Upload app.py

app.py

@@ -54,7 +54,8 @@ tokenizer = CLIPTokenizer.from_pretrained("SG161222/Realistic_Vision_V4.0_noVAE"
 text_encoder = CLIPTextModel.from_pretrained("SG161222/Realistic_Vision_V4.0_noVAE", subfolder="text_encoder").to(dtype=torch.float16, device=args.device)
 image_encoder = CLIPVisionModelWithProjection.from_pretrained("h94/IP-Adapter", subfolder="models/image_encoder").to(dtype=torch.float16, device=args.device)
 unet = UNet2DConditionModel.from_pretrained("SG161222/Realistic_Vision_V4.0_noVAE", subfolder="unet").to(dtype=torch.float16,device=args.device)
-
+image_face_fusion = pipeline('face_fusion_torch', model='damo/cv_unet_face_fusion_torch',
+                                     model_revision='v1.0.3')
 #face_model
 app = FaceAnalysis(model_path="buffalo_l", providers=[('CUDAExecutionProvider', {"device_id": args.device})]) ##使用GPU:0, 默认使用buffalo_l就可以了
 app.prepare(ctx_id=0, det_size=(640, 640))
@@ -235,8 +236,7 @@ def dress_process(garm_img, face_img, pose_img, prompt, cloth_guidance_scale, ca
     ).images
     
     if if_post and if_ipa:
-        image_face_fusion = pipeline('face_fusion_torch', model='damo/cv_unet_face_fusion_torch',
-                                     model_revision='v1.0.3')
+
         output_array = np.array(output[0])
 
         bgr_array = cv2.cvtColor(output_array, cv2.COLOR_RGB2BGR)
@@ -256,6 +256,8 @@ face_list = os.listdir(os.path.join(example_path,"face"))
 face_list_path = [os.path.join(example_path,"face",face) for face in face_list]
 
 pose_list = os.listdir(os.path.join(example_path,"pose"))
+print(pose_list)
+print('====', len(pose_list))
 pose_list_path = [os.path.join(example_path,"pose",pose) for pose in pose_list]
 
 def process_image(image):