F403 + from ..utils.dummy_transformers_and_torch_and_note_seq_objects import * # noqa F403 else: - from .spectrogram_diffusion import MidiProcessor + from .spectrogram_diffusion import SpectrogramDiffusionPipeline diff --git a/src/diffusers/pipelines/spectrogram_diffusion/__init__.py b/src/diffusers/pipelines/spectrogram_diffusion/__init__.py index 64acafc80e3b..196402c71af5 100644 --- a/src/diffusers/pipelines/spectrogram_diffusion/__init__.py +++ b/src/diffusers/pipelines/spectrogram_diffusion/__init__.py @@ -1,13 +1,26 @@ # flake8: noqa -from ...utils import is_note_seq_available +from ...utils import is_note_seq_available, is_transformers_available +from ...utils import OptionalDependencyNotAvailable -from .notes_encoder import SpectrogramNotesEncoder -from .continous_encoder import SpectrogramContEncoder -from .pipeline_spectrogram_diffusion import ( - SpectrogramContEncoder, - SpectrogramDiffusionPipeline, - T5FilmDecoder, -) -if is_note_seq_available(): +try: + if not (is_transformers_available() and is_torch_available()): + raise OptionalDependencyNotAvailable() +except OptionalDependencyNotAvailable: + from ...utils.dummy_torch_and_transformers_objects import * # noqa F403 +else: + from .notes_encoder import SpectrogramNotesEncoder + from .continous_encoder import SpectrogramContEncoder + from .pipeline_spectrogram_diffusion import ( + SpectrogramContEncoder, + SpectrogramDiffusionPipeline, + T5FilmDecoder, + ) + +try: + if not (is_transformers_available() and is_torch_available() and is_note_seq_available()): + raise OptionalDependencyNotAvailable() +except OptionalDependencyNotAvailable: + from ...utils.dummy_transformers_and_torch_and_note_seq_objects import * # noqa F403 +else: from .midi_utils import MidiProcessor diff --git a/src/diffusers/utils/dummy_torch_and_note_seq_objects.py b/src/diffusers/utils/dummy_transformers_and_torch_and_note_seq_objects.py similarity index 57% rename from src/diffusers/utils/dummy_torch_and_note_seq_objects.py rename to src/diffusers/utils/dummy_transformers_and_torch_and_note_seq_objects.py index 997333630763..fbde04e33f0a 100644 --- a/src/diffusers/utils/dummy_torch_and_note_seq_objects.py +++ b/src/diffusers/utils/dummy_transformers_and_torch_and_note_seq_objects.py @@ -3,15 +3,15 @@ class SpectrogramDiffusionPipeline(metaclass=DummyObject): - _backends = ["torch", "note_seq"] + _backends = ["transformers", "torch", "note_seq"] def __init__(self, *args, **kwargs): - requires_backends(self, ["torch", "note_seq"]) + requires_backends(self, ["transformers", "torch", "note_seq"]) @classmethod def from_config(cls, *args, **kwargs): - requires_backends(cls, ["torch", "note_seq"]) + requires_backends(cls, ["transformers", "torch", "note_seq"]) @classmethod def from_pretrained(cls, *args, **kwargs): - requires_backends(cls, ["torch", "note_seq"]) + requires_backends(cls, ["transformers", "torch", "note_seq"]) From 7447f75b9f8badb073636ed163417b0947c59e9f Mon Sep 17 00:00:00 2001 From: Patrick von Platen Date: Fri, 31 Mar 2023 15:59:50 +0200 Subject: [PATCH 51/56] Update pipeline_stable_diffusion_controlnet.py (#2917) --- .../stable_diffusion/pipeline_stable_diffusion_controlnet.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_controlnet.py b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_controlnet.py index 93cbc03b12ed..b8272a4ef3d6 100644 --- a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_controlnet.py +++ b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_controlnet.py @@ -698,7 +698,7 @@ def _default_height_width(self, height, width, image): if isinstance(image, PIL.Image.Image): height = image.height elif isinstance(image, torch.Tensor): - height = image.shape[3] + height = image.shape[2] height = (height // 8) * 8 # round down to nearest multiple of 8 @@ -706,7 +706,7 @@ def _default_height_width(self, height, width, image): if isinstance(image, PIL.Image.Image): width = image.width elif isinstance(image, torch.Tensor): - width = image.shape[2] + width = image.shape[3] width = (width // 8) * 8 # round down to nearest multiple of 8 From cd634a8fbba38883d421248e910c5d5bac219549 Mon Sep 17 00:00:00 2001 From: wfng92 <43742196+wfng92@users.noreply.github.com> Date: Fri, 31 Mar 2023 22:00:59 +0800 Subject: [PATCH 52/56] Check for all different packages of opencv (#2901) Co-authored-by: Patrick von Platen --- src/diffusers/utils/import_utils.py | 19 ++++++++++++++++--- 1 file changed, 16 insertions(+), 3 