update

.github/workflows/nightly_tests.yml

@@ -32,7 +32,7 @@ jobs:
           fetch-depth: 2
       - name: Install dependencies
         run: |
-          pip install -e .
+          pip install -e .[test]
           pip install huggingface_hub
       - name: Fetch Pipeline Matrix
         id: fetch_pipeline_matrix

docs/source/en/api/pipelines/pag.md

@@ -20,11 +20,6 @@ The abstract from the paper is:
 
 *Recent studies have demonstrated that diffusion models are capable of generating high-quality samples, but their quality heavily depends on sampling guidance techniques, such as classifier guidance (CG) and classifier-free guidance (CFG). These techniques are often not applicable in unconditional generation or in various downstream tasks such as image restoration. In this paper, we propose a novel sampling guidance, called Perturbed-Attention Guidance (PAG), which improves diffusion sample quality across both unconditional and conditional settings, achieving this without requiring additional training or the integration of external modules. PAG is designed to progressively enhance the structure of samples throughout the denoising process. It involves generating intermediate samples with degraded structure by substituting selected self-attention maps in diffusion U-Net with an identity matrix, by considering the self-attention mechanisms' ability to capture structural information, and guiding the denoising process away from these degraded samples. In both ADM and Stable Diffusion, PAG surprisingly improves sample quality in conditional and even unconditional scenarios. Moreover, PAG significantly improves the baseline performance in various downstream tasks where existing guidances such as CG or CFG cannot be fully utilized, including ControlNet with empty prompts and image restoration such as inpainting and deblurring.*
 
-## AnimateDiffPAGPipeline
-[[autodoc]] AnimateDiffPAGPipeline
-  - all
-  - __call__
-
 ## StableDiffusionPAGPipeline
 [[autodoc]] StableDiffusionPAGPipeline
 	- all

src/diffusers/__init__.py

@@ -233,7 +233,6 @@ else:
             "AmusedInpaintPipeline",
             "AmusedPipeline",
             "AnimateDiffControlNetPipeline",
-            "AnimateDiffPAGPipeline",
             "AnimateDiffPipeline",
             "AnimateDiffSDXLPipeline",
             "AnimateDiffSparseControlNetPipeline",
@@ -655,7 +654,6 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             AmusedInpaintPipeline,
             AmusedPipeline,
             AnimateDiffControlNetPipeline,
-            AnimateDiffPAGPipeline,
             AnimateDiffPipeline,
             AnimateDiffSDXLPipeline,
             AnimateDiffSparseControlNetPipeline,

src/diffusers/pipelines/__init__.py

@@ -143,7 +143,6 @@ else:
     )
     _import_structure["pag"].extend(
         [
-            "AnimateDiffPAGPipeline",
             "StableDiffusionPAGPipeline",
             "StableDiffusionControlNetPAGPipeline",
             "StableDiffusionXLPAGPipeline",
@@ -528,7 +527,6 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
         )
         from .musicldm import MusicLDMPipeline
         from .pag import (
-            AnimateDiffPAGPipeline,
             StableDiffusionControlNetPAGPipeline,
             StableDiffusionPAGPipeline,
             StableDiffusionXLControlNetPAGPipeline,

src/diffusers/pipelines/pag/__init__.py

@@ -25,7 +25,6 @@ else:
     _import_structure["pipeline_pag_controlnet_sd"] = ["StableDiffusionControlNetPAGPipeline"]
     _import_structure["pipeline_pag_controlnet_sd_xl"] = ["StableDiffusionXLControlNetPAGPipeline"]
     _import_structure["pipeline_pag_sd"] = ["StableDiffusionPAGPipeline"]
-    _import_structure["pipeline_pag_sd_animatediff"] = ["AnimateDiffPAGPipeline"]
     _import_structure["pipeline_pag_sd_xl"] = ["StableDiffusionXLPAGPipeline"]
     _import_structure["pipeline_pag_sd_xl_img2img"] = ["StableDiffusionXLPAGImg2ImgPipeline"]
     _import_structure["pipeline_pag_sd_xl_inpaint"] = ["StableDiffusionXLPAGInpaintPipeline"]
@@ -41,7 +40,6 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
         from .pipeline_pag_controlnet_sd import StableDiffusionControlNetPAGPipeline
         from .pipeline_pag_controlnet_sd_xl import StableDiffusionXLControlNetPAGPipeline
         from .pipeline_pag_sd import StableDiffusionPAGPipeline
-        from .pipeline_pag_sd_animatediff import AnimateDiffPAGPipeline
         from .pipeline_pag_sd_xl import StableDiffusionXLPAGPipeline
         from .pipeline_pag_sd_xl_img2img import StableDiffusionXLPAGImg2ImgPipeline
         from .pipeline_pag_sd_xl_inpaint import StableDiffusionXLPAGInpaintPipeline

src/diffusers/pipelines/pag/pag_utils.py

@@ -33,7 +33,7 @@ class PAGMixin:
         Check if each layer input in `applied_pag_layers` is valid. It should be either one of these 3 formats:
         "{block_type}", "{block_type}.{block_index}", or "{block_type}.{block_index}.{attention_index}". `block_type`
         can be "down", "mid", "up". `block_index` should be in the format of "block_{i}". `attention_index` should be
-        in the format of "attentions_{j}". `motion_modules_index` should be in the format of "motion_modules_{j}"
+        in the format of "attentions_{j}".
         """
 
         layer_splits = layer.split(".")
@@ -52,11 +52,8 @@ class PAGMixin:
                 raise ValueError(f"Invalid block_index in pag layer: {layer}. Should start with 'block_'")
 
         if len(layer_splits) == 3:
-            layer_2 = layer_splits[2]
-            if not layer_2.startswith("attentions_") and not layer_2.startswith("motion_modules_"):
-                raise ValueError(
-                    f"Invalid attention_index in pag layer: {layer}. Should start with 'attentions_' or 'motion_modules_'"
-                )
+            if not layer_splits[2].startswith("attentions_"):
+                raise ValueError(f"Invalid attention_index in pag layer: {layer}. Should start with 'attentions_'")
 
     def _set_pag_attn_processor(self, pag_applied_layers, do_classifier_free_guidance):
         r"""
@@ -75,46 +72,33 @@ class PAGMixin:
 
         def get_block_type(module_name):
             r"""
-            Get the block type from the module name. Can be "down", "mid", "up".
+            Get the block type from the module name. can be "down", "mid", "up".
             """
             # down_blocks.1.attentions.0.transformer_blocks.0.attn1 -> "down"
-            # down_blocks.1.motion_modules.0.transformer_blocks.0.attn1 -> "down"
             return module_name.split(".")[0].split("_")[0]
 
         def get_block_index(module_name):
             r"""
-            Get the block index from the module name. Can be "block_0", "block_1", ... If there is only one block (e.g.
+            Get the block index from the module name. can be "block_0", "block_1", ... If there is only one block (e.g.
             mid_block) and index is ommited from the name, it will be "block_0".
             """
             # down_blocks.1.attentions.0.transformer_blocks.0.attn1 -> "block_1"
             # mid_block.attentions.0.transformer_blocks.0.attn1 -> "block_0"
-            module_name_splits = module_name.split(".")
-            block_index = module_name_splits[1]
-            if "attentions" in block_index or "motion_modules" in block_index:
+            if "attentions" in module_name.split(".")[1]:
                 return "block_0"
             else:
-                return f"block_{block_index}"
+                return f"block_{module_name.split('.')[1]}"
 
