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# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved.
import gc
import logging
import math
import os
import random
import sys
import types
from contextlib import contextmanager
from functools import partial
import torch
import torch.cuda.amp as amp
import torch.distributed as dist
from tqdm import tqdm
from .distributed.fsdp import shard_model
from .modules.model import WanModel
from .modules.t5 import T5EncoderModel
from .modules.vae import WanVAE
from .utils.fm_solvers import (FlowDPMSolverMultistepScheduler,
get_sampling_sigmas, retrieve_timesteps)
from .utils.fm_solvers_unipc import FlowUniPCMultistepScheduler
class WanT2V:
def __init__(
self,
config,
checkpoint_dir,
device_id=0,
rank=0,
t5_fsdp=False,
dit_fsdp=False,
use_usp=False,
t5_cpu=False,
):
r"""
Initializes the Wan text-to-video generation model components.
Args:
config (EasyDict):
Object containing model parameters initialized from config.py
checkpoint_dir (`str`):
Path to directory containing model checkpoints
device_id (`int`, *optional*, defaults to 0):
Id of target GPU device
rank (`int`, *optional*, defaults to 0):
Process rank for distributed training
t5_fsdp (`bool`, *optional*, defaults to False):
Enable FSDP sharding for T5 model
dit_fsdp (`bool`, *optional*, defaults to False):
Enable FSDP sharding for DiT model
use_usp (`bool`, *optional*, defaults to False):
Enable distribution strategy of USP.
t5_cpu (`bool`, *optional*, defaults to False):
Whether to place T5 model on CPU. Only works without t5_fsdp.
"""
self.device = torch.device(f"cuda:{device_id}")
self.config = config
self.rank = rank
self.t5_cpu = t5_cpu
self.num_train_timesteps = config.num_train_timesteps
self.param_dtype = config.param_dtype
shard_fn = partial(shard_model, device_id=device_id)
self.text_encoder = T5EncoderModel(
text_len=config.text_len,
dtype=config.t5_dtype,
device=torch.device('cpu'),
checkpoint_path=os.path.join(checkpoint_dir, config.t5_checkpoint),
tokenizer_path=os.path.join(checkpoint_dir, config.t5_tokenizer),
shard_fn=shard_fn if t5_fsdp else None)
self.vae_stride = config.vae_stride
self.patch_size = config.patch_size
self.vae = WanVAE(
vae_pth=os.path.join(checkpoint_dir, config.vae_checkpoint),
device=self.device)
logging.info(f"Creating WanModel from {checkpoint_dir}")
self.model = WanModel.from_pretrained(checkpoint_dir)
self.model.eval().requires_grad_(False)
if use_usp:
from xfuser.core.distributed import \
get_sequence_parallel_world_size
from .distributed.xdit_context_parallel import (usp_attn_forward,
usp_dit_forward)
for block in self.model.blocks:
block.self_attn.forward = types.MethodType(
usp_attn_forward, block.self_attn)
self.model.forward = types.MethodType(usp_dit_forward, self.model)
self.sp_size = get_sequence_parallel_world_size()
else:
self.sp_size = 1
if dist.is_initialized():
dist.barrier()
if dit_fsdp:
self.model = shard_fn(self.model)
else:
self.model.to(self.device)
self.sample_neg_prompt = config.sample_neg_prompt
def generate(self,
input_prompt,
size=(1280, 720),
frame_num=81,
shift=5.0,
sample_solver='unipc',
sampling_steps=50,
guide_scale=5.0,
n_prompt="",
seed=-1,
offload_model=True):
r"""
Generates video frames from text prompt using diffusion process.
Args:
input_prompt (`str`):
Text prompt for content generation
size (tupele[`int`], *optional*, defaults to (1280,720)):
Controls video resolution, (width,height).
frame_num (`int`, *optional*, defaults to 81):
How many frames to sample from a video. The number should be 4n+1
shift (`float`, *optional*, defaults to 5.0):
Noise schedule shift parameter. Affects temporal dynamics
sample_solver (`str`, *optional*, defaults to 'unipc'):
Solver used to sample the video.
