Update app.py

app.py

@@ -156,6 +156,7 @@ def generate_video(input_image, prompt, height, width,
                    pattern, dense_layers, dense_timesteps, decay_factor,
                    progress=gr.Progress(track_tqdm=True)):
 
+    print("Test")
     @torch.no_grad()
     def WanImageToVideoPipeline_Sparse__call__(
         self,
@@ -309,7 +310,7 @@ def generate_video(input_image, prompt, height, width,
         if not return_dict:
             return (video,)
         return WanPipelineOutput(frames=video)
-    
+    print("Test2")
     def replace_wan_attention_for_i2v(
         pipe,
         height,
@@ -361,7 +362,7 @@ def generate_video(input_image, prompt, height, width,
             if isinstance(m, Attention) and hasattr(m.processor, "layer_idx"):
                 layer_idx = m.processor.layer_idx
                 m.set_processor(AttnModule(layer_idx))
-
+    print("Test3")
     if input_image is None:
         raise gr.Error("Please upload an input image.")
 
@@ -375,7 +376,7 @@ def generate_video(input_image, prompt, height, width,
         decay_factor=decay_factor,
         sparsity_type=pattern,
     )
-
+    print("Test4")
     target_h = max(MOD_VALUE, (int(height) // MOD_VALUE) * MOD_VALUE)
     target_w = max(MOD_VALUE, (int(width) // MOD_VALUE) * MOD_VALUE)
     
@@ -384,7 +385,7 @@ def generate_video(input_image, prompt, height, width,
     current_seed = random.randint(0, MAX_SEED) if randomize_seed else int(seed)
 
     resized_image = input_image.resize((target_w, target_h))
-
+    print("Test5")
     with torch.inference_mode():
         output_frames_list = pipe(
             image=resized_image, prompt=prompt, negative_prompt=negative_prompt,
@@ -393,7 +394,7 @@ def generate_video(input_image, prompt, height, width,
             generator=torch.Generator(device="cuda").manual_seed(current_seed),
             callback_on_step_end=progress
         ).frames[0]
-
+    print("Test6")
     with tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) as tmpfile:
         video_path = tmpfile.name
     export_to_video(output_frames_list, video_path, fps=FIXED_FPS)