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import gc |
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import logging |
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import math |
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import os |
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import random |
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import sys |
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import types |
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from contextlib import contextmanager |
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from functools import partial |
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import numpy as np |
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import torch |
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import torch.cuda.amp as amp |
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import torch.distributed as dist |
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import torchvision.transforms.functional as TF |
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from tqdm import tqdm |
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from .distributed.fsdp import shard_model |
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from .distributed.sequence_parallel import sp_attn_forward, sp_dit_forward |
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from .distributed.util import get_world_size |
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from .modules.model import WanModel |
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from .modules.t5 import T5EncoderModel |
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from .modules.vae2_1 import Wan2_1_VAE |
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from .utils.fm_solvers import ( |
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FlowDPMSolverMultistepScheduler, |
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get_sampling_sigmas, |
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retrieve_timesteps, |
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) |
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from .utils.fm_solvers_unipc import FlowUniPCMultistepScheduler |
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class WanI2V: |
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def __init__( |
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self, |
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config, |
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checkpoint_dir, |
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device_id=0, |
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rank=0, |
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t5_fsdp=False, |
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dit_fsdp=False, |
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use_sp=False, |
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t5_cpu=False, |
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init_on_cpu=True, |
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convert_model_dtype=False, |
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): |
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r""" |
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Initializes the image-to-video generation model components. |
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Args: |
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config (EasyDict): |
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Object containing model parameters initialized from config.py |
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checkpoint_dir (`str`): |
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Path to directory containing model checkpoints |
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device_id (`int`, *optional*, defaults to 0): |
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Id of target GPU device |
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rank (`int`, *optional*, defaults to 0): |
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Process rank for distributed training |
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t5_fsdp (`bool`, *optional*, defaults to False): |
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Enable FSDP sharding for T5 model |
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dit_fsdp (`bool`, *optional*, defaults to False): |
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Enable FSDP sharding for DiT model |
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use_sp (`bool`, *optional*, defaults to False): |
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Enable distribution strategy of sequence parallel. |
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t5_cpu (`bool`, *optional*, defaults to False): |
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Whether to place T5 model on CPU. Only works without t5_fsdp. |
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init_on_cpu (`bool`, *optional*, defaults to True): |
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Enable initializing Transformer Model on CPU. Only works without FSDP or USP. |
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convert_model_dtype (`bool`, *optional*, defaults to False): |
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Convert DiT model parameters dtype to 'config.param_dtype'. |
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Only works without FSDP. |
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""" |
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self.device = torch.device(f"cuda:{device_id}") |
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self.config = config |
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self.rank = rank |
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self.t5_cpu = t5_cpu |
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self.init_on_cpu = init_on_cpu |
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self.num_train_timesteps = config.num_train_timesteps |
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self.boundary = config.boundary |
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self.param_dtype = config.param_dtype |
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if t5_fsdp or dit_fsdp or use_sp: |
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self.init_on_cpu = False |
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shard_fn = partial(shard_model, device_id=device_id) |
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self.text_encoder = T5EncoderModel( |
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text_len=config.text_len, |
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dtype=config.t5_dtype, |
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device=torch.device('cpu'), |
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checkpoint_path=os.path.join(checkpoint_dir, config.t5_checkpoint), |
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tokenizer_path=os.path.join(checkpoint_dir, config.t5_tokenizer), |
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shard_fn=shard_fn if t5_fsdp else None, |
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) |
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self.vae_stride = config.vae_stride |
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self.patch_size = config.patch_size |
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self.vae = Wan2_1_VAE( |
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vae_pth=os.path.join(checkpoint_dir, config.vae_checkpoint), |
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device=self.device) |
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logging.info(f"Creating WanModel from {checkpoint_dir}") |
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self.low_noise_model = WanModel.from_pretrained( |
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checkpoint_dir, subfolder=config.low_noise_checkpoint) |
