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https://github.com/modelscope/DiffSynth-Studio.git
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53 lines
3.9 KiB
Python
53 lines
3.9 KiB
Python
import torch, os
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from PIL import Image
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from diffsynth.utils.data import save_video, VideoData
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from diffsynth.pipelines.wan_video import WanVideoPipeline, ModelConfig
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from modelscope import dataset_snapshot_download
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pipe = WanVideoPipeline.from_pretrained(
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torch_dtype=torch.bfloat16,
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device="cuda",
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model_configs=[
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ModelConfig(model_id="Wan-AI/WanToDance-14B", origin_file_pattern="local_model.safetensors"),
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ModelConfig(model_id="Wan-AI/WanToDance-14B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"),
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ModelConfig(model_id="Wan-AI/WanToDance-14B", origin_file_pattern="Wan2.1_VAE.pth"),
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ModelConfig(model_id="Wan-AI/WanToDance-14B", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth"),
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],
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tokenizer_config=ModelConfig(model_id="Wan-AI/Wan2.1-T2V-1.3B", origin_file_pattern="google/umt5-xxl/"),
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)
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dataset_snapshot_download(
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"DiffSynth-Studio/diffsynth_example_dataset",
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local_dir="data/diffsynth_example_dataset",
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allow_file_pattern="wanvideo/WanToDance-14B-local/*"
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)
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# This is a specialized model with the following constraints on its input parameters:
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# * The model renders and outputs video based on a sequence of keyframes; therefore, `wantodance_keyframes` must be provided correctly.
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# * If you need to generate a long video, please generate it in segments, and ensure that `wantodance_music_path`, `wantodance_keyframes`, and `wantodance_keyframes_mask` are properly split accordingly.
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# * The audio file specified by `wantodance_music_path` must match the video duration, calculated as (`num_frames` / 30) seconds.
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# * The width and height of `wantodance_reference_image` must be multiples of 16.
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# * `wantodance_fps` is configurable, but since the model appears to have been trained exclusively at 30 FPS, setting it to other values is not recommended.
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# * In `wantodance_keyframes`, frames that are not keyframes should be solid black.
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# * `wantodance_keyframes_mask` indicates the positions of valid frames within `wantodance_keyframes`.
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wantodance_keyframes = VideoData("data/diffsynth_example_dataset/wanvideo/WanToDance-14B-local/keyframes.mp4")
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wantodance_keyframes = [wantodance_keyframes[i] for i in range(149)]
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video = pipe(
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prompt="一个人正在跳舞，舞蹈种类是古典舞,图像清晰程度高,人物动作平均幅度中等,人物动作最大幅度中等。, 帧率是30fps。",
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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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seed=0, tiled=True,
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height=1280, width=720, num_frames=149,
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num_inference_steps=24,
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wantodance_music_path="data/diffsynth_example_dataset/wanvideo/WanToDance-14B-local/music.wav",
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wantodance_reference_image=Image.open("data/diffsynth_example_dataset/wanvideo/WanToDance-14B-local/refimage.jpg"),
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wantodance_fps=30,
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wantodance_keyframes=wantodance_keyframes,
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wantodance_keyframes_mask=[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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1],
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)
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save_video(video, "video_WanToDance-14B-local.mp4", fps=30, quality=5)
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