# 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, Tuple, Union import numpy as np import torch import torch.nn.functional as F from PIL import Image from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection from diffusers.image_processor import PipelineImageInput, VaeImageProcessor from diffusers.loaders import IPAdapterMixin, LoraLoaderMixin, TextualInversionLoaderMixin from diffusers.models import AutoencoderKL, ControlNetModel, ImageProjection, UNet2DConditionModel, UNetMotionModel from diffusers.models.lora import adjust_lora_scale_text_encoder from diffusers.models.unets.unet_motion_model import MotionAdapter from diffusers.pipelines.animatediff.pipeline_output import AnimateDiffPipelineOutput from diffusers.pipelines.controlnet.multicontrolnet import MultiControlNetModel from diffusers.pipelines.pipeline_utils import DiffusionPipeline, StableDiffusionMixin from diffusers.schedulers import ( DDIMScheduler, DPMSolverMultistepScheduler, EulerAncestralDiscreteScheduler, EulerDiscreteScheduler, LMSDiscreteScheduler, PNDMScheduler, ) from diffusers.utils import USE_PEFT_BACKEND, deprecate, logging, scale_lora_layers, unscale_lora_layers from diffusers.utils.torch_utils import is_compiled_module, randn_tensor logger = logging.get_logger(__name__) # pylint: disable=invalid-name EXAMPLE_DOC_STRING = """ Examples: ```py >>> import torch >>> from diffusers import AutoencoderKL, ControlNetModel, MotionAdapter >>> from diffusers.pipelines import DiffusionPipeline >>> from diffusers.schedulers import DPMSolverMultistepScheduler >>> from PIL import Image >>> motion_id = "guoyww/animatediff-motion-adapter-v1-5-2" >>> adapter = MotionAdapter.from_pretrained(motion_id) >>> controlnet = ControlNetModel.from_pretrained("lllyasviel/control_v11p_sd15_openpose", torch_dtype=torch.float16) >>> vae = AutoencoderKL.from_pretrained("stabilityai/sd-vae-ft-mse", torch_dtype=torch.float16) >>> model_id = "SG161222/Realistic_Vision_V5.1_noVAE" >>> pipe = DiffusionPipeline.from_pretrained( ... model_id, ... motion_adapter=adapter, ... controlnet=controlnet, ... vae=vae, ... custom_pipeline="pipeline_animatediff_controlnet", ... ).to(device="cuda", dtype=torch.float16) >>> pipe.scheduler = DPMSolverMultistepScheduler.from_pretrained( ... model_id, subfolder="scheduler", clip_sample=False, timestep_spacing="linspace", steps_offset=1, beta_schedule="linear", ... ) >>> pipe.enable_vae_slicing() >>> conditioning_frames = [] >>> for i in range(1, 16 + 1): ... conditioning_frames.append(Image.open(f"frame_{i}.png")) >>> prompt = "astronaut in space, dancing" >>> negative_prompt = "bad quality, worst quality, jpeg artifacts, ugly" >>> result = pipe( ... prompt=prompt, ... negative_prompt=negative_prompt, ... width=512, ... height=768, ... conditioning_frames=conditioning_frames, ... num_inference_steps=12, ... ) >>> from diffusers.utils import export_to_gif >>> export_to_gif(result.frames[0], "result.gif") ``` """ # Copied from diffusers.pipelines.animatediff.pipeline_animatediff.tensor2vid def tensor2vid(video: torch.Tensor, processor, output_type="np"): batch_size, channels, num_frames, height, width = video.shape outputs = [] for batch_idx in range(batch_size): batch_vid = video[batch_idx].permute(1, 0, 2, 3) batch_output = processor.postprocess(batch_vid, output_type) outputs.append(batch_output) if output_type == "np": outputs = np.stack(outputs) elif output_type == "pt": outputs = torch.stack(outputs) elif not output_type == "pil": raise ValueError(f"{output_type} does not exist. Please choose one of ['np', 'pt', 'pil']") return outputs class AnimateDiffControlNetPipeline( DiffusionPipeline, StableDiffusionMixin, TextualInversionLoaderMixin, IPAdapterMixin, LoraLoaderMixin ): r""" Pipeline for text-to-video generation. 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.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights - [`~loaders.LoraLoaderMixin.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->unet->vae" _optional_components = ["feature_extractor", "image_encoder"] _callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds"] def __init__( self, vae: AutoencoderKL, text_encoder: CLIPTextModel, tokenizer: CLIPTokenizer, unet: UNet2DConditionModel, motion_adapter: MotionAdapter, controlnet: Union[ControlNetModel, List[ControlNetModel], Tuple[ControlNetModel], MultiControlNetModel], scheduler: Union[ DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler, EulerDiscreteScheduler, EulerAncestralDiscreteScheduler, DPMSolverMultistepScheduler, ], feature_extractor: Optional[CLIPImageProcessor] = None, image_encoder: Optional[CLIPVisionModelWithProjection] = None, ): super().