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self.low_noise_model = self._configure_model( |
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model=self.low_noise_model, |
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use_sp=use_sp, |
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dit_fsdp=dit_fsdp, |
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shard_fn=shard_fn, |
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convert_model_dtype=convert_model_dtype) |
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self.high_noise_model = WanModel.from_pretrained( |
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checkpoint_dir, subfolder=config.high_noise_checkpoint) |
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self.high_noise_model = self._configure_model( |
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model=self.high_noise_model, |
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use_sp=use_sp, |
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dit_fsdp=dit_fsdp, |
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shard_fn=shard_fn, |
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convert_model_dtype=convert_model_dtype) |
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if use_sp: |
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self.sp_size = get_world_size() |
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else: |
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self.sp_size = 1 |
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self.sample_neg_prompt = config.sample_neg_prompt |
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def _configure_model(self, model, use_sp, dit_fsdp, shard_fn, |
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convert_model_dtype): |
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""" |
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Configures a model object. This includes setting evaluation modes, |
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applying distributed parallel strategy, and handling device placement. |
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Args: |
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model (torch.nn.Module): |
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The model instance to configure. |
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use_sp (`bool`): |
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Enable distribution strategy of sequence parallel. |
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dit_fsdp (`bool`): |
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Enable FSDP sharding for DiT model. |
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shard_fn (callable): |
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The function to apply FSDP sharding. |
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convert_model_dtype (`bool`): |
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Convert DiT model parameters dtype to 'config.param_dtype'. |
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Only works without FSDP. |
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Returns: |
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torch.nn.Module: |
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The configured model. |
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""" |
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model.eval().requires_grad_(False) |
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if use_sp: |
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for block in model.blocks: |
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block.self_attn.forward = types.MethodType( |
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sp_attn_forward, block.self_attn) |
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model.forward = types.MethodType(sp_dit_forward, model) |
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if dist.is_initialized(): |
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dist.barrier() |
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if dit_fsdp: |
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model = shard_fn(model) |
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else: |
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if convert_model_dtype: |
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model.to(self.param_dtype) |
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if not self.init_on_cpu: |
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model.to(self.device) |
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return model |
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def _prepare_model_for_timestep(self, t, boundary, offload_model): |
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r""" |
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Prepares and returns the required model for the current timestep. |
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Args: |
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t (torch.Tensor): |
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current timestep. |
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boundary (`int`): |
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The timestep threshold. If `t` is at or above this value, |
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the `high_noise_model` is considered as the required model. |
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offload_model (`bool`): |
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A flag intended to control the offloading behavior. |
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Returns: |
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torch.nn.Module: |
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The active model on the target device for the current timestep. |
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""" |
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if t.item() >= boundary: |
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required_model_name = 'high_noise_model' |
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offload_model_name = 'low_noise_model' |
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else: |
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required_model_name = 'low_noise_model' |
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offload_model_name = 'high_noise_model' |
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if offload_model or self.init_on_cpu: |
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if next(getattr( |
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self, |
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offload_model_name).parameters()).device.type == 'cuda': |
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getattr(self, offload_model_name).to('cpu') |
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if next(getattr( |
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self, |
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required_model_name).parameters()).device.type == 'cpu': |
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getattr(self, required_model_name).to(self.device) |
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return getattr(self, required_model_name) |
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def generate(self, |
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input_prompt, |
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img, |
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max_area=720 * 1280, |
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frame_num=81, |
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shift=5.0, |
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sample_solver='unipc', |
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sampling_steps=40, |
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guide_scale=5.0, |
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n_prompt="", |
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seed=-1, |
