mirror of
https://github.com/Wan-Video/Wan2.1.git
synced 2025-11-04 06:15:17 +00:00
fdbc5f0588
Add --vae_cpu argument to enable VAE offloading for consumer GPUs with limited VRAM. When enabled, VAE initializes on CPU and moves to GPU only when needed for encoding/decoding operations. Key changes: - Add --vae_cpu argument to generate.py (mirrors --t5_cpu pattern) - Update all 4 pipelines (T2V, I2V, FLF2V, VACE) with conditional VAE offloading - Fix DiT offloading to free VRAM before T5 loading when offload_model=True - Handle VAE scale tensors (mean/std) during device transfers Benefits: - Saves ~100-200MB VRAM without performance degradation - Enables T2V-1.3B on more consumer GPUs (tested on 11.49GB GPU) - Backward compatible (default=False) - Consistent with existing --t5_cpu flag Test results on 11.49 GiB VRAM GPU: - Baseline: OOM (needed 80MB, only 85MB free) - With --vae_cpu: Success - With --t5_cpu: Success - With both flags: Success (maximum VRAM savings) 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
298 lines
11 KiB
Python
298 lines
11 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 torch
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import torch.cuda.amp as amp
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import torch.distributed as dist
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from tqdm import tqdm
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from .distributed.fsdp import shard_model
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from .modules.model import WanModel
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from .modules.t5 import T5EncoderModel
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from .modules.vae import WanVAE
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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 WanT2V:
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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_usp=False,
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t5_cpu=False,
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vae_cpu=False,
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):
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r"""
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Initializes the Wan text-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_usp (`bool`, *optional*, defaults to False):
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Enable distribution strategy of USP.
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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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vae_cpu (`bool`, *optional*, defaults to False):
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Whether to place VAE model on CPU to save VRAM. VAE will be moved to GPU only when needed.
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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.vae_cpu = vae_cpu
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self.num_train_timesteps = config.num_train_timesteps
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self.param_dtype = config.param_dtype
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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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self.vae_stride = config.vae_stride
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self.patch_size = config.patch_size
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# Initialize VAE on CPU if vae_cpu=True to save VRAM during pipeline initialization and diffusion loop
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# VAE is only needed at the end for decoding latents to pixels
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vae_device = torch.device('cpu') if vae_cpu else self.device
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self.vae = WanVAE(
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vae_pth=os.path.join(checkpoint_dir, config.vae_checkpoint),
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device=vae_device)
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logging.info(f"Creating WanModel from {checkpoint_dir}")
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self.model = WanModel.from_pretrained(checkpoint_dir)
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self.model.eval().requires_grad_(False)
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if use_usp:
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from xfuser.core.distributed import get_sequence_parallel_world_size
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from .distributed.xdit_context_parallel import (
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usp_attn_forward,
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usp_dit_forward,
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)
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for block in self.model.blocks:
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block.self_attn.forward = types.MethodType(
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usp_attn_forward, block.self_attn)
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self.model.forward = types.MethodType(usp_dit_forward, self.model)
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self.sp_size = get_sequence_parallel_world_size()
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else:
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self.sp_size = 1
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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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self.model = shard_fn(self.model)
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else:
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self.model.to(self.device)
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self.sample_neg_prompt = config.sample_neg_prompt
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def generate(self,
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input_prompt,
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size=(1280, 720),
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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=50,
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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 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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size (tupele[`int`], *optional*, defaults to (1280,720)):
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Controls video resolution, (width,height).
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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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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`, *optional*, defaults 5.0):
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Classifier-free guidance scale. Controls prompt adherence vs. creativity
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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 size)
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- W: Frame width from size)
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"""
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# preprocess
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F = frame_num
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target_shape = (self.vae.model.z_dim, (F - 1) // self.vae_stride[0] + 1,
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size[1] // self.vae_stride[1],
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size[0] // self.vae_stride[2])
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seq_len = math.ceil((target_shape[2] * target_shape[3]) /
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(self.patch_size[1] * self.patch_size[2]) *
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target_shape[1] / self.sp_size) * self.sp_size
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if n_prompt == "":
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n_prompt = self.sample_neg_prompt
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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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if not self.t5_cpu:
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# Offload DiT to CPU first if needed to make room for T5
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if offload_model:
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self.model.cpu()
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torch.cuda.empty_cache()
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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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torch.cuda.empty_cache()
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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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noise = [
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torch.randn(
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target_shape[0],
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target_shape[1],
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target_shape[2],
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target_shape[3],
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dtype=torch.float32,
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device=self.device,
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generator=seed_g)
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]
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@contextmanager
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def noop_no_sync():
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yield
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no_sync = getattr(self.model, 'no_sync', noop_no_sync)
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# evaluation mode
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with amp.autocast(dtype=self.param_dtype), torch.no_grad(), no_sync():
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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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latents = noise
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arg_c = {'context': context, 'seq_len': seq_len}
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arg_null = {'context': context_null, 'seq_len': seq_len}
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for _, t in enumerate(tqdm(timesteps)):
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latent_model_input = latents
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timestep = [t]
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timestep = torch.stack(timestep)
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self.model.to(self.device)
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noise_pred_cond = self.model(
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latent_model_input, t=timestep, **arg_c)[0]
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noise_pred_uncond = self.model(
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latent_model_input, t=timestep, **arg_null)[0]
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noise_pred = noise_pred_uncond + 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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latents[0].unsqueeze(0),
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return_dict=False,
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generator=seed_g)[0]
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latents = [temp_x0.squeeze(0)]
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x0 = latents
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if offload_model:
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self.model.cpu()
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torch.cuda.empty_cache()
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if self.rank == 0:
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# Move VAE to GPU for decoding if it's on CPU
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if self.vae_cpu:
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self.vae.model.to(self.device)
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# Also move scale tensors to GPU
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self.vae.mean = self.vae.mean.to(self.device)
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self.vae.std = self.vae.std.to(self.device)
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self.vae.scale = [self.vae.mean, 1.0 / self.vae.std]
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videos = self.vae.decode(x0)
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# Offload VAE back to CPU after decoding to free VRAM
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if self.vae_cpu and offload_model:
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self.vae.model.cpu()
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self.vae.mean = self.vae.mean.cpu()
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self.vae.std = self.vae.std.cpu()
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self.vae.scale = [self.vae.mean, 1.0 / self.vae.std]
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torch.cuda.empty_cache()
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del noise, latents
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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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