# Copyright 2024 EasyAnimate Authors and 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 Callable, Dict, List, Optional, Tuple, Union import torch import torch.nn.functional as F from diffusers import DiffusionPipeline from diffusers.callbacks import MultiPipelineCallbacks, PipelineCallback from diffusers.image_processor import VaeImageProcessor from diffusers.models import AutoencoderKL, HunyuanDiT2DModel from diffusers.models.embeddings import (get_2d_rotary_pos_embed, get_3d_rotary_pos_embed) from diffusers.pipelines.pipeline_utils import DiffusionPipeline from diffusers.pipelines.stable_diffusion.safety_checker import \ StableDiffusionSafetyChecker from diffusers.schedulers import DDIMScheduler from diffusers.utils import (is_torch_xla_available, logging, replace_example_docstring) from diffusers.utils.torch_utils import randn_tensor from einops import rearrange from PIL import Image from tqdm import tqdm from transformers import (BertModel, BertTokenizer, CLIPImageProcessor, CLIPVisionModelWithProjection, T5Tokenizer, T5EncoderModel) from .pipeline_easyanimate import EasyAnimatePipelineOutput from ..models import AutoencoderKLMagvit, EasyAnimateTransformer3DModel if is_torch_xla_available(): import torch_xla.core.xla_model as xm XLA_AVAILABLE = True else: XLA_AVAILABLE = False logger = logging.get_logger(__name__) # pylint: disable=invalid-name EXAMPLE_DOC_STRING = """ Examples: ```py >>> pass ``` """ def get_resize_crop_region_for_grid(src, tgt_width, tgt_height): tw = tgt_width th = tgt_height h, w = src r = h / w if r > (th / tw): resize_height = th resize_width = int(round(th / h * w)) else: resize_width = tw resize_height = int(round(tw / w * h)) crop_top = int(round((th - resize_height) / 2.0)) crop_left = int(round((tw - resize_width) / 2.0)) return (crop_top, crop_left), (crop_top + resize_height, crop_left + resize_width) # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.rescale_noise_cfg def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0): """ Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4 """ std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True) std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True) # rescale the results from guidance (fixes overexposure) noise_pred_rescaled = noise_cfg * (std_text / std_cfg) # mix with the original results from guidance by factor guidance_rescale to avoid "plain looking" images noise_cfg = guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg return noise_cfg def resize_mask(mask, latent, process_first_frame_only=True): latent_size = latent.size() if process_first_frame_only: target_size = list(latent_size[2:]) target_size[0] = 1 first_frame_resized = F.interpolate( mask[:, :, 0:1, :, :], size=target_size, mode='trilinear', align_corners=False ) target_size = list(latent_size[2:]) target_size[0] = target_size[0] - 1 if target_size[0] != 0: remaining_frames_resized = F.interpolate( mask[:, :, 1:, :, :], size=target_size, mode='trilinear', align_corners=False ) resized_mask = torch.cat([first_frame_resized, remaining_frames_resized], dim=2) else: resized_mask = first_frame_resized else: target_size = list(latent_size[2:]) resized_mask = F.interpolate( mask, size=target_size, mode='trilinear', align_corners=False ) return resized_mask def add_noise_to_reference_video(image, ratio=None): if ratio is None: sigma = torch.normal(mean=-3.0, std=0.5, size=(image.shape[0],)).to(image.device) sigma = torch.exp(sigma).to(image.dtype) else: sigma = torch.ones((image.shape[0],)).to(image.device, image.dtype) * ratio image_noise = torch.randn_like(image) * sigma[:, None, None, None, None] image_noise = torch.where(image==-1, torch.zeros_like(image), image_noise) image = image + image_noise return image class EasyAnimatePipeline_Multi_Text_Encoder_Inpaint(DiffusionPipeline): r""" Pipeline for text-to-video generation using EasyAnimate. This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) EasyAnimate uses two text encoders: [mT5](https://huggingface.co/google/mt5-base) and [bilingual CLIP](fine-tuned by HunyuanDiT team) Args: vae ([`AutoencoderKLMagvit`]): Variational Auto-Encoder (VAE) Model to encode and decode video to and from latent representations. text_encoder (Optional[`~transformers.BertModel`, `~transformers.CLIPTextModel`]): Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)). EasyAnimate uses a fine-tuned [bilingual CLIP]. tokenizer (Optional[`~transformers.BertTokenizer`, `~transformers.CLIPTokenizer`]): A `BertTokenizer` or `CLIPTokenizer` to tokenize text. transformer ([`EasyAnimateTransformer3DModel`]): The EasyAnimate model designed by Tencent Hunyuan. text_encoder_2 (`T5EncoderModel`): The mT5 embedder. tokenizer_2 (`T5Tokenizer`): The tokenizer for the mT5 embedder. scheduler ([`DDIMScheduler`]): A scheduler to be used in combination with EasyAnimate to denoise the encoded image latents. clip_image_processor (`CLIPImageProcessor`): The CLIP image embedder. clip_image_encoder (`CLIPVisionModelWithProjection`): The image processor for the CLIP image embedder. """ model_cpu_offload_seq = "text_encoder->text_encoder_2->clip_image_encoder->transformer->vae" _optional_components = [ "safety_checker", "feature_extractor", "text_encoder_2", "tokenizer_2", "text_encoder", "tokenizer", "clip_image_encoder", ] _exclude_from_cpu_offload = ["safety_checker"] _callback_tensor_inputs = [ "latents", "prompt_embeds", "negative_prompt_embeds", "prompt_embeds_2", "negative_prompt_embeds_2", ] def __init__( self, vae: AutoencoderKLMagvit, text_encoder: BertModel, tokenizer: BertTokenizer, text_encoder_2: T5EncoderModel, tokenizer_2: T5Tokenizer, transformer: EasyAnimateTransformer3DModel, scheduler: DDIMScheduler, safety_checker: StableDiffusionSafetyChecker, feature_extractor: CLIPImageProcessor, requires_safety_checker: bool = True, clip_image_processor: CLIPImageProcessor = None, clip_image_encoder: CLIPVisionModelWithProjection = None, ): super().