docs: little update

app.py

@@ -116,7 +116,7 @@ train_transforms = transforms.Compose(
 
 
 # Download to avoid uneasy git lfs install
-os.system("wget https://github.com/user-attachments/files/23571020/assets.zip")
+os.system("wget -q https://github.com/user-attachments/files/23571020/assets.zip")
 os.system("unzip assets.zip")
 
 
@@ -179,7 +179,7 @@ pipe.enable_model_cpu_offload()
 model_type = "vit_h"        #vit-h has the most number of paramter
 sam_pretrained_path = "pretrained/sam_vit_h_4b8939.pth"
 if not os.path.exists(sam_pretrained_path):
-    os.system("wget https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth -P pretrained/")
+    os.system("wget -q https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth -P pretrained/")
 sam = sam_model_registry[model_type](checkpoint = sam_pretrained_path).to(device="cuda")
 sam_predictor = SamPredictor(sam)     # There is a lot of setting here
 
@@ -512,7 +512,7 @@ def sample_traj_by_length(points, num_samples):
 
 
 
-@spaces.GPU(duration=120)
+@spaces.GPU(duration=200)
 def inference(inference_canvas, visual_canvas, text_prompt, traj_lists, main_reference_img, 
                 resized_height, resized_width, top_left_height, top_left_width, bottom_right_height, bottom_right_width):
 
@@ -607,6 +607,7 @@ def inference(inference_canvas, visual_canvas, text_prompt, traj_lists, main_ref
 
             
     # Create the traj tensor
+    print("inference_canvas is", inference_canvas)
     traj_tensor, traj_imgs_np, _, img_with_traj = VideoDataset_Motion.prepare_traj_tensor(
                                                                                             full_pred_tracks, canvas_height, canvas_width, 
                                                                                             [], dot_radius, canvas_width, canvas_height,