# Copyright 2023 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 dataclasses import dataclass from typing import Any, Callable, Dict, List, Optional, Union, Tuple import numpy as np import torch from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection # Updated to use absolute paths from diffusers.image_processor import PipelineImageInput, VaeImageProcessor from diffusers.loaders import IPAdapterMixin, LoraLoaderMixin, TextualInversionLoaderMixin from diffusers.models import AutoencoderKL, ImageProjection, UNet2DConditionModel, UNetMotionModel, ControlNetModel from diffusers.models.lora import adjust_lora_scale_text_encoder from diffusers.models.unet_motion_model import MotionAdapter from diffusers.schedulers import ( DDIMScheduler, DPMSolverMultistepScheduler, EulerAncestralDiscreteScheduler, EulerDiscreteScheduler, LMSDiscreteScheduler, PNDMScheduler, ) from diffusers.utils import ( USE_PEFT_BACKEND, BaseOutput, logging, scale_lora_layers, unscale_lora_layers, ) from diffusers.utils.torch_utils import is_compiled_module, randn_tensor # Added imports based on the working paths from diffusers.models import ControlNetModel from diffusers.pipelines.controlnet.multicontrolnet import MultiControlNetModel from diffusers.pipelines.pipeline_utils import DiffusionPipeline from diffusers.utils import deprecate import torchvision import PIL import PIL.Image import math import time logger = logging.get_logger(__name__) # pylint: disable=invalid-name EXAMPLE_DOC_STRING = """ Examples: ```py >>> import torch >>> from diffusers import MotionAdapter, AnimateDiffPipeline, DDIMScheduler >>> from diffusers.utils import export_to_gif >>> adapter = MotionAdapter.from_pretrained("guoyww/animatediff-motion-adapter-v1-5-2") >>> pipe = AnimateDiffPipeline.from_pretrained("frankjoshua/toonyou_beta6", motion_adapter=adapter) >>> pipe.scheduler = DDIMScheduler(beta_schedule="linear", steps_offset=1, clip_sample=False) >>> output = pipe(prompt="A corgi walking in the park") >>> frames = output.frames[0] >>> export_to_gif(frames, "animation.gif") ``` """ # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.retrieve_latents def retrieve_latents( encoder_output: torch.Tensor, generator: Optional[torch.Generator] = None, sample_mode: str = "sample" ): if hasattr(encoder_output, "latent_dist") and sample_mode == "sample": return encoder_output.latent_dist.sample(generator) elif hasattr(encoder_output, "latent_dist") and sample_mode == "argmax": return encoder_output.latent_dist.mode() elif hasattr(encoder_output, "latents"): return encoder_output.latents else: raise AttributeError("Could not access latents of provided encoder_output") # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps def retrieve_timesteps( scheduler, num_inference_steps: Optional[int] = None, device: Optional[Union[str, torch.device]] = None, timesteps: Optional[List[int]] = None, **kwargs, ): """ Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`. Args: scheduler (`SchedulerMixin`): The scheduler to get timesteps from. num_inference_steps (`int`): The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps` must be `None`. device (`str` or `torch.device`, *optional*): The device to which the timesteps should be moved to. If `None`, the timesteps are not moved. timesteps (`List[int]`, *optional*): Custom timesteps used to support arbitrary spacing between timesteps. If `None`, then the default timestep spacing strategy of the scheduler is used. If `timesteps` is passed, `num_inference_steps` must be `None`. Returns: `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the second element is the number of inference steps. """ if timesteps is not None: accepts_timesteps = "timesteps" in set(inspect.signature(scheduler.set_timesteps).parameters.keys()) # if not accepts_timesteps: # raise ValueError( # f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom" # f" timestep schedules. Please check whether you are using the correct scheduler." # ) scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs) timesteps = scheduler.timesteps num_inference_steps = len(timesteps) else: scheduler.set_timesteps(num_inference_steps, device=device, **kwargs) timesteps = scheduler.timesteps return timesteps, num_inference_steps def tensor2vid(video: torch.Tensor, processor, output_type="np"): # Based on: # https://github.com/modelscope/modelscope/blob/1509fdb973e5871f37148a4b5e5964cafd43e64d/modelscope/pipelines/multi_modal/text_to_video_synthesis_pipeline.py#L78 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) return outputs @dataclass class AnimateDiffPipelineOutput(BaseOutput): frames: Union[torch.Tensor, np.ndarray] class AnimateDiffPipeline(DiffusionPipeline, 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->image_encoder->unet->vae" _optional_components = ["feature_extractor", "image_encoder","controlnet"] def __init__( self, vae: AutoencoderKL, text_encoder: CLIPTextModel, tokenizer: CLIPTokenizer, unet: UNet2DConditionModel, motion_adapter: MotionAdapter, scheduler: Union[ DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler, EulerDiscreteScheduler, EulerAncestralDiscreteScheduler, DPMSolverMultistepScheduler, ], controlnet: Optional[Union[ControlNetModel, MultiControlNetModel]]=None, feature_extractor: Optional[CLIPImageProcessor] = None, image_encoder: Optional[CLIPVisionModelWithProjection] = None, ): super().__init__() unet = UNetMotionModel.from_unet2d(unet, motion_adapter) 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 ) def load_motion_adapter(self,motion_adapter): self.register_modules(motion_adapter=motion_adapter) # 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: procecss multi-vector tokens if necessary if isinstance(self, TextualInversionLoaderMixin): prompt = self.maybe_convert_prompt(prompt, self.tokenizer) text_inputs = self.tokenizer( prompt, padding="max_length", 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: procecss multi-vector tokens if necessary if isinstance(self, TextualInversionLoaderMixin): uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer) max_length = prompt_embeds.shape[1] uncond_input = self.tokenizer( uncond_tokens, 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.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.enable_vae_slicing def enable_vae_slicing(self): r""" Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to compute decoding in several steps. This is useful to save some memory and allow larger batch sizes. """ self.vae.enable_slicing() # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_slicing def disable_vae_slicing(self): r""" Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to computing decoding in one step. """ self.vae.disable_slicing() # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_tiling def enable_vae_tiling(self): r""" Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow processing larger images. """ self.vae.enable_tiling() # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_tiling def disable_vae_tiling(self): r""" Disable tiled VAE decoding. If `enable_vae_tiling` was previously enabled, this method will go back to computing decoding in one step. """ self.vae.disable_tiling() # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_freeu def enable_freeu(self, s1: float, s2: float, b1: float, b2: float): r"""Enables the FreeU mechanism as in https://arxiv.org/abs/2309.11497. The suffixes after the scaling factors represent the stages where they are being applied. Please refer to the [official repository](https://github.com/ChenyangSi/FreeU) for combinations of the values that are known to work well for different pipelines such as Stable Diffusion v1, v2, and Stable Diffusion XL. Args: s1 (`float`): Scaling factor for stage 1 to attenuate the contributions of the skip features. This is done to mitigate "oversmoothing effect" in the enhanced denoising process. s2 (`float`): Scaling factor for stage 2 to attenuate the contributions of the skip features. This is done to mitigate "oversmoothing effect" in the enhanced denoising process. b1 (`float`): Scaling factor for stage 1 to amplify the contributions of backbone features. b2 (`float`): Scaling factor for stage 2 to amplify the contributions of backbone features. """ if not hasattr(self, "unet"): raise ValueError("The pipeline must have `unet` for using FreeU.") self.unet.enable_freeu(s1=s1, s2=s2, b1=b1, b2=b2) # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_freeu def disable_freeu(self): """Disables the FreeU mechanism if enabled.""" self.unet.disable_freeu() # 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, callback_steps, negative_prompt=None, prompt_embeds=None, negative_prompt_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_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}." ) # 