         def get_attn_index(module_name):
             r"""
-            Get the attention index from the module name. Can be "attentions_0", "attentions_1", "motion_modules_0",
-            "motion_modules_1", ...
+            Get the attention index from the module name. can be "attentions_0", "attentions_1", ...
             """
             # down_blocks.1.attentions.0.transformer_blocks.0.attn1 -> "attentions_0"
             # mid_block.attentions.0.transformer_blocks.0.attn1 -> "attentions_0"
-            # down_blocks.1.motion_modules.0.transformer_blocks.0.attn1 -> "motion_modules_0"
-            # mid_block.motion_modules.0.transformer_blocks.0.attn1 -> "motion_modules_0"
-            module_name_split = module_name.split(".")
-            mid_name = module_name_split[1]
-            down_name = module_name_split[2]
-            if "attentions" in down_name:
-                return f"attentions_{module_name_split[3]}"
-            if "attentions" in mid_name:
-                return f"attentions_{module_name_split[2]}"
-            if "motion_modules" in down_name:
-                return f"motion_modules_{module_name_split[3]}"
-            if "motion_modules" in mid_name:
-                return f"motion_modules_{module_name_split[2]}"
+            if "attentions" in module_name.split(".")[2]:
+                return f"attentions_{module_name.split('.')[3]}"
+            elif "attentions" in module_name.split(".")[1]:
+                return f"attentions_{module_name.split('.')[2]}"
 
         for pag_layer_input in pag_applied_layers:
             # for each PAG layer input, we find corresponding self-attention layers in the unet model
@@ -130,7 +114,7 @@ class PAGMixin:
                         target_modules.append(module)
 
             elif len(pag_layer_input_splits) == 2:
-                # when the layer input contains both block_type and block_index. e.g. "down.block_1", "mid.block_0"
+                # when the layer inpput contains both block_type and block_index. e.g. "down.block_1", "mid.block_0"
                 block_type = pag_layer_input_splits[0]
                 block_index = pag_layer_input_splits[1]
                 for name, module in self.unet.named_modules():
@@ -142,8 +126,7 @@ class PAGMixin:
                         target_modules.append(module)
 
             elif len(pag_layer_input_splits) == 3:
-                # when the layer input contains block_type, block_index and attention_index.
-                # e.g. "down.block_1.attentions_1" or "down.block_1.motion_modules_1"
+                # when the layer input contains block_type, block_index and attention_index. e.g. "down.blocks_1.attentions_1"
                 block_type = pag_layer_input_splits[0]
                 block_index = pag_layer_input_splits[1]
                 attn_index = pag_layer_input_splits[2]