__init__() unet = UNetMotionModel.from_unet2d(unet, motion_adapter) if isinstance(controlnet, (list, tuple)): controlnet = MultiControlNetModel(controlnet) self.register_modules( vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet, motion_adapter=motion_adapter, controlnet=controlnet, scheduler=scheduler, feature_extractor=feature_extractor, image_encoder=image_encoder, ) self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor) self.control_image_processor = VaeImageProcessor( vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True, do_normalize=False ) # 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.FloatTensor] = None, negative_prompt_embeds: Optional[torch.FloatTensor] = 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.FloatTensor`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument. negative_prompt_embeds (`torch.FloatTensor`, *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, LoraLoaderMixin): 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 isinstance(self, LoraLoaderMixin) 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 ): 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." ) image_embeds = [] 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 ) single_image_embeds = torch.stack([single_image_embeds] * num_images_per_prompt, dim=0) single_negative_image_embeds = torch.stack( [single_negative_image_embeds] * num_images_per_prompt, dim=0 ) if self.do_classifier_free_guidance: single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds]) single_image_embeds = single_image_embeds.to(device) image_embeds.append(single_image_embeds) else: repeat_dims = [1] image_embeds = [] 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) single_image_embeds = single_image_embeds.repeat( num_images_per_prompt, *(repeat_dims * len(single_image_embeds.shape[1:])) ) single_negative_image_embeds = single_negative_image_embeds.repeat( num_images_per_prompt, *(repeat_dims * len(single_negative_image_embeds.shape[1:])) ) single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds]) else: single_image_embeds = single_image_embeds.repeat( num_images_per_prompt, *(repeat_dims * len(single_image_embeds.shape[1:])) ) image_embeds.append(single_image_embeds) return 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.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.check_inputs def check_inputs( self, prompt, height, width, num_frames, callback_steps, negative_prompt=None, prompt_embeds=None, negative_prompt_embeds=None, callback_on_step_end_tensor_inputs=None, image=None, controlnet_conditioning_scale=1.0, control_guidance_start=0.0, control_guidance_end=1.0, ): if height % 8 != 0 or width % 8 != 0: raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.") if callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0): raise ValueError( f"`callback_steps` has to be a positive integer but is {callback_steps} of type" f" {type(callback_steps)}." ) if 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}." ) # `prompt` needs more sophisticated handling when there are multiple # conditionings. if isinstance(self.controlnet, MultiControlNetModel): if isinstance(prompt, list): logger.warning( f"You have {len(self.controlnet.nets)} ControlNets and you have passed {len(prompt)}" " prompts. The conditionings will be fixed across the prompts." ) # Check `image` is_compiled = hasattr(F, "scaled_dot_product_attention") and isinstance( self.controlnet, torch._dynamo.eval_frame.OptimizedModule ) if ( isinstance(self.controlnet, ControlNetModel) or is_compiled and isinstance(self.controlnet._orig_mod, ControlNetModel) ): if not isinstance(image, list): raise TypeError(f"For single controlnet, `image` must be of type `list` but got {type(image)}") if len(image) != num_frames: raise ValueError(f"Excepted image to have length {num_frames} but got {len(image)=}") elif ( isinstance(self.controlnet, MultiControlNetModel) or is_compiled and isinstance(self.controlnet._orig_mod, MultiControlNetModel) ): if not isinstance(image, list) or not isinstance(image[0], list): raise TypeError(f"For multiple controlnets: `image` must be type list of lists but got {type(image)=}") if len(image[0]) != num_frames: raise ValueError(f"Expected length of image sublist as {num_frames} but got {len(image[0])=}") if any(len(img) != len(image[0]) for img in image): raise ValueError("All conditioning frame batches for multicontrolnet must be same