# Adapted from CogVideo # This source code is licensed under the license found in the # LICENSE file in the root directory of this source tree. # -------------------------------------------------------- # References: # CogVideo: https://github.com/THUDM/CogVideo # diffusers: https://github.com/huggingface/diffusers # -------------------------------------------------------- import inspect import math from typing import Callable, Dict, List, Optional, Tuple, Union import torch from diffusers.callbacks import MultiPipelineCallbacks, PipelineCallback from diffusers.utils import logging from diffusers.utils.torch_utils import randn_tensor from diffusers.video_processor import VideoProcessor from transformers import T5EncoderModel, T5Tokenizer from videosys.core.pipeline import VideoSysPipeline, VideoSysPipelineOutput from videosys.utils.utils import save_video from .autoencoder_kl import AutoencoderKLCogVideoX from .cogvideox_transformer_3d import CogVideoXTransformer3DModel from .retrieve_timesteps import retrieve_timesteps from .scheduling import CogVideoXDDIMScheduler, CogVideoXDPMScheduler logger = logging.get_logger(__name__) # pylint: disable=invalid-name from videosys.core.pab_mgr import ( PABConfig, get_diffusion_skip, get_diffusion_skip_timestep, set_pab_manager, skip_diffusion_timestep, update_steps, ) class CogVideoPABConfig(PABConfig): def __init__( self, steps: int = 150, spatial_broadcast: bool = True, spatial_threshold: list = [100, 850], spatial_gap: int = 2, temporal_broadcast: bool = True, temporal_threshold: list = [100, 850], temporal_gap: int = 4, cross_broadcast: bool = True, cross_threshold: list = [100, 850], cross_gap: int = 6, diffusion_skip: bool = False, diffusion_timestep_respacing: list = None, diffusion_skip_timestep: list = None, mlp_skip: bool = True, mlp_spatial_skip_config: dict = { 738: {"block": [0, 1, 2, 3, 4, 5, 6], "skip_count": 2}, 714: {"block": [0, 1, 2, 3, 4, 5, 6], "skip_count": 2}, }, mlp_temporal_skip_config: dict = { 738: {"block": [0, 1, 2, 3, 4, 5, 6], "skip_count": 2}, 714: {"block": [0, 1, 2, 3, 4, 5, 6], "skip_count": 2}, }, full_broadcast: bool = True, full_threshold: list = [100, 850], full_gap: int = 3, ): super().__init__( steps=steps, spatial_broadcast=spatial_broadcast, spatial_threshold=spatial_threshold, spatial_gap=spatial_gap, temporal_broadcast=temporal_broadcast, temporal_threshold=temporal_threshold, temporal_gap=temporal_gap, cross_broadcast=cross_broadcast, cross_threshold=cross_threshold, cross_gap=cross_gap, diffusion_skip=diffusion_skip, diffusion_timestep_respacing=diffusion_timestep_respacing, diffusion_skip_timestep=diffusion_skip_timestep, mlp_skip=mlp_skip, mlp_spatial_skip_config=mlp_spatial_skip_config, mlp_temporal_skip_config=mlp_temporal_skip_config, full_broadcast=full_broadcast, full_threshold=full_threshold, full_gap=full_gap, ) class CogVideoConfig: def __init__( self, world_size: int = 1, model_path: str = "THUDM/CogVideoX-2b", num_inference_steps: int = 50, guidance_scale: float = 6.0, enable_pab: bool = False, pab_config = CogVideoPABConfig() ): # ======= engine ======== self.world_size = world_size # ======= pipeline ======== self.pipeline_cls = CogVideoPipeline # ======= model ======== self.model_path = model_path self.num_inference_steps = num_inference_steps self.guidance_scale = guidance_scale self.enable_pab = enable_pab self.pab_config = pab_config class CogVideoPipeline(VideoSysPipeline): _callback_tensor_inputs = [ "latents", "prompt_embeds", "negative_prompt_embeds", ] def __init__( self, config: CogVideoConfig, tokenizer: Optional[T5Tokenizer] = None, text_encoder: Optional[T5EncoderModel] = None, vae: Optional[AutoencoderKLCogVideoX] = None, transformer: Optional[CogVideoXTransformer3DModel] = None, scheduler: Optional[CogVideoXDDIMScheduler] = None, device: torch.device = torch.device("cuda"), dtype: torch.dtype = torch.float16, ): super().__init__() self._config = config self._device = device self._dtype = dtype if transformer is None: transformer = CogVideoXTransformer3DModel.from_pretrained( config.model_path, subfolder="transformer", torch_dtype=self._dtype ) if vae is None: vae = AutoencoderKLCogVideoX.from_pretrained(config.model_path, subfolder="vae", torch_dtype=self._dtype) if tokenizer is None: tokenizer = T5Tokenizer.from_pretrained(config.model_path, subfolder="tokenizer") if text_encoder is None: text_encoder = T5EncoderModel.from_pretrained( config.model_path, subfolder="text_encoder", torch_dtype=self._dtype ) if scheduler is None: scheduler = CogVideoXDDIMScheduler.from_pretrained( config.model_path, subfolder="scheduler", ) # set eval and device self.set_eval_and_device(self._device, text_encoder, vae, transformer) self.register_modules( tokenizer=tokenizer, text_encoder=text_encoder, vae=vae, transformer=transformer, scheduler=scheduler ) if config.enable_pab: set_pab_manager(config.pab_config) self.vae_scale_factor_spatial = ( 2 ** (len(self.vae.config.block_out_channels) - 1) if hasattr(self, "vae") and self.vae is not None else 8 ) self.vae_scale_factor_temporal = ( self.vae.config.temporal_compression_ratio if hasattr(self, "vae") and self.vae is not None else 4 ) self.video_processor = VideoProcessor(vae_scale_factor=self.vae_scale_factor_spatial) def _get_t5_prompt_embeds( self, prompt: Union[str, List[str]] = None, num_videos_per_prompt: int = 1, max_sequence_length: int = 226, device: Optional[torch.device] = None, dtype: Optional[torch.dtype] = None, ): device = device or self._device dtype = dtype or self.text_encoder.dtype prompt = [prompt] if isinstance(prompt, str) else prompt batch_size = len(prompt) text_inputs = self.tokenizer( prompt, padding="max_length", max_length=max_sequence_length, truncation=True, add_special_tokens=True, return_tensors="pt", ) text_input_ids = text_inputs.input_ids untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids): removed_text = self.tokenizer.batch_decode(untruncated_ids[:, max_sequence_length - 1 : -1]) logger.warning( "The following part of your input was truncated because `max_sequence_length` is set to " f" {max_sequence_length} tokens: {removed_text}" ) prompt_embeds = self.text_encoder(text_input_ids.to(device))[0] prompt_embeds = prompt_embeds.to(dtype=dtype, device=device) # duplicate text embeddings for each generation per prompt, using mps friendly method _, seq_len, _ = prompt_embeds.shape prompt_embeds = prompt_embeds.repeat(1, num_videos_per_prompt, 1) prompt_embeds = prompt_embeds.view(batch_size * num_videos_per_prompt, seq_len, -1) return prompt_embeds def encode_prompt( self, prompt: Union[str, List[str]], negative_prompt: Optional[Union[str, List[str]]] = None, do_classifier_free_guidance: bool = True, num_videos_per_prompt: int = 1, prompt_embeds: Optional[torch.Tensor] = None, negative_prompt_embeds: Optional[torch.Tensor] = None, max_sequence_length: int = 226, device: Optional[torch.device] = None, dtype: Optional[torch.dtype] = None, ): r""" Encodes the prompt into text encoder hidden states. Args: prompt (`str` or `List[str]`, *optional*): prompt to be encoded 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`). do_classifier_free_guidance (`bool`, *optional*, defaults to `True`): Whether to use classifier free guidance or not. num_videos_per_prompt (`int`, *optional*, defaults to 1): Number of videos that should be generated per prompt. torch device to place the resulting embeddings on 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. device: (`torch.device`, *optional*): torch device dtype: (`torch.dtype`, *optional*): torch dtype """ device = device or self._device prompt = [prompt] if isinstance(prompt, str) else prompt if prompt is not None: batch_size = len(prompt) else: batch_size = prompt_embeds.shape[0] if prompt_embeds is None: prompt_embeds = self._get_t5_prompt_embeds( prompt=prompt, num_videos_per_prompt=num_videos_per_prompt, max_sequence_length=max_sequence_length, device=device, dtype=dtype, ) if do_classifier_free_guidance and negative_prompt_embeds is None: negative_prompt = negative_prompt or "" negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt if 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 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`." ) negative_prompt_embeds = self._get_t5_prompt_embeds( prompt=negative_prompt, num_videos_per_prompt=num_videos_per_prompt, max_sequence_length=max_sequence_length, device=device, dtype=dtype, ) return prompt_embeds, negative_prompt_embeds def prepare_latents( self, batch_size, num_channels_latents, num_frames, height, width, dtype, device, generator, latents=None ): shape = ( batch_size, (num_frames - 1) // self.vae_scale_factor_temporal + 1, num_channels_latents, height // self.vae_scale_factor_spatial, width // self.vae_scale_factor_spatial, ) 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 torch.cuda.empty_cache() return latents def decode_latents(self, latents: torch.Tensor, num_seconds: int): print("hhhhhhhh") latents = latents.permute(0, 2, 1, 3, 4) # [batch_size, num_channels, num_frames, height, width] latents = 1 / self.vae.config.scaling_factor * latents frames = [] num_frames = latents.size(2) segment_size = num_frames // num_frames # 每段处理的帧数 for i in range(num_frames): # 显存问题，逐帧处理 start_frame = i * segment_size end_frame = start_frame + segment_size if i < num_frames-1 else num_frames current_latents = latents[:, :, start_frame:end_frame, :, :] try: current_frames = self.vae.decode(current_latents).sample frames.append(current_frames) except RuntimeError as e: logger.error(f"CUDA out of memory