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https://github.com/modelscope/DiffSynth-Studio.git
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DiffSynth-Studio 2.0 major update
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@@ -1,10 +1,13 @@
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# This script can generate a single video clip.
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# If you need generate long videos, please refer to `Wan2.2-S2V-14B_multi_clips.py`.
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import torch
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from PIL import Image
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import librosa
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from diffsynth import VideoData, save_video_with_audio
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from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig
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from diffsynth.utils.data import VideoData, save_video_with_audio
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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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@@ -14,6 +17,7 @@ pipe = WanVideoPipeline.from_pretrained(
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ModelConfig(model_id="Wan-AI/Wan2.2-S2V-14B", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth"),
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ModelConfig(model_id="Wan-AI/Wan2.2-S2V-14B", origin_file_pattern="Wan2.1_VAE.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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audio_processor_config=ModelConfig(model_id="Wan-AI/Wan2.2-S2V-14B", origin_file_pattern="wav2vec2-large-xlsr-53-english/"),
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)
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dataset_snapshot_download(
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@@ -46,7 +50,7 @@ video = pipe(
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input_audio=input_audio,
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num_inference_steps=40,
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)
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save_video_with_audio(video[1:], "video_with_audio.mp4", audio_path, fps=16, quality=5)
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save_video_with_audio(video[1:], "video_1_Wan2.2-S2V-14B.mp4", audio_path, fps=16, quality=5)
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# s2v will use the first (num_frames) frames as reference. height and width must be the same as input_image. And fps should be 16, the same as output video fps.
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pose_video_path = 'data/example_video_dataset/wans2v/pose.mp4'
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@@ -66,4 +70,4 @@ video = pipe(
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s2v_pose_video=pose_video,
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num_inference_steps=40,
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)
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save_video_with_audio(video[1:], "video_pose_with_audio.mp4", audio_path, fps=16, quality=5)
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save_video_with_audio(video[1:], "video_2_Wan2.2-S2V-14B.mp4", audio_path, fps=16, quality=5)
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