# 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 numpy as np import torch from diffusers.callbacks import MultiPipelineCallbacks, PipelineCallback from diffusers.image_processor import VaeImageProcessor 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 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 tqdm import tqdm from transformers import (BertModel, BertTokenizer, CLIPImageProcessor, 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 class EasyAnimatePipeline_Multi_Text_Encoder(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.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 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, 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 + 1) # 5. Prepare latent variables num_channels_latents = self.transformer.config.in_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) # 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, )[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)