Update app.py

app.py

@@ -42,25 +42,27 @@ def get_duration(prompt, height, width, negative_prompt, duration_seconds, guida
 # Gradio inference function with spaces GPU decorator
 @spaces.GPU(duration=get_duration)
 def generate_video(prompt, height, width, negative_prompt, duration_seconds, guidance_scale, steps, seed, randomize_seed, progress=gr.Progress(track_tqdm=True)):
-    generator = torch.manual_seed(seed) if seed else None
-    fps = 8
-    num_frames = int(duration_seconds * fps) if duration_seconds else 16
+    num_frames = np.clip(int(round(duration_seconds * FIXED_FPS)), MIN_FRAMES_MODEL, MAX_FRAMES_MODEL)
+    current_seed = random.randint(0, MAX_SEED) if randomize_seed else int(seed)
 
-    video_frames = pipe(
+    output_frames_list = pipe(
         prompt=prompt,
+        negative_prompt=negative_prompt,
+        height=int(height),
+        width=int(width),
         num_frames=num_frames,
-        generator=generator,
-        num_inference_steps=steps
+        guidance_scale=float(guidance_scale),
+        num_inference_steps=int(steps),
+        generator=torch.Generator(device="cuda").manual_seed(current_seed),
     ).frames[0]
 
-    processed_frames = [
-        (np.clip(frame * 255, 0, 255).astype(np.uint8) if frame.dtype in [np.float32, np.float64] else frame)
-        for frame in video_frames
-    ]
+    with tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) as tmpfile:
+        video_path = tmpfile.name
+
+    export_to_video(output_frames_list, video_path, fps=FIXED_FPS)
+
+    return video_path, current_seed
 
-    out_path = "output.gif"
-    imageio.mimsave(out_path, processed_frames, fps=fps)
-    return out_path
 
 # Build Gradio UI with all parameters
 with gr.Blocks(css="body { max-width: 100vw; overflow-x: hidden; }") as demo: