Update app.py

app.py

@@ -27,13 +27,6 @@ pipeline.to(device)
 pipe_upsample.to(device)
 pipeline.vae.enable_tiling()
 
-canny_processor = CannyDetector()
-
-# Initialize MediaPipe pose estimation
-# mp_drawing = mp.solutions.drawing_utils
-# mp_drawing_styles = mp.solutions.drawing_styles
-# mp_pose = mp.solutions.pose
-
 CONTROL_LORAS = {
     "canny": {
         "repo": "Lightricks/LTX-Video-ICLoRA-canny-13b-0.9.7",
@@ -60,6 +53,13 @@ pipeline.load_lora_weights(
         )
 pipeline.set_adapters([CONTROL_LORAS["canny"]["adapter_name"]], adapter_weights=[1.0])
 
+# Initialize MediaPipe pose estimation
+# mp_drawing = mp.solutions.drawing_utils
+# mp_drawing_styles = mp.solutions.drawing_styles
+# mp_pose = mp.solutions.pose
+
+canny_processor = CannyDetector()
+
 @spaces.GPU()
 def read_video(video) -> torch.Tensor:
     """
@@ -180,11 +180,12 @@ def process_video_for_control(reference_video, control_type):
         processed_video = process_video_for_pose(video)
     else:
         processed_video = reference_video
-    fps = 24
-    with tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) as tmp2_file:
-        output2_path = tmp2_file.name
-        export_to_video(processed_video, output2_path, fps=fps)
-    return output2_path
+    # fps = 24
+    # with tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) as tmp2_file:
+    #     output2_path = tmp2_file.name
+    #     export_to_video(processed_video, output2_path, fps=fps)
+    # return output2_path
+    return processed_video
 
 
 @spaces.GPU(duration=160)
@@ -224,7 +225,7 @@ def generate_video(
         temporal_compression = pipeline.vae_temporal_compression_ratio
         num_frames = ((num_frames - 1) // temporal_compression) * temporal_compression + 1
         
-        progress(0.05, desc="Loading control LoRA...")
+    
         
         # Load the appropriate control LoRA and update state
         # updated_lora_state = load_control_lora(control_type, current_lora_state)