src/diffusers/pipelines/pag/pipeline_pag_sd_animatediff.py

@@ -1,846 +0,0 @@
-# Copyright 2024 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import inspect
-from typing import Any, Callable, Dict, List, Optional, Union
-
-import torch
-from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection
-
-from ...image_processor import PipelineImageInput
-from ...loaders import IPAdapterMixin, StableDiffusionLoraLoaderMixin, TextualInversionLoaderMixin
-from ...models import AutoencoderKL, ImageProjection, UNet2DConditionModel, UNetMotionModel
-from ...models.lora import adjust_lora_scale_text_encoder
-from ...models.unets.unet_motion_model import MotionAdapter
-from ...schedulers import KarrasDiffusionSchedulers
-from ...utils import (
-    USE_PEFT_BACKEND,
-    logging,
-    replace_example_docstring,
-    scale_lora_layers,
-    unscale_lora_layers,
-)
-from ...utils.torch_utils import randn_tensor
-from ...video_processor import VideoProcessor
-from ..animatediff.pipeline_output import AnimateDiffPipelineOutput
-from ..free_init_utils import FreeInitMixin
-from ..pipeline_utils import DiffusionPipeline, StableDiffusionMixin
-from .pag_utils import PAGMixin
-
-
-logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
-
-EXAMPLE_DOC_STRING = """
-    Examples:
-        ```py
-        >>> import torch
-        >>> from diffusers import AnimateDiffPAGPipeline, MotionAdapter, DDIMScheduler
-        >>> from diffusers.utils import export_to_gif
-
-        >>> model_id = "SG161222/Realistic_Vision_V5.1_noVAE"
-        >>> motion_adapter_id = "guoyww/animatediff-motion-adapter-v1-5-2"
-        >>> motion_adapter = MotionAdapter.from_pretrained(motion_adapter_id)
-        >>> scheduler = DDIMScheduler.from_pretrained(
-        ...     model_id, subfolder="scheduler", beta_schedule="linear", steps_offset=1, clip_sample=False
-        ... )
-        >>> pipe = AnimateDiffPAGPipeline.from_pretrained(
-        ...     model_id,
-        ...     motion_adapter=motion_adapter,
-        ...     scheduler=scheduler,
-        ...     pag_applied_layers=["mid"],
-        ...     torch_dtype=torch.float16,
-        ... ).to("cuda")
-
-        >>> video = pipe(
-        ...     prompt="car, futuristic cityscape with neon lights, street, no human",
-        ...     negative_prompt="low quality, bad quality",
-        ...     num_inference_steps=25,
-        ...     guidance_scale=6.0,
-        ...     pag_scale=3.0,
-        ...     generator=torch.Generator().manual_seed(42),
-        ... ).frames[0]
-
-        >>> export_to_gif(video, "animatediff_pag.gif")
-        ```
-"""
-
-
-class AnimateDiffPAGPipeline(
-    DiffusionPipeline,
-    StableDiffusionMixin,
-    TextualInversionLoaderMixin,
-    IPAdapterMixin,
-    StableDiffusionLoraLoaderMixin,
-    FreeInitMixin,
-    PAGMixin,
-):
-    r"""
-    Pipeline for text-to-video generation using
-    [AnimateDiff](https://huggingface.co/docs/diffusers/en/api/pipelines/animatediff) and [Perturbed Attention
-    Guidance](https://huggingface.co/docs/diffusers/en/using-diffusers/pag).
-
-    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
-    implemented for all pipelines (downloading, saving, running on a particular device, etc.).
-
-    The pipeline also inherits the following loading methods:
-        - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings
-        - [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_weights`] for loading LoRA weights
-        - [`~loaders.StableDiffusionLoraLoaderMixin.save_lora_weights`] for saving LoRA weights
-        - [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters
-
-    Args:
-        vae ([`AutoencoderKL`]):
-            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
-        text_encoder ([`CLIPTextModel`]):
-            Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
-        tokenizer (`CLIPTokenizer`):
-            A [`~transformers.CLIPTokenizer`] to tokenize text.
-        unet ([`UNet2DConditionModel`]):
-            A [`UNet2DConditionModel`] used to create a UNetMotionModel to denoise the encoded video latents.
-        motion_adapter ([`MotionAdapter`]):
-            A [`MotionAdapter`] to be used in combination with `unet` to denoise the encoded video latents.
-        scheduler ([`SchedulerMixin`]):
-            A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
-            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
-    """
-
-    model_cpu_offload_seq = "text_encoder->image_encoder->unet->vae"
-    _optional_components = ["feature_extractor", "image_encoder", "motion_adapter"]
-    _callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds"]
-
-    def __init__(
-        self,
-        vae: AutoencoderKL,
-        text_encoder: CLIPTextModel,
-        tokenizer: CLIPTokenizer,
-        unet: Union[UNet2DConditionModel, UNetMotionModel],
-        motion_adapter: MotionAdapter,
-        scheduler: KarrasDiffusionSchedulers,
-        feature_extractor: CLIPImageProcessor = None,
-        image_encoder: CLIPVisionModelWithProjection = None,
-        pag_applied_layers: Union[str, List[str]] = "mid",  # ["mid"], ["down.block_1"], ["up.block_0.attentions_0"]
-    ):
-        super().__init__()
-        if isinstance(unet, UNet2DConditionModel):
-            unet = UNetMotionModel.from_unet2d(unet, motion_adapter)
-
-        self.register_modules(
-            vae=vae,
-            text_encoder=text_encoder,
-            tokenizer=tokenizer,
-            unet=unet,
-            motion_adapter=motion_adapter,
-            scheduler=scheduler,
-            feature_extractor=feature_extractor,
-            image_encoder=image_encoder,
-        )
-        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
-        self.video_processor = VideoProcessor(do_resize=False, vae_scale_factor=self.vae_scale_factor)
-
-        self.set_pag_applied_layers(pag_applied_layers)
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_prompt with num_images_per_prompt -> num_videos_per_prompt
-    def encode_prompt(
-        self,
-        prompt,
-        device,
-        num_images_per_prompt,
-        do_classifier_free_guidance,
-        negative_prompt=None,
-        prompt_embeds: Optional[torch.Tensor] = None,
-        negative_prompt_embeds: Optional[torch.Tensor] = None,
-        lora_scale: Optional[float] = None,
-        clip_skip: Optional[int] = None,
-    ):
-        r"""
-        Encodes the prompt into text encoder hidden states.
-
-        Args:
-            prompt (`str` or `List[str]`, *optional*):
-                prompt to be encoded
-            device: (`torch.device`):
-                torch device
-            num_images_per_prompt (`int`):
-                number of images that should be generated per 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. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
-                less than `1`).
-            prompt_embeds (`torch.Tensor`, *optional*):
-                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
-                provided, text embeddings will be generated from `prompt` input argument.
-            negative_prompt_embeds (`torch.Tensor`, *optional*):
-                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
-                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
-                argument.
-            lora_scale (`float`, *optional*):
-                A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
-            clip_skip (`int`, *optional*):
-                Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
-                the output of the pre-final layer will be used for computing the prompt embeddings.
-        """
-        # set lora scale so that monkey patched LoRA
-        # function of text encoder can correctly access it
-        if lora_scale is not None and isinstance(self, StableDiffusionLoraLoaderMixin):
-            self._lora_scale = lora_scale
-
-            # dynamically adjust the LoRA scale
-            if not USE_PEFT_BACKEND:
-                adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)
-            else:
-                scale_lora_layers(self.text_encoder, lora_scale)
-
-        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]
-
-        if prompt_embeds is None:
-            # textual inversion: process 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",
-                max_length=self.tokenizer.model_max_length,
-                truncation=True,
-                return_tensors="pt",
-            )
-            text_input_ids = text_inputs.input_ids
-            untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
-
-            if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
-                text_input_ids, untruncated_ids
-            ):
-                removed_text = self.tokenizer.batch_decode(
-                    untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
-                )
-                logger.warning(