sampling_steps (`int`, *optional*, defaults to 40):
Number of diffusion sampling steps. Higher values improve quality but slow generation
guide_scale (`float`, *optional*, defaults 5.0):
Classifier-free guidance scale. Controls prompt adherence vs. creativity
n_prompt (`str`, *optional*, defaults to ""):
Negative prompt for content exclusion. If not given, use `config.sample_neg_prompt`
seed (`int`, *optional*, defaults to -1):
Random seed for noise generation. If -1, use random seed.
offload_model (`bool`, *optional*, defaults to True):
If True, offloads models to CPU during generation to save VRAM
Returns:
torch.Tensor:
Generated video frames tensor. Dimensions: (C, N H, W) where:
- C: Color channels (3 for RGB)
- N: Number of frames (81)
- H: Frame height (from size)
- W: Frame width from size)
"""
# preprocess
F = frame_num
target_shape = (self.vae.model.z_dim, (F - 1) // self.vae_stride[0] + 1,
size[1] // self.vae_stride[1],
size[0] // self.vae_stride[2])
seq_len = math.ceil((target_shape[2] * target_shape[3]) /
(self.patch_size[1] * self.patch_size[2]) *
target_shape[1] / self.sp_size) * self.sp_size
if n_prompt == "":
n_prompt = self.sample_neg_prompt
seed = seed if seed >= 0 else random.randint(0, sys.maxsize)
seed_g = torch.Generator(device=self.device)
seed_g.manual_seed(seed)
if not self.t5_cpu:
self.text_encoder.model.to(self.device)
context = self.text_encoder([input_prompt], self.device)
context_null = self.text_encoder([n_prompt], self.device)
if offload_model:
self.text_encoder.model.cpu()
else:
context = self.text_encoder([input_prompt], torch.device('cpu'))
context_null = self.text_encoder([n_prompt], torch.device('cpu'))
context = [t.to(self.device) for t in context]
context_null = [t.to(self.device) for t in context_null]
noise = [
torch.randn(
target_shape[0],
target_shape[1],
target_shape[2],
target_shape[3],
dtype=torch.float32,
device=self.device,
generator=seed_g)
]
@contextmanager
def noop_no_sync():
yield
no_sync = getattr(self.model, 'no_sync', noop_no_sync)
# evaluation mode
with amp.autocast(dtype=self.param_dtype), torch.no_grad(), no_sync():
if sample_solver == 'unipc':
sample_scheduler = FlowUniPCMultistepScheduler(
num_train_timesteps=self.num_train_timesteps,
shift=1,
use_dynamic_shifting=False)
sample_scheduler.set_timesteps(
sampling_steps, device=self.device, shift=shift)
timesteps = sample_scheduler.timesteps
elif sample_solver == 'dpm++':
sample_scheduler = FlowDPMSolverMultistepScheduler(
num_train_timesteps=self.num_train_timesteps,
shift=1,
use_dynamic_shifting=False)
sampling_sigmas = get_sampling_sigmas(sampling_steps, shift)
timesteps, _ = retrieve_timesteps(
sample_scheduler,
device=self.device,
sigmas=sampling_sigmas)
else:
raise NotImplementedError("Unsupported solver.")
# sample videos
latents = noise
arg_c = {'context': context, 'seq_len': seq_len}
arg_null = {'context': context_null, 'seq_len': seq_len}
for _, t in enumerate(tqdm(timesteps)):
latent_model_input = latents
timestep = [t]
timestep = torch.stack(timestep)
self.model.to(self.device)
noise_pred_cond = self.model(
latent_model_input, t=timestep, **arg_c)[0]
noise_pred_uncond = self.model(
latent_model_input, t=timestep, **arg_null)[0]
noise_pred = noise_pred_uncond + guide_scale * (
noise_pred_cond - noise_pred_uncond)
temp_x0 = sample_scheduler.step(
noise_pred.unsqueeze(0),
t,
latents[0].unsqueeze(0),
return_dict=False,
generator=seed_g)[0]
latents = [temp_x0.squeeze(0)]
x0 = latents
if offload_model:
self.model.cpu()
torch.cuda.empty_cache()
if self.rank == 0:
videos = self.vae.decode(x0)
del noise, latents
del sample_scheduler
if offload_model:
gc.collect()
torch.cuda.synchronize()
if dist.is_initialized():
dist.barrier()
return videos[0] if self.rank == 0 else None
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