update

examples/dreambooth/train_dreambooth_lora_sana.py

@@ -158,9 +158,6 @@ def log_validation(
         f"Running validation... \n Generating {args.num_validation_images} images with prompt:"
         f" {args.validation_prompt}."
     )
-    if args.enable_vae_tiling:
-        pipeline.vae.enable_tiling(tile_sample_min_height=1024, tile_sample_stride_width=1024)
-
     pipeline.text_encoder = pipeline.text_encoder.to(torch.bfloat16)
     pipeline = pipeline.to(accelerator.device)
     pipeline.set_progress_bar_config(disable=True)
@@ -600,7 +597,6 @@ def parse_args(input_args=None):
         help="Whether to offload the VAE and the text encoder to CPU when they are not used.",
     )
     parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
-    parser.add_argument("--enable_vae_tiling", action="store_true", help="Enabla vae tiling in log validation")
 
     if input_args is not None:
         args = parser.parse_args(input_args)

setup.py

@@ -74,9 +74,8 @@ To create the package for PyPI.
    twine upload dist/* -r pypi
 
 10. Prepare the release notes and publish them on GitHub once everything is looking hunky-dory. You can use the following
-    Space to fetch all the commits applicable for the release: https://huggingface.co/spaces/sayakpaul/auto-release-notes-diffusers.
-    It automatically fetches the correct tag and branch but also provides the option to configure them.
-    `tag` should be the previous release tag (v0.26.1, for example), and `branch` should be
+    Space to fetch all the commits applicable for the release: https://huggingface.co/spaces/lysandre/github-release. Repo should
+    be `huggingface/diffusers`. `tag` should be the previous release tag (v0.26.1, for example), and `branch` should be
     the latest release branch (v0.27.0-release, for example). It denotes all commits that have happened on branch
     v0.27.0-release after the tag v0.26.1 was created.
 

src/diffusers/loaders/lora_pipeline.py

@@ -21,7 +21,6 @@ from huggingface_hub.utils import validate_hf_hub_args
 from ..utils import (
     USE_PEFT_BACKEND,
     deprecate,
-    get_submodule_by_name,
     is_peft_available,
     is_peft_version,
     is_torch_version,
@@ -1982,17 +1981,10 @@ class FluxLoraLoaderMixin(LoraBaseMixin):
                 in_features = state_dict[lora_A_weight_name].shape[1]
                 out_features = state_dict[lora_B_weight_name].shape[0]
 
-                # Model maybe loaded with different quantization schemes which may flatten the params.
-                # `bitsandbytes`, for example, flatten the weights when using 4bit. 8bit bnb models
-                # preserve weight shape.
-                module_weight_shape = cls._calculate_module_shape(model=transformer, base_module=module)
-
                 # This means there's no need for an expansion in the params, so we simply skip.
-                if tuple(module_weight_shape) == (out_features, in_features):
+                if tuple(module_weight.shape) == (out_features, in_features):
                     continue
 
-                # TODO (sayakpaul): We still need to consider if the module we're expanding is
-                # quantized and handle it accordingly if that is the case.
                 module_out_features, module_in_features = module_weight.shape
                 debug_message = ""
                 if in_features > module_in_features:
@@ -2088,16 +2080,13 @@ class FluxLoraLoaderMixin(LoraBaseMixin):
             base_weight_param = transformer_state_dict[base_param_name]
             lora_A_param = lora_state_dict[f"{prefix}{k}.lora_A.weight"]
 
