# 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 import re import urllib.parse as ul from dataclasses import dataclass from typing import Callable, Dict, List, Optional, Tuple, Union import numpy as np import torch import torch.nn.functional as F from diffusers import DiffusionPipeline, ImagePipelineOutput 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 import StableDiffusionPipelineOutput from diffusers.pipelines.stable_diffusion.safety_checker import \ StableDiffusionSafetyChecker from diffusers.schedulers import DDIMScheduler, DPMSolverMultistepScheduler from diffusers.utils import (BACKENDS_MAPPING, BaseOutput, deprecate, is_bs4_available, is_ftfy_available, 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, T5EncoderModel, T5Tokenizer) from ..models import AutoencoderKLMagvit, EasyAnimateTransformer3DModel from .pipeline_easyanimate import EasyAnimatePipelineOutput 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 class EasyAnimatePipeline_Multi_Text_Encoder_Control(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. """ model_cpu_offload_seq = "text_encoder->text_encoder_2->transformer->vae" _optional_components = [ "safety_checker", "feature_extractor", "text_encoder_2", "tokenizer_2", "text_encoder", "tokenizer", ] _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 ): 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 ) 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.StableDiffusionPipeline.prepare_latents def prepare_latents(self, batch_size, num_channels_latents, video_length, height, width, dtype, device, generator, latents=None): 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 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_control_latents( self, mask, masked_image, batch_size, height, width, dtype, device, generator, do_classifier_free_guidance ): # 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) 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 if masked_image is not None: masked_image = masked_image.to(device=device, dtype=self.vae.dtype) 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: masked_image_latents = None return mask, masked_image_latents 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, height: Optional[int] = None, width: Optional[int] = None, control_video: Union[torch.FloatTensor] = 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), comfyui_progressbar: bool = False, ): r""" Generates images or video using the EasyAnimate pipeline based on the provided prompts. Examples: prompt (`str` or `List[str]`, *optional*): Text prompts to guide the image or video generation. If not provided, use `prompt_embeds` instead. video_length (`int`, *optional*): Length of the generated video (in frames). height (`int`, *optional*): Height of the generated image in pixels. width (`int`, *optional*): Width of the generated image in pixels. num_inference_steps (`int`, *optional*, defaults to 50): Number of denoising steps during generation. More steps generally yield higher quality images but slow down inference. guidance_scale (`float`, *optional*, defaults to 5.0): Encourages the model to align outputs with prompts. A higher value may decrease image quality. negative_prompt (`str` or `List[str]`, *optional*): Prompts indicating what to exclude in generation. If not specified, use `negative_prompt_embeds`. num_images_per_prompt (`int`, *optional*, defaults to 1): Number of images to generate for each prompt. eta (`float`, *optional*, defaults to 0.0): Applies to DDIM scheduling. Controlled by the eta parameter from the related literature. generator (`torch.Generator` or `List[torch.Generator]`, *optional*): A generator to ensure reproducibility in image generation. latents (`torch.Tensor`, *optional*): Predefined latent tensors to condition generation. prompt_embeds (`torch.Tensor`, *optional*): Text embeddings for the prompts. Overrides prompt string inputs for more flexibility. prompt_embeds_2 (`torch.Tensor`, *optional*): Secondary text embeddings to supplement or replace the initial prompt embeddings. negative_prompt_embeds (`torch.Tensor`, *optional*): Embeddings for negative prompts. Overrides string inputs if defined. negative_prompt_embeds_2 (`torch.Tensor`, *optional*): Secondary embeddings for negative prompts, similar to `negative_prompt_embeds`. prompt_attention_mask (`torch.Tensor`, *optional*): Attention mask for the primary prompt embeddings. prompt_attention_mask_2 (`torch.Tensor`, *optional*): Attention mask for the secondary prompt embeddings. negative_prompt_attention_mask (`torch.Tensor`, *optional*): Attention mask for negative prompt embeddings. negative_prompt_attention_mask_2 (`torch.Tensor`, *optional*): Attention mask for secondary negative prompt embeddings. output_type (`str`, *optional*, defaults to "latent"): Format of the generated output, either as a PIL image or as a NumPy array. return_dict (`bool`, *optional*, defaults to `True`): If `True`, returns a structured output. Otherwise returns a simple tuple. callback_on_step_end (`Callable`, *optional*): Functions called at the end of each denoising step. callback_on_step_end_tensor_inputs (`List[str]`, *optional*): Tensor names to be included in callback function calls. guidance_rescale (`float`, *optional*, defaults to 0.0): Adjusts noise levels based on guidance scale. original_size (`Tuple[int, int]`, *optional*, defaults to `(1024, 1024)`): Original dimensions of the output. target_size (`Tuple[int, int]`, *optional*): Desired output dimensions for calculations. crops_coords_top_left (`Tuple[int, int]`, *optional*, defaults to `(0, 0)`): Coordinates for cropping. Returns: [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`: If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned, otherwise a `tuple` is returned where the first element is a list with the generated images and the second element is a list of `bool`s indicating whether the corresponding generated image contains "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. Prepare timesteps self.scheduler.set_timesteps(num_inference_steps, device=device) timesteps = self.scheduler.timesteps if comfyui_progressbar: from comfy.utils import ProgressBar pbar = ProgressBar(num_inference_steps + 2) # 5. Prepare latent variables num_channels_latents = self.vae.config.latent_channels latents = self.prepare_latents( batch_size * num_images_per_prompt, num_channels_latents, video_length, height, width, prompt_embeds.dtype, device, generator, latents, ) if comfyui_progressbar: pbar.update(1) if control_video is not None: video_length = control_video.shape[2] control_video = self.image_processor.preprocess(rearrange(control_video, "b c f h w -> (b f) c h w"), height=height, width=width) control_video = control_video.to(dtype=torch.float32) control_video = rearrange(control_video, "(b f) c h w -> b c f h w", f=video_length) else: control_video = None control_video_latents = self.prepare_control_latents( None, control_video, batch_size, height, width, prompt_embeds.dtype, device, generator, self.do_classifier_free_guidance )[1] control_latents = ( torch.cat([control_video_latents] * 2) if self.do_classifier_free_guidance else control_video_latents ) if comfyui_progressbar: pbar.update(1) # 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 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) # To latents.device 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) # 8. 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) # 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, return_dict=False, control_latents=control_latents, )[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 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)