mirror of
https://github.com/modelscope/DiffSynth-Studio.git
synced 2026-03-22 16:50:47 +00:00
129 lines
10 KiB
Python
129 lines
10 KiB
Python
import torch
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from PIL import Image
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from diffsynth import save_video, VideoData
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from diffsynth.pipelines.wan_video_new import WanVideoPipeline, ModelConfig
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from modelscope import dataset_snapshot_download
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from diffsynth.models.wan_video_camera_controller import process_pose_file
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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="PAI/Wan2.1-Fun-V1.1-1.3B-Control-Camera", origin_file_pattern="diffusion_pytorch_model*.safetensors", offload_device="cpu"),
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ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-1.3B-Control-Camera", origin_file_pattern="models_t5_umt5-xxl-enc-bf16.pth", offload_device="cpu"),
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ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-1.3B-Control-Camera", origin_file_pattern="Wan2.1_VAE.pth", offload_device="cpu"),
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ModelConfig(model_id="PAI/Wan2.1-Fun-V1.1-1.3B-Control-Camera", origin_file_pattern="models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth", offload_device="cpu"),
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],
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)
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pipe.enable_vram_management()
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# Control camera video
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# text or txt file path, e.g. control_camera_text = "Pan_Left.txt"
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control_camera_text = '''
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0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.018518518518518517 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.037037037037037035 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.05555555555555555 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.07407407407407407 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.09259259259259259 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.1111111111111111 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.12962962962962962 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.14814814814814814 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.16666666666666666 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.18518518518518517 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.2037037037037037 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.2222222222222222 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.24074074074074073 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.25925925925925924 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.2777777777777778 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.2962962962962963 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.31481481481481477 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.3333333333333333 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.35185185185185186 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.37037037037037035 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.38888888888888884 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.4074074074074074 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.42592592592592593 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.4444444444444444 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.4629629629629629 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.48148148148148145 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.5 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.5185185185185185 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.537037037037037 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.5555555555555556 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.5740740740740741 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.5925925925925926 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.611111111111111 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.6296296296296295 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.6481481481481481 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.6666666666666666 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.6851851851851851 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.7037037037037037 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.7222222222222222 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.7407407407407407 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.7592592592592593 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.7777777777777777 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.7962962962962963 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.8148148148148148 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.8333333333333334 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.8518518518518519 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.8703703703703705 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.8888888888888888 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.9074074074074074 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.9259259259259258 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.9444444444444444 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.9629629629629629 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 0.9814814814814815 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 1.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 1.0185185185185186 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 1.037037037037037 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 1.0555555555555556 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 1.074074074074074 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 1.0925925925925926 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 1.1111111111111112 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 1.1296296296296298 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 1.1481481481481481 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 1.1666666666666667 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 1.1851851851851851 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 1.2037037037037037 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 1.222222222222222 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 1.2407407407407407 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 1.259259259259259 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 1.2777777777777777 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 1.2962962962962963 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 1.3148148148148149 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 1.3333333333333333 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 1.3518518518518519 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 1.3703703703703702 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 1.3888888888888888 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 1.4074074074074074 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 1.425925925925926 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 1.4444444444444444 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 1.462962962962963 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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0 0.532139961 0.946026558 0.5 0.5 0 0 1.0 0.0 0.0 1.4814814814814814 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0
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'''
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dataset_snapshot_download(
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dataset_id="DiffSynth-Studio/examples_in_diffsynth",
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local_dir="./",
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allow_file_pattern=f"data/examples/wan/input_image.jpg"
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)
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input_image = Image.open("data/examples/wan/input_image.jpg")
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height = 480
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width = 832
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control_camera_video = process_pose_file(control_camera_text, width, height)
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video = pipe(
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prompt="一艘小船正勇敢地乘风破浪前行。蔚蓝的大海波涛汹涌，白色的浪花拍打着船身，但小船毫不畏惧，坚定地驶向远方。阳光洒在水面上，闪烁着金色的光芒，为这壮丽的场景增添了一抹温暖。镜头拉近，可以看到船上的旗帜迎风飘扬，象征着不屈的精神与冒险的勇气。这段画面充满力量，激励人心，展现了面对挑战时的无畏与执着。",
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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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height=height, width=width, num_frames=81,
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seed=43, tiled=True,
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control_camera_video = control_camera_video,
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input_image = input_image,
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)
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save_video(video, "video.mp4", fps=15, quality=5) |