-            # TODO (sayakpaul): Handle the cases when we actually need to expand when using quantization.
-            base_module_shape = cls._calculate_module_shape(model=transformer, base_weight_param_name=base_param_name)
-
-            if base_module_shape[1] > lora_A_param.shape[1]:
+            if base_weight_param.shape[1] > lora_A_param.shape[1]:
                 shape = (lora_A_param.shape[0], base_weight_param.shape[1])
                 expanded_state_dict_weight = torch.zeros(shape, device=base_weight_param.device)
                 expanded_state_dict_weight[:, : lora_A_param.shape[1]].copy_(lora_A_param)
                 lora_state_dict[f"{prefix}{k}.lora_A.weight"] = expanded_state_dict_weight
                 expanded_module_names.add(k)
-            elif base_module_shape[1] < lora_A_param.shape[1]:
+            elif base_weight_param.shape[1] < lora_A_param.shape[1]:
                 raise NotImplementedError(
                     f"This LoRA param ({k}.lora_A.weight) has an incompatible shape {lora_A_param.shape}. Please open an issue to file for a feature request - https://github.com/huggingface/diffusers/issues/new."
                 )
@@ -2109,28 +2098,6 @@ class FluxLoraLoaderMixin(LoraBaseMixin):
 
         return lora_state_dict
 
-    @staticmethod
-    def _calculate_module_shape(
-        model: "torch.nn.Module",
-        base_module: "torch.nn.Linear" = None,
-        base_weight_param_name: str = None,
-    ) -> "torch.Size":
-        def _get_weight_shape(weight: torch.Tensor):
-            return weight.quant_state.shape if weight.__class__.__name__ == "Params4bit" else weight.shape
-
-        if base_module is not None:
-            return _get_weight_shape(base_module.weight)
-        elif base_weight_param_name is not None:
-            if not base_weight_param_name.endswith(".weight"):
-                raise ValueError(
-                    f"Invalid `base_weight_param_name` passed as it does not end with '.weight' {base_weight_param_name=}."
-                )
-            module_path = base_weight_param_name.rsplit(".weight", 1)[0]
-            submodule = get_submodule_by_name(model, module_path)
-            return _get_weight_shape(submodule.weight)
-
-        raise ValueError("Either `base_module` or `base_weight_param_name` must be provided.")
-
 
 # The reason why we subclass from `StableDiffusionLoraLoaderMixin` here is because Amused initially
 # relied on `StableDiffusionLoraLoaderMixin` for its LoRA support.

src/diffusers/loaders/textual_inversion.py

@@ -40,7 +40,7 @@ def load_textual_inversion_state_dicts(pretrained_model_name_or_paths, **kwargs)
     force_download = kwargs.pop("force_download", False)
     proxies = kwargs.pop("proxies", None)
     local_files_only = kwargs.pop("local_files_only", None)
-    hf_token = kwargs.pop("hf_token", None)
+    token = kwargs.pop("token", None)
     revision = kwargs.pop("revision", None)
     subfolder = kwargs.pop("subfolder", None)
     weight_name = kwargs.pop("weight_name", None)
@@ -73,7 +73,7 @@ def load_textual_inversion_state_dicts(pretrained_model_name_or_paths, **kwargs)
                         force_download=force_download,
                         proxies=proxies,
                         local_files_only=local_files_only,
-                        token=hf_token,
+                        token=token,
                         revision=revision,
                         subfolder=subfolder,
                         user_agent=user_agent,
@@ -93,7 +93,7 @@ def load_textual_inversion_state_dicts(pretrained_model_name_or_paths, **kwargs)
                     force_download=force_download,
                     proxies=proxies,
                     local_files_only=local_files_only,
-                    token=hf_token,
+                    token=token,
                     revision=revision,
                     subfolder=subfolder,
                     user_agent=user_agent,
@@ -312,7 +312,7 @@ class TextualInversionLoaderMixin:
             local_files_only (`bool`, *optional*, defaults to `False`):
                 Whether to only load local model weights and configuration files or not. If set to `True`, the model
                 won't be downloaded from the Hub.
-            hf_token (`str` or *bool*, *optional*):
+            token (`str` or *bool*, *optional*):
                 The token to use as HTTP bearer authorization for remote files. If `True`, the token generated from
                 `diffusers-cli login` (stored in `~/.huggingface`) is used.
             revision (`str`, *optional*, defaults to `"main"`):

