wan_14b_text_to_video_usp.py

import torch
from diffsynth import ModelManager, WanVideoPipeline, save_video, VideoData
from modelscope import snapshot_download
import torch.distributed as dist


# Download models
snapshot_download("Wan-AI/Wan2.1-T2V-14B", local_dir="models/Wan-AI/Wan2.1-T2V-14B")

# Load models
model_manager = ModelManager(device="cpu")
model_manager.load_models(
    [
        [
            "models/Wan-AI/Wan2.1-T2V-14B/diffusion_pytorch_model-00001-of-00006.safetensors",
            "models/Wan-AI/Wan2.1-T2V-14B/diffusion_pytorch_model-00002-of-00006.safetensors",
            "models/Wan-AI/Wan2.1-T2V-14B/diffusion_pytorch_model-00003-of-00006.safetensors",
            "models/Wan-AI/Wan2.1-T2V-14B/diffusion_pytorch_model-00004-of-00006.safetensors",
            "models/Wan-AI/Wan2.1-T2V-14B/diffusion_pytorch_model-00005-of-00006.safetensors",
            "models/Wan-AI/Wan2.1-T2V-14B/diffusion_pytorch_model-00006-of-00006.safetensors",
        ],
        "models/Wan-AI/Wan2.1-T2V-14B/models_t5_umt5-xxl-enc-bf16.pth",
        "models/Wan-AI/Wan2.1-T2V-14B/Wan2.1_VAE.pth",
    ],
    torch_dtype=torch.float8_e4m3fn, # You can set `torch_dtype=torch.bfloat16` to disable FP8 quantization.
)

dist.init_process_group(
    backend="nccl",
    init_method="env://",
)
from xfuser.core.distributed import (initialize_model_parallel,
                                     init_distributed_environment)
init_distributed_environment(
    rank=dist.get_rank(), world_size=dist.get_world_size())

initialize_model_parallel(
    sequence_parallel_degree=dist.get_world_size(),
    ring_degree=1,
    ulysses_degree=dist.get_world_size(),
)
torch.cuda.set_device(dist.get_rank())

pipe = WanVideoPipeline.from_model_manager(model_manager, 
                                           torch_dtype=torch.bfloat16, 
                                           device=f"cuda:{dist.get_rank()}", 
                                           use_usp=True if dist.get_world_size() > 1 else False)
pipe.enable_vram_management(num_persistent_param_in_dit=None) # You can set `num_persistent_param_in_dit` to a small number to reduce VRAM required.

# Text-to-video
video = pipe(
    prompt="一名宇航员身穿太空服，面朝镜头骑着一匹机械马在火星表面驰骋。红色的荒凉地表延伸至远方，点缀着巨大的陨石坑和奇特的岩石结构。机械马的步伐稳健，扬起微弱的尘埃，展现出未来科技与原始探索的完美结合。宇航员手持操控装置，目光坚定，仿佛正在开辟人类的新疆域。背景是深邃的宇宙和蔚蓝的地球，画面既科幻又充满希望，让人不禁畅想未来的星际生活。",
    negative_prompt="色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走",
    num_inference_steps=50,
    seed=0, tiled=True
)
if dist.get_rank() == 0:
    save_video(video, "video1.mp4", fps=25, quality=5)