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 def prepare_latents_same_start(self, batch_size, num_channels_latents, num_frames, height, width, dtype, device, generator, latents=None, context_size=16, overlap=4, strength=0.5): 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) # make every (context_size-overlap) frames have the same noise loop_size = context_size - overlap loop_count = num_frames // loop_size for i in range(loop_count): # repeat the first frames noise for i*loop_size frame # lerp the first frames noise latents[:, :, i*loop_size:(i*loop_size)+overlap, :, :] = torch.lerp(latents[:, :, i*loop_size:(i*loop_size)+overlap, :, :], latents[:, :, 0:overlap, :, :], strength) # scale the initial noise by the standard deviation required by the scheduler latents = latents * self.scheduler.init_noise_sigma return latents def prepare_latents_consistent(self, batch_size, num_channels_latents, num_frames, height, width, dtype, device, generator, latents=None,smooth_weight=0.5,smooth_steps=3): 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) # blend each frame with the surrounding N frames making sure to wrap around at the end for i in range(num_frames): blended_latent = torch.zeros_like(latents[:, :, i]) for s in range(-smooth_steps, smooth_steps + 1): if s == 0: continue frame_index = (i + s) % num_frames weight = (smooth_steps - abs(s)) / smooth_steps blended_latent += latents[:, :, frame_index] * weight latents[:, :, i] = blended_latent / (2 * smooth_steps) latents = torch.lerp(randn_tensor(shape, generator=generator, device=device, dtype=dtype),latents, smooth_weight) 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 def prepare_motion_latents(self, batch_size, num_channels_latents, num_frames, height, width, dtype, device, generator, latents=None, x_velocity=0, y_velocity=0, scale_velocity=0): 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 for frame in range(num_frames): x_offset = int(frame * x_velocity) # Convert to int y_offset = int(frame * y_velocity) # Convert to int scale_factor = 1 + frame * scale_velocity # Apply offsets latents[:, :, frame] = torch.roll(latents[:, :, frame], shifts=(x_offset,), dims=3) # x direction latents[:, :, frame] = torch.roll(latents[:, :, frame], shifts=(y_offset,), dims=2) # y direction # Apply scaling - This is a simple approach and might not be ideal for all applications if scale_factor != 1: scaled_size = ( int(latents.shape[3] * scale_factor), int(latents.shape[4] * scale_factor) ) latents[:, :, frame] = torch.nn.functional.interpolate( latents[:, :, frame].unsqueeze(0), size=scaled_size, mode='bilinear', align_corners=False ).squeeze(0) return latents def generate_correlated_noise(self, latents, init_noise_correlation): cloned_latents = latents.clone() p = init_noise_correlation flattened_latents = torch.flatten(cloned_latents) noise = torch.randn_like(flattened_latents) correlated_noise = flattened_latents * p + math.sqrt(1 - p**2) * noise return correlated_noise.reshape(cloned_latents.shape) def generate_correlated_latents(self, latents, init_noise_correlation): cloned_latents = latents.clone() for i in range(1, cloned_latents.shape[2]): p = init_noise_correlation flattened_latents = torch.flatten(cloned_latents[:, :, i]) prev_flattened_latents = torch.flatten(cloned_latents[:, :, i - 1]) correlated_latents = (prev_flattened_latents * p/math.sqrt((1+p**2))+flattened_latents * math.sqrt(1/(1 + p**2))) cloned_latents[:, :, i] = correlated_latents.reshape(cloned_latents[:, :, i].shape) return cloned_latents def generate_correlated_latents_legacy(self, latents, init_noise_correlation): cloned_latents = latents.clone() for i in range(1, cloned_latents.shape[2]): p = init_noise_correlation flattened_latents = torch.flatten(cloned_latents[:, :, i]) prev_flattened_latents = torch.flatten(cloned_latents[:, :, i - 1]) correlated_latents = ( prev_flattened_latents * p + flattened_latents * math.sqrt(1 - p**2) ) cloned_latents[:, :, i] = correlated_latents.reshape( cloned_latents[:, :, i].shape ) return cloned_latents def generate_mixed_noise(self, noise, init_noise_correlation): shared_noise = torch.randn_like(noise[0, :, 0]) for b in range(noise.shape[0]): for f in range(noise.shape[2]): p = init_noise_correlation flattened_latents = torch.flatten(noise[b, :, f]) shared_latents = torch.flatten(shared_noise) correlated_latents = ( shared_latents * math.sqrt(p**2/(1+p**2)) + flattened_latents * math.sqrt(1/(1+p**2)) ) noise[b, :, f] = correlated_latents.reshape(noise[b, :, f].shape) return noise def prepare_correlated_latents( self, init_image, init_image_strength, init_noise_correlation, batch_size, num_channels_latents, video_length, height, width, dtype, device, generator, latents=None, ): shape = ( batch_size, num_channels_latents, video_length, height // self.vae_scale_factor, width // self.vae_scale_factor, ) if init_image is not None: start_image = ((torchvision.transforms.functional.pil_to_tensor(init_image))/ 255 )[:3, :, :].to("cuda").to(dtype).unsqueeze(0) start_image = ( self.vae.encode(start_image.mul(2).sub(1)) .latent_dist.sample() .view(1, 4, height // 8, width // 8) * 0.18215 ) init_latents = start_image.unsqueeze(2).repeat(1, 1, video_length, 1, 1) else: init_latents = None 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: rand_device = "cpu" if device.type == "mps" else device if isinstance(generator, list): shape = shape # shape = (1,) + shape[1:] # ignore init latents for batch model latents = [torch.randn(shape, generator=generator[i], device=rand_device, dtype=dtype)for i in range(batch_size)] latents = torch.cat(latents, dim=0).to(device) else: if init_latents is not None: offset = int( init_image_strength * (len(self.scheduler.timesteps) - 1) ) noise = torch.randn_like(init_latents) noise = self.generate_correlated_latents(noise, init_noise_correlation) # Eric - some black magic here # We should be only adding the noise at timestep[offset], but I noticed that # we get more motion and cooler motion if we add the noise at timestep[offset - 1] # or offset - 2. However, this breaks the fewer timesteps there are, so let's interpolate timesteps = self.scheduler.timesteps average_timestep = None if offset == 0: average_timestep = timesteps[0] elif offset == 1: average_timestep = ( timesteps[offset - 1] * (1 - init_image_strength) + timesteps[offset] * init_image_strength ) else: average_timestep = timesteps[offset - 1] latents = self.scheduler.add_noise( init_latents, noise, average_timestep.long() ) latents = self.scheduler.add_noise( latents, torch.randn_like(init_latents), timesteps[-2] ) else: latents = torch.randn( shape, generator=generator, device=rand_device, dtype=dtype ).to(device) latents = self.generate_correlated_latents( latents, init_noise_correlation ) else: if latents.shape != shape: raise ValueError( f"Unexpected latents shape, got {latents.shape}, expected {shape}" ) latents = latents.to(device) # scale the initial noise by the standard deviation required by the scheduler if init_latents is None: latents = latents * self.scheduler.init_noise_sigma # elif self.unet.trained_initial_frames and init_latents is not None: # # we only want to use this as the first frame # init_latents[:, :, 1:] = torch.zeros_like(init_latents[:, :, 1:]) latents = latents.to(device) return latents, init_latents def prepare_video_latents( self, video, height, width, num_channels_latents, batch_size, timestep, dtype, device, generator, latents=None, ): # video must be a list of list of images # the outer list denotes having multiple videos as input, whereas inner list means the frames of the video # as a list of images if not isinstance(video[0], list): video = [video] if latents is None: video = torch.cat( [self.image_processor.preprocess(vid, height=height, width=width).unsqueeze(0) for vid in video], dim=0 ) video = video.to(device=device, dtype=dtype) num_frames = video.shape[1] else: num_frames = latents.shape[2] 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: # make sure the VAE is in float32 mode, as it overflows in float16 if self.vae.config.force_upcast: video = video.float() self.vae.to(dtype=torch.float32) if isinstance(generator, list): if 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." ) init_latents = [ retrieve_latents(self.vae.encode(video[i]), generator=generator[i]).unsqueeze(0) for i in range(batch_size) ] else: init_latents = [ retrieve_latents(self.vae.encode(vid), generator=generator).unsqueeze(0) for vid in video ] init_latents = torch.cat(init_latents, dim=0) # restore vae to original dtype if self.vae.config.force_upcast: self.vae.to(dtype) init_latents = init_latents.to(dtype) init_latents = self.vae.config.scaling_factor * init_latents if batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] == 