src/diffusers/pipelines/mochi/pipeline_mochi.py

@@ -21,7 +21,7 @@ from transformers import T5EncoderModel, T5TokenizerFast
 
 from ...callbacks import MultiPipelineCallbacks, PipelineCallback
 from ...loaders import Mochi1LoraLoaderMixin
-from ...models.autoencoders import AutoencoderKLMochi
+from ...models.autoencoders import AutoencoderKL
 from ...models.transformers import MochiTransformer3DModel
 from ...schedulers import FlowMatchEulerDiscreteScheduler
 from ...utils import (
@@ -151,8 +151,8 @@ class MochiPipeline(DiffusionPipeline, Mochi1LoraLoaderMixin):
             Conditional Transformer architecture to denoise the encoded video latents.
         scheduler ([`FlowMatchEulerDiscreteScheduler`]):
             A scheduler to be used in combination with `transformer` to denoise the encoded image latents.
-        vae ([`AutoencoderKLMochi`]):
-            Variational Auto-Encoder (VAE) Model to encode and decode videos to and from latent representations.
+        vae ([`AutoencoderKL`]):
+            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
         text_encoder ([`T5EncoderModel`]):
             [T5](https://huggingface.co/docs/transformers/en/model_doc/t5#transformers.T5EncoderModel), specifically
             the [google/t5-v1_1-xxl](https://huggingface.co/google/t5-v1_1-xxl) variant.
@@ -171,7 +171,7 @@ class MochiPipeline(DiffusionPipeline, Mochi1LoraLoaderMixin):
     def __init__(
         self,
         scheduler: FlowMatchEulerDiscreteScheduler,
-        vae: AutoencoderKLMochi,
+        vae: AutoencoderKL,
         text_encoder: T5EncoderModel,
         tokenizer: T5TokenizerFast,
         transformer: MochiTransformer3DModel,

src/diffusers/pipelines/pag/pipeline_pag_sana.py

@@ -16,7 +16,6 @@ import html
 import inspect
 import re
 import urllib.parse as ul
-import warnings
 from typing import Callable, Dict, List, Optional, Tuple, Union
 
 import torch
@@ -42,7 +41,6 @@ from ..pixart_alpha.pipeline_pixart_alpha import (
     ASPECT_RATIO_1024_BIN,
 )
 from ..pixart_alpha.pipeline_pixart_sigma import ASPECT_RATIO_2048_BIN
-from ..sana.pipeline_sana import ASPECT_RATIO_4096_BIN
 from .pag_utils import PAGMixin
 
 
@@ -641,7 +639,7 @@ class SanaPAGPipeline(DiffusionPipeline, PAGMixin):
         negative_prompt_attention_mask: Optional[torch.Tensor] = None,
         output_type: Optional[str] = "pil",
         return_dict: bool = True,
-        clean_caption: bool = False,
+        clean_caption: bool = True,
         use_resolution_binning: bool = True,
         callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
         callback_on_step_end_tensor_inputs: List[str] = ["latents"],
@@ -757,9 +755,7 @@ class SanaPAGPipeline(DiffusionPipeline, PAGMixin):
             callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs
 
         if use_resolution_binning:
-            if self.transformer.config.sample_size == 128:
-                aspect_ratio_bin = ASPECT_RATIO_4096_BIN
-            elif self.transformer.config.sample_size == 64:
+            if self.transformer.config.sample_size == 64:
                 aspect_ratio_bin = ASPECT_RATIO_2048_BIN
             elif self.transformer.config.sample_size == 32:
                 aspect_ratio_bin = ASPECT_RATIO_1024_BIN
@@ -916,14 +912,7 @@ class SanaPAGPipeline(DiffusionPipeline, PAGMixin):
             image = latents
         else:
             latents = latents.to(self.vae.dtype)
-            try:
-                image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
-            except torch.cuda.OutOfMemoryError as e:
-                warnings.warn(
-                    f"{e}. \n"
-                    f"Try to use VAE tiling for large images. For example: \n"
-                    f"pipe.vae.enable_tiling(tile_sample_min_width=512, tile_sample_min_height=512)"
-                )
+            image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
             if use_resolution_binning:
                 image = self.image_processor.resize_and_crop_tensor(image, orig_width, orig_height)
 

