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39 lines
1.5 KiB
Python
39 lines
1.5 KiB
Python
import torch
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from PIL import Image
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import librosa
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from diffsynth import save_video, VideoData, save_video_with_audio
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from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig
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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/Wan2.2-S2V-14B", origin_file_pattern="diffusion_pytorch_model*.safetensors"),
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ModelConfig(model_id="Wan-AI/Wan2.2-S2V-14B", origin_file_pattern="wav2vec2-large-xlsr-53-english/model.safetensors"),
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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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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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prompt = "a person is singing"
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input_image = Image.open("/mnt/nas1/zhanghong/project/aigc/Wan2.2_s2v/examples/pose.png").convert("RGB").resize((width, height))
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# s2v audio input, recommend 16kHz sampling rate
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audio_path = '/mnt/nas1/zhanghong/project/aigc/Wan2.2_s2v/examples/sing.MP3'
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input_audio, sample_rate = librosa.load(audio_path, sr=16000)
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# Speech-to-video
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video = pipe(
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prompt=prompt,
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input_image=input_image,
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negative_prompt="",
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seed=0,
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num_frames=81,
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height=1280,
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width=720,
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audio_sample_rate=sample_rate,
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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, "video_with_audio.mp4", audio_path, fps=16, quality=5)
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