-                    "The following part of your input was truncated because CLIP can only handle sequences up to"
-                    f" {self.tokenizer.model_max_length} tokens: {removed_text}"
-                )
-
-            if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
-                attention_mask = text_inputs.attention_mask.to(device)
-            else:
-                attention_mask = None
-
-            if clip_skip is None:
-                prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask)
-                prompt_embeds = prompt_embeds[0]
-            else:
-                prompt_embeds = self.text_encoder(
-                    text_input_ids.to(device), attention_mask=attention_mask, output_hidden_states=True
-                )
-                # Access the `hidden_states` first, that contains a tuple of
-                # all the hidden states from the encoder layers. Then index into
-                # the tuple to access the hidden states from the desired layer.
-                prompt_embeds = prompt_embeds[-1][-(clip_skip + 1)]
-                # We also need to apply the final LayerNorm here to not mess with the
-                # representations. The `last_hidden_states` that we typically use for
-                # obtaining the final prompt representations passes through the LayerNorm
-                # layer.
-                prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds)
-
-        if self.text_encoder is not None:
-            prompt_embeds_dtype = self.text_encoder.dtype
-        elif self.unet is not None:
-            prompt_embeds_dtype = self.unet.dtype
-        else:
-            prompt_embeds_dtype = prompt_embeds.dtype
-
-        prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)
-
-        bs_embed, seq_len, _ = prompt_embeds.shape
-        # duplicate text embeddings for each generation per prompt, using mps friendly method
-        prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
-        prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
-
-        # get unconditional embeddings for classifier free guidance
-        if do_classifier_free_guidance and negative_prompt_embeds is None:
-            uncond_tokens: List[str]
-            if negative_prompt is None:
-                uncond_tokens = [""] * batch_size
-            elif prompt is not None and type(prompt) is not type(negative_prompt):
-                raise TypeError(
-                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
-                    f" {type(prompt)}."
-                )
-            elif isinstance(negative_prompt, str):
-                uncond_tokens = [negative_prompt]
-            elif batch_size != len(negative_prompt):
-                raise ValueError(
-                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
-                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
-                    " the batch size of `prompt`."
-                )
-            else:
-                uncond_tokens = negative_prompt
-
-            # textual inversion: process 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,
-                padding="max_length",
-                max_length=max_length,
-                truncation=True,
-                return_tensors="pt",
-            )
-
-            if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
-                attention_mask = uncond_input.attention_mask.to(device)
-            else:
-                attention_mask = None
-
-            negative_prompt_embeds = self.text_encoder(
-                uncond_input.input_ids.to(device),
-                attention_mask=attention_mask,
-            )
-            negative_prompt_embeds = negative_prompt_embeds[0]
-
-        if do_classifier_free_guidance:
-            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
-            seq_len = negative_prompt_embeds.shape[1]
-
-            negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)
-
-            negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
-            negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
-
-        if self.text_encoder is not None:
-            if isinstance(self, StableDiffusionLoraLoaderMixin) and USE_PEFT_BACKEND:
-                # Retrieve the original scale by scaling back the LoRA layers
-                unscale_lora_layers(self.text_encoder, lora_scale)
-
-        return prompt_embeds, negative_prompt_embeds
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_image
-    def encode_image(self, image, device, num_images_per_prompt, output_hidden_states=None):
-        dtype = next(self.image_encoder.parameters()).dtype
-
-        if not isinstance(image, torch.Tensor):
-            image = self.feature_extractor(image, return_tensors="pt").pixel_values
-
-        image = image.to(device=device, dtype=dtype)
-        if output_hidden_states:
-            image_enc_hidden_states = self.image_encoder(image, output_hidden_states=True).hidden_states[-2]
-            image_enc_hidden_states = image_enc_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)
-            uncond_image_enc_hidden_states = self.image_encoder(
-                torch.zeros_like(image), output_hidden_states=True
-            ).hidden_states[-2]
-            uncond_image_enc_hidden_states = uncond_image_enc_hidden_states.repeat_interleave(
-                num_images_per_prompt, dim=0
-            )
-            return image_enc_hidden_states, uncond_image_enc_hidden_states
-        else:
-            image_embeds = self.image_encoder(image).image_embeds
-            image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)
-            uncond_image_embeds = torch.zeros_like(image_embeds)
-
-            return image_embeds, uncond_image_embeds
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_ip_adapter_image_embeds
-    def prepare_ip_adapter_image_embeds(
-        self, ip_adapter_image, ip_adapter_image_embeds, device, num_images_per_prompt, do_classifier_free_guidance
-    ):
-        image_embeds = []
-        if do_classifier_free_guidance:
-            negative_image_embeds = []
-        if ip_adapter_image_embeds is None:
-            if not isinstance(ip_adapter_image, list):
-                ip_adapter_image = [ip_adapter_image]
-
-            if len(ip_adapter_image) != len(self.unet.encoder_hid_proj.image_projection_layers):
-                raise ValueError(
-                    f"`ip_adapter_image` must have same length as the number of IP Adapters. Got {len(ip_adapter_image)} images and {len(self.unet.encoder_hid_proj.image_projection_layers)} IP Adapters."
-                )
-
-            for single_ip_adapter_image, image_proj_layer in zip(
-                ip_adapter_image, self.unet.encoder_hid_proj.image_projection_layers
-            ):
-                output_hidden_state = not isinstance(image_proj_layer, ImageProjection)
-                single_image_embeds, single_negative_image_embeds = self.encode_image(
-                    single_ip_adapter_image, device, 1, output_hidden_state
-                )
-
-                image_embeds.append(single_image_embeds[None, :])
-                if do_classifier_free_guidance:
-                    negative_image_embeds.append(single_negative_image_embeds[None, :])
-        else:
-            for single_image_embeds in ip_adapter_image_embeds:
-                if do_classifier_free_guidance:
-                    single_negative_image_embeds, single_image_embeds = single_image_embeds.chunk(2)
-                    negative_image_embeds.append(single_negative_image_embeds)
-                image_embeds.append(single_image_embeds)
-
-        ip_adapter_image_embeds = []
-        for i, single_image_embeds in enumerate(image_embeds):
-            single_image_embeds = torch.cat([single_image_embeds] * num_images_per_prompt, dim=0)
-            if do_classifier_free_guidance:
-                single_negative_image_embeds = torch.cat([negative_image_embeds[i]] * num_images_per_prompt, dim=0)
-                single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds], dim=0)
-
-            single_image_embeds = single_image_embeds.to(device=device)
-            ip_adapter_image_embeds.append(single_image_embeds)
-
-        return ip_adapter_image_embeds
-
-    # Copied from diffusers.pipelines.text_to_video_synthesis/pipeline_text_to_video_synth.TextToVideoSDPipeline.decode_latents
-    def decode_latents(self, latents):
-        latents = 1 / self.vae.config.scaling_factor * latents
-
-        batch_size, channels, num_frames, height, width = latents.shape
-        latents = latents.permute(0, 2, 1, 3, 4).reshape(batch_size * num_frames, channels, height, width)
-
-        image = self.vae.decode(latents).sample
-        video = image[None, :].reshape((batch_size, num_frames, -1) + image.shape[2:]).permute(0, 2, 1, 3, 4)
-        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
-        video = video.float()
-        return video
-