src/diffusers/pipelines/sana/pipeline_sana.py

@@ -16,7 +16,6 @@ import html
 import inspect
 import re
 import urllib.parse as ul
-import warnings
 from typing import Any, Callable, Dict, List, Optional, Tuple, Union
 
 import torch
@@ -954,14 +953,7 @@ class SanaPipeline(DiffusionPipeline, SanaLoraLoaderMixin):
             image = latents
         else:
             latents = latents.to(self.vae.dtype)
-            try:
-                image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
-            except torch.cuda.OutOfMemoryError as e:
-                warnings.warn(
-                    f"{e}. \n"
-                    f"Try to use VAE tiling for large images. For example: \n"
-                    f"pipe.vae.enable_tiling(tile_sample_min_width=512, tile_sample_min_height=512)"
-                )
+            image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
             if use_resolution_binning:
                 image = self.image_processor.resize_and_crop_tensor(image, orig_width, orig_height)
 

src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3_inpaint.py

@@ -13,21 +13,19 @@
 # limitations under the License.
 
 import inspect
-from typing import Any, Callable, Dict, List, Optional, Union
+from typing import Callable, Dict, List, Optional, Union
 
 import torch
 from transformers import (
-    BaseImageProcessor,
     CLIPTextModelWithProjection,
     CLIPTokenizer,
-    PreTrainedModel,
     T5EncoderModel,
     T5TokenizerFast,
 )
 
 from ...callbacks import MultiPipelineCallbacks, PipelineCallback
 from ...image_processor import PipelineImageInput, VaeImageProcessor
-from ...loaders import FromSingleFileMixin, SD3IPAdapterMixin, SD3LoraLoaderMixin
+from ...loaders import FromSingleFileMixin, SD3LoraLoaderMixin
 from ...models.autoencoders import AutoencoderKL
 from ...models.transformers import SD3Transformer2DModel
 from ...schedulers import FlowMatchEulerDiscreteScheduler
@@ -164,7 +162,7 @@ def retrieve_timesteps(
     return timesteps, num_inference_steps
 
 
-class StableDiffusion3InpaintPipeline(DiffusionPipeline, SD3LoraLoaderMixin, FromSingleFileMixin, SD3IPAdapterMixin):
+class StableDiffusion3InpaintPipeline(DiffusionPipeline, SD3LoraLoaderMixin, FromSingleFileMixin):
     r"""
     Args:
         transformer ([`SD3Transformer2DModel`]):
@@ -196,14 +194,10 @@ class StableDiffusion3InpaintPipeline(DiffusionPipeline, SD3LoraLoaderMixin, Fro
         tokenizer_3 (`T5TokenizerFast`):
             Tokenizer of class
             [T5Tokenizer](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5Tokenizer).
-        image_encoder (`PreTrainedModel`, *optional*):
-            Pre-trained Vision Model for IP Adapter.
-        feature_extractor (`BaseImageProcessor`, *optional*):
-            Image processor for IP Adapter.
     """
 
-    model_cpu_offload_seq = "text_encoder->text_encoder_2->text_encoder_3->image_encoder->transformer->vae"
-    _optional_components = ["image_encoder", "feature_extractor"]
+    model_cpu_offload_seq = "text_encoder->text_encoder_2->text_encoder_3->transformer->vae"
+    _optional_components = []
     _callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds", "negative_pooled_prompt_embeds"]
 
     def __init__(
@@ -217,8 +211,6 @@ class StableDiffusion3InpaintPipeline(DiffusionPipeline, SD3LoraLoaderMixin, Fro
         tokenizer_2: CLIPTokenizer,
         text_encoder_3: T5EncoderModel,
         tokenizer_3: T5TokenizerFast,
-        image_encoder: PreTrainedModel = None,
-        feature_extractor: BaseImageProcessor = None,
     ):
         super().__init__()
 
