Fix config prints and save, load of pipelines

docs/source/en/tutorials/basic_training.mdx

@@ -344,7 +344,7 @@ Now you can wrap all these components together in a training loop with 🤗 Acce
 
 ...             # Sample a random timestep for each image
 ...             timesteps = torch.randint(
-...                 0, noise_scheduler.num_train_timesteps, (bs,), device=clean_images.device
+...                 0, noise_scheduler.config.num_train_timesteps, (bs,), device=clean_images.device
 ...             ).long()
 
 ...             # Add noise to the clean images according to the noise magnitude at each timestep

docs/source/en/using-diffusers/contribute_pipeline.mdx

@@ -62,7 +62,7 @@ class UnetSchedulerOneForwardPipeline(DiffusionPipeline):
 
     def __call__(self):
         image = torch.randn(
-            (1, self.unet.in_channels, self.unet.sample_size, self.unet.sample_size),
+            (1, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size),
         )
         timestep = 1
 
@@ -108,7 +108,7 @@ class UnetSchedulerOneForwardPipeline(DiffusionPipeline):
 
     def __call__(self):
         image = torch.randn(
-            (1, self.unet.in_channels, self.unet.sample_size, self.unet.sample_size),
+            (1, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size),
         )
         timestep = 1
 

docs/source/en/using-diffusers/custom_pipeline_overview.mdx

@@ -89,7 +89,9 @@ class MyPipeline(DiffusionPipeline):
     @torch.no_grad()
     def __call__(self, batch_size: int = 1, num_inference_steps: int = 50):
         # Sample gaussian noise to begin loop
-        image = torch.randn((batch_size, self.unet.in_channels, self.unet.sample_size, self.unet.sample_size))
+        image = torch.randn(
+            (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)
+        )
 
         image = image.to(self.device)
 

examples/community/bit_diffusion.py

@@ -238,7 +238,7 @@ def __call__(
         **kwargs,
     ) -> Union[Tuple, ImagePipelineOutput]:
         latents = torch.randn(
-            (batch_size, self.unet.in_channels, height, width),
+            (batch_size, self.unet.config.in_channels, height, width),
             generator=generator,
         )
         latents = decimal_to_bits(latents) * self.bit_scale

examples/community/clip_guided_stable_diffusion.py

@@ -254,7 +254,7 @@ def __call__(
         # Unlike in other pipelines, latents need to be generated in the target device
         # for 1-to-1 results reproducibility with the CompVis implementation.
         # However this currently doesn't work in `mps`.
-        latents_shape = (batch_size * num_images_per_prompt, self.unet.in_channels, height // 8, width // 8)
+        latents_shape = (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
         latents_dtype = text_embeddings.dtype
         if latents is None:
             if self.device.type == "mps":

examples/community/clip_guided_stable_diffusion_img2img.py

@@ -414,7 +414,7 @@ def __call__(
         # Unlike in other pipelines, latents need to be generated in the target device
         # for 1-to-1 results reproducibility with the CompVis implementation.
         # However this currently doesn't work in `mps`.
-        latents_shape = (batch_size * num_images_per_prompt, self.unet.in_channels, height // 8, width // 8)
+        latents_shape = (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
         latents_dtype = text_embeddings.dtype
         if latents is None:
             if self.device.type == "mps":

examples/community/composable_stable_diffusion.py

@@ -513,7 +513,7 @@ def __call__(
         timesteps = self.scheduler.timesteps
 
         # 5. Prepare latent variables
-        num_channels_latents = self.unet.in_channels
+        num_channels_latents = self.unet.config.in_channels
         latents = self.prepare_latents(
             batch_size * num_images_per_prompt,
             num_channels_latents,

examples/community/imagic_stable_diffusion.py

@@ -424,7 +424,7 @@ def __call__(
         # Unlike in other pipelines, latents need to be generated in the target device
         # for 1-to-1 results reproducibility with the CompVis implementation.
         # However this currently doesn't work in `mps`.
-        latents_shape = (1, self.unet.in_channels, height // 8, width // 8)
+        latents_shape = (1, self.unet.config.in_channels, height // 8, width // 8)
         latents_dtype = text_embeddings.dtype
         if self.device.type == "mps":
             # randn does not exist on mps