__init__() self.register_modules( vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, tokenizer_2=tokenizer_2, transformer=transformer, scheduler=scheduler, safety_checker=safety_checker, feature_extractor=feature_extractor, text_encoder_2=text_encoder_2, clip_image_processor=clip_image_processor, clip_image_encoder=clip_image_encoder, ) if safety_checker is None and requires_safety_checker: logger.warning( f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure" " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered" " results in services or applications open to the public. Both the diffusers team and Hugging Face" " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling" " it only for use-cases that involve analyzing network behavior or auditing its results. For more" " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ." ) if safety_checker is not None and feature_extractor is None: raise ValueError( "Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety" " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead." ) 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.mask_processor = VaeImageProcessor( vae_scale_factor=self.vae_scale_factor, do_normalize=False, do_binarize=True, do_convert_grayscale=True ) self.enable_autocast_float8_transformer_flag = False self.register_to_config(requires_safety_checker=requires_safety_checker) def enable_sequential_cpu_offload(self, *args, **kwargs): super().enable_sequential_cpu_offload(*args, **kwargs) if hasattr(self.transformer, "clip_projection") and self.transformer.clip_projection is not None: import accelerate accelerate.hooks.remove_hook_from_module(self.transformer.clip_projection, recurse=True) self.transformer.clip_projection = self.transformer.clip_projection.to("cuda") def encode_prompt( self, prompt: str, device: torch.device, dtype: torch.dtype, num_images_per_prompt: int = 1, do_classifier_free_guidance: bool = True, negative_prompt: Optional[str] = None, prompt_embeds: Optional[torch.Tensor] = None, negative_prompt_embeds: Optional[torch.Tensor] = None, prompt_attention_mask: Optional[torch.Tensor] = None, negative_prompt_attention_mask: Optional[torch.Tensor] = None, max_sequence_length: Optional[int] = None, text_encoder_index: int = 0, actual_max_sequence_length: int = 256 ): 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 dtype (`torch.dtype`): torch dtype num_images_per_prompt (`int`): number of images that should be generated per prompt do_classifier_free_guidance (`bool`): whether to use classifier free guidance or not negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). prompt_embeds (`torch.Tensor`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument. negative_prompt_embeds (`torch.Tensor`, *optional*): Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument. prompt_attention_mask (`torch.Tensor`, *optional*): Attention mask for the prompt. Required when `prompt_embeds` is passed directly. negative_prompt_attention_mask (`torch.Tensor`, *optional*): Attention mask for the negative prompt. Required when `negative_prompt_embeds` is passed directly. max_sequence_length (`int`, *optional*): maximum sequence length to use for the prompt. text_encoder_index (`int`, *optional*): Index of the text encoder to use. `0` for clip and `1` for T5. """ tokenizers = [self.tokenizer, self.tokenizer_2] text_encoders = [self.text_encoder, self.text_encoder_2] tokenizer = tokenizers[text_encoder_index] text_encoder = text_encoders[text_encoder_index] if max_sequence_length is None: if text_encoder_index == 0: max_length = min(self.tokenizer.model_max_length, actual_max_sequence_length) if text_encoder_index == 1: max_length = min(self.tokenizer_2.model_max_length, actual_max_sequence_length) else: max_length = max_sequence_length 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: text_inputs = tokenizer( prompt, padding="max_length", max_length=max_length, truncation=True, return_attention_mask=True, return_tensors="pt", ) text_input_ids = text_inputs.input_ids if text_input_ids.shape[-1] > actual_max_sequence_length: reprompt = tokenizer.batch_decode(text_input_ids[:, :actual_max_sequence_length], skip_special_tokens=True) text_inputs = tokenizer( reprompt, padding="max_length", max_length=max_length, truncation=True, return_attention_mask=True, return_tensors="pt", ) text_input_ids = text_inputs.input_ids untruncated_ids = 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 ): _actual_max_sequence_length = min(tokenizer.model_max_length, actual_max_sequence_length) removed_text = tokenizer.batch_decode(untruncated_ids[:, _actual_max_sequence_length - 1 : -1]) logger.warning( "The following part of your input was truncated because CLIP can only handle sequences up to" f" {_actual_max_sequence_length} tokens: {removed_text}" ) prompt_attention_mask = text_inputs.attention_mask.to(device) if self.transformer.config.enable_text_attention_mask: prompt_embeds = text_encoder( text_input_ids.to(device), attention_mask=prompt_attention_mask, ) else: prompt_embeds = text_encoder( text_input_ids.to(device) ) prompt_embeds = prompt_embeds[0] prompt_attention_mask = prompt_attention_mask.repeat(num_images_per_prompt, 1) prompt_embeds = prompt_embeds.to(dtype=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 max_length = prompt_embeds.shape[1] uncond_input = tokenizer( uncond_tokens, padding="max_length", max_length=max_length, truncation=True, return_tensors="pt", ) uncond_input_ids = uncond_input.input_ids if uncond_input_ids.shape[-1] > actual_max_sequence_length: reuncond_tokens = tokenizer.batch_decode(uncond_input_ids[:, :actual_max_sequence_length], skip_special_tokens=True) uncond_input = tokenizer( reuncond_tokens, padding="max_length", max_length=max_length, truncation=True, return_attention_mask=True, return_tensors="pt", ) uncond_input_ids = uncond_input.input_ids negative_prompt_attention_mask = uncond_input.attention_mask.to(device) if self.transformer.config.enable_text_attention_mask: negative_prompt_embeds = text_encoder( uncond_input.input_ids.to(device), attention_mask=negative_prompt_attention_mask, ) else: negative_prompt_embeds = text_encoder( uncond_input.input_ids.to(device) ) negative_prompt_embeds = negative_prompt_embeds[0] negative_prompt_attention_mask = negative_prompt_attention_mask.repeat(num_images_per_prompt, 1) 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=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) return prompt_embeds, negative_prompt_embeds, prompt_attention_mask, negative_prompt_attention_mask # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker def run_safety_checker(self, image, device, dtype): if self.safety_checker is None: has_nsfw_concept = None else: if torch.is_tensor(image): feature_extractor_input = self.image_processor.postprocess(image, output_type="pil") else: feature_extractor_input = self.image_processor.numpy_to_pil(image) safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors="pt").to(device) image, has_nsfw_concept = self.safety_checker( images=image, clip_input=safety_checker_input.pixel_values.to(dtype) ) return image, has_nsfw_concept # 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 def check_inputs( self, prompt, height, width, negative_prompt=None, prompt_embeds=None, negative_prompt_embeds=None, prompt_attention_mask=None, negative_prompt_attention_mask=None, prompt_embeds_2=None, negative_prompt_embeds_2=None, prompt_attention_mask_2=None, negative_prompt_attention_mask_2=None, callback_on_step_end_tensor_inputs=None, ): if height % 8 != 0 or width % 8 != 0: raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.") if callback_on_step_end_tensor_inputs is not None and not all( k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs ): raise ValueError( f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}" ) if prompt is not None and prompt_embeds is not None: raise ValueError( f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to" " only forward one of the two." ) elif prompt is None and prompt_embeds is None: raise ValueError( "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined." ) elif prompt is None and prompt_embeds_2 is None: raise ValueError( "Provide either `prompt` or `prompt_embeds_2`. Cannot leave both `prompt` and `prompt_embeds_2` 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 prompt_embeds is not None and prompt_attention_mask is None: raise ValueError("Must provide `prompt_attention_mask` when specifying `prompt_embeds`.") if prompt_embeds_2 is not None and prompt_attention_mask_2 is None: raise ValueError("Must provide `prompt_attention_mask_2` when specifying `prompt_embeds_2`.") 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 negative_prompt_embeds is not None and negative_prompt_attention_mask is None: raise ValueError("Must provide `negative_prompt_attention_mask` when specifying `negative_prompt_embeds`.") if negative_prompt_embeds_2 is not None and negative_prompt_attention_mask_2 is None: raise ValueError( "Must provide `negative_prompt_attention_mask_2` when specifying `negative_prompt_embeds_2`." ) if prompt_embeds is not None and negative_prompt_embeds is not None: if prompt_embeds.shape != negative_prompt_embeds.shape: raise ValueError( "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but" f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`" f" {negative_prompt_embeds.shape}." ) if prompt_embeds_2 is not None and negative_prompt_embeds_2 is not None: if prompt_embeds_2.shape != negative_prompt_embeds_2.shape: raise ValueError( "`prompt_embeds_2` and `negative_prompt_embeds_2` must have the same shape when passed directly, but" f" got: `prompt_embeds_2` {prompt_embeds_2.shape} != `negative_prompt_embeds_2`" f" {negative_prompt_embeds_2.shape}." ) # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.get_timesteps def get_timesteps(self, num_inference_steps, strength, device): # get the original timestep using init_timestep init_timestep = min(int(num_inference_steps * strength), num_inference_steps) t_start = max(num_inference_steps - init_timestep, 0) timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :] return timesteps, num_inference_steps - t_start def prepare_mask_latents( self, mask, masked_image, batch_size, height, width, dtype, device, generator, do_classifier_free_guidance, noise_aug_strength ): # resize the mask to latents shape as we concatenate the mask to the latents # we do that before converting to dtype to avoid breaking in case we're using cpu_offload # and half precision if mask is not None: mask = mask.to(device=device, dtype=self.vae.dtype) if self.vae.quant_conv is None or self.vae.quant_conv.weight.ndim==5: bs = 1 new_mask = [] for i in range(0, mask.shape[0], bs): mask_bs = mask[i : i + bs] mask_bs = self.vae.encode(mask_bs)[0] mask_bs = mask_bs.mode() new_mask.append(mask_bs) mask = torch.cat(new_mask, dim = 0) mask = mask * self.vae.config.scaling_factor else: if mask.shape[1] == 4: mask = mask else: video_length = mask.shape[2] mask = rearrange(mask, "b c f h w -> (b f) c h w") mask = self._encode_vae_image(mask, generator=generator) mask = rearrange(mask, "(b f) c h w -> b c f h w", f=video_length) if masked_image is not None: masked_image = masked_image.to(device=device, dtype=self.vae.dtype) if self.transformer.config.add_noise_in_inpaint_model: masked_image = add_noise_to_reference_video(masked_image, ratio=noise_aug_strength) if self.vae.quant_conv is None or self.vae.quant_conv.weight.ndim==5: bs = 1 new_mask_pixel_values = [] for i in range(0, masked_image.shape[0], bs): mask_pixel_values_bs = masked_image[i : i + bs] mask_pixel_values_bs = self.vae.encode(mask_pixel_values_bs)[0] mask_pixel_values_bs = mask_pixel_values_bs.mode() new_mask_pixel_values.append(mask_pixel_values_bs) masked_image_latents = torch.cat(new_mask_pixel_values, dim = 0) masked_image_latents = masked_image_latents * self.vae.config.scaling_factor else: if masked_image.shape[1] == 4: masked_image_latents = masked_image else: video_length = masked_image.shape[2] masked_image = rearrange(masked_image, "b c f h w -> (b f) c h w") masked_image_latents = self._encode_vae_image(masked_image, generator=generator) masked_image_latents = rearrange(masked_image_latents, "(b f) c h w -> b c f h w", f=video_length) # aligning device to prevent device errors when concating it with the latent model input masked_image_latents = masked_image_latents.to(device=device, dtype=dtype) else: masked_image_latents = None return mask, masked_image_latents def prepare_latents( self, batch_size, num_channels_latents, height, width, video_length, dtype, device, generator, latents=None, video=None, timestep=None, is_strength_max=True, return_noise=False, return_video_latents=False, ): if self.vae.quant_conv is None or self.vae.quant_conv.weight.ndim==5: if self.vae.cache_mag_vae: mini_batch_encoder = self.vae.mini_batch_encoder mini_batch_decoder = self.vae.mini_batch_decoder shape = (batch_size, num_channels_latents, int((video_length - 1) // mini_batch_encoder * mini_batch_decoder + 1) if video_length != 1 else 1, height // self.vae_scale_factor, width // self.vae_scale_factor) else: mini_batch_encoder = self.vae.mini_batch_encoder mini_batch_decoder = self.vae.mini_batch_decoder shape = (batch_size, num_channels_latents, int(video_length // mini_batch_encoder * mini_batch_decoder) if video_length != 1 else 1, height // self.vae_scale_factor, width // self.vae_scale_factor) else: shape = (batch_size, num_channels_latents, video_length, 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 return_video_latents or (latents is None and not is_strength_max): video = video.to(device=device, dtype=self.vae.dtype) if self.vae.quant_conv is None or self.vae.quant_conv.weight.ndim==5: bs = 1 new_video = [] for i in range(0, video.shape[0], bs): video_bs = video[i : i + bs] video_bs = self.vae.encode(video_bs)[0] video_bs = video_bs.sample() new_video.append(video_bs) video = torch.cat(new_video, dim = 0) video = video * self.vae.config.scaling_factor else: if video.shape[1] == 4: video = video else: video_length = video.shape[2] video = rearrange(video, "b c f h w -> (b f) c h w") video = self._encode_vae_image(video, generator=generator) video = rearrange(video, "(b f) c h w -> b c f h w", f=video_length) video_latents = video.repeat(batch_size // video.shape[0], 1, 1, 1, 1) video_latents = video_latents.to(device=device, dtype=dtype) if latents is None: noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype) # if strength is 1. then initialise the latents to noise, else initial to image + noise latents = noise if is_strength_max else self.scheduler.add_noise(video_latents, noise, timestep) # if pure noise then scale the initial latents by the Scheduler's init sigma latents = latents * self.scheduler.init_noise_sigma if is_strength_max else latents else: noise = latents.to(device) latents = noise * self.scheduler.init_noise_sigma # scale the initial noise by the standard deviation required by the scheduler outputs = (latents,) if return_noise: outputs += (noise,) if return_video_latents: outputs += (video_latents,) return outputs def smooth_output(self, video, mini_batch_encoder, mini_batch_decoder): if video.size()[2] <= mini_batch_encoder: return video prefix_index_before = mini_batch_encoder // 2 prefix_index_after = mini_batch_encoder - prefix_index_before pixel_values = video[:, :, prefix_index_before:-prefix_index_after] # Encode middle videos latents = self.vae.encode(pixel_values)[0] latents = latents.mode() # Decode middle videos middle_video = self.vae.decode(latents)[0] video[:, :, prefix_index_before:-prefix_index_after] = (video[:, :, prefix_index_before:-prefix_index_after] + middle_video) / 2 return video def decode_latents(self, latents): video_length = latents.shape[2] latents = 1 / self.vae.config.scaling_factor * latents if self.vae.quant_conv is None or self.vae.quant_conv.weight.ndim==5: mini_batch_encoder = self.vae.mini_batch_encoder mini_batch_decoder = self.vae.mini_batch_decoder video = self.vae.decode(latents)[0] video = video.clamp(-1, 1) if not self.vae.cache_compression_vae and not self.vae.cache_mag_vae: video = self.smooth_output(video, mini_batch_encoder, mini_batch_decoder).cpu().clamp(-1, 1) else: latents = rearrange(latents, "b c f h w -> (b f) c h w") video = [] for frame_idx in tqdm(range(latents.shape[0])): video.append(self.vae.decode(latents[frame_idx:frame_idx+1]).sample) video = torch.cat(video) video = rearrange(video, "(b f) c h w -> b c f h w", f=video_length) video = (video / 2 + 0.5).clamp(0, 1) # we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16 video = video.cpu().float().numpy() return video @property def guidance_scale(self): return self._guidance_scale @property def guidance_rescale(self): return self._guidance_rescale # 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 num_timesteps(self): return self._num_timesteps @property def interrupt(self): return self._interrupt def enable_autocast_float8_transformer(self): self.enable_autocast_float8_transformer_flag = True @torch.no_grad() @replace_example_docstring(EXAMPLE_DOC_STRING) def __call__( self, prompt: Union[str, List[str]] = None, video_length: Optional[int] = None, video: Union[torch.FloatTensor] = None, mask_video: Union[torch.FloatTensor] = None, masked_video_latents: Union[torch.FloatTensor] = None, height: Optional[int] = None, width: Optional[int] = None, num_inference_steps: Optional[int] = 50, guidance_scale: Optional[float] = 5.0, negative_prompt: Optional[Union[str, List[str]]] = None, num_images_per_prompt: Optional[int] = 1, eta: Optional[float] = 0.0, generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, latents: Optional[torch.Tensor] = None, prompt_embeds: Optional[torch.Tensor] = None, prompt_embeds_2: Optional[torch.Tensor] = None, negative_prompt_embeds: Optional[torch.Tensor] = None, negative_prompt_embeds_2: Optional[torch.Tensor] = None, prompt_attention_mask: Optional[torch.Tensor] = None, prompt_attention_mask_2: Optional[torch.Tensor] = None, negative_prompt_attention_mask: Optional[torch.Tensor] = None, negative_prompt_attention_mask_2: Optional[torch.Tensor] = None, output_type: Optional[str] = "latent", return_dict: bool = True, callback_on_step_end: Optional[ Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks] ] = None, callback_on_step_end_tensor_inputs: List[str] = ["latents"], guidance_rescale: float = 0.0, original_size: Optional[Tuple[int, int]] = (1024, 1024), target_size: Optional[Tuple[int, int]] = None, crops_coords_top_left: Tuple[int, int] = (0, 0), clip_image: Image = None, clip_apply_ratio: float = 0.40, strength: float = 1.0, noise_aug_strength: float = 0.0563, comfyui_progressbar: bool = False, ): r""" The call function to the pipeline for generation with HunyuanDiT. Examples: prompt (`str` or `List[str]`, *optional*): The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`. video_length (`int`, *optional*): Length of the video to be generated in seconds. This parameter influences the number of frames and continuity of generated content. video (`torch.FloatTensor`, *optional*): A tensor representing an input video, which can be modified depending on the prompts provided. mask_video (`torch.FloatTensor`, *optional*): A tensor to specify areas of the video to be masked (omitted from generation). masked_video_latents (`torch.FloatTensor`, *optional*): Latents from masked portions of the video, utilized during image generation. height (`int`, *optional*): The height in pixels of the generated image or video frames. width (`int`, *optional*): The width in pixels of the generated image or video frames. num_inference_steps (`int`, *optional*, defaults to 50): The number of denoising steps. More denoising steps usually lead to a higher quality image but slower inference time. This parameter is modulated by `strength`. guidance_scale (`float`, *optional*, defaults to 5.0): 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 effective when `guidance_scale > 1`. negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts to guide what to exclude in image generation. If not defined, you need to provide `negative_prompt_embeds`. This parameter is ignored when not using guidance (`guidance_scale < 1`). num_images_per_prompt (`int`, *optional*, defaults to 1): The number of images to generate per prompt. eta (`float`, *optional*, defaults to 0.0): A parameter defined in the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies to the [`~schedulers.DDIMScheduler`] and is ignored in other schedulers. It adjusts noise level during the inference process. generator (`torch.Generator` or `List[torch.Generator]`, *optional*): A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) for setting random seeds which helps in making generation deterministic. latents (`torch.Tensor`, *optional*): A pre-computed latent representation which can be used to guide the generation process. prompt_embeds (`torch.Tensor`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not provided, embeddings are generated from the `prompt` input argument. prompt_embeds_2 (`torch.Tensor`, *optional*): Secondary set of pre-generated text embeddings, useful for advanced prompt weighting. negative_prompt_embeds (`torch.Tensor`, *optional*): Pre-generated negative text embeddings, aiding in fine-tuning what should not be represented in the outputs. If not provided, embeddings are generated from the `negative_prompt` argument. negative_prompt_embeds_2 (`torch.Tensor`, *optional*): Secondary set of pre-generated negative text embeddings for further control. prompt_attention_mask (`torch.Tensor`, *optional*): Attention mask guiding the focus of the model on specific parts of the prompt text. Required when using `prompt_embeds`. prompt_attention_mask_2 (`torch.Tensor`, *optional*): Attention mask for the secondary prompt embedding. negative_prompt_attention_mask (`torch.Tensor`, *optional*): Attention mask for the negative prompt, needed when `negative_prompt_embeds` are used. negative_prompt_attention_mask_2 (`torch.Tensor`, *optional*): Attention mask for the secondary negative prompt embedding. output_type (`str`, *optional*, defaults to `"latent"`): The output format of the generated image. Choose between `PIL.Image` and `np.array` to define how you want the results to be formatted. return_dict (`bool`, *optional*, defaults to `True`): If set to `True`, a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] will be returned; otherwise, a tuple containing the generated images and safety flags will be returned. callback_on_step_end (`Callable[[int, int, Dict], None]`, `PipelineCallback`, `MultiPipelineCallbacks`, *optional*): A callback function (or a list of them) that will be executed at the end of each denoising step, allowing for custom processing during generation. callback_on_step_end_tensor_inputs (`List[str]`, *optional*): Specifies which tensor inputs should be included in the callback function. If not defined, all tensor inputs will be passed, facilitating enhanced logging or monitoring of the generation process. guidance_rescale (`float`, *optional*, defaults to 0.0): Rescale parameter for adjusting noise configuration based on guidance rescale. Based on findings from [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). original_size (`Tuple[int, int]`, *optional*, defaults to `(1024, 1024)`): The original dimensions of the image. Used to compute time ids during the generation process. target_size (`Tuple[int, int]`, *optional*): The targeted dimensions of the generated image, also utilized in the time id calculations. crops_coords_top_left (`Tuple[int, int]`, *optional*, defaults to `(0, 0)`): Coordinates defining the top left corner of any cropping, utilized while calculating the time ids. clip_image (`Image`, *optional*): An optional image to assist in the generation process. It may be used as an additional visual cue. clip_apply_ratio (`float`, *optional*, defaults to 0.40): Ratio indicating how much influence the clip image should exert over the generated content. strength (`float`, *optional*, defaults to 1.0): Affects the overall styling or quality of the generated output. Values closer to 1 usually provide direct adherence to prompts. comfyui_progressbar (`bool`, *optional*, defaults to `False`): Enables a progress bar in ComfyUI, providing visual feedback during the generation process. Examples: # Example usage of the function for generating images based on prompts. Returns: [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`: Returns either a structured output containing generated images and their metadata when `return_dict` is `True`, or a simpler tuple, where the first element is a list of generated images and the second element indicates if any of them contain "not-safe-for-work" (NSFW) content. """ if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)): callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs # 0. default height and width height = int(height // 16 * 16) width = int(width // 16 * 16) # 1. Check inputs. Raise error if not correct self.check_inputs( prompt, height, width, negative_prompt, prompt_embeds, negative_prompt_embeds, prompt_attention_mask, negative_prompt_attention_mask, prompt_embeds_2, negative_prompt_embeds_2, prompt_attention_mask_2, negative_prompt_attention_mask_2, callback_on_step_end_tensor_inputs, ) self._guidance_scale = guidance_scale self._guidance_rescale = guidance_rescale self._interrupt = False # 2. Define call parameters if prompt is not None and isinstance(prompt, str): batch_size = 1 elif prompt is not None and isinstance(prompt, list): batch_size = len(prompt) else: batch_size = prompt_embeds.shape[0] device = self._execution_device # 3. Encode input prompt ( prompt_embeds, negative_prompt_embeds, prompt_attention_mask, negative_prompt_attention_mask, ) = self.encode_prompt( prompt=prompt, device=device, dtype=self.transformer.dtype, num_images_per_prompt=num_images_per_prompt, do_classifier_free_guidance=self.do_classifier_free_guidance, negative_prompt=negative_prompt, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_prompt_embeds, prompt_attention_mask=prompt_attention_mask, negative_prompt_attention_mask=negative_prompt_attention_mask, text_encoder_index=0, ) ( prompt_embeds_2, negative_prompt_embeds_2, prompt_attention_mask_2, negative_prompt_attention_mask_2, ) = self.encode_prompt( prompt=prompt, device=device, dtype=self.transformer.dtype, num_images_per_prompt=num_images_per_prompt, do_classifier_free_guidance=self.do_classifier_free_guidance, negative_prompt=negative_prompt, prompt_embeds=prompt_embeds_2, negative_prompt_embeds=negative_prompt_embeds_2, prompt_attention_mask=prompt_attention_mask_2, negative_prompt_attention_mask=negative_prompt_attention_mask_2, text_encoder_index=1, ) torch.cuda.empty_cache() # 4. set timesteps self.scheduler.set_timesteps(num_inference_steps, device=device) timesteps, num_inference_steps = self.get_timesteps( num_inference_steps=num_inference_steps, strength=strength, device=device ) if comfyui_progressbar: from comfy.utils import ProgressBar pbar = ProgressBar(num_inference_steps + 3) # at which timestep to set the initial noise (n.b. 50% if strength is 0.5) latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt) # create a boolean to check if the strength is set to 1. if so then initialise the latents with pure noise is_strength_max = strength == 1.0 if video is not None: video_length = video.shape[2] init_video = self.image_processor.preprocess(rearrange(video, "b c f h w -> (b f) c h w"), height=height, width=width) init_video = init_video.to(dtype=torch.float32) init_video = rearrange(init_video, "(b f) c h w -> b c f h w", f=video_length) else: init_video = None # Prepare latent variables num_channels_latents = self.vae.config.latent_channels num_channels_transformer = self.transformer.config.in_channels return_image_latents = num_channels_transformer == num_channels_latents # 5. Prepare latents. latents_outputs = self.prepare_latents( batch_size * num_images_per_prompt, num_channels_latents, height, width, video_length, prompt_embeds.dtype, device, generator, latents, video=init_video, timestep=latent_timestep, is_strength_max=is_strength_max, return_noise=True, return_video_latents=return_image_latents, ) if return_image_latents: latents, noise, image_latents = latents_outputs else: latents, noise = latents_outputs if comfyui_progressbar: pbar.update(1) # 6. Prepare clip latents if it needs. if clip_image is not None and self.transformer.enable_clip_in_inpaint: inputs = self.clip_image_processor(images=clip_image, return_tensors="pt") inputs["pixel_values"] = inputs["pixel_values"].to(latents.device, dtype=latents.dtype) clip_encoder_hidden_states = self.clip_image_encoder(**inputs).last_hidden_state[:, 1:] clip_encoder_hidden_states_neg = torch.zeros( [ batch_size, int(self.clip_image_encoder.config.image_size / self.clip_image_encoder.config.patch_size) ** 2, int(self.clip_image_encoder.config.hidden_size) ] ).to(latents.device, dtype=latents.dtype) clip_attention_mask = torch.ones([batch_size, self.transformer.n_query]).to(latents.device, dtype=latents.dtype) clip_attention_mask_neg = torch.zeros([batch_size, self.transformer.n_query]).to(latents.device, dtype=latents.dtype) clip_encoder_hidden_states_input = torch.cat([clip_encoder_hidden_states_neg, clip_encoder_hidden_states]) if self.do_classifier_free_guidance else clip_encoder_hidden_states clip_attention_mask_input = torch.cat([clip_attention_mask_neg, clip_attention_mask]) if self.do_classifier_free_guidance else clip_attention_mask elif clip_image is None and num_channels_transformer != num_channels_latents and self.transformer.enable_clip_in_inpaint: clip_encoder_hidden_states = torch.zeros( [ batch_size, int(self.clip_image_encoder.config.image_size / self.clip_image_encoder.config.patch_size) ** 2, int(self.clip_image_encoder.config.hidden_size) ] ).to(latents.device, dtype=latents.dtype) clip_attention_mask = torch.zeros([batch_size, self.transformer.n_query]) clip_attention_mask = clip_attention_mask.to(latents.device, dtype=latents.dtype) clip_encoder_hidden_states_input = torch.cat([clip_encoder_hidden_states] * 2) if self.do_classifier_free_guidance else clip_encoder_hidden_states clip_attention_mask_input = torch.cat([clip_attention_mask] * 2) if self.do_classifier_free_guidance else clip_attention_mask else: clip_encoder_hidden_states_input = None clip_attention_mask_input = None if comfyui_progressbar: pbar.update(1) # 7. Prepare inpaint latents if it needs. if mask_video is not None: if (mask_video == 255).all(): # Use zero latents if we want to t2v. if self.transformer.resize_inpaint_mask_directly: mask_latents = torch.zeros_like(latents)[:, :1].to(latents.device, latents.dtype) else: mask_latents = torch.zeros_like(latents).to(latents.device, latents.dtype) masked_video_latents = torch.zeros_like(latents).to(latents.device, latents.dtype) mask_input = torch.cat([mask_latents] * 2) if self.do_classifier_free_guidance else mask_latents masked_video_latents_input = ( torch.cat([masked_video_latents] * 2) if self.do_classifier_free_guidance else masked_video_latents ) inpaint_latents = torch.cat([mask_input, masked_video_latents_input], dim=1).to(latents.dtype) else: # Prepare mask latent variables video_length = video.shape[2] mask_condition = self.mask_processor.preprocess(rearrange(mask_video, "b c f h w -> (b f) c h w"), height=height, width=width) mask_condition = mask_condition.to(dtype=torch.float32) mask_condition = rearrange(mask_condition, "(b f) c h w -> b c f h w", f=video_length) if num_channels_transformer != num_channels_latents: mask_condition_tile = torch.tile(mask_condition, [1, 3, 1, 1, 1]) if masked_video_latents is None: masked_video = init_video * (mask_condition_tile < 0.5) + torch.ones_like(init_video) * (mask_condition_tile > 0.5) * -1 else: masked_video = masked_video_latents if self.transformer.resize_inpaint_mask_directly: _, masked_video_latents = self.prepare_mask_latents( None, masked_video, batch_size, height, width, prompt_embeds.dtype, device, generator, self.do_classifier_free_guidance, noise_aug_strength=noise_aug_strength, ) mask_latents = resize_mask(1 - mask_condition, masked_video_latents, self.vae.cache_mag_vae) mask_latents = mask_latents.to(masked_video_latents.device) * self.vae.config.scaling_factor else: mask_latents, masked_video_latents = self.prepare_mask_latents( mask_condition_tile, masked_video, batch_size, height, width, prompt_embeds.dtype, device, generator, self.do_classifier_free_guidance, noise_aug_strength=noise_aug_strength, ) mask_input = torch.cat([mask_latents] * 2) if self.do_classifier_free_guidance else mask_latents masked_video_latents_input = ( torch.cat([masked_video_latents] * 2) if self.do_classifier_free_guidance else masked_video_latents ) inpaint_latents = torch.cat([mask_input, masked_video_latents_input], dim=1).to(latents.dtype) else: inpaint_latents = None mask = torch.tile(mask_condition, [1, num_channels_latents, 1, 1, 1]) mask = F.interpolate(mask, size=latents.size()[-3:], mode='trilinear', align_corners=True).to(latents.device, latents.dtype) else: if num_channels_transformer != num_channels_latents: mask = torch.zeros_like(latents).to(latents.device, latents.dtype) masked_video_latents = torch.zeros_like(latents).to(latents.device, latents.dtype) mask_input = torch.cat([mask] * 2) if self.do_classifier_free_guidance else mask masked_video_latents_input = ( torch.cat([masked_video_latents] * 2) if self.do_classifier_free_guidance else masked_video_latents ) inpaint_latents = torch.cat([mask_input, masked_video_latents_input], dim=1).to(latents.dtype) else: mask = torch.zeros_like(init_video[:, :1]) mask = torch.tile(mask, [1, num_channels_latents, 1, 1, 1]) mask = F.interpolate(mask, size=latents.size()[-3:], mode='trilinear', align_corners=True).to(latents.device, latents.dtype) inpaint_latents = None if comfyui_progressbar: pbar.update(1) # Check that sizes of mask, masked image and latents match if num_channels_transformer != num_channels_latents: num_channels_mask = mask_latents.shape[1] num_channels_masked_image = masked_video_latents.shape[1] if num_channels_latents + num_channels_mask + num_channels_masked_image != self.transformer.config.in_channels: raise ValueError( f"Incorrect configuration settings! The config of `pipeline.transformer`: {self.transformer.config} expects" f" {self.transformer.config.in_channels} but received `num_channels_latents`: {num_channels_latents} +" f" `num_channels_mask`: {num_channels_mask} + `num_channels_masked_image`: {num_channels_masked_image}" f" = {num_channels_latents+num_channels_masked_image+num_channels_mask}. Please verify the config of" " `pipeline.transformer` or your `mask_image` or `image` input." ) # 8. 