@@ -232,8 +224,6 @@ class StableDiffusion3InpaintPipeline(DiffusionPipeline, SD3LoraLoaderMixin, Fro
             tokenizer_3=tokenizer_3,
             transformer=transformer,
             scheduler=scheduler,
-            image_encoder=image_encoder,
-            feature_extractor=feature_extractor,
         )
         self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) if getattr(self, "vae", None) else 8
         latent_channels = self.vae.config.latent_channels if getattr(self, "vae", None) else 16
@@ -828,10 +818,6 @@ class StableDiffusion3InpaintPipeline(DiffusionPipeline, SD3LoraLoaderMixin, Fro
     def do_classifier_free_guidance(self):
         return self._guidance_scale > 1
 
-    @property
-    def joint_attention_kwargs(self):
-        return self._joint_attention_kwargs
-
     @property
     def num_timesteps(self):
         return self._num_timesteps
@@ -840,84 +826,6 @@ class StableDiffusion3InpaintPipeline(DiffusionPipeline, SD3LoraLoaderMixin, Fro
     def interrupt(self):
         return self._interrupt
 
-    # Copied from diffusers.pipelines.stable_diffusion_3.pipeline_stable_diffusion_3.StableDiffusion3Pipeline.encode_image
-    def encode_image(self, image: PipelineImageInput, device: torch.device) -> torch.Tensor:
-        """Encodes the given image into a feature representation using a pre-trained image encoder.
-
-        Args:
-            image (`PipelineImageInput`):
-                Input image to be encoded.
-            device: (`torch.device`):
-                Torch device.
-
-        Returns:
-            `torch.Tensor`: The encoded image feature representation.
-        """
-        if not isinstance(image, torch.Tensor):
-            image = self.feature_extractor(image, return_tensors="pt").pixel_values
-
-        image = image.to(device=device, dtype=self.dtype)
-
-        return self.image_encoder(image, output_hidden_states=True).hidden_states[-2]
-
-    # Copied from diffusers.pipelines.stable_diffusion_3.pipeline_stable_diffusion_3.StableDiffusion3Pipeline.prepare_ip_adapter_image_embeds
-    def prepare_ip_adapter_image_embeds(
-        self,
-        ip_adapter_image: Optional[PipelineImageInput] = None,
-        ip_adapter_image_embeds: Optional[torch.Tensor] = None,
-        device: Optional[torch.device] = None,
-        num_images_per_prompt: int = 1,
-        do_classifier_free_guidance: bool = True,
-    ) -> torch.Tensor:
-        """Prepares image embeddings for use in the IP-Adapter.
-
-        Either `ip_adapter_image` or `ip_adapter_image_embeds` must be passed.
-
-        Args:
-            ip_adapter_image (`PipelineImageInput`, *optional*):
-                The input image to extract features from for IP-Adapter.
-            ip_adapter_image_embeds (`torch.Tensor`, *optional*):
-                Precomputed image embeddings.
-            device: (`torch.device`, *optional*):
-                Torch device.
-            num_images_per_prompt (`int`, defaults to 1):
-                Number of images that should be generated per prompt.
-            do_classifier_free_guidance (`bool`, defaults to True):
-                Whether to use classifier free guidance or not.
-        """
-        device = device or self._execution_device
-
-        if ip_adapter_image_embeds is not None:
-            if do_classifier_free_guidance:
-                single_negative_image_embeds, single_image_embeds = ip_adapter_image_embeds.chunk(2)
-            else:
-                single_image_embeds = ip_adapter_image_embeds
-        elif ip_adapter_image is not None:
-            single_image_embeds = self.encode_image(ip_adapter_image, device)
-            if do_classifier_free_guidance:
-                single_negative_image_embeds = torch.zeros_like(single_image_embeds)
-        else:
-            raise ValueError("Neither `ip_adapter_image_embeds` or `ip_adapter_image_embeds` were provided.")
-
-        image_embeds = torch.cat([single_image_embeds] * num_images_per_prompt, dim=0)
-
-        if do_classifier_free_guidance:
-            negative_image_embeds = torch.cat([single_negative_image_embeds] * num_images_per_prompt, dim=0)
-            image_embeds = torch.cat([negative_image_embeds, image_embeds], dim=0)
-
-        return image_embeds.to(device=device)
-
-    # Copied from diffusers.pipelines.stable_diffusion_3.pipeline_stable_diffusion_3.StableDiffusion3Pipeline.enable_sequential_cpu_offload
-    def enable_sequential_cpu_offload(self, *args, **kwargs):
