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import gradio as gr |
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import torch |
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from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler |
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from diffusers.utils import export_to_video |
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import tempfile |
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model_id = "damo-vilab/text-to-video-ms-1.7b" |
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print("جاري تحميل النموذج على CPU...") |
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pipe = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float32) |
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pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config) |
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pipe.to("cpu") |
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def generate_video(prompt): |
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if not prompt: |
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return None |
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video_frames = pipe( |
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prompt, |
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num_inference_steps=5, |
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num_frames=8, |
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height=256, |
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width=256 |
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).frames[0] |
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temp_file = tempfile.NamedTemporaryFile(suffix='.mp4', delete=False) |
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export_to_video(video_frames, temp_file.name, fps=8) |
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return temp_file.name |
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with gr.Blocks() as demo: |
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gr.Markdown("### 🎥 Light Video Generator (CPU Friendly)") |
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with gr.Row(): |
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input_text = gr.Textbox(label="اكتب الوصف هنا", placeholder="A panda eating bamboo...") |
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btn = gr.Button("إنشاء الفيديو") |
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output_video = gr.Video(label="الفيديو الناتج") |
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btn.click(fn=generate_video, inputs=input_text, outputs=output_video) |
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demo.launch() |
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