Upload app.py

app.py

@@ -80,7 +80,7 @@ text_encoder = CLIPTextModel.from_pretrained("SG161222/Realistic_Vision_V4.0_noV
 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')
+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
@@ -278,18 +278,18 @@ def dress_process(garm_img, face_img, pose_img, prompt, cloth_guidance_scale, ca
         num_inference_steps=denoise_steps,
     ).images
     
-    # if if_post and if_ipa:
-    #     # 将 PIL 图像转换为 NumPy 数组
-    #     output_array = np.array(output[0])
-    #     # 将 RGB 图像转换为 BGR 图像
-    #     bgr_array = cv2.cvtColor(output_array, cv2.COLOR_RGB2BGR)
-    #     # 将 NumPy 数组转换为 PIL 图像
-    #     bgr_image = Image.fromarray(bgr_array)
-    #     result = image_face_fusion(dict(template=bgr_image, user=Image.fromarray(face_image.astype('uint8'))))
-    #     return result[OutputKeys.OUTPUT_IMG]
+    if if_post and if_ipa:
+        # 将 PIL 图像转换为 NumPy 数组
+        output_array = np.array(output[0])
+        # 将 RGB 图像转换为 BGR 图像
+        bgr_array = cv2.cvtColor(output_array, cv2.COLOR_RGB2BGR)
+        # 将 NumPy 数组转换为 PIL 图像
+        bgr_image = Image.fromarray(bgr_array)
+        result = image_face_fusion(dict(template=bgr_image, user=Image.fromarray(face_image.astype('uint8'))))
+        return result[OutputKeys.OUTPUT_IMG]
     return output[0]
 
-base_path = 'yisol/IDM-VTON'
+
 example_path = os.path.join(os.path.dirname(__file__), 'example')
 
 garm_list = os.listdir(os.path.join(example_path,"cloth"))