-        if self.image_encoder is not None and "image_encoder" not in self._exclude_from_cpu_offload:
-            logger.warning(
-                "`pipe.enable_sequential_cpu_offload()` might fail for `image_encoder` if it uses "
-                "`torch.nn.MultiheadAttention`. You can exclude `image_encoder` from CPU offloading by calling "
-                "`pipe._exclude_from_cpu_offload.append('image_encoder')` before `pipe.enable_sequential_cpu_offload()`."
-            )
-
-        super().enable_sequential_cpu_offload(*args, **kwargs)
-
     @torch.no_grad()
     @replace_example_docstring(EXAMPLE_DOC_STRING)
     def __call__(
@@ -945,11 +853,8 @@ class StableDiffusion3InpaintPipeline(DiffusionPipeline, SD3LoraLoaderMixin, Fro
         negative_prompt_embeds: Optional[torch.Tensor] = None,
         pooled_prompt_embeds: Optional[torch.Tensor] = None,
         negative_pooled_prompt_embeds: Optional[torch.Tensor] = None,
-        ip_adapter_image: Optional[PipelineImageInput] = None,
-        ip_adapter_image_embeds: Optional[torch.Tensor] = None,
         output_type: Optional[str] = "pil",
         return_dict: bool = True,
-        joint_attention_kwargs: Optional[Dict[str, Any]] = None,
         clip_skip: Optional[int] = None,
         callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
         callback_on_step_end_tensor_inputs: List[str] = ["latents"],
@@ -985,9 +890,9 @@ class StableDiffusion3InpaintPipeline(DiffusionPipeline, SD3LoraLoaderMixin, Fro
             mask_image_latent (`torch.Tensor`, `List[torch.Tensor]`):
                 `Tensor` representing an image batch to mask `image` generated by VAE. If not provided, the mask
                 latents tensor will ge generated by `mask_image`.
-            height (`int`, *optional*, defaults to self.transformer.config.sample_size * self.vae_scale_factor):
+            height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
                 The height in pixels of the generated image. This is set to 1024 by default for the best results.
-            width (`int`, *optional*, defaults to self.transformer.config.sample_size * self.vae_scale_factor):
+            width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
                 The width in pixels of the generated image. This is set to 1024 by default for the best results.
             padding_mask_crop (`int`, *optional*, defaults to `None`):
                 The size of margin in the crop to be applied to the image and masking. If `None`, no crop is applied to
@@ -1048,22 +953,12 @@ class StableDiffusion3InpaintPipeline(DiffusionPipeline, SD3LoraLoaderMixin, Fro
                 Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
                 weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`
                 input argument.
-            ip_adapter_image (`PipelineImageInput`, *optional*):
-                Optional image input to work with IP Adapters.
-            ip_adapter_image_embeds (`torch.Tensor`, *optional*):
-                Pre-generated image embeddings for IP-Adapter. Should be a tensor of shape `(batch_size, num_images,
-                emb_dim)`. It should contain the negative image embedding if `do_classifier_free_guidance` is set to
-                `True`. If not provided, embeddings are computed from the `ip_adapter_image` input argument.
             output_type (`str`, *optional*, defaults to `"pil"`):
                 The output format of the generate image. Choose between
                 [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
             return_dict (`bool`, *optional*, defaults to `True`):
                 Whether or not to return a [`~pipelines.stable_diffusion_3.StableDiffusion3PipelineOutput`] instead of
                 a plain tuple.
-            joint_attention_kwargs (`dict`, *optional*):
-                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
-                `self.processor` in
-                [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
             callback_on_step_end (`Callable`, *optional*):
                 A function that calls at the end of each denoising steps during the inference. The function is called
                 with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,
@@ -1111,7 +1006,6 @@ class StableDiffusion3InpaintPipeline(DiffusionPipeline, SD3LoraLoaderMixin, Fro
 
         self._guidance_scale = guidance_scale
         self._clip_skip = clip_skip
-        self._joint_attention_kwargs = joint_attention_kwargs
         self._interrupt = False
 
