Update app.py

app.py

@@ -175,7 +175,17 @@ def generate(
     if rife_status:
         video_pt = rife_inference_with_latents(frame_interpolation_model, video_pt)
 
-    return (video_pt, seed)
+    batch_size = video_pt.shape[0]
+    batch_video_frames = []
+    for batch_idx in range(batch_size):
+        pt_image = video_pt[batch_idx]
+        pt_image = torch.stack([pt_image[i] for i in range(pt_image.shape[0])])
+
+        image_np = VaeImageProcessor.pt_to_numpy(pt_image)
+        image_pil = VaeImageProcessor.numpy_to_pil(image_np)
+        batch_video_frames.append(image_pil)
+
+    return (batch_video_frames, seed)
 
 
 def convert_to_gif(video_path):
@@ -337,7 +347,7 @@ with gr.Blocks() as demo:
         rife_status,
         progress=gr.Progress(track_tqdm=True)
     ):
-        latents, seed = generate(
+        batch_video_frames, seed = generate(
             prompt,
             image_input,
             num_inference_steps=50,
@@ -347,16 +357,6 @@ with gr.Blocks() as demo:
             rife_status=rife_status,
         )   
 
-        batch_size = latents.shape[0]
-        batch_video_frames = []
-        for batch_idx in range(batch_size):
-            pt_image = latents[batch_idx]
-            pt_image = torch.stack([pt_image[i] for i in range(pt_image.shape[0])])
-
-            image_np = VaeImageProcessor.pt_to_numpy(pt_image)
-            image_pil = VaeImageProcessor.numpy_to_pil(image_np)
-            batch_video_frames.append(image_pil)
-
         video_path = save_video(batch_video_frames[0], fps=math.ceil((len(batch_video_frames[0]) - 1) / 6))
         video_update = gr.update(visible=True, value=video_path)
         gif_path = convert_to_gif(video_path)