-    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
-    def prepare_extra_step_kwargs(self, generator, eta):
-        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
-        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
-        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
-        # and should be between [0, 1]
-
-        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
-        extra_step_kwargs = {}
-        if accepts_eta:
-            extra_step_kwargs["eta"] = eta
-
-        # check if the scheduler accepts generator
-        accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
-        if accepts_generator:
-            extra_step_kwargs["generator"] = generator
-        return extra_step_kwargs
-
-    # Copied from diffusers.pipelines.pia.pipeline_pia.PIAPipeline.check_inputs
-    def check_inputs(
-        self,
-        prompt,
-        height,
-        width,
-        negative_prompt=None,
-        prompt_embeds=None,
-        negative_prompt_embeds=None,
-        ip_adapter_image=None,
-        ip_adapter_image_embeds=None,
-        callback_on_step_end_tensor_inputs=None,
-    ):
-        if height % 8 != 0 or width % 8 != 0:
-            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
-
-        if callback_on_step_end_tensor_inputs is not None and not all(
-            k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs
-        ):
-            raise ValueError(
-                f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"
-            )
-
-        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 ip_adapter_image is not None and ip_adapter_image_embeds is not None:
-            raise ValueError(
-                "Provide either `ip_adapter_image` or `ip_adapter_image_embeds`. Cannot leave both `ip_adapter_image` and `ip_adapter_image_embeds` defined."
-            )
-
-        if ip_adapter_image_embeds is not None:
-            if not isinstance(ip_adapter_image_embeds, list):
-                raise ValueError(
-                    f"`ip_adapter_image_embeds` has to be of type `list` but is {type(ip_adapter_image_embeds)}"
-                )
-            elif ip_adapter_image_embeds[0].ndim not in [3, 4]:
-                raise ValueError(
-                    f"`ip_adapter_image_embeds` has to be a list of 3D or 4D tensors but is {ip_adapter_image_embeds[0].ndim}D"
-                )
-
-    # Copied from diffusers.pipelines.text_to_video_synthesis.pipeline_text_to_video_synth.TextToVideoSDPipeline.prepare_latents
-    def prepare_latents(
-        self, batch_size, num_channels_latents, num_frames, height, width, dtype, device, generator, latents=None
-    ):
-        shape = (
-            batch_size,
-            num_channels_latents,
-            num_frames,
-            height // self.vae_scale_factor,
-            width // self.vae_scale_factor,
-        )
-        if isinstance(generator, list) and len(generator) != batch_size:
-            raise ValueError(
-                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
-                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
-            )
-
-        if latents is None:
-            latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
-        else:
-            latents = latents.to(device)
-
-        # scale the initial noise by the standard deviation required by the scheduler
-        latents = latents * self.scheduler.init_noise_sigma
-        return latents
-
-    @property
-    def guidance_scale(self):
-        return self._guidance_scale
-
-    @property
-    def clip_skip(self):
-        return self._clip_skip
-
-    # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
-    # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
-    # corresponds to doing no classifier free guidance.
-    @property
-    def do_classifier_free_guidance(self):
-        return self._guidance_scale > 1
-
-    @property
-    def cross_attention_kwargs(self):
-        return self._cross_attention_kwargs
-
-    @property
-    def num_timesteps(self):
-        return self._num_timesteps
-
-    @torch.no_grad()
-    @replace_example_docstring(EXAMPLE_DOC_STRING)
-    def __call__(
-        self,
-        prompt: Optional[Union[str, List[str]]] = None,
-        num_frames: Optional[int] = 16,
-        height: Optional[int] = None,
-        width: Optional[int] = None,
-        num_inference_steps: int = 50,
-        guidance_scale: float = 7.5,
-        negative_prompt: Optional[Union[str, List[str]]] = None,
-        num_videos_per_prompt: Optional[int] = 1,
-        eta: float = 0.0,
-        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
-        latents: Optional[torch.Tensor] = None,
-        prompt_embeds: Optional[torch.Tensor] = None,
-        negative_prompt_embeds: Optional[torch.Tensor] = None,
-        ip_adapter_image: Optional[PipelineImageInput] = None,
-        ip_adapter_image_embeds: Optional[List[torch.Tensor]] = None,
-        output_type: Optional[str] = "pil",
-        return_dict: bool = True,
-        cross_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"],
-        pag_scale: float = 3.0,
-        pag_adaptive_scale: float = 0.0,
-    ):
-        r"""
-        The call function to the pipeline for generation.
-
-        Args:
-            prompt (`str` or `List[str]`, *optional*):
-                The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
-            height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
-                The height in pixels of the generated video.
-            width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
-                The width in pixels of the generated video.
-            num_frames (`int`, *optional*, defaults to 16):
-                The number of video frames that are generated. Defaults to 16 frames which at 8 frames per seconds
-                amounts to 2 seconds of video.
-            num_inference_steps (`int`, *optional*, defaults to 50):
-                The number of denoising steps. More denoising steps usually lead to a higher quality videos at the
-                expense of slower inference.
-            guidance_scale (`float`, *optional*, defaults to 7.5):
-                A higher guidance scale value encourages the model to generate images closely linked to the text
-                `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
-            negative_prompt (`str` or `List[str]`, *optional*):
-                The prompt or prompts to guide what to not include in image generation. If not defined, you need to
-                pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
-            eta (`float`, *optional*, defaults to 0.0):
-                Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies
-                to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers.
-            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
-                A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
-                generation deterministic.
-            latents (`torch.Tensor`, *optional*):
-                Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for video
-                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
-                tensor is generated by sampling using the supplied random `generator`. Latents should be of shape
-                `(batch_size, num_channel, num_frames, height, width)`.
-            prompt_embeds (`torch.Tensor`, *optional*):
-                Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
-                provided, text embeddings are generated from the `prompt` input argument.
-            negative_prompt_embeds (`torch.Tensor`, *optional*):
-                Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
-                not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
-            ip_adapter_image: (`PipelineImageInput`, *optional*):
-                Optional image input to work with IP Adapters.
-            ip_adapter_image_embeds (`List[torch.Tensor]`, *optional*):
-                Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of
-                IP-adapters. Each element 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 generated video. Choose between `torch.Tensor`, `PIL.Image` or `np.array`.
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~pipelines.text_to_video_synthesis.TextToVideoSDPipelineOutput`] instead
-                of a plain tuple.
-            cross_attention_kwargs (`dict`, *optional*):
-                A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in
-                [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
-            clip_skip (`int`, *optional*):
-                Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
-                the output of the pre-final layer will be used for computing the prompt embeddings.
-            callback_on_step_end (`Callable`, *optional*):
-                A function that calls at the end of each denoising steps during the inference. The function is called