         # 2. Define call parameters
@@ -1266,22 +1160,7 @@ class StableDiffusion3InpaintPipeline(DiffusionPipeline, SD3LoraLoaderMixin, Fro
                 f"The transformer {self.transformer.__class__} should have 16 input channels or 33 input channels, not {self.transformer.config.in_channels}."
             )
 
-        # 7. Prepare image embeddings
-        if (ip_adapter_image is not None and self.is_ip_adapter_active) or ip_adapter_image_embeds is not None:
-            ip_adapter_image_embeds = self.prepare_ip_adapter_image_embeds(
-                ip_adapter_image,
-                ip_adapter_image_embeds,
-                device,
-                batch_size * num_images_per_prompt,
-                self.do_classifier_free_guidance,
-            )
-
-            if self.joint_attention_kwargs is None:
-                self._joint_attention_kwargs = {"ip_adapter_image_embeds": ip_adapter_image_embeds}
-            else:
-                self._joint_attention_kwargs.update(ip_adapter_image_embeds=ip_adapter_image_embeds)
-
-        # 8. Denoising loop
+        # 7. Denoising loop
         num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
         self._num_timesteps = len(timesteps)
         with self.progress_bar(total=num_inference_steps) as progress_bar:
@@ -1302,7 +1181,6 @@ class StableDiffusion3InpaintPipeline(DiffusionPipeline, SD3LoraLoaderMixin, Fro
                     timestep=timestep,
                     encoder_hidden_states=prompt_embeds,
                     pooled_projections=pooled_prompt_embeds,
-                    joint_attention_kwargs=self.joint_attention_kwargs,
                     return_dict=False,
                 )[0]
 

src/diffusers/utils/__init__.py

@@ -101,7 +101,7 @@ from .import_utils import (
     is_xformers_available,
     requires_backends,
 )
-from .loading_utils import get_module_from_name, get_submodule_by_name, load_image, load_video
+from .loading_utils import get_module_from_name, load_image, load_video
 from .logging import get_logger
 from .outputs import BaseOutput
 from .peft_utils import (

src/diffusers/utils/loading_utils.py

@@ -148,15 +148,3 @@ def get_module_from_name(module, tensor_name: str) -> Tuple[Any, str]:
             module = new_module
         tensor_name = splits[-1]
     return module, tensor_name
-
-
-def get_submodule_by_name(root_module, module_path: str):
-    current = root_module
-    parts = module_path.split(".")
-    for part in parts:
-        if part.isdigit():
-            idx = int(part)
-            current = current[idx]  # e.g., for nn.ModuleList or nn.Sequential
-        else:
-            current = getattr(current, part)
-    return current

tests/models/transformers/test_models_transformer_cogvideox.py

@@ -33,7 +33,6 @@ class CogVideoXTransformerTests(ModelTesterMixin, unittest.TestCase):
     model_class = CogVideoXTransformer3DModel
     main_input_name = "hidden_states"
     uses_custom_attn_processor = True
-    model_split_percents = [0.7, 0.7, 0.8]
 
     @property
     def dummy_input(self):

tests/models/transformers/test_models_transformer_cogview3plus.py

@@ -33,7 +33,6 @@ class CogView3PlusTransformerTests(ModelTesterMixin, unittest.TestCase):
     model_class = CogView3PlusTransformer2DModel
     main_input_name = "hidden_states"
     uses_custom_attn_processor = True
-    model_split_percents = [0.7, 0.6, 0.6]
 
