Update app.py

app.py

@@ -9,22 +9,17 @@ pipe = AutoPipelineForImage2Image.from_pretrained(
     "nitrosocke/Arcane-Diffusion",
     safety_checker=None,
 )
-pipe.to("cuda")
+# Do NOT move to cuda; let it run on CPU by default
 
 # Function to process a single frame
 def process_frame(frame, prompt):
-    # Convert frame from BGR (OpenCV format) to RGB (expected by the model)
     frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
-    # Load the frame as an image for the diffusion model
     image = load_image(frame_rgb)
-    # Apply the anime-style transformation
     result = pipe(prompt=prompt, image=image, strength=0.75).images[0]
-    # Convert back to numpy array
     return np.array(result)
 
 # Function to convert the entire video
 def video_to_anime(video_path, prompt="Arcane style"):
-    # Read the input video using OpenCV
     cap = cv2.VideoCapture(video_path)
     fps = cap.get(cv2.CAP_PROP_FPS)
     frames = []
@@ -35,16 +30,15 @@ def video_to_anime(video_path, prompt="Arcane style"):
         frames.append(frame)
     cap.release()
 
-    # Process each frame to anime style
+    # Process each frame
     processed_frames = [process_frame(frame, prompt) for frame in frames]
 
-    # Write the output video using OpenCV
+    # Write the output video
     height, width, _ = processed_frames[0].shape
-    fourcc = cv2.VideoWriter_fourcc(*'mp4v')  # Codec for MP4
+    fourcc = cv2.VideoWriter_fourcc(*'mp4v')
     output_path = "output.mp4"
     out = cv2.VideoWriter(output_path, fourcc, fps, (width, height))
     for frame in processed_frames:
-        # Convert back to BGR for OpenCV
         frame_bgr = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
         out.write(frame_bgr)
     out.release()