size") else: assert False # Check `controlnet_conditioning_scale` if ( isinstance(self.controlnet, ControlNetModel) or is_compiled and isinstance(self.controlnet._orig_mod, ControlNetModel) ): if not isinstance(controlnet_conditioning_scale, float): raise TypeError("For single controlnet: `controlnet_conditioning_scale` must be type `float`.") elif ( isinstance(self.controlnet, MultiControlNetModel) or is_compiled and isinstance(self.controlnet._orig_mod, MultiControlNetModel) ): if isinstance(controlnet_conditioning_scale, list): if any(isinstance(i, list) for i in controlnet_conditioning_scale): raise ValueError("A single batch of multiple conditionings are supported at the moment.") elif isinstance(controlnet_conditioning_scale, list) and len(controlnet_conditioning_scale) != len( self.controlnet.nets ): raise ValueError( "For multiple controlnets: When `controlnet_conditioning_scale` is specified as `list`, it must have" " the same length as the number of controlnets" ) else: assert False if not isinstance(control_guidance_start, (tuple, list)): control_guidance_start = [control_guidance_start] if not isinstance(control_guidance_end, (tuple, list)): control_guidance_end = [control_guidance_end] if len(control_guidance_start) != len(control_guidance_end): raise ValueError( f"`control_guidance_start` has {len(control_guidance_start)} elements, but `control_guidance_end` has {len(control_guidance_end)} elements. Make sure to provide the same number of elements to each list." ) if isinstance(self.controlnet, MultiControlNetModel): if len(control_guidance_start) != len(self.controlnet.nets): raise ValueError( f"`control_guidance_start`: {control_guidance_start} has {len(control_guidance_start)} elements but there are {len(self.controlnet.nets)} controlnets available. Make sure to provide {len(self.controlnet.nets)}." ) for start, end in zip(control_guidance_start, control_guidance_end): if start >= end: raise ValueError( f"control guidance start: {start} cannot be larger or equal to control guidance end: {end}." ) if start < 0.0: raise ValueError(f"control guidance start: {start} can't be smaller than 0.") if end > 1.0: raise ValueError(f"control guidance end: {end} can't be larger than 1.0.") # Copied from diffusers.pipelines.controlnet.pipeline_controlnet.StableDiffusionControlNetPipeline.check_image def check_image(self, image, prompt, prompt_embeds): image_is_pil = isinstance(image, Image.Image) image_is_tensor = isinstance(image, torch.Tensor) image_is_np = isinstance(image, np.ndarray) image_is_pil_list = isinstance(image, list) and isinstance(image[0], Image.Image) image_is_tensor_list = isinstance(image, list) and isinstance(image[0], torch.Tensor) image_is_np_list = isinstance(image, list) and isinstance(image[0], np.ndarray) if ( not image_is_pil and not image_is_tensor and not image_is_np and not image_is_pil_list and not image_is_tensor_list and not image_is_np_list ): raise TypeError( f"image must be passed and be one of PIL image, numpy array, torch tensor, list of PIL images, list of numpy arrays or list of torch tensors, but is {type(image)}" ) if image_is_pil: image_batch_size = 1 else: image_batch_size = len(image) if prompt is not None and isinstance(prompt, str): prompt_batch_size = 1 elif prompt is not None and isinstance(prompt, list): prompt_batch_size = len(prompt) elif prompt_embeds is not None: prompt_batch_size = prompt_embeds.shape[0] if image_batch_size != 1 and image_batch_size != prompt_batch_size: raise ValueError( f"If image batch size is not 1, image batch size must be same as prompt batch size. image batch size: {image_batch_size}, prompt batch size: {prompt_batch_size}" ) # 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 # Copied from diffusers.pipelines.controlnet.pipeline_controlnet.StableDiffusionControlNetPipeline.prepare_image def prepare_image( self, image, width, height, batch_size, num_images_per_prompt, device, dtype, do_classifier_free_guidance=False, guess_mode=False, ): image = self.control_image_processor.preprocess(image, height=height, width=width).to(dtype=torch.float32) image_batch_size = image.shape[0] if image_batch_size == 1: repeat_by = batch_size else: # image batch size is the same as prompt batch size repeat_by = num_images_per_prompt image = image.repeat_interleave(repeat_by, dim=0) image = image.to(device=device, dtype=dtype) if do_classifier_free_guidance and not guess_mode: image = torch.cat([image] * 2) return image @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() def __call__( self, prompt: 