examples/community/interpolate_stable_diffusion.py

@@ -320,7 +320,7 @@ def __call__(
         # Unlike in other pipelines, latents need to be generated in the target device
         # for 1-to-1 results reproducibility with the CompVis implementation.
         # However this currently doesn't work in `mps`.
-        latents_shape = (batch_size * num_images_per_prompt, self.unet.in_channels, height // 8, width // 8)
+        latents_shape = (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
         latents_dtype = text_embeddings.dtype
         if latents is None:
             if self.device.type == "mps":
@@ -416,7 +416,7 @@ def embed_text(self, text):
     def get_noise(self, seed, dtype=torch.float32, height=512, width=512):
         """Takes in random seed and returns corresponding noise vector"""
         return torch.randn(
-            (1, self.unet.in_channels, height // 8, width // 8),
+            (1, self.unet.config.in_channels, height // 8, width // 8),
             generator=torch.Generator(device=self.device).manual_seed(seed),
             device=self.device,
             dtype=dtype,

examples/community/lpw_stable_diffusion.py

@@ -627,7 +627,7 @@ def prepare_latents(self, image, timestep, batch_size, height, width, dtype, dev
         if image is None:
             shape = (
                 batch_size,
-                self.unet.in_channels,
+                self.unet.config.in_channels,
                 height // self.vae_scale_factor,
                 width // self.vae_scale_factor,
             )

examples/community/lpw_stable_diffusion_onnx.py

@@ -486,7 +486,7 @@ def __init__(
             self.__init__additional__()
 
     def __init__additional__(self):
-        self.unet_in_channels = 4
+        self.unet.config.in_channels = 4
         self.vae_scale_factor = 8
 
     def _encode_prompt(
@@ -621,7 +621,7 @@ def prepare_latents(self, image, timestep, batch_size, height, width, dtype, gen
         if image is None:
             shape = (
                 batch_size,
-                self.unet_in_channels,
+                self.unet.config.in_channels,
                 height // self.vae_scale_factor,
                 width // self.vae_scale_factor,
             )

examples/community/magic_mix.py

@@ -93,7 +93,7 @@ def __call__(
 
         torch.manual_seed(seed)
         noise = torch.randn(
-            (1, self.unet.in_channels, height // 8, width // 8),
+            (1, self.unet.config.in_channels, height // 8, width // 8),
         ).to(self.device)
 
         latents = self.scheduler.add_noise(

examples/community/multilingual_stable_diffusion.py

@@ -355,7 +355,7 @@ def __call__(
         # Unlike in other pipelines, latents need to be generated in the target device
         # for 1-to-1 results reproducibility with the CompVis implementation.
         # However this currently doesn't work in `mps`.
-        latents_shape = (batch_size * num_images_per_prompt, self.unet.in_channels, height // 8, width // 8)
+        latents_shape = (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
         latents_dtype = text_embeddings.dtype
         if latents is None:
             if self.device.type == "mps":

examples/community/sd_text2img_k_diffusion.py

@@ -433,7 +433,7 @@ def __call__(
         sigmas = sigmas.to(text_embeddings.dtype)
 
         # 5. Prepare latent variables
-        num_channels_latents = self.unet.in_channels
+        num_channels_latents = self.unet.config.in_channels
         latents = self.prepare_latents(
             batch_size * num_images_per_prompt,
             num_channels_latents,

examples/community/seed_resize_stable_diffusion.py

@@ -262,8 +262,8 @@ def __call__(
         # Unlike in other pipelines, latents need to be generated in the target device
         # for 1-to-1 results reproducibility with the CompVis implementation.
         # However this currently doesn't work in `mps`.
-        latents_shape = (batch_size * num_images_per_prompt, self.unet.in_channels, height // 8, width // 8)
-        latents_shape_reference = (batch_size * num_images_per_prompt, self.unet.in_channels, 64, 64)
+        latents_shape = (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
+        latents_shape_reference = (batch_size * num_images_per_prompt, self.unet.config.in_channels, 64, 64)
         latents_dtype = text_embeddings.dtype
         if latents is None:
             if self.device.type == "mps":

examples/community/speech_to_image_diffusion.py

@@ -190,7 +190,7 @@ def __call__(
         # Unlike in other pipelines, latents need to be generated in the target device
         # for 1-to-1 results reproducibility with the CompVis implementation.
         # However this currently doesn't work in `mps`.
-        latents_shape = (batch_size * num_images_per_prompt, self.unet.in_channels, height // 8, width // 8)
+        latents_shape = (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
         latents_dtype = text_embeddings.dtype
         if latents is None:
             if self.device.type == "mps":

