diff --git a/api/onnx_web/convert.py b/api/onnx_web/convert.py
index 6a9d167d..5889646d 100644
--- a/api/onnx_web/convert.py
+++ b/api/onnx_web/convert.py
@@ -16,28 +16,30 @@ sources: Dict[str, List[Tuple[str, str]]] = {
     'diffusers': [
         # v1.x
         ('stable-diffusion-onnx-v1-5', 'runwayml/stable-diffusion-v1-5'),
-        ('stable-diffusion-onnx-v1-inpainting', 'runwayml/stable-diffusion-inpainting'),
+        ('stable-diffusion-onnx-v1-inpainting',
+         'runwayml/stable-diffusion-inpainting'),
         # v2.x
         ('stable-diffusion-onnx-v2-1', 'stabilityai/stable-diffusion-2-1'),
-        ('stable-diffusion-onnx-v2-inpainting', 'stabilityai/stable-diffusion-2-inpainting'),
+        ('stable-diffusion-onnx-v2-inpainting',
+         'stabilityai/stable-diffusion-2-inpainting'),
     ],
     'gfpgan': [
-        ('GFPGANv1.3', 'https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth'),
+        ('correction-gfpgan-v1-3',
+         'https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth'),
     ],
     'real_esrgan': [
-        ('RealESRGAN_x4plus', 'https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth'),
+        ('upscaling-real-esrgan-x4-plus',
+         'https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth', 4),
     ],
 }
 
 model_path = environ.get('ONNX_WEB_MODEL_PATH',
                          path.join('..', 'models'))
-
-
 training_device = 'cuda' if torch.cuda.is_available() else 'cpu'
 
 
 @torch.no_grad()
-def convert_real_esrgan(name: str, url: str, opset: int):
+def convert_real_esrgan(name: str, url: str, scale: int, opset: int):
     dest_path = path.join(model_path, name)
     dest_onnx = path.join(model_path, name + '.onnx')
     print('converting Real ESRGAN model: %s -> %s' % (name, dest_path))
@@ -53,7 +55,7 @@ def convert_real_esrgan(name: str, url: str, opset: int):
 
     print('loading and training model')
     model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64,
-                    num_block=23, num_grow_ch=32, scale=4)
+                    num_block=23, num_grow_ch=32, scale=scale)
     model.load_state_dict(torch.load(dest_path)['params_ema'])
     model.to(training_device).train(False)
     model.eval()