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.FloatTensor] = None, prompt_embeds: Optional[torch.FloatTensor] = None, negative_prompt_embeds: Optional[torch.FloatTensor] = None, ip_adapter_image: Optional[PipelineImageInput] = None, ip_adapter_image_embeds: Optional[PipelineImageInput] = None, conditioning_frames: Optional[List[PipelineImageInput]] = None, output_type: Optional[str] = "pil", return_dict: bool = True, cross_attention_kwargs: Optional[Dict[str, Any]] = None, controlnet_conditioning_scale: Union[float, List[float]] = 1.0, guess_mode: bool = False, control_guidance_start: Union[float, List[float]] = 0.0, control_guidance_end: Union[float, List[float]] = 1.0, 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"], **kwargs, ): 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.FloatTensor`, *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.FloatTensor`, *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.FloatTensor`, *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.FloatTensor]`, *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. conditioning_frames (`List[PipelineImageInput]`, *optional*): The ControlNet input condition to provide guidance to the `unet` for generation. If multiple ControlNets are specified, images must be passed as a list such that each element of the list can be correctly batched for input to a single ControlNet. output_type (`str`, *optional*, defaults to `"pil"`): The output format of the generated video. Choose between `torch.FloatTensor`, `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). controlnet_conditioning_scale (`float` or `List[float]`, *optional*, defaults to 1.0): The outputs of the ControlNet are multiplied by `controlnet_conditioning_scale` before they are added to the residual in the original `unet`. If multiple ControlNets are specified in `init`, you can set the corresponding scale as a list. guess_mode (`bool`, *optional*, defaults to `False`): The ControlNet encoder tries to recognize the content of the input image even if you remove all prompts. A `guidance_scale` value between 3.0 and 5.0 is recommended. control_guidance_start (`float` or `List[float]`, *optional*, defaults to 0.0): The percentage of total steps at which the ControlNet starts applying. control_guidance_end (`float` or `List[float]`, *optional*, defaults to 1.0): The percentage of total steps at which the ControlNet stops applying. 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. allback_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 pipeine class. 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. """ callback = kwargs.pop("callback", None) callback_steps = kwargs.pop("callback_steps", None) if callback is not None: deprecate( "callback", "1.0.0", "Passing `callback` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`", ) if callback_steps is not None: deprecate( "callback_steps", "1.0.0", "Passing `callback_steps` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`", ) controlnet = self.controlnet._orig_mod if is_compiled_module(self.controlnet) else self.controlnet # align format for control guidance if not isinstance(control_guidance_start, list) and isinstance(control_guidance_end, list): control_guidance_start = len(control_guidance_end) * [control_guidance_start] elif not isinstance(control_guidance_end, list) and isinstance(control_guidance_start, list): control_guidance_end = len(control_guidance_start) * [control_guidance_end] elif not isinstance(control_guidance_start, list) and not isinstance(control_guidance_end, list): mult = len(controlnet.nets) if isinstance(controlnet, MultiControlNetModel) else 1 control_guidance_start, control_guidance_end = ( mult * [control_guidance_start], mult * [control_guidance_end], ) # 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=prompt, height=height, width=width, num_frames=num_frames, callback_steps=callback_steps, negative_prompt=negative_prompt, callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_prompt_embeds, image=conditioning_frames, controlnet_conditioning_scale=controlnet_conditioning_scale, control_guidance_start=control_guidance_start, control_guidance_end=control_guidance_end, ) self._guidance_scale = guidance_scale self._clip_skip = clip_skip self._cross_attention_kwargs = cross_attention_kwargs # 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 if isinstance(controlnet, MultiControlNetModel) and isinstance(controlnet_conditioning_scale, float): controlnet_conditioning_scale = [controlnet_conditioning_scale] * len(controlnet.nets) global_pool_conditions = ( controlnet.config.global_pool_conditions if isinstance(controlnet, ControlNetModel) else controlnet.nets[0].config.global_pool_conditions ) guess_mode = guess_mode or global_pool_conditions # 3. Encode input prompt text_encoder_lora_scale = ( cross_attention_kwargs.get("scale", None) if 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_classifier_free_guidance: prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds]) if ip_adapter_image is not None: image_embeds = self.prepare_ip_adapter_image_embeds( ip_adapter_image, ip_adapter_image_embeds, device, batch_size * num_videos_per_prompt ) if isinstance(controlnet, ControlNetModel): conditioning_frames = self.prepare_image( image=conditioning_frames, width=width, height=height, batch_size=batch_size * num_videos_per_prompt * num_frames, num_images_per_prompt=num_videos_per_prompt, device=device, dtype=controlnet.dtype, do_classifier_free_guidance=self.do_classifier_free_guidance, guess_mode=guess_mode, ) elif isinstance(controlnet, MultiControlNetModel): cond_prepared_frames = [] for frame_ in conditioning_frames: prepared_frame = self.prepare_image( image=frame_, width=width, height=height, batch_size=batch_size * num_videos_per_prompt * num_frames, num_images_per_prompt=num_videos_per_prompt, device=device, dtype=controlnet.dtype, do_classifier_free_guidance=self.do_classifier_free_guidance, guess_mode=guess_mode, ) cond_prepared_frames.append(prepared_frame) conditioning_frames = cond_prepared_frames else: assert False # 4. Prepare timesteps self.scheduler.set_timesteps(num_inference_steps, device=device) timesteps = self.scheduler.timesteps self._num_timesteps = len(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": image_embeds} if ip_adapter_image is not None or ip_adapter_image_embeds is not None else None ) # 7.1 Create tensor stating which controlnets to keep controlnet_keep = [] for i in range(len(timesteps)): keeps = [ 1.0 - float(i / len(timesteps) < s or (i + 1) / len(timesteps) > e) for s, e in zip(control_guidance_start, control_guidance_end) ] controlnet_keep.append(keeps[0] if isinstance(controlnet, ControlNetModel) else keeps) # 8. Denoising loop num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order with self.progress_bar(total=num_inference_steps) as progress_bar: for i, t in enumerate(timesteps): # expand the latents if we are doing classifier free guidance latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) if guess_mode and self.do_classifier_free_guidance: # Infer ControlNet only for the conditional batch. control_model_input = latents control_model_input = self.scheduler.scale_model_input(control_model_input, t) controlnet_prompt_embeds = prompt_embeds.chunk(2)[1] else: control_model_input = latent_model_input controlnet_prompt_embeds = prompt_embeds controlnet_prompt_embeds = controlnet_prompt_embeds.repeat_interleave(num_frames, dim=0) if isinstance(controlnet_keep[i], list): cond_scale = [c * s for c, s in zip(controlnet_conditioning_scale, controlnet_keep[i])] else: controlnet_cond_scale = controlnet_conditioning_scale if isinstance(controlnet_cond_scale, list): controlnet_cond_scale = controlnet_cond_scale[0] cond_scale = controlnet_cond_scale * controlnet_keep[i] control_model_input = torch.transpose(control_model_input, 1, 2) control_model_input = control_model_input.reshape( (-1, control_model_input.shape[2], control_model_input.shape[3], control_model_input.shape[4]) ) down_block_res_samples, mid_block_res_sample = self.controlnet( control_model_input, t, encoder_hidden_states=controlnet_prompt_embeds, controlnet_cond=conditioning_frames, conditioning_scale=cond_scale, guess_mode=guess_mode, return_dict=False, ) # predict the noise residual noise_pred = self.unet( latent_model_input, t, encoder_hidden_states=prompt_embeds, cross_attention_kwargs=self.cross_attention_kwargs, added_cond_kwargs=added_cond_kwargs, down_block_additional_residuals=down_block_res_samples, mid_block_additional_residual=mid_block_res_sample, ).sample # perform guidance if 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() if callback is not None and i % callback_steps == 0: callback(i, t, latents) # 9. Post processing if output_type == "latent": video = latents else: video_tensor = self.decode_latents(latents) video = tensor2vid(video_tensor, self.image_processor, output_type=output_type) # 10. Offload all models self.maybe_free_model_hooks() if not return_dict: return (video,) return AnimateDiffPipelineOutput(frames=video)