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offload_model=True): |
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r""" |
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Generates video frames from input image and text prompt using diffusion process. |
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Args: |
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input_prompt (`str`): |
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Text prompt for content generation. |
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img (PIL.Image.Image): |
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Input image tensor. Shape: [3, H, W] |
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max_area (`int`, *optional*, defaults to 720*1280): |
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Maximum pixel area for latent space calculation. Controls video resolution scaling |
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frame_num (`int`, *optional*, defaults to 81): |
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How many frames to sample from a video. The number should be 4n+1 |
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shift (`float`, *optional*, defaults to 5.0): |
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Noise schedule shift parameter. Affects temporal dynamics |
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[NOTE]: If you want to generate a 480p video, it is recommended to set the shift value to 3.0. |
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sample_solver (`str`, *optional*, defaults to 'unipc'): |
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Solver used to sample the video. |
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sampling_steps (`int`, *optional*, defaults to 40): |
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Number of diffusion sampling steps. Higher values improve quality but slow generation |
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guide_scale (`float` or tuple[`float`], *optional*, defaults 5.0): |
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Classifier-free guidance scale. Controls prompt adherence vs. creativity. |
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If tuple, the first guide_scale will be used for low noise model and |
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the second guide_scale will be used for high noise model. |
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n_prompt (`str`, *optional*, defaults to ""): |
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Negative prompt for content exclusion. If not given, use `config.sample_neg_prompt` |
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seed (`int`, *optional*, defaults to -1): |
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Random seed for noise generation. If -1, use random seed |
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offload_model (`bool`, *optional*, defaults to True): |
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If True, offloads models to CPU during generation to save VRAM |
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Returns: |
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torch.Tensor: |
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Generated video frames tensor. Dimensions: (C, N H, W) where: |
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- C: Color channels (3 for RGB) |
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- N: Number of frames (81) |
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- H: Frame height (from max_area) |
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- W: Frame width from max_area) |
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""" |
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guide_scale = (guide_scale, guide_scale) if isinstance( |
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guide_scale, float) else guide_scale |
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img = TF.to_tensor(img).sub_(0.5).div_(0.5).to(self.device) |
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F = frame_num |
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h, w = img.shape[1:] |
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aspect_ratio = h / w |
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lat_h = round( |
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np.sqrt(max_area * aspect_ratio) // self.vae_stride[1] // |
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self.patch_size[1] * self.patch_size[1]) |
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lat_w = round( |
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np.sqrt(max_area / aspect_ratio) // self.vae_stride[2] // |
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self.patch_size[2] * self.patch_size[2]) |
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h = lat_h * self.vae_stride[1] |
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w = lat_w * self.vae_stride[2] |
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max_seq_len = ((F - 1) // self.vae_stride[0] + 1) * lat_h * lat_w // ( |
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self.patch_size[1] * self.patch_size[2]) |
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max_seq_len = int(math.ceil(max_seq_len / self.sp_size)) * self.sp_size |
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seed = seed if seed >= 0 else random.randint(0, sys.maxsize) |
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seed_g = torch.Generator(device=self.device) |
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seed_g.manual_seed(seed) |
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noise = torch.randn( |
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16, |
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(F - 1) // self.vae_stride[0] + 1, |
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lat_h, |
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lat_w, |
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dtype=torch.float32, |
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generator=seed_g, |
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device=self.device) |
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msk = torch.ones(1, F, lat_h, lat_w, device=self.device) |
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msk[:, 1:] = 0 |
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msk = torch.concat([ |
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torch.repeat_interleave(msk[:, 0:1], repeats=4, dim=1), msk[:, 1:] |
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], |
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dim=1) |
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msk = msk.view(1, msk.shape[1] // 4, 4, lat_h, lat_w) |
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msk = msk.transpose(1, 2)[0] |
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if n_prompt == "": |
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n_prompt = self.sample_neg_prompt |
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if not self.t5_cpu: |
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self.text_encoder.model.to(self.device) |
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context = self.text_encoder([input_prompt], self.device) |
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context_null = self.text_encoder([n_prompt], self.device) |
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if offload_model: |
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self.text_encoder.model.cpu() |
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else: |
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context = self.text_encoder([input_prompt], torch.device('cpu')) |
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context_null = self.text_encoder([n_prompt], torch.device('cpu')) |
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context = [t.to(self.device) for t in context] |
