update code

app.py

@@ -480,11 +480,7 @@ def avatar_generation(items, save_path_base, video_path_input, source_type, is_s
     # ws_avg.to(device)
     # DiT_model.to(device)
     # Set up face verse for amimation
-    base_coff = np.load(
-        'pretrained_model/temp.npy').astype(
-        np.float32)
-    base_coff = torch.from_numpy(base_coff).float()
-    Faceverse = Faceverse_manager(device=device, base_coeff=base_coff)
+
 
     if source_type == 'example':
         input_img_fvid = './demo_data/source_img/img_generate_different_domain/coeffs/demo_imgs'
@@ -527,9 +523,9 @@ def avatar_generation(items, save_path_base, video_path_input, source_type, is_s
         samples = samples * std + mean
         torch.cuda.empty_cache()
         torch.cuda.ipc_collect()
-        save_frames_path_out = os.path.join(save_path_base, image_name, 'out')
-        save_frames_path_outshow = os.path.join(save_path_base, image_name, 'out_show')
-        save_frames_path_depth = os.path.join(save_path_base, image_name, 'depth')
+        save_frames_path_out = os.path.join(save_path_base, image_name, video_name, 'out')
+        save_frames_path_outshow = os.path.join(save_path_base, image_name, video_name,'out_show')
+        save_frames_path_depth = os.path.join(save_path_base, image_name, video_name, 'depth')
 
         os.makedirs(save_frames_path_out, exist_ok=True)
         os.makedirs(save_frames_path_outshow, exist_ok=True)
@@ -611,12 +607,10 @@ def avatar_generation(items, save_path_base, video_path_input, source_type, is_s
 
         # Generate videos
         images_to_video(save_frames_path_out, os.path.join(save_path_base, image_name + '_out.mp4'))
-        images_to_video(save_frames_path_outshow, os.path.join(save_path_base, image_name + '_outshow.mp4'))
         images_to_video(save_frames_path_depth, os.path.join(save_path_base, image_name + '_depth.mp4'))
 
         logging.info(f"✅ Video generation completed successfully!")
-        return os.path.join(save_path_base, image_name + '_out.mp4'), os.path.join(save_path_base,
-                                                                                   image_name + '_outshow.mp4'),  os.path.join(save_path_base, image_name + '_depth.mp4')
+        return os.path.join(save_path_base, image_name + video_name+ '_out.mp4'),  os.path.join(save_path_base, image_name + video_name+'_depth.mp4')
 
 
 def get_image_base64(path):
@@ -630,7 +624,6 @@ def assert_input_image(input_image):
         raise gr.Error("No image selected or uploaded!")
 
 @spaces.GPU(duration=100)
-@torch.no_grad()
 def process_image(input_image, source_type, is_style, save_dir):
     """ 🎯 处理 input_image，根据是否是示例图片执行不同逻辑 """
     process_img_input_dir = os.path.join(save_dir, 'input_image')
@@ -934,13 +927,7 @@ def launch_gradio_app():
                             autoplay=True
                         )
 
-                        output_video_2 = gr.Video(
-                            label="Generated Animation Rotate View",
-                            format="mp4", height=512, width=512,
-                            autoplay=True
-                        )
-
-                        output_video_3 = gr.Video(
+                        output_video_1 = gr.Video(
                             label="Generated Animation Rotate View Depth",
                             format="mp4", height=512, width=512,
                             autoplay=True
@@ -971,7 +958,7 @@ def launch_gradio_app():
         submit.click(
             fn=avatar_generation,
             inputs=[processed_image, working_dir, video_input, source_type, is_styled, style_image],
-            outputs=[output_video, output_video_2, output_video_3],  # ⏳ 稍后展示视频
+            outputs=[output_video, output_video_1],  # ⏳ 稍后展示视频
             queue=True
         )
 
@@ -1011,4 +998,9 @@ if __name__ == '__main__':
         use_safetensors=True, controlnet=controlnet, variant="fp16"
     ).to(device)
     demo_cam = False
+    base_coff = np.load(
+    'pretrained_model/temp.npy').astype(
+    np.float32)
+    base_coff = torch.from_numpy(base_coff).float()
+    Faceverse = Faceverse_manager(device=device, base_coeff=base_coff)
     launch_gradio_app()