Create app.py

app.py

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+import gradio as gr
+import torch
+from diffusers import WanPipeline, UniPCMultistepScheduler
+
+pipe = WanPipeline.from_pretrained(
+    "Wan-AI/Wan2.2-T2V-1.3B-Diffusers",
+    torch_dtype=torch.float16,
+    revision="fp16"
+)
+pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
+pipe = pipe.to("cpu")
+
+def generate_video(prompt, duration):
+    """
+    Generates a video from a text prompt locally using Wan 2.2.
+    Duration is clamped between 1 and 8 seconds.
+    Returns the path to the saved MP4 file.
+    """
+    duration = max(1, min(duration, 8)) 
+    video = pipe(prompt=prompt, height=512, width=512, duration_seconds=duration, guidance_scale=1.0).videos[0]
+    video_path = "output.mp4"
+    video.save(video_path)
+    return video_path
+
+with gr.Blocks() as demo:
+    gr.Markdown("## Wan 2.2 Video Generator (Local)")
+    
+    with gr.Row():
+        prompt_input = gr.Textbox(label="Prompt", placeholder="Describe your scene")
+        duration_input = gr.Slider(label="Duration (sec)", minimum=1, maximum=8, step=1, value=6)
+        submit_btn = gr.Button("Generate Video")
+        output_video = gr.Video(label="Generated Video")
+    
+    submit_btn.click(generate_video, inputs=[prompt_input, duration_input], outputs=output_video)
+
+
+demo.launch(api_name="generate-video")