0: # expand init_latents for batch_size error_message = ( f"You have passed {batch_size} text prompts (`prompt`), but only {init_latents.shape[0]} initial" " images (`image`). Please make sure to update your script to pass as many initial images as text prompts" ) raise ValueError(error_message) elif batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] != 0: raise ValueError( f"Cannot duplicate `image` of batch size {init_latents.shape[0]} to {batch_size} text prompts." ) else: init_latents = torch.cat([init_latents], dim=0) noise = randn_tensor(init_latents.shape, generator=generator, device=device, dtype=dtype) latents = self.scheduler.add_noise(init_latents, noise, timestep).permute(0, 2, 1, 3, 4) else: if shape != latents.shape: # [B, C, F, H, W] raise ValueError(f"`latents` expected to have {shape=}, but found {latents.shape=}") latents = latents.to(device, dtype=dtype) return latents # Copied from diffusers.pipelines.controlnet.pipeline_controlnet.StableDiffusionControlNetPipeline.prepare_image def prepare_control_frames( 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] image_batch_size = len(image) # 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 @torch.no_grad() def __call__( self, prompt: Union[str, List[str]] = None, num_frames: Optional[int] = 16, context_size=16, overlap=2, step=1, 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, output_type: Optional[str] = "pil", output_path: Optional[str] = None, return_dict: bool = True, callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, callback_steps: Optional[int] = 1, cross_attention_kwargs: Optional[Dict[str, Any]] = None, clip_skip: Optional[int] = None, x_velocity: Optional[float] = 0, y_velocity: Optional[float] = 0, scale_velocity: Optional[float] = 0, init_image: Optional[PipelineImageInput] = None, init_image_strength: Optional[float] = 1.0, init_noise_correlation: Optional[float] = 0.0, latent_mode: Optional[str] = "normal", smooth_weight: Optional[float] = 0.5, smooth_steps: Optional[int] = 3, initial_context_scale: Optional[float] = 1.0, conditioning_frames: Optional[List[PipelineImageInput]] = None, controlnet_conditioning_scale: Union[float, List[float]] = 1.0, control_guidance_start: Union[float, List[float]] = 0.0, control_guidance_end: Union[float, List[float]] = 1.0, guess_mode: bool = False, ): 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. 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. callback (`Callable`, *optional*): A function that calls every `callback_steps` steps during inference. The function is called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`. callback_steps (`int`, *optional*, defaults to 1): The frequency at which the `callback` function is called. If not specified, the callback is called at every step. 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. Examples: Returns: [`~pipelines.text_to_video_synthesis.TextToVideoSDPipelineOutput`] or `tuple`: If `return_dict` is `True`, [`~pipelines.text_to_video_synthesis.TextToVideoSDPipelineOutput`] is returned, otherwise a `tuple` is returned where the first element is a list with the generated frames. """ if self.controlnet != None: controlnet = self.controlnet._orig_mod if is_compiled_module(self.controlnet) else self.controlnet # align format for control guidance control_end = control_guidance_end 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, height, width, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds ) # 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 self.controlnet != None: 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 # 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. do_classifier_free_guidance = guidance_scale > 1.0 # 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, do_classifier_free_guidance, negative_prompt, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_prompt_embeds, lora_scale=text_encoder_lora_scale, clip_skip=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 do_classifier_free_guidance: prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds]) if ip_adapter_image is not None: output_hidden_state = False if isinstance(self.unet.encoder_hid_proj, ImageProjection) else True image_embeds, negative_image_embeds = self.encode_image( ip_adapter_image, device, num_videos_per_prompt, output_hidden_state ) if do_classifier_free_guidance: image_embeds = torch.cat([negative_image_embeds, image_embeds]) if self.controlnet != None: 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, # ) conditioning_frames = self.prepare_control_frames( 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=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, # ) prepared_frame = self.prepare_control_frames( 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=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 # round num frames to the nearest multiple of context size - overlap num_frames = (num_frames // (context_size - overlap)) * (context_size - overlap) # 5. Prepare latent variables num_channels_latents = self.unet.config.in_channels if(latent_mode == "normal"): latents = self.prepare_latents( batch_size * num_videos_per_prompt, num_channels_latents, num_frames, height, width, prompt_embeds.dtype, device, generator, latents, ) if(latent_mode == "same_start"): latents = self.prepare_latents_same_start( batch_size * num_videos_per_prompt, num_channels_latents, num_frames, height, width, prompt_embeds.dtype, device, generator, latents, context_size=context_size, overlap=overlap, strength=init_image_strength, ) elif(latent_mode == "motion"): latents = self.prepare_motion_latents( batch_size * num_videos_per_prompt, num_channels_latents, num_frames, height, width, prompt_embeds.dtype, device, generator, latents, x_velocity=x_velocity, y_velocity=y_velocity, scale_velocity=scale_velocity, ) elif(latent_mode == "correlated"): latents, init_latents = self.prepare_correlated_latents( init_image, init_image_strength, init_noise_correlation, batch_size, num_channels_latents, num_frames, height, width, prompt_embeds.dtype, device, generator, ) elif(latent_mode == "consistent"): latents = self.prepare_latents_consistent( batch_size * num_videos_per_prompt, num_channels_latents, num_frames, height, width, prompt_embeds.dtype, device, generator, latents, smooth_weight, smooth_steps, ) elif(latent_mode == "video"): # 4. Prepare timesteps # timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, timesteps) # timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, init_image_strength, device) latent_timestep = timesteps[:1].repeat(batch_size * num_videos_per_prompt) self._num_timesteps = len(timesteps) num_channels_latents = self.unet.config.in_channels latents = self.prepare_video_latents( video=init_image, height=height, width=width, num_channels_latents=num_channels_latents, batch_size=batch_size * num_videos_per_prompt, timestep=latent_timestep, dtype=prompt_embeds.dtype, device=device, generator=generator, latents=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 else None # 7.1 Create tensor stating which controlnets to keep if self.controlnet != None: 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) # divide the initial latents into context groups def context_scheduler(context_size, overlap, offset, total_frames, total_timesteps): # Calculate the number of context groups based on frame count and context size number_of_context_groups = (total_frames // (context_size - overlap)) # Initialize a list to store context indexes for all timesteps all_context_indexes = [] # Iterate over each timestep for timestep in range(total_timesteps): # Initialize a list to store groups of context indexes for this timestep timestep_context_groups = [] # Iterate over each context group for group_index in range(number_of_context_groups): # Initialize a list to store context indexes for this group context_group_indexes = [] # Iterate over each index in the context group local_context_size = context_size if timestep <= 1: local_context_size = int(context_size * initial_context_scale) for index in range(local_context_size): # if its the first timestep, spread the indexes out evenly over the full frame range, offset by the group index frame_index = (group_index * (local_context_size - overlap)) + (offset * timestep) + index # If frame index exceeds total frames, wrap around if frame_index >= total_frames: frame_index %= total_frames # Add the frame index to the group's list context_group_indexes.append(frame_index) # Add the group's indexes to the timestep's list timestep_context_groups.append(context_group_indexes) # Add the timestep's context groups to the overall list all_context_indexes.append(timestep_context_groups) return all_context_indexes context_indexes = context_scheduler(context_size, overlap, step, num_frames, len(timesteps)) # Denoising loop num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order with self.progress_bar(total=len(timesteps)) as progress_bar: for i, t in enumerate(timesteps): noise_pred_uncond_sum = torch.zeros_like(latents).to(device).to(dtype=torch.float16) noise_pred_text_sum = torch.zeros_like(latents).to(device).to(dtype=torch.float16) latent_counter = torch.zeros(num_frames).to(device).to(dtype=torch.float16) # foreach context group seperately denoise the current timestep for context_group in range(len(context_indexes[i])): # calculate to current indexes, considering overlapa current_context_indexes = context_indexes[i][context_group] # select the relevent context from the latents current_context_latents = latents[:, :, current_context_indexes, :, :] # expand the latents if we are doing classifier free guidance latent_model_input = torch.cat([current_context_latents] * 2) if do_classifier_free_guidance else current_context_latents latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) if self.controlnet != None and i < int(control_end*num_inference_steps): torch.cuda.synchronize() # Synchronize GPU control_start = time.time() current_context_conditioning_frames = conditioning_frames[current_context_indexes, :, :, :] current_context_conditioning_frames = torch.cat([current_context_conditioning_frames] * 2) if do_classifier_free_guidance else current_context_conditioning_frames 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(len(current_context_indexes), 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=current_context_conditioning_frames, conditioning_scale=cond_scale, guess_mode=guess_mode, return_dict=False, ) unet_start = time.time() # predict the noise residual with the added controlnet residuals 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, down_block_additional_residuals=down_block_res_samples, mid_block_additional_residual=mid_block_res_sample, ).sample else: # predict the noise residual without contorlnet torch.cuda.synchronize() unet_start = time.time() 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 if do_classifier_free_guidance: # Start timing for overall guidance process torch.cuda.synchronize() # Synchronize GPU before starting timing start_guidance_time = time.time() # Timing for chunk operation torch.cuda.synchronize() # Synchronize GPU before chunking time_chunk_start = time.time() noise_pred_uncond, noise_pred_text = torch.chunk(noise_pred, 2, dim=0) # Timing for batch addition and latent counter increment torch.cuda.synchronize() # Synchronize GPU before batch addition time_batch_addition_start = time.time() # Perform batch addition noise_pred_uncond_sum[..., current_context_indexes, :, :] += noise_pred_uncond noise_pred_text_sum[..., current_context_indexes, :, :] += noise_pred_text latent_counter[current_context_indexes] += 1 # set the step index to the current batch self.scheduler._step_index = i # perform guidance if do_classifier_free_guidance: latent_counter = latent_counter.reshape(1, 1, num_frames, 1, 1) noise_pred_uncond = noise_pred_uncond_sum / latent_counter noise_pred_text = noise_pred_text_sum / latent_counter 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 # 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, None) if output_type == "latent": return AnimateDiffPipelineOutput(frames=latents) # save frames if output_path is not None: output_batch_size = 2 # prevents out of memory errors with large videos num_digits = output_path.count('#') # count the number of '#' characters frame_format = output_path.replace('#' * num_digits, '{:0' + str(num_digits) + 'd}') for batch in range((num_frames + output_batch_size - 1) // output_batch_size): start_id = batch * output_batch_size end_id = min((batch + 1) * output_batch_size, num_frames) video_tensor = self.decode_latents(latents[:, :, start_id:end_id, :, :]) video = tensor2vid(video_tensor, self.image_processor, output_type=output_type) for f_id, frame in enumerate(video[0]): frame.save(frame_format.format(start_id + f_id)) return output_path # Post-processing video_tensor = self.decode_latents(latents) if output_type == "pt": video = video_tensor else: video = tensor2vid(video_tensor, self.image_processor, output_type=output_type) # Offload all models self.maybe_free_model_hooks() if not return_dict: return (video,) return AnimateDiffPipelineOutput(frames=video)