diff --git a/src/live_portrait_pipeline.py b/src/live_portrait_pipeline.py
index 4f2f777..59df314 100644
--- a/src/live_portrait_pipeline.py
+++ b/src/live_portrait_pipeline.py
@@ -112,8 +112,6 @@ class LivePortraitPipeline(object):
             c_d_lip_lst = driving_template_dct['c_lip_lst'] if 'c_lip_lst' in driving_template_dct.keys() else driving_template_dct['c_d_lip_lst']
             driving_n_frames = driving_template_dct['n_frames']
             flag_is_driving_video = True if driving_n_frames > 1 else False
-            # if flag_is_source_video and not flag_is_driving_video:
-            #     raise Exception(f"Animating a source video with a driving image is not supported!")
             if flag_is_source_video and flag_is_driving_video:
                 n_frames = min(len(source_rgb_lst), driving_n_frames)  # minimum number as the number of the animated frames
             elif flag_is_source_video and not flag_is_driving_video:
@@ -138,8 +136,6 @@ class LivePortraitPipeline(object):
                 driving_rgb_lst = load_video(args.driving)
             elif is_image(args.driving):
                 flag_is_driving_video = False
-                # if flag_is_source_video:
-                #     raise Exception(f"Animating a source video with a driving image is not supported!")
                 driving_img_rgb = load_image_rgb(args.driving)
                 output_fps = 25
                 log(f"Load driving image from {args.driving}")
@@ -217,7 +213,6 @@ class LivePortraitPipeline(object):
                     x_d_exp_lst = [source_template_dct['motion'][i]['exp'] + driving_template_dct['motion'][i]['exp'] - driving_template_dct['motion'][0]['exp'] for i in range(n_frames)]
                     x_d_exp_lst_smooth = smooth(x_d_exp_lst, source_template_dct['motion'][0]['exp'].shape, device, inf_cfg.driving_smooth_observation_variance)
                 else:
-                    # x_d_exp_lst = [source_template_dct['motion'][i]['exp'] + (driving_template_dct['motion'][0]['exp'] - 0) for i in range(n_frames)] if driving_template_dct['motion'][0]['exp'].mean() > 0 else [source_template_dct['motion'][i]['exp'] + (driving_template_dct['motion'][0]['exp'] - inf_cfg.lip_array) for i in range(n_frames)]
                     x_d_exp_lst = [source_template_dct['motion'][i]['exp'] + (driving_template_dct['motion'][0]['exp'] - inf_cfg.lip_array) for i in range(n_frames)]
                     x_d_exp_lst_smooth = [torch.tensor(x_d_exp[0], dtype=torch.float32, device=device) for x_d_exp in x_d_exp_lst]
                 if inf_cfg.animation_region == "all" or inf_cfg.animation_region == "pose":