examples/community/wildcard_stable_diffusion.py

@@ -337,7 +337,7 @@ def __call__(
         # Unlike in other pipelines, latents need to be generated in the target device
         # for 1-to-1 results reproducibility with the CompVis implementation.
         # However this currently doesn't work in `mps`.
-        latents_shape = (batch_size * num_images_per_prompt, self.unet.in_channels, height // 8, width // 8)
+        latents_shape = (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
         latents_dtype = text_embeddings.dtype
         if latents is None:
             if self.device.type == "mps":

examples/instruct_pix2pix/train_instruct_pix2pix.py

@@ -794,7 +794,7 @@ def collate_fn(examples):
                 noise = torch.randn_like(latents)
                 bsz = latents.shape[0]
                 # Sample a random timestep for each image
-                timesteps = torch.randint(0, noise_scheduler.num_train_timesteps, (bsz,), device=latents.device)
+                timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device)
                 timesteps = timesteps.long()
 
                 # Add noise to the latents according to the noise magnitude at each timestep

examples/research_projects/lora/train_text_to_image_lora.py

@@ -794,7 +794,7 @@ def collate_fn(examples):
                 noise = torch.randn_like(latents)
                 bsz = latents.shape[0]
                 # Sample a random timestep for each image
-                timesteps = torch.randint(0, noise_scheduler.num_train_timesteps, (bsz,), device=latents.device)
+                timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device)
                 timesteps = timesteps.long()
 
                 # Add noise to the latents according to the noise magnitude at each timestep

examples/research_projects/onnxruntime/text_to_image/train_text_to_image.py

@@ -641,7 +641,7 @@ def collate_fn(examples):
                 noise = torch.randn_like(latents)
                 bsz = latents.shape[0]
                 # Sample a random timestep for each image
-                timesteps = torch.randint(0, noise_scheduler.num_train_timesteps, (bsz,), device=latents.device)
+                timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device)
                 timesteps = timesteps.long()
 
                 # Add noise to the latents according to the noise magnitude at each timestep

examples/text_to_image/train_text_to_image.py

@@ -804,7 +804,7 @@ def collate_fn(examples):
 
                 bsz = latents.shape[0]
                 # Sample a random timestep for each image
-                timesteps = torch.randint(0, noise_scheduler.num_train_timesteps, (bsz,), device=latents.device)
+                timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device)
                 timesteps = timesteps.long()
 
                 # Add noise to the latents according to the noise magnitude at each timestep

examples/text_to_image/train_text_to_image_lora.py

@@ -707,7 +707,7 @@ def collate_fn(examples):
 
                 bsz = latents.shape[0]
                 # Sample a random timestep for each image
-                timesteps = torch.randint(0, noise_scheduler.num_train_timesteps, (bsz,), device=latents.device)
+                timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device)
                 timesteps = timesteps.long()
 
                 # Add noise to the latents according to the noise magnitude at each timestep

src/diffusers/configuration_utils.py

@@ -109,13 +109,6 @@ def register_to_config(self, **kwargs):
         # TODO: remove this when we remove the deprecation warning, and the `kwargs` argument,
         # or solve in a more general way.
         kwargs.pop("kwargs", None)
-        for key, value in kwargs.items():
-            try:
-                setattr(self, key, value)
-            except AttributeError as err:
-                logger.error(f"Can't set {key} with value {value} for {self}")
-                raise err
-
         if not hasattr(self, "_internal_dict"):
             internal_dict = kwargs
         else:

src/diffusers/image_processor.py

@@ -99,8 +99,8 @@ 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 = (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])
+        w, h = (x - x % self.config.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.config.resample])
         return images
 
     def preprocess(
@@ -119,7 +119,7 @@ def preprocess(
             )
 
         if isinstance(image[0], PIL.Image.Image):
-            if self.do_resize:
+            if self.config.do_resize:
                 image = [self.resize(i) for i in image]
             image = [np.array(i).astype(np.float32) / 255.0 for i in image]
             image = np.stack(image, axis=0)  # to np
@@ -129,23 +129,27 @@ def preprocess(
             image = np.concatenate(image, axis=0) if image[0].ndim == 4 else np.stack(image, axis=0)
             image = self.numpy_to_pt(image)
             _, _, height, width = image.shape
-            if self.do_resize and (height % self.vae_scale_factor != 0 or width % self.vae_scale_factor != 0):
+            if self.config.do_resize and (
+                height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
+            ):
                 raise ValueError(
-                    f"Currently we only support resizing for PIL image - please resize your numpy array to be divisible by {self.vae_scale_factor}"
+                    f"Currently we only support resizing for PIL image - please resize your numpy array to be divisible by {self.config.vae_scale_factor}"
                     f"currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor"
                 )
 