error: {str(e)}") raise e # 清理缓存 torch.cuda.empty_cache() self.vae.clear_fake_context_parallel_cache() frames = torch.cat(frames, dim=2) return frames # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs def prepare_extra_step_kwargs(self, generator, eta): # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 # and should be between [0, 1] accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) extra_step_kwargs = {} if accepts_eta: extra_step_kwargs["eta"] = eta # check if the scheduler accepts generator accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys()) if accepts_generator: extra_step_kwargs["generator"] = generator return extra_step_kwargs # Copied from diffusers.pipelines.latte.pipeline_latte.LattePipeline.check_inputs def check_inputs( self, prompt, height, width, negative_prompt, callback_on_step_end_tensor_inputs, prompt_embeds=None, negative_prompt_embeds=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 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 is not None and negative_prompt_embeds is not None: raise ValueError( f"Cannot forward both `prompt`: {prompt} and `negative_prompt_embeds`:" f" {negative_prompt_embeds}. Please make sure to only forward one of the two." ) if negative_prompt is not None and negative_prompt_embeds is not None: raise ValueError( f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:" f" {negative_prompt_embeds}. Please make sure to only forward one of the two." ) if prompt_embeds is not None and negative_prompt_embeds is not None: if prompt_embeds.shape != negative_prompt_embeds.shape: raise ValueError( "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but" f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`" f" {negative_prompt_embeds.shape}." ) @property def guidance_scale(self): return self._guidance_scale @property def num_timesteps(self): return self._num_timesteps @property def interrupt(self): return self._interrupt @torch.no_grad() def generate( self, prompt: Optional[Union[str, List[str]]] = None, negative_prompt: Optional[Union[str, List[str]]] = None, height: int = 480, width: int = 720, num_frames: int = 48, num_inference_steps: int = 50, timesteps: Optional[List[int]] = None, guidance_scale: float = 6, use_dynamic_cfg: bool = False, num_videos_per_prompt: int = 1, eta: float = 0.0, generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, latents: Optional[torch.FloatTensor] = None, prompt_embeds: Optional[torch.FloatTensor] = None, negative_prompt_embeds: Optional[torch.FloatTensor] = None, output_type: str = "pil", 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"], max_sequence_length: int = 226, ) -> Union[VideoSysPipelineOutput, Tuple]: """ Function invoked when calling the pipeline for generation. Args: prompt (`str` or `List[str]`, *optional*): The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`. instead. 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`). height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): The height in pixels of the generated image. This is set to 1024 by default for the best results. width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): The width in pixels of the generated image. This is set to 1024 by default for the best results. num_frames (`int`, defaults to `48`): Number of frames to generate. Must be divisible by self.vae_scale_factor_temporal. Generated video will contain 1 extra frame because CogVideoX is conditioned with (num_seconds * fps + 1) frames where num_seconds is 6 and fps is 4. However, since videos can be saved at any fps, the only condition that needs to be satisfied is that of divisibility mentioned above. num_inference_steps (`int`, *optional*, defaults to 50): The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. timesteps (`List[int]`, *optional*): Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed will be used. Must be in descending order. guidance_scale (`float`, *optional*, defaults to 7.0): Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). `guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, usually at the expense of lower image quality. num_videos_per_prompt (`int`, *optional*, defaults to 1): The number of videos to generate per prompt. generator (`torch.Generator` or `List[torch.Generator]`, *optional*): One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation deterministic. latents (`torch.FloatTensor`, *optional*): Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image generation. Can be used to tweak the same generation with different prompts. If not provided, a latents tensor will ge generated by sampling using the supplied random `generator`. prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument. negative_prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument. output_type (`str`, *optional*, defaults to `"pil"`): The output format of the generate image. Choose between [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] instead of a plain tuple. callback_on_step_end (`Callable`, *optional*): A function that calls at the end of each denoising steps during the inference. The function is called with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by `callback_on_step_end_tensor_inputs`. callback_on_step_end_tensor_inputs (`List`, *optional*): The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the `._callback_tensor_inputs` attribute of your pipeline class. max_sequence_length (`int`, defaults to `226`): Maximum sequence length in encoded prompt. Must be consistent with `self.transformer.config.max_text_seq_length` otherwise may lead to poor results. Examples: Returns: [`~pipelines.cogvideo.pipeline_cogvideox.CogVideoXPipelineOutput`] or `tuple`: [`~pipelines.cogvideo.pipeline_cogvideox.CogVideoXPipelineOutput`] if `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is a list with the generated images. """ fps = 8 assert ( num_frames <= 48 and num_frames % fps == 0 and fps == 8 ), f"The number of frames must be divisible by {fps=} and less than 48 frames (for now). Other values are not supported in CogVideoX." if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)): callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs height = height or self.transformer.config.sample_size * self.vae_scale_factor_spatial width = width or self.transformer.config.sample_size * self.vae_scale_factor_spatial num_videos_per_prompt = 1 # 1. Check inputs. Raise error if not correct self.check_inputs( prompt, height, width, negative_prompt, callback_on_step_end_tensor_inputs, prompt_embeds, negative_prompt_embeds, ) self._guidance_scale = guidance_scale self._interrupt = False # 2. Default 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._device # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` # corresponds to doing no classifier free guidance. do_classifier_free_guidance = guidance_scale > 1.0 # 3. Encode input prompt prompt_embeds, negative_prompt_embeds = self.encode_prompt( prompt, negative_prompt, do_classifier_free_guidance, num_videos_per_prompt=num_videos_per_prompt, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_prompt_embeds, max_sequence_length=max_sequence_length, device=device, ) if do_classifier_free_guidance: prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0) # 4. Prepare timesteps timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, timesteps) self._num_timesteps = len(timesteps) # 5. Prepare latents. latent_channels = self.transformer.config.in_channels num_frames += 1 latents = self.prepare_latents( batch_size * num_videos_per_prompt, latent_channels, num_frames, height, width, prompt_embeds.dtype, device, generator, latents, ) # 6. Prepare extra step kwargs. extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) # 7. Denoising loop num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0) with self.progress_bar(total=num_inference_steps) as progress_bar: # for DPM-solver++ old_pred_original_sample = None for i, t in enumerate(timesteps): if self.interrupt: continue latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) # broadcast to batch dimension in a way that's compatible with ONNX/Core ML timestep = t.expand(latent_model_input.shape[0]) # predict noise model_output noise_pred = self.transformer( hidden_states=latent_model_input, encoder_hidden_states=prompt_embeds, timestep=timestep, return_dict=False, )[0] noise_pred = noise_pred.float() # perform guidance if use_dynamic_cfg: self._guidance_scale = 1 + guidance_scale * ( (1 - math.cos(math.pi * ((num_inference_steps - t.item()) / num_inference_steps) ** 5.0)) / 2 ) if do_classifier_free_guidance: noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond) # compute the previous noisy sample x_t -> x_t-1 if not isinstance(self.scheduler, CogVideoXDPMScheduler): latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0] else: latents, old_pred_original_sample = self.scheduler.step( noise_pred, old_pred_original_sample, t, timesteps[i - 1] if i > 0 else None, latents, **extra_step_kwargs, return_dict=False, ) latents = latents.to(prompt_embeds.dtype) # call the callback, if provided 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) if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0): progress_bar.update() if not output_type == "latent": video = self.decode_latents(latents, num_frames // fps) video = self.video_processor.postprocess_video(video=video, output_type=output_type) else: video = latents if not return_dict: return (video,) return VideoSysPipelineOutput(video=video) def save_video(self, video, output_path): save_video(video, output_path, fps=8)