resolution

gradio_app.py

@@ -131,8 +131,11 @@ def inference(
         "--controlnet_input_channels", str(controlnet_input_channels),
         "--controlnet_transformer_num_layers", str(controlnet_transformer_num_layers)
     ]
-
-    result = subprocess.run(command, capture_output=True, text=True)
+    try:
+        result = subprocess.run(command, capture_output=True, text=True)
+    except subprocess.CalledProcessError as e:
+        logs = f"❌ Inference failed:\n{e.stderr}{e.stdout}"
+        return None, logs
     logs = result.stdout
     video_output = f"{out_dir}/00000_{seed}_out.mp4"
     return video_output if os.path.exists(video_output) else None, logs

inference/v2v_data/demo.py

@@ -111,7 +111,7 @@ class GetAnchorVideos:
 
     def infer_gradual(self, opts):
         frames = read_video_frames(
-            opts.video_path, opts.video_length, opts.stride, opts.max_res, opts.height, opts.width
+            opts.video_path, opts.video_length, opts.stride, opts.max_res, height = opts.height, width = opts.width
         )
         vr = VideoReader(opts.video_path, ctx=cpu(0))
         frame_shape = vr[0].shape  # (H, W, 3)
@@ -177,7 +177,7 @@ class GetAnchorVideos:
         os.makedirs(f'{opts.out_dir}/intrinsics', exist_ok=True)
         os.makedirs(f'{opts.out_dir}/captions', exist_ok=True)
         
-        mask_save = process_mask_tensor(torch.cat(masks)).squeeze().cpu().numpy()
+        mask_save = process_mask_tensor(torch.cat(masks), resolution=(opts.height, opts.width)).squeeze().cpu().numpy()
         np.save(f"{opts.out_dir}/depth/{save_name}.npy",depths.cpu().numpy())
         np.savez_compressed(f"{opts.out_dir}/masks/{save_name}.npz",mask=mask_save)
         save_video_as_mp4(ori_video_save,f"{opts.out_dir}/videos/{save_name}.mp4", fps=opts.fps)