     @property
     def dummy_input(self):

tests/pipelines/stable_diffusion_3/test_pipeline_stable_diffusion_3_inpaint.py

@@ -106,8 +106,6 @@ class StableDiffusion3InpaintPipelineFastTests(PipelineLatentTesterMixin, unitte
             "tokenizer_3": tokenizer_3,
             "transformer": transformer,
             "vae": vae,
-            "image_encoder": None,
-            "feature_extractor": None,
         }
 
     def get_dummy_inputs(self, device, seed=0):

tests/quantization/bnb/test_4bit.py

@@ -20,7 +20,6 @@ import unittest
 import numpy as np
 import pytest
 import safetensors.torch
-from huggingface_hub import hf_hub_download
 
 from diffusers import BitsAndBytesConfig, DiffusionPipeline, FluxTransformer2DModel, SD3Transformer2DModel
 from diffusers.utils import is_accelerate_version, logging
@@ -569,27 +568,6 @@ class SlowBnb4BitFluxTests(Base4bitTests):
         max_diff = numpy_cosine_similarity_distance(expected_slice, out_slice)
         self.assertTrue(max_diff < 1e-3)
 
-    def test_lora_loading(self):
-        self.pipeline_4bit.load_lora_weights(
-            hf_hub_download("ByteDance/Hyper-SD", "Hyper-FLUX.1-dev-8steps-lora.safetensors"), adapter_name="hyper-sd"
-        )
-        self.pipeline_4bit.set_adapters("hyper-sd", adapter_weights=0.125)
-
-        output = self.pipeline_4bit(
-            prompt=self.prompt,
-            height=256,
-            width=256,
-            max_sequence_length=64,
-            output_type="np",
-            num_inference_steps=8,
-            generator=torch.Generator().manual_seed(42),
-        ).images
-        out_slice = output[0, -3:, -3:, -1].flatten()
-        expected_slice = np.array([0.5347, 0.5342, 0.5283, 0.5093, 0.4988, 0.5093, 0.5044, 0.5015, 0.4946])
-
-        max_diff = numpy_cosine_similarity_distance(expected_slice, out_slice)
-        self.assertTrue(max_diff < 1e-3)
-
 
 @slow
 class BaseBnb4BitSerializationTests(Base4bitTests):

tests/quantization/bnb/test_mixed_int8.py

@@ -18,7 +18,6 @@ import unittest
 
 import numpy as np
 import pytest
-from huggingface_hub import hf_hub_download
 
 from diffusers import BitsAndBytesConfig, DiffusionPipeline, FluxTransformer2DModel, SD3Transformer2DModel, logging
 from diffusers.utils import is_accelerate_version
@@ -31,7 +30,6 @@ from diffusers.utils.testing_utils import (
     numpy_cosine_similarity_distance,
     require_accelerate,
     require_bitsandbytes_version_greater,
-    require_peft_version_greater,
     require_torch,
     require_torch_gpu,
     require_transformers_version_greater,
@@ -511,29 +509,6 @@ class SlowBnb8bitFluxTests(Base8bitTests):
         max_diff = numpy_cosine_similarity_distance(expected_slice, out_slice)
         self.assertTrue(max_diff < 1e-3)
 
-    @require_peft_version_greater("0.14.0")
-    def test_lora_loading(self):
-        self.pipeline_8bit.load_lora_weights(
-            hf_hub_download("ByteDance/Hyper-SD", "Hyper-FLUX.1-dev-8steps-lora.safetensors"), adapter_name="hyper-sd"
-        )
-        self.pipeline_8bit.set_adapters("hyper-sd", adapter_weights=0.125)
-
-        output = self.pipeline_8bit(
-            prompt=self.prompt,
-            height=256,
-            width=256,
-            max_sequence_length=64,
-            output_type="np",
-            num_inference_steps=8,
-            generator=torch.manual_seed(42),
-        ).images
-        out_slice = output[0, -3:, -3:, -1].flatten()
-
-        expected_slice = np.array([0.3916, 0.3916, 0.3887, 0.4243, 0.4155, 0.4233, 0.4570, 0.4531, 0.4248])
-
-        max_diff = numpy_cosine_similarity_distance(expected_slice, out_slice)
-        self.assertTrue(max_diff < 1e-3)
-
 
 @slow
 class BaseBnb8bitSerializationTests(Base8bitTests):