Upload app.py

app.py

@@ -46,9 +46,9 @@ parser.add_argument('--if_resampler', type=bool, default=True)
 parser.add_argument('--if_ipa', type=bool, default=True)
 parser.add_argument('--if_control', type=bool, default=True)
 
-parser.add_argument('--pretrained_model_name_or_path',
-                    default="./ckpt/Realistic_Vision_V4.0_noVAE",
-                    type=str)
+# parser.add_argument('--pretrained_model_name_or_path',
+#                     default="./ckpt/Realistic_Vision_V4.0_noVAE",
+#                     type=str)
 parser.add_argument('--ip_ckpt',
                     default="./ckpt/ip-adapter-faceid-plus_sd15.bin",
                     type=str)
@@ -86,7 +86,7 @@ image_encoder = CLIPVisionModelWithProjection.from_pretrained(args.pretrained_im
 unet = UNet2DConditionModel.from_pretrained("./ckpt/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(providers=[('CUDAExecutionProvider', {"device_id": args.device})]) ##使用GPU:0, 默认使用buffalo_l就可以了
@@ -186,7 +186,7 @@ img_transform = transforms.Compose([
     transforms.Normalize([0.5], [0.5]),
 ])
 
-openpose_model = OpenposeDetector.from_pretrained("/home/sf/ControlNet").to(args.device)
+openpose_model = OpenposeDetector.from_pretrained("./ckpt/ControlNet").to(args.device)
 
 def resize_img(input_image, max_side=640, min_side=512, size=None, 
                pad_to_max_side=False, mode=Image.BILINEAR, base_pixel_number=64):
@@ -282,15 +282,15 @@ def tryon_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]
 
 example_path = os.path.dirname(__file__)
@@ -396,4 +396,4 @@ with image_blocks as demo:
     try_button.click(fn=tryon_process, inputs=[garm_img, imgs, pose_img, prompt, cloth_guidance_scale, caption_guidance_scale, face_guidance_scale,self_guidance_scale, cross_guidance_scale, is_checked_face, is_checked_postprocess, is_checked_pose, denoise_steps, seed],
                      outputs=[image_out], api_name='tryon')
 
-image_blocks.launch(server_port=20021)  # 指定固定端口
+image_blocks.launch()