wehub-resource-sync
432 lines
16 KiB
Python
432 lines
16 KiB
Python
# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved.
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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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# preprocess
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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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# preprocess
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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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# evaluation mode
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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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# sample videos
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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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