Update app.py

app.py

@@ -1,4 +1,3 @@
-import spaces
 import gradio as gr
 import torch
 from diffusers import DiffusionPipeline
@@ -29,9 +28,13 @@ pipe.transformer.compile()
 pipe.to("cuda")
 
 # Gradio inference function
-@spaces.GPU(duration=20)
-def generate_video(prompt, num_frames, seed):
+def generate_video(prompt, seed):
     generator = torch.manual_seed(seed) if seed else None
+    
+    # Force ~2 second video (e.g., fps=8, frames=16)
+    num_frames = 16
+    fps = 8
+
     video_frames = pipe(
         prompt=prompt,
         num_frames=num_frames,
@@ -41,22 +44,21 @@ def generate_video(prompt, num_frames, seed):
     # Save as GIF for Gradio preview
     import imageio
     out_path = "output.gif"
-    imageio.mimsave(out_path, video_frames, fps=8)
+    imageio.mimsave(out_path, video_frames, fps=fps)
     return out_path
 
 # Build Gradio UI
 with gr.Blocks() as demo:
-    gr.Markdown("## 🚀 Wan2.1 T2V - Text to Video Generator")
+    gr.Markdown("## 🚀 Wan2.1 T2V - Text to Video Generator (2 sec duration)")
     with gr.Row():
         with gr.Column():
             prompt = gr.Textbox(label="Prompt", lines=3, value="A futuristic cityscape with flying cars and neon lights.")
-            num_frames = gr.Slider(8, 64, value=16, step=1, label="Frames")
             seed = gr.Number(value=42, label="Seed (optional)")
             run_btn = gr.Button("Generate Video")
         with gr.Column():
             output_video = gr.Video(label="Generated Video")
 
-    run_btn.click(fn=generate_video, inputs=[prompt, num_frames, seed], outputs=output_video)
+    run_btn.click(fn=generate_video, inputs=[prompt, seed], outputs=output_video)
 
 # Launch demo
 demo.launch()