-                with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,
-                callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by
-                `callback_on_step_end_tensor_inputs`.
-            callback_on_step_end_tensor_inputs (`List`, *optional*):
-                The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
-                will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
-                `._callback_tensor_inputs` attribute of your pipeline class.
-            pag_scale (`float`, *optional*, defaults to 3.0):
-                The scale factor for the perturbed attention guidance. If it is set to 0.0, the perturbed attention
-                guidance will not be used.
-            pag_adaptive_scale (`float`, *optional*, defaults to 0.0):
-                The adaptive scale factor for the perturbed attention guidance. If it is set to 0.0, `pag_scale` is
-                used.
-
-        Examples:
-
-        Returns:
-            [`~pipelines.animatediff.pipeline_output.AnimateDiffPipelineOutput`] or `tuple`:
-                If `return_dict` is `True`, [`~pipelines.animatediff.pipeline_output.AnimateDiffPipelineOutput`] is
-                returned, otherwise a `tuple` is returned where the first element is a list with the generated frames.
-        """
-
-        # 0. Default height and width to unet
-        height = height or self.unet.config.sample_size * self.vae_scale_factor
-        width = width or self.unet.config.sample_size * self.vae_scale_factor
-
-        num_videos_per_prompt = 1
-
-        # 1. Check inputs. Raise error if not correct
-        self.check_inputs(
-            prompt,
-            height,
-            width,
-            negative_prompt,
-            prompt_embeds,
-            negative_prompt_embeds,
-            ip_adapter_image,
-            ip_adapter_image_embeds,
-            callback_on_step_end_tensor_inputs,
-        )
-
-        self._guidance_scale = guidance_scale
-        self._clip_skip = clip_skip
-        self._cross_attention_kwargs = cross_attention_kwargs
-        self._pag_scale = pag_scale
-        self._pag_adaptive_scale = pag_adaptive_scale
-
-        # 2. Define call parameters
-        if prompt is not None and isinstance(prompt, str):
-            batch_size = 1
-        elif prompt is not None and isinstance(prompt, list):
-            batch_size = len(prompt)
-        else:
-            batch_size = prompt_embeds.shape[0]
-
-        device = self._execution_device
-
-        # 3. Encode input prompt
-        text_encoder_lora_scale = (
-            self.cross_attention_kwargs.get("scale", None) if self.cross_attention_kwargs is not None else None
-        )
-        prompt_embeds, negative_prompt_embeds = self.encode_prompt(
-            prompt,
-            device,
-            num_videos_per_prompt,
-            self.do_classifier_free_guidance,
-            negative_prompt,
-            prompt_embeds=prompt_embeds,
-            negative_prompt_embeds=negative_prompt_embeds,
-            lora_scale=text_encoder_lora_scale,
-            clip_skip=self.clip_skip,
-        )
-
-        # For classifier free guidance, we need to do two forward passes.
-        # Here we concatenate the unconditional and text embeddings into a single batch
-        # to avoid doing two forward passes
-        if self.do_perturbed_attention_guidance:
-            prompt_embeds = self._prepare_perturbed_attention_guidance(
-                prompt_embeds, negative_prompt_embeds, self.do_classifier_free_guidance
-            )
-        elif self.do_classifier_free_guidance:
-            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
-
-        if ip_adapter_image is not None 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_videos_per_prompt,
-                self.do_classifier_free_guidance,
-            )
-
-            for i, image_embeds in enumerate(ip_adapter_image_embeds):
-                negative_image_embeds = None
-                if self.do_classifier_free_guidance:
-                    negative_image_embeds, image_embeds = image_embeds.chunk(2)
-                if self.do_perturbed_attention_guidance:
-                    image_embeds = self._prepare_perturbed_attention_guidance(
-                        image_embeds, negative_image_embeds, self.do_classifier_free_guidance
-                    )
-                elif self.do_classifier_free_guidance:
-                    image_embeds = torch.cat([negative_image_embeds, image_embeds], dim=0)
-                image_embeds = image_embeds.to(device)
-                ip_adapter_image_embeds[i] = image_embeds
-
-        # 4. Prepare timesteps
-        self.scheduler.set_timesteps(num_inference_steps, device=device)
-        timesteps = self.scheduler.timesteps
-
-        # 5. Prepare latent variables
-        num_channels_latents = self.unet.config.in_channels
-        latents = self.prepare_latents(
-            batch_size * num_videos_per_prompt,
-            num_channels_latents,
-            num_frames,
-            height,
-            width,
-            prompt_embeds.dtype,
-            device,
-            generator,
-            latents,
-        )
-
-        # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
-        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
-
-        # 7. Add image embeds for IP-Adapter
-        added_cond_kwargs = (
-            {"image_embeds": ip_adapter_image_embeds}
-            if ip_adapter_image is not None or ip_adapter_image_embeds is not None
-            else None
-        )
-
-        if self.do_perturbed_attention_guidance:
-            original_attn_proc = self.unet.attn_processors
-            self._set_pag_attn_processor(
-                pag_applied_layers=self.pag_applied_layers,
-                do_classifier_free_guidance=self.do_classifier_free_guidance,
-            )
-
-        num_free_init_iters = self._free_init_num_iters if self.free_init_enabled else 1
-        for free_init_iter in range(num_free_init_iters):
-            if self.free_init_enabled:
-                latents, timesteps = self._apply_free_init(
-                    latents, free_init_iter, num_inference_steps, device, latents.dtype, generator
-                )
-
-            self._num_timesteps = len(timesteps)
-            num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
-
-            # 8. Denoising loop
-            with self.progress_bar(total=self._num_timesteps) as progress_bar:
-                for i, t in enumerate(timesteps):
-                    # expand the latents if we are doing classifier free guidance
-                    latent_model_input = torch.cat([latents] * (prompt_embeds.shape[0] // latents.shape[0]))
-                    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,
-                        cross_attention_kwargs=cross_attention_kwargs,
-                        added_cond_kwargs=added_cond_kwargs,
-                    ).sample
-
-                    # perform guidance
-                    if self.do_perturbed_attention_guidance:
-                        noise_pred = self._apply_perturbed_attention_guidance(
-                            noise_pred, self.do_classifier_free_guidance, self.guidance_scale, t
-                        )
-                    elif self.do_classifier_free_guidance:
-                        noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
-                        noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
-
-                    # compute the previous noisy sample x_t -> x_t-1
-                    latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
-
-                    if callback_on_step_end is not None:
-                        callback_kwargs = {}
-                        for k in callback_on_step_end_tensor_inputs:
-                            callback_kwargs[k] = locals()[k]
-                        callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
-
-                        latents = callback_outputs.pop("latents", latents)
-                        prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
-                        negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)
-
-                    # call the callback, if provided
-                    if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
-                        progress_bar.update()
-
-        # 9. Post processing
-        if output_type == "latent":
-            video = latents
-        else:
-            video_tensor = self.decode_latents(latents)
-            video = self.video_processor.postprocess_video(video=video_tensor, output_type=output_type)
-
-        # 10. Offload all models
-        self.maybe_free_model_hooks()
-
-        if self.do_perturbed_attention_guidance:
-            self.unet.set_attn_processor(original_attn_proc)
-
-        if not return_dict:
-            return (video,)
-
-        return AnimateDiffPipelineOutput(frames=video)

