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Browse files- app.py +48 -0
- requirements.txt +5 -0
app.py
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import os
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if os.environ.get("SPACES_ZERO_GPU") is not None:
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import spaces
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else:
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class spaces:
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@staticmethod
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def GPU(func):
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def wrapper(*args, **kwargs):
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return func(*args, **kwargs)
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return wrapper
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import torch
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from diffusers import MochiPipeline
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from diffusers.utils import export_to_video
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import gradio as gr
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# Caricare il modello pre-addestrato
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pipe = MochiPipeline.from_pretrained("genmo/mochi-1-preview", variant="bf16", torch_dtype=torch.bfloat16)
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# Abilitare le ottimizzazioni per il risparmio di memoria
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pipe.enable_model_cpu_offload()
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pipe.enable_vae_tiling()
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@spaces.GPU(duration=80)
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def generate_video(prompt):
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# Generare i frame del video
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frames = pipe(prompt, num_frames=84).frames[0]
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# Esportare i frame come video
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video_path = "mochi.mp4"
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export_to_video(frames, video_path, fps=30)
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return video_path
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# Creare l'interfaccia Gradio
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interface = gr.Interface(
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fn=generate_video,
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inputs="text",
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outputs="video",
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title="Mochi Video Generator",
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description="Genera un video basato su un prompt di testo utilizzando MochiPipeline."
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)
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# Avviare l'applicazione
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if __name__ == "__main__":
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interface.launch()
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requirements.txt
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accelerate
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torch
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gradio
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transformers
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git+https://github.com/huggingface/diffusers
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