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) # 9 create image_rotary_emb, style embedding & time ids grid_height = height // 8 // self.transformer.config.patch_size grid_width = width // 8 // self.transformer.config.patch_size if self.transformer.config.get("time_position_encoding_type", "2d_rope") == "3d_rope": base_size_width = 720 // 8 // self.transformer.config.patch_size base_size_height = 480 // 8 // self.transformer.config.patch_size grid_crops_coords = get_resize_crop_region_for_grid( (grid_height, grid_width), base_size_width, base_size_height ) image_rotary_emb = get_3d_rotary_pos_embed( self.transformer.config.attention_head_dim, grid_crops_coords, grid_size=(grid_height, grid_width), temporal_size=latents.size(2), use_real=True, ) else: base_size = 512 // 8 // self.transformer.config.patch_size grid_crops_coords = get_resize_crop_region_for_grid( (grid_height, grid_width), base_size, base_size ) image_rotary_emb = get_2d_rotary_pos_embed( self.transformer.config.attention_head_dim, grid_crops_coords, (grid_height, grid_width) ) # Get other hunyuan params style = torch.tensor([0], device=device) target_size = target_size or (height, width) add_time_ids = list(original_size + target_size + crops_coords_top_left) add_time_ids = torch.tensor([add_time_ids], dtype=prompt_embeds.dtype) if self.do_classifier_free_guidance: prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds]) prompt_attention_mask = torch.cat([negative_prompt_attention_mask, prompt_attention_mask]) prompt_embeds_2 = torch.cat([negative_prompt_embeds_2, prompt_embeds_2]) prompt_attention_mask_2 = torch.cat([negative_prompt_attention_mask_2, prompt_attention_mask_2]) add_time_ids = torch.cat([add_time_ids] * 2, dim=0) style = torch.cat([style] * 2, dim=0) prompt_embeds = prompt_embeds.to(device=device) prompt_attention_mask = prompt_attention_mask.to(device=device) prompt_embeds_2 = prompt_embeds_2.to(device=device) prompt_attention_mask_2 = prompt_attention_mask_2.to(device=device) add_time_ids = add_time_ids.to(dtype=prompt_embeds.dtype, device=device).repeat( batch_size * num_images_per_prompt, 1 ) style = style.to(device=device).repeat(batch_size * num_images_per_prompt) torch.cuda.empty_cache() if self.enable_autocast_float8_transformer_flag: origin_weight_dtype = self.transformer.dtype self.transformer = self.transformer.to(torch.float8_e4m3fn) # 10. Denoising loop num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order self._num_timesteps = len(timesteps) with self.progress_bar(total=num_inference_steps) as progress_bar: for i, t in enumerate(timesteps): if self.interrupt: continue # 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 i < len(timesteps) * (1 - clip_apply_ratio) and clip_encoder_hidden_states_input is not None: clip_encoder_hidden_states_actual_input = torch.zeros_like(clip_encoder_hidden_states_input) clip_attention_mask_actual_input = torch.zeros_like(clip_attention_mask_input) else: clip_encoder_hidden_states_actual_input = clip_encoder_hidden_states_input clip_attention_mask_actual_input = clip_attention_mask_input # expand scalar t to 1-D tensor to match the 1st dim of latent_model_input t_expand = torch.tensor([t] * latent_model_input.shape[0], device=device).to( dtype=latent_model_input.dtype ) # predict the noise residual noise_pred = self.transformer( latent_model_input, t_expand, encoder_hidden_states=prompt_embeds, text_embedding_mask=prompt_attention_mask, encoder_hidden_states_t5=prompt_embeds_2, text_embedding_mask_t5=prompt_attention_mask_2, image_meta_size=add_time_ids, style=style, image_rotary_emb=image_rotary_emb, inpaint_latents=inpaint_latents, clip_encoder_hidden_states=clip_encoder_hidden_states_actual_input, clip_attention_mask=clip_attention_mask_actual_input, return_dict=False, )[0] if noise_pred.size()[1] != self.vae.config.latent_channels: noise_pred, _ = noise_pred.chunk(2, dim=1) # 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) if self.do_classifier_free_guidance and guidance_rescale > 0.0: # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale) # compute the previous noisy sample x_t -> x_t-1 latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0] if num_channels_transformer == 4: init_latents_proper = image_latents init_mask = mask if i < len(timesteps) - 1: noise_timestep = timesteps[i + 1] init_latents_proper = self.scheduler.add_noise( init_latents_proper, noise, torch.tensor([noise_timestep]) ) latents = (1 - init_mask) * init_latents_proper + init_mask * latents 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) prompt_embeds_2 = callback_outputs.pop("prompt_embeds_2", prompt_embeds_2) negative_prompt_embeds_2 = callback_outputs.pop( "negative_prompt_embeds_2", negative_prompt_embeds_2 ) if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0): progress_bar.update() if XLA_AVAILABLE: xm.mark_step() if comfyui_progressbar: pbar.update(1) if self.enable_autocast_float8_transformer_flag: self.transformer = self.transformer.to("cpu", origin_weight_dtype) torch.cuda.empty_cache() # Post-processing video = self.decode_latents(latents) # Convert to tensor if output_type == "latent": video = torch.from_numpy(video) # Offload all models self.maybe_free_model_hooks() if not return_dict: return video return EasyAnimatePipelineOutput(videos=video)