src/diffusers/utils/dummy_torch_and_transformers_objects.py

@@ -92,21 +92,6 @@ class AnimateDiffControlNetPipeline(metaclass=DummyObject):
         requires_backends(cls, ["torch", "transformers"])
 
 
-class AnimateDiffPAGPipeline(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 AnimateDiffPipeline(metaclass=DummyObject):
     _backends = ["torch", "transformers"]
 

tests/pipelines/pag/test_pag_animatediff.py

@@ -1,492 +0,0 @@
-import inspect
-import unittest
-
-import numpy as np
-import torch
-from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
-
-from diffusers import (
-    AnimateDiffPAGPipeline,
-    AnimateDiffPipeline,
-    AutoencoderKL,
-    DDIMScheduler,
-    DPMSolverMultistepScheduler,
-    LCMScheduler,
-    MotionAdapter,
-    StableDiffusionPipeline,
-    UNet2DConditionModel,
-    UNetMotionModel,
-)
-from diffusers.utils import is_xformers_available
-from diffusers.utils.testing_utils import torch_device
-
-from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
-from ..test_pipelines_common import (
-    IPAdapterTesterMixin,
-    PipelineFromPipeTesterMixin,
-    PipelineTesterMixin,
-    SDFunctionTesterMixin,
-)
-
-
-def to_np(tensor):
-    if isinstance(tensor, torch.Tensor):
-        tensor = tensor.detach().cpu().numpy()
-
-    return tensor
-
-
-class AnimateDiffPAGPipelineFastTests(
-    IPAdapterTesterMixin, SDFunctionTesterMixin, PipelineTesterMixin, PipelineFromPipeTesterMixin, unittest.TestCase
-):
-    pipeline_class = AnimateDiffPAGPipeline
-    params = TEXT_TO_IMAGE_PARAMS.union({"pag_scale", "pag_adaptive_scale"})
-    batch_params = TEXT_TO_IMAGE_BATCH_PARAMS
-    required_optional_params = frozenset(
-        [
-            "num_inference_steps",
-            "generator",
-            "latents",
-            "return_dict",
-            "callback_on_step_end",
-            "callback_on_step_end_tensor_inputs",
-        ]
-    )
-
-    def get_dummy_components(self):
-        cross_attention_dim = 8
-        block_out_channels = (8, 8)
-
-        torch.manual_seed(0)
-        unet = UNet2DConditionModel(
-            block_out_channels=block_out_channels,
-            layers_per_block=2,
-            sample_size=8,
-            in_channels=4,
-            out_channels=4,
-            down_block_types=("CrossAttnDownBlock2D", "DownBlock2D"),
-            up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
-            cross_attention_dim=cross_attention_dim,
-            norm_num_groups=2,
-        )
-        scheduler = DDIMScheduler(
-            beta_start=0.00085,
-            beta_end=0.012,
-            beta_schedule="linear",
-            clip_sample=False,
-        )
-        torch.manual_seed(0)
-        vae = AutoencoderKL(
-            block_out_channels=block_out_channels,
-            in_channels=3,
-            out_channels=3,
-            down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
-            up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
-            latent_channels=4,
-            norm_num_groups=2,
-        )
-        torch.manual_seed(0)
-        text_encoder_config = CLIPTextConfig(
-            bos_token_id=0,
-            eos_token_id=2,
-            hidden_size=cross_attention_dim,
-            intermediate_size=37,
-            layer_norm_eps=1e-05,
-            num_attention_heads=4,
-            num_hidden_layers=5,
-            pad_token_id=1,
-            vocab_size=1000,
-        )
-        text_encoder = CLIPTextModel(text_encoder_config)
-        tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
-        motion_adapter = MotionAdapter(
-            block_out_channels=block_out_channels,
-            motion_layers_per_block=2,
-            motion_norm_num_groups=2,
-            motion_num_attention_heads=4,
-        )
-
-        components = {
-            "unet": unet,
-            "scheduler": scheduler,
-            "vae": vae,
-            "motion_adapter": motion_adapter,
-            "text_encoder": text_encoder,
-            "tokenizer": tokenizer,
-            "feature_extractor": None,
-            "image_encoder": None,
-        }
-        return components
-
-    def get_dummy_inputs(self, device, seed=0):
-        if str(device).startswith("mps"):
-            generator = torch.manual_seed(seed)
-        else:
-            generator = torch.Generator(device=device).manual_seed(seed)
-
-        inputs = {
-            "prompt": "A painting of a squirrel eating a burger",
-            "generator": generator,
-            "num_inference_steps": 2,
-            "guidance_scale": 7.5,
-            "pag_scale": 3.0,
-            "output_type": "pt",
-        }
-        return inputs
-
-    def test_from_pipe_consistent_config(self):
-        assert self.original_pipeline_class == StableDiffusionPipeline
-        original_repo = "hf-internal-testing/tinier-stable-diffusion-pipe"
-        original_kwargs = {"requires_safety_checker": False}
-
-        # create original_pipeline_class(sd)
-        pipe_original = self.original_pipeline_class.from_pretrained(original_repo, **original_kwargs)
-
-        # original_pipeline_class(sd) -> pipeline_class
-        pipe_components = self.get_dummy_components()
-        pipe_additional_components = {}
-        for name, component in pipe_components.items():
-            if name not in pipe_original.components:
-                pipe_additional_components[name] = component
-
-        pipe = self.pipeline_class.from_pipe(pipe_original, **pipe_additional_components)
-
-        # pipeline_class -> original_pipeline_class(sd)
-        original_pipe_additional_components = {}
-        for name, component in pipe_original.components.items():
-            if name not in pipe.components or not isinstance(component, pipe.components[name].__class__):
-                original_pipe_additional_components[name] = component
-
-        pipe_original_2 = self.original_pipeline_class.from_pipe(pipe, **original_pipe_additional_components)
-
-        # compare the config
-        original_config = {k: v for k, v in pipe_original.config.items() if not k.startswith("_")}
-        original_config_2 = {k: v for k, v in pipe_original_2.config.items() if not k.startswith("_")}
-        assert original_config_2 == original_config
-
-    def test_motion_unet_loading(self):
-        components = self.get_dummy_components()
-        pipe = self.pipeline_class(**components)
-
-        assert isinstance(pipe.unet, UNetMotionModel)
-
-    @unittest.skip("Attention slicing is not enabled in this pipeline")
-    def test_attention_slicing_forward_pass(self):
-        pass
-
-    def test_ip_adapter_single(self):
-        expected_pipe_slice = None
-
-        if torch_device == "cpu":
-            expected_pipe_slice = np.array(
-                [
-                    0.5068,
-                    0.5294,
-                    0.4926,
-                    0.4810,
-                    0.4188,
-                    0.5935,
-                    0.5295,
-                    0.3947,
-                    0.5300,
-                    0.4706,
-                    0.3950,
-                    0.4737,
-                    0.4072,
-                    0.3227,
-                    0.5481,
-                    0.4864,
-                    0.4518,
-                    0.5315,
-                    0.5979,
-                    0.5374,
-                    0.3503,
-                    0.5275,
-                    0.6067,
-                    0.4914,
-                    0.5440,
-                    0.4775,
-                    0.5538,
-                ]
-            )
-        return super().test_ip_adapter_single(expected_pipe_slice=expected_pipe_slice)
-
-    def test_dict_tuple_outputs_equivalent(self):
-        expected_slice = None
-        if torch_device == "cpu":
-            expected_slice = np.array([0.5295, 0.3947, 0.5300, 0.4864, 0.4518, 0.5315, 0.5440, 0.4775, 0.5538])
-        return super().test_dict_tuple_outputs_equivalent(expected_slice=expected_slice)
-
-    @unittest.skipIf(torch_device != "cuda", reason="CUDA and CPU are required to switch devices")
-    def test_to_device(self):
-        components = self.get_dummy_components()
-        pipe = self.pipeline_class(**components)
-        pipe.set_progress_bar_config(disable=None)
-
-        pipe.to("cpu")
-        # pipeline creates a new motion UNet under the hood. So we need to check the device from pipe.components
-        model_devices = [
-            component.device.type for component in pipe.components.values() if hasattr(component, "device")
-        ]
-        self.assertTrue(all(device == "cpu" for device in model_devices))
-
-        output_cpu = pipe(**self.get_dummy_inputs("cpu"))[0]
-        self.assertTrue(np.isnan(output_cpu).sum() == 0)
-
-        pipe.to("cuda")
-        model_devices = [
-            component.device.type for component in pipe.components.values() if hasattr(component, "device")
-        ]
-        self.assertTrue(all(device == "cuda" for device in model_devices))
-
-        output_cuda = pipe(**self.get_dummy_inputs("cuda"))[0]
-        self.assertTrue(np.isnan(to_np(output_cuda)).sum() == 0)
-
-    def test_to_dtype(self):
-        components = self.get_dummy_components()
-        pipe = self.pipeline_class(**components)
-        pipe.set_progress_bar_config(disable=None)
-
-        # pipeline creates a new motion UNet under the hood. So we need to check the dtype from pipe.components
-        model_dtypes = [component.dtype for component in pipe.components.values() if hasattr(component, "dtype")]
-        self.assertTrue(all(dtype == torch.float32 for dtype in model_dtypes))
-
-        pipe.to(dtype=torch.float16)
-        model_dtypes = [component.dtype for component in pipe.components.values() if hasattr(component, "dtype")]
-        self.assertTrue(all(dtype == torch.float16 for dtype in model_dtypes))
-
-    def test_prompt_embeds(self):
-        components = self.get_dummy_components()
-        pipe = self.pipeline_class(**components)
-        pipe.set_progress_bar_config(disable=None)
-        pipe.to(torch_device)
-
-        inputs = self.get_dummy_inputs(torch_device)
-        inputs.pop("prompt")
-        inputs["prompt_embeds"] = torch.randn((1, 4, pipe.text_encoder.config.hidden_size), device=torch_device)
-        pipe(**inputs)
-
-    def test_free_init(self):
-        components = self.get_dummy_components()
-        pipe: AnimateDiffPAGPipeline = self.pipeline_class(**components)
-        pipe.set_progress_bar_config(disable=None)
-        pipe.to(torch_device)
-
-        inputs_normal = self.get_dummy_inputs(torch_device)
-        frames_normal = pipe(**inputs_normal).frames[0]
-
-        pipe.enable_free_init(
-            num_iters=2,
-            use_fast_sampling=True,
-            method="butterworth",
-            order=4,
-            spatial_stop_frequency=0.25,
-            temporal_stop_frequency=0.25,
-        )
-        inputs_enable_free_init = self.get_dummy_inputs(torch_device)
-        frames_enable_free_init = pipe(**inputs_enable_free_init).frames[0]
-
-        pipe.disable_free_init()
-        inputs_disable_free_init = self.get_dummy_inputs(torch_device)
-        frames_disable_free_init = pipe(**inputs_disable_free_init).frames[0]
-
-        sum_enabled = np.abs(to_np(frames_normal) - to_np(frames_enable_free_init)).sum()
-        max_diff_disabled = np.abs(to_np(frames_normal) - to_np(frames_disable_free_init)).max()
-        self.assertGreater(
-            sum_enabled, 1e1, "Enabling of FreeInit should lead to results different from the default pipeline results"
-        )
-        self.assertLess(
-            max_diff_disabled,
-            1e-3,
-            "Disabling of FreeInit should lead to results similar to the default pipeline results",
-        )
-
-    def test_free_init_with_schedulers(self):
-        components = self.get_dummy_components()
-        pipe: AnimateDiffPAGPipeline = self.pipeline_class(**components)
-        pipe.set_progress_bar_config(disable=None)
-        pipe.to(torch_device)
-
-        inputs_normal = self.get_dummy_inputs(torch_device)
-        frames_normal = pipe(**inputs_normal).frames[0]
-
-        schedulers_to_test = [
-            DPMSolverMultistepScheduler.from_config(
-                components["scheduler"].config,
-                timestep_spacing="linspace",
-                beta_schedule="linear",
-                algorithm_type="dpmsolver++",
-                steps_offset=1,
-                clip_sample=False,
-            ),
-            LCMScheduler.from_config(
-                components["scheduler"].config,
-                timestep_spacing="linspace",
-                beta_schedule="linear",
-                steps_offset=1,
-                clip_sample=False,
-            ),
-        ]
-        components.pop("scheduler")
-
-        for scheduler in schedulers_to_test:
-            components["scheduler"] = scheduler
-            pipe: AnimateDiffPAGPipeline = self.pipeline_class(**components)
-            pipe.set_progress_bar_config(disable=None)
-            pipe.to(torch_device)
-
-            pipe.enable_free_init(num_iters=2, use_fast_sampling=False)
-
-            inputs = self.get_dummy_inputs(torch_device)
-            frames_enable_free_init = pipe(**inputs).frames[0]
-            sum_enabled = np.abs(to_np(frames_normal) - to_np(frames_enable_free_init)).sum()
-
-            self.assertGreater(
-                sum_enabled,
-                1e1,
-                "Enabling of FreeInit should lead to results different from the default pipeline results",
-            )
-
-    @unittest.skipIf(
-        torch_device != "cuda" or not is_xformers_available(),
-        reason="XFormers attention is only available with CUDA and `xformers` installed",
-    )
-    def test_xformers_attention_forwardGenerator_pass(self):
-        components = self.get_dummy_components()
-        pipe = self.pipeline_class(**components)
-        for component in pipe.components.values():
-            if hasattr(component, "set_default_attn_processor"):
-                component.set_default_attn_processor()
-        pipe.to(torch_device)
-        pipe.set_progress_bar_config(disable=None)
-
-        inputs = self.get_dummy_inputs(torch_device)
-        output_without_offload = pipe(**inputs).frames[0]
-        output_without_offload = (
-            output_without_offload.cpu() if torch.is_tensor(output_without_offload) else output_without_offload
-        )
-
-        pipe.enable_xformers_memory_efficient_attention()
-        inputs = self.get_dummy_inputs(torch_device)
-        output_with_offload = pipe(**inputs).frames[0]
-        output_with_offload = (
-            output_with_offload.cpu() if torch.is_tensor(output_with_offload) else output_without_offload
-        )
-
-        max_diff = np.abs(to_np(output_with_offload) - to_np(output_without_offload)).max()
-        self.assertLess(max_diff, 1e-4, "XFormers attention should not affect the inference results")
-
-    def test_vae_slicing(self):
-        return super().test_vae_slicing(image_count=2)
-
-    def test_pag_disable_enable(self):
-        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
-        components = self.get_dummy_components()
-
-        # base pipeline (expect same output when pag is disabled)
-        components.pop("pag_applied_layers", None)
-        pipe_sd = AnimateDiffPipeline(**components)
-        pipe_sd = pipe_sd.to(device)
-        pipe_sd.set_progress_bar_config(disable=None)
-
-        inputs = self.get_dummy_inputs(device)
-        del inputs["pag_scale"]
-        assert (
-            "pag_scale" not in inspect.signature(pipe_sd.__call__).parameters
-        ), f"`pag_scale` should not be a call parameter of the base pipeline {pipe_sd.__class__.__name__}."
-        out = pipe_sd(**inputs).frames[0, -3:, -3:, -1]
-
-        components = self.get_dummy_components()
-
-        # pag disabled with pag_scale=0.0
-        pipe_pag = self.pipeline_class(**components)
-        pipe_pag = pipe_pag.to(device)
-        pipe_pag.set_progress_bar_config(disable=None)
-
-        inputs = self.get_dummy_inputs(device)
-        inputs["pag_scale"] = 0.0
-        out_pag_disabled = pipe_pag(**inputs).frames[0, -3:, -3:, -1]
-
-        # pag enabled
-        pipe_pag = self.pipeline_class(**components)
-        pipe_pag = pipe_pag.to(device)
-        pipe_pag.set_progress_bar_config(disable=None)
-
-        inputs = self.get_dummy_inputs(device)
-        out_pag_enabled = pipe_pag(**inputs).frames[0, -3:, -3:, -1]
-
-        assert np.abs(out.flatten() - out_pag_disabled.flatten()).max() < 1e-3
-        assert np.abs(out.flatten() - out_pag_enabled.flatten()).max() > 1e-3
-
-    def test_pag_applied_layers(self):
-        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
-        components = self.get_dummy_components()
-
-        # base pipeline
-        components.pop("pag_applied_layers", None)
-        pipe = self.pipeline_class(**components)
-        pipe = pipe.to(device)
-        pipe.set_progress_bar_config(disable=None)
-
-        # pag_applied_layers = ["mid","up","down"] should apply to all self-attention layers
-        all_self_attn_layers = [k for k in pipe.unet.attn_processors.keys() if "attn1" in k]
-        original_attn_procs = pipe.unet.attn_processors
-        pag_layers = [
-            "down",
-            "mid",
-            "up",
-        ]
-        pipe._set_pag_attn_processor(pag_applied_layers=pag_layers, do_classifier_free_guidance=False)
-        assert set(pipe.pag_attn_processors) == set(all_self_attn_layers)
-
-        # pag_applied_layers = ["mid"], or ["mid.block_0"] or ["mid.block_0.motion_modules_0"] should apply to all self-attention layers in mid_block, i.e.
-        # mid_block.motion_modules.0.transformer_blocks.0.attn1.processor
-        # mid_block.attentions.0.transformer_blocks.0.attn1.processor
-        all_self_attn_mid_layers = [
-            "mid_block.motion_modules.0.transformer_blocks.0.attn1.processor",
-            "mid_block.attentions.0.transformer_blocks.0.attn1.processor",
-        ]
-        pipe.unet.set_attn_processor(original_attn_procs.copy())
-        pag_layers = ["mid"]
-        pipe._set_pag_attn_processor(pag_applied_layers=pag_layers, do_classifier_free_guidance=False)
-        assert set(pipe.pag_attn_processors) == set(all_self_attn_mid_layers)
-
-        pipe.unet.set_attn_processor(original_attn_procs.copy())
-        pag_layers = ["mid.block_0"]
-        pipe._set_pag_attn_processor(pag_applied_layers=pag_layers, do_classifier_free_guidance=False)
-        assert set(pipe.pag_attn_processors) == set(all_self_attn_mid_layers)
-
-        pipe.unet.set_attn_processor(original_attn_procs.copy())
-        pag_layers = ["mid.block_0.attentions_0", "mid.block_0.motion_modules_0"]
-        pipe._set_pag_attn_processor(pag_applied_layers=pag_layers, do_classifier_free_guidance=False)
-        assert set(pipe.pag_attn_processors) == set(all_self_attn_mid_layers)
-
-        pipe.unet.set_attn_processor(original_attn_procs.copy())
-        pag_layers = ["mid.block_0.attentions_1"]
-        with self.assertRaises(ValueError):
-            pipe._set_pag_attn_processor(pag_applied_layers=pag_layers, do_classifier_free_guidance=False)
-
-        # pag_applied_layers = "down" should apply to all self-attention layers in down_blocks
-        # down_blocks.1.(attentions|motion_modules).0.transformer_blocks.0.attn1.processor
-        # down_blocks.1.(attentions|motion_modules).0.transformer_blocks.1.attn1.processor
-        # down_blocks.1.(attentions|motion_modules).0.transformer_blocks.0.attn1.processor
-
-        pipe.unet.set_attn_processor(original_attn_procs.copy())
-        pag_layers = ["down"]
-        pipe._set_pag_attn_processor(pag_applied_layers=pag_layers, do_classifier_free_guidance=False)
-        assert len(pipe.pag_attn_processors) == 6
-
-        pipe.unet.set_attn_processor(original_attn_procs.copy())
-        pag_layers = ["down.block_0"]
-        pipe._set_pag_attn_processor(pag_applied_layers=pag_layers, do_classifier_free_guidance=False)
-        assert (len(pipe.pag_attn_processors)) == 4
-
-        pipe.unet.set_attn_processor(original_attn_procs.copy())
-        pag_layers = ["down.block_1"]
-        pipe._set_pag_attn_processor(pag_applied_layers=pag_layers, do_classifier_free_guidance=False)
-        assert len(pipe.pag_attn_processors) == 2
-
-        pipe.unet.set_attn_processor(original_attn_procs.copy())
-        pag_layers = ["down.block_1.motion_modules_2"]
-        with self.assertRaises(ValueError):
-            pipe._set_pag_attn_processor(pag_applied_layers=pag_layers, do_classifier_free_guidance=False)