         elif isinstance(image[0], torch.Tensor):
             image = torch.cat(image, axis=0) if image[0].ndim == 4 else torch.stack(image, axis=0)
             _, _, height, width = image.shape
-            if self.do_resize and (height % self.vae_scale_factor != 0 or width % self.vae_scale_factor != 0):
+            if self.config.do_resize and (
+                height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
+            ):
                 raise ValueError(
-                    f"Currently we only support resizing for PIL image - please resize your pytorch tensor to be divisible by {self.vae_scale_factor}"
+                    f"Currently we only support resizing for PIL image - please resize your pytorch tensor to be divisible by {self.config.vae_scale_factor}"
                     f"currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor"
                 )
 
         # expected range [0,1], normalize to [-1,1]
-        do_normalize = self.do_normalize
+        do_normalize = self.config.do_normalize
         if image.min() < 0:
             warnings.warn(
                 "Passing `image` as torch tensor with value range in [-1,1] is deprecated. The expected value range for image tensor is [0,1] "

src/diffusers/models/autoencoder_kl.py

@@ -18,7 +18,7 @@
 import torch.nn as nn
 
 from ..configuration_utils import ConfigMixin, register_to_config
-from ..utils import BaseOutput, apply_forward_hook
+from ..utils import BaseOutput, apply_forward_hook, deprecate
 from .modeling_utils import ModelMixin
 from .vae import Decoder, DecoderOutput, DiagonalGaussianDistribution, Encoder
 
@@ -120,9 +120,19 @@ def __init__(
             if isinstance(self.config.sample_size, (list, tuple))
             else self.config.sample_size
         )
-        self.tile_latent_min_size = int(sample_size / (2 ** (len(self.block_out_channels) - 1)))
+        self.tile_latent_min_size = int(sample_size / (2 ** (len(self.config.block_out_channels) - 1)))
         self.tile_overlap_factor = 0.25
 
+    @property
+    def block_out_channels(self):
+        deprecate(
+            "block_out_channels",
+            "1.0.0",
+            "Accessing `block_out_channels` directly via vae.block_out_channels is deprecated. Please use `vae.config.block_out_channels instead`",
+            standard_warn=False,
+        )
+        return self.config.block_out_channels
+
     def _set_gradient_checkpointing(self, module, value=False):
         if isinstance(module, (Encoder, Decoder)):
             module.gradient_checkpointing = value

src/diffusers/models/unet_1d.py

@@ -19,7 +19,7 @@
 import torch.nn as nn
 
 from ..configuration_utils import ConfigMixin, register_to_config
-from ..utils import BaseOutput
+from ..utils import BaseOutput, deprecate
 from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
 from .modeling_utils import ModelMixin
 from .unet_1d_blocks import get_down_block, get_mid_block, get_out_block, get_up_block
@@ -190,6 +190,16 @@ def __init__(
             fc_dim=block_out_channels[-1] // 4,
         )
 
+    @property
+    def in_channels(self):
+        deprecate(
+            "in_channels",
+            "1.0.0",
+            "Accessing `in_channels` directly via unet.in_channels is deprecated. Please use `unet.config.in_channels` instead",
+            standard_warn=False,
+        )
+        return self.config.in_channels
+
     def forward(
         self,
         sample: torch.FloatTensor,

src/diffusers/models/unet_2d.py

@@ -18,7 +18,7 @@
 import torch.nn as nn
 
 from ..configuration_utils import ConfigMixin, register_to_config
-from ..utils import BaseOutput
+from ..utils import BaseOutput, deprecate
 from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
 from .modeling_utils import ModelMixin
 from .unet_2d_blocks import UNetMidBlock2D, get_down_block, get_up_block
@@ -215,6 +215,16 @@ def __init__(
         self.conv_act = nn.SiLU()
         self.conv_out = nn.Conv2d(block_out_channels[0], out_channels, kernel_size=3, padding=1)
 
+    @property
+    def in_channels(self):
+        deprecate(
+            "in_channels",
+            "1.0.0",
+            "Accessing `in_channels` directly via unet.in_channels is deprecated. Please use `unet.config.in_channels` instead",
+            standard_warn=False,
+        )
+        return self.config.in_channels
+
     def forward(
         self,
         sample: torch.FloatTensor,