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Merge pull request #485 from modelscope/usp
support Unified Sequence Parallel
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@@ -49,6 +49,20 @@ We present a detailed table here. The model is tested on a single A100.
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https://github.com/user-attachments/assets/3908bc64-d451-485a-8b61-28f6d32dd92f
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### Parallel Inference
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1. Unified Sequence Parallel (USP)
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```bash
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pip install xfuser>=0.4.3
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```
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```bash
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torchrun --standalone --nproc_per_node=8 examples/wanvideo/wan_14b_text_to_video_usp.py
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```
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2. Tensor Parallel
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Tensor parallel module of Wan-Video-14B-T2V is still under development. An example script is provided in [`./wan_14b_text_to_video_tensor_parallel.py`](./wan_14b_text_to_video_tensor_parallel.py).
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### Wan-Video-14B-I2V
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58
examples/wanvideo/wan_14b_text_to_video_usp.py
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58
examples/wanvideo/wan_14b_text_to_video_usp.py
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@@ -0,0 +1,58 @@
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import torch
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from diffsynth import ModelManager, WanVideoPipeline, save_video, VideoData
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from modelscope import snapshot_download
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import torch.distributed as dist
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# Download models
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snapshot_download("Wan-AI/Wan2.1-T2V-14B", local_dir="models/Wan-AI/Wan2.1-T2V-14B")
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# Load models
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model_manager = ModelManager(device="cpu")
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model_manager.load_models(
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[
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[
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"models/Wan-AI/Wan2.1-T2V-14B/diffusion_pytorch_model-00001-of-00006.safetensors",
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"models/Wan-AI/Wan2.1-T2V-14B/diffusion_pytorch_model-00002-of-00006.safetensors",
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"models/Wan-AI/Wan2.1-T2V-14B/diffusion_pytorch_model-00003-of-00006.safetensors",
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"models/Wan-AI/Wan2.1-T2V-14B/diffusion_pytorch_model-00004-of-00006.safetensors",
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"models/Wan-AI/Wan2.1-T2V-14B/diffusion_pytorch_model-00005-of-00006.safetensors",
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"models/Wan-AI/Wan2.1-T2V-14B/diffusion_pytorch_model-00006-of-00006.safetensors",
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],
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"models/Wan-AI/Wan2.1-T2V-14B/models_t5_umt5-xxl-enc-bf16.pth",
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"models/Wan-AI/Wan2.1-T2V-14B/Wan2.1_VAE.pth",
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],
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torch_dtype=torch.float8_e4m3fn, # You can set `torch_dtype=torch.bfloat16` to disable FP8 quantization.
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)
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dist.init_process_group(
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backend="nccl",
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init_method="env://",
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)
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from xfuser.core.distributed import (initialize_model_parallel,
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init_distributed_environment)
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init_distributed_environment(
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rank=dist.get_rank(), world_size=dist.get_world_size())
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initialize_model_parallel(
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sequence_parallel_degree=dist.get_world_size(),
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ring_degree=1,
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ulysses_degree=dist.get_world_size(),
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)
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torch.cuda.set_device(dist.get_rank())
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pipe = WanVideoPipeline.from_model_manager(model_manager,
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torch_dtype=torch.bfloat16,
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device=f"cuda:{dist.get_rank()}",
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use_usp=True if dist.get_world_size() > 1 else False)
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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.
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# Text-to-video
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video = pipe(
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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。",
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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",
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num_inference_steps=50,
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seed=0, tiled=True
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)
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if dist.get_rank() == 0:
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save_video(video, "video1.mp4", fps=25, quality=5)
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