deletions(-) diff --git a/src/diffusers/utils/import_utils.py b/src/diffusers/utils/import_utils.py index 5757ded65dac..fd7538b1b5e9 100644 --- a/src/diffusers/utils/import_utils.py +++ b/src/diffusers/utils/import_utils.py @@ -175,9 +175,22 @@ # (sayakpaul): importlib.util.find_spec("opencv-python") returns None even when it's installed. # _opencv_available = importlib.util.find_spec("opencv-python") is not None try: - _opencv_version = importlib_metadata.version("opencv-python") - _opencv_available = True - logger.debug(f"Successfully imported cv2 version {_opencv_version}") + candidates = ( + "opencv-python", + "opencv-contrib-python", + "opencv-python-headless", + "opencv-contrib-python-headless", + ) + _opencv_version = None + for pkg in candidates: + try: + _opencv_version = importlib_metadata.version(pkg) + break + except importlib_metadata.PackageNotFoundError: + pass + _opencv_available = _opencv_version is not None + if _opencv_available: + logger.debug(f"Successfully imported cv2 version {_opencv_version}") except importlib_metadata.PackageNotFoundError: _opencv_available = False From f23d6eb8f2a618c924a2e9f928edbc2e3b0e274f Mon Sep 17 00:00:00 2001 From: Patrick von Platen Date: Fri, 31 Mar 2023 23:37:58 +0200 Subject: [PATCH 53/56] fix missing import --- src/diffusers/pipelines/spectrogram_diffusion/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/diffusers/pipelines/spectrogram_diffusion/__init__.py b/src/diffusers/pipelines/spectrogram_diffusion/__init__.py index 196402c71af5..05b14a857630 100644 --- a/src/diffusers/pipelines/spectrogram_diffusion/__init__.py +++ b/src/diffusers/pipelines/spectrogram_diffusion/__init__.py @@ -1,5 +1,5 @@ # flake8: noqa -from ...utils import is_note_seq_available, is_transformers_available +from ...utils import is_note_seq_available, is_transformers_available, is_torch_available from ...utils import OptionalDependencyNotAvailable From 723933f5f18ffe6889f17e08eb3fa0866b27f494 Mon Sep 17 00:00:00 2001 From: Patrick von Platen Date: Fri, 31 Mar 2023 23:45:05 +0200 Subject: [PATCH 54/56] add another import --- src/diffusers/pipelines/__init__.py | 1 + 1 file changed, 1 insertion(+) diff --git a/src/diffusers/pipelines/__init__.py b/src/diffusers/pipelines/__init__.py index f73eb8383f79..d9bbdeeb3867 100644 --- a/src/diffusers/pipelines/__init__.py +++ b/src/diffusers/pipelines/__init__.py @@ -137,3 +137,4 @@ from ..utils.dummy_transformers_and_torch_and_note_seq_objects import * # noqa F403 else: from .spectrogram_diffusion import SpectrogramDiffusionPipeline + from .spectrogram_diffusion import MidiProcessor From 8c530fc2f6a76a2aefb6b285dce6df1675092ac6 Mon Sep 17 00:00:00 2001 From: Patrick von Platen Date: Fri, 31 Mar 2023 23:46:28 +0200 Subject: [PATCH 55/56] make style --- src/diffusers/pipelines/__init__.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/src/diffusers/pipelines/__init__.py b/src/diffusers/pipelines/__init__.py index d9bbdeeb3867..421099a6d746 100644 --- a/src/diffusers/pipelines/__init__.py +++ b/src/diffusers/pipelines/__init__.py @@ -136,5 +136,4 @@ except OptionalDependencyNotAvailable: from ..utils.dummy_transformers_and_torch_and_note_seq_objects import * # noqa F403 else: - from .spectrogram_diffusion import SpectrogramDiffusionPipeline - from .spectrogram_diffusion import MidiProcessor + from .spectrogram_diffusion import MidiProcessor, SpectrogramDiffusionPipeline From 7139f0e874f10b2463caa8cbd585762a309d12d6 Mon Sep 17 00:00:00 2001 From: Sayak Paul Date: Tue, 4 Apr 2023 13:31:15 +0530 Subject: [PATCH 56/56] fix: norm group test for UNet3D. (#2959) --- tests/models/test_models_unet_3d_condition.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/tests/models/test_models_unet_3d_condition.py b/tests/models/test_models_unet_3d_condition.py index 729367a0c164..5a0d74a3ea5a 100644 --- a/tests/models/test_models_unet_3d_condition.py +++ b/tests/models/test_models_unet_3d_condition.py @@ -119,12 +119,11 @@ def test_xformers_enable_works(self): == "XFormersAttnProcessor" ), "xformers is not enabled" - # Overriding because `block_out_channels` needs to be different for this model. + # Overriding to set `norm_num_groups` needs to be different for this model. def test_forward_with_norm_groups(self): init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common() init_dict["norm_num_groups"] = 32 - init_dict["block_out_channels"] = (32, 64, 64, 64) model = self.model_class(**init_dict) model.to(torch_device)