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context_null = [t.to(self.device) for t in context_null] |
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y = self.vae.encode([ |
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torch.concat([ |
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torch.nn.functional.interpolate( |
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img[None].cpu(), size=(h, w), mode='bicubic').transpose( |
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0, 1), |
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torch.zeros(3, F - 1, h, w) |
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], |
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dim=1).to(self.device) |
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])[0] |
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y = torch.concat([msk, y]) |
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@contextmanager |
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def noop_no_sync(): |
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yield |
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no_sync_low_noise = getattr(self.low_noise_model, 'no_sync', |
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noop_no_sync) |
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no_sync_high_noise = getattr(self.high_noise_model, 'no_sync', |
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noop_no_sync) |
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with ( |
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torch.amp.autocast('cuda', dtype=self.param_dtype), |
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torch.no_grad(), |
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no_sync_low_noise(), |
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no_sync_high_noise(), |
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): |
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boundary = self.boundary * self.num_train_timesteps |
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if sample_solver == 'unipc': |
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sample_scheduler = FlowUniPCMultistepScheduler( |
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num_train_timesteps=self.num_train_timesteps, |
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shift=1, |
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use_dynamic_shifting=False) |
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sample_scheduler.set_timesteps( |
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sampling_steps, device=self.device, shift=shift) |
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timesteps = sample_scheduler.timesteps |
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elif sample_solver == 'dpm++': |
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sample_scheduler = FlowDPMSolverMultistepScheduler( |
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num_train_timesteps=self.num_train_timesteps, |
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shift=1, |
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use_dynamic_shifting=False) |
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sampling_sigmas = get_sampling_sigmas(sampling_steps, shift) |
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timesteps, _ = retrieve_timesteps( |
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sample_scheduler, |
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device=self.device, |
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sigmas=sampling_sigmas) |
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else: |
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raise NotImplementedError("Unsupported solver.") |
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latent = noise |
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arg_c = { |
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'context': [context[0]], |
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'seq_len': max_seq_len, |
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'y': [y], |
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} |
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arg_null = { |
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'context': context_null, |
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'seq_len': max_seq_len, |
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'y': [y], |
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} |
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if offload_model: |
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torch.cuda.empty_cache() |
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for _, t in enumerate(tqdm(timesteps)): |
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latent_model_input = [latent.to(self.device)] |
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timestep = [t] |
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timestep = torch.stack(timestep).to(self.device) |
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model = self._prepare_model_for_timestep( |
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t, boundary, offload_model) |
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sample_guide_scale = guide_scale[1] if t.item( |
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) >= boundary else guide_scale[0] |
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noise_pred_cond = model( |
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latent_model_input, t=timestep, **arg_c)[0] |
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if offload_model: |
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torch.cuda.empty_cache() |
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noise_pred_uncond = model( |
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latent_model_input, t=timestep, **arg_null)[0] |
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if offload_model: |
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torch.cuda.empty_cache() |
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noise_pred = noise_pred_uncond + sample_guide_scale * ( |
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noise_pred_cond - noise_pred_uncond) |
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temp_x0 = sample_scheduler.step( |
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noise_pred.unsqueeze(0), |
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t, |
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latent.unsqueeze(0), |
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return_dict=False, |
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generator=seed_g)[0] |
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latent = temp_x0.squeeze(0) |
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x0 = [latent] |
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del latent_model_input, timestep |
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if offload_model: |
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self.low_noise_model.cpu() |
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self.high_noise_model.cpu() |
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torch.cuda.empty_cache() |
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if self.rank == 0: |
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videos = self.vae.decode(x0) |
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del noise, latent, x0 |
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del sample_scheduler |
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if offload_model: |
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gc.collect() |
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torch.cuda.synchronize() |
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if dist.is_initialized(): |
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dist.barrier() |
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return videos[0] if self.rank == 0 else None |
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|
