update demo

app.py

@@ -24,14 +24,14 @@ os.makedirs("./output", exist_ok=True)
 os.makedirs("./gradio_tmp", exist_ok=True)
 
 @spaces.GPU(duration=120)
-def infer(prompt: str, num_inference_steps: int, guidance_scale: float, progress=gr.Progress(track_tqdm=True)):
+def infer(prompt: str, progress=gr.Progress(track_tqdm=True)):
     torch.cuda.empty_cache()
     with torch.cuda.amp.autocast(dtype=torch.bfloat16):
         video = pipe(
             prompt,
             negative_prompt="",
-            num_inference_steps=num_inference_steps,
-            guidance_scale=guidance_scale,
+            num_inference_steps=50,
+            guidance_scale=7.5,
             width=432,
             height=240, #480x288  624x352 432x240 768x432
             frames=16
@@ -117,8 +117,8 @@ with gr.Blocks() as demo:
 
 
 
-    def generate(prompt, num_inference_steps, guidance_scale, model_choice, progress=gr.Progress(track_tqdm=True)):
-        tensor = infer(prompt, num_inference_steps, guidance_scale, progress=progress)
+    def generate(prompt, model_choice, progress=gr.Progress(track_tqdm=True)):
+        tensor = infer(prompt, progress=progress)
         video_path = save_video(tensor)
         video_update = gr.update(visible=True, value=video_path)
         gif_path = convert_to_gif(video_path)
@@ -133,7 +133,7 @@ with gr.Blocks() as demo:
 
     generate_button.click(
         generate,
-        inputs=[prompt, num_inference_steps, guidance_scale],
+        inputs=[prompt],
         outputs=[video_output, download_video_button, download_gif_button]
     )