diff --git a/api/onnx_web/convert.py b/api/onnx_web/convert.py
index b3e2f922..5c33cd35 100644
--- a/api/onnx_web/convert.py
+++ b/api/onnx_web/convert.py
@@ -7,31 +7,40 @@ from torch.onnx import export
 
 import torch
 
-from .upscale import (
-    gfpgan_url,
-    resrgan_url,
-    resrgan_name,
-)
+sources = {
+    'diffusers': [
+        # TODO: add stable diffusion models
+    ],
+    'gfpgan': [
+        ('GFPGANv1.3', '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'),
+    ],
+}
 
 model_path = environ.get('ONNX_WEB_MODEL_PATH',
-                             path.join('..', 'models'))
+                         path.join('..', 'models'))
 
 
 @torch.no_grad()
-def convert_real_esrgan():
-    dest_path = path.join(model_path, resrgan_name + '.pth')
+def convert_real_esrgan(name: str, url: str):
+    dest_path = path.join(model_path, name)
+    dest_onnx = path.join(model_path, name + '.onnx')
     print('converting Real ESRGAN into %s' % dest_path)
 
-    if not path.isfile(dest_path):
-        print('existing model not found, downloading...')
-        for url in resrgan_url:
-            dest_path = load_file_from_url(
-                url=url, model_dir=path.join(dest_path, resrgan_name), progress=True, file_name=None)
+    if path.isfile(dest_onnx):
+        print('Real ESRGAN ONNX model already exists, skipping.')
+        return
 
-    model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64,
-                    num_block=23, num_grow_ch=32, scale=4)
+    if not path.isfile(dest_path):
+        print('PTH model not found, downloading...')
+        dest_path = load_file_from_url(
+            url=url, model_dir=path.join(dest_path, name), progress=True, file_name=None)
 
     print('loading and training Real ESRGAN model')
+    model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64,
+                    num_block=23, num_grow_ch=32, scale=4)
     model.load_state_dict(torch.load(dest_path)['params_ema'])
     model.train(False)
     model.eval()
@@ -42,24 +51,62 @@ def convert_real_esrgan():
     dynamic_axes = {'data': {2: 'width', 3: 'height'},
                     'output': {2: 'width', 3: 'height'}}
 
-    with torch.no_grad():
-        dest_onnx = path.join(model_path, resrgan_name + '.onnx')
-        print('exporting Real ESRGAN model to %s' % dest_onnx)
-        export(
-            model,
-            rng,
-            dest_onnx,
-            input_names=input_names,
-            output_names=output_names,
-            dynamic_axes=dynamic_axes,
-            opset_version=11,
-            export_params=True
-        )
-        print('Real ESRGAN exported to ONNX')
+    print('exporting Real ESRGAN model to %s' % dest_onnx)
+    export(
+        model,
+        rng,
+        dest_onnx,
+        input_names=input_names,
+        output_names=output_names,
+        dynamic_axes=dynamic_axes,
+        opset_version=11,
+        export_params=True
+    )
+    print('Real ESRGAN exported to ONNX')
 
 
-def convert_gfpgan():
-    pass
+def convert_gfpgan(name: str, url: str):
+    dest_path = path.join(model_path, name)
+    dest_onnx = path.join(model_path, name + '.onnx')
+
+    print('converting GFPGAN into %s' % dest_path)
+
+    if path.isfile(dest_onnx):
+        print('GFPGAN ONNX model already exists, skipping.')
+        return
+
+    if not path.isfile(dest_path):
+        print('existing model not found, downloading...')
+        dest_path = load_file_from_url(
+            url=url, model_dir=path.join(dest_path, name), progress=True, file_name=None)
+
+    print('loading and training GFPGAN model')
+    model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64,
+                    num_block=23, num_grow_ch=32, scale=4)
+
+    # TODO: make sure strict=False is safe here
+    model.load_state_dict(torch.load(dest_path)['params_ema'], strict=False)
+    model.train(False)
+    model.eval()
+
+    rng = torch.rand(1, 3, 64, 64)
+    input_names = ['data']
+    output_names = ['output']
+    dynamic_axes = {'data': {2: 'width', 3: 'height'},
+                    'output': {2: 'width', 3: 'height'}}
+
+    print('exporting GFPGAN model to %s' % dest_onnx)
+    export(
+        model,
+        rng,
+        dest_onnx,
+        input_names=input_names,
+        output_names=output_names,
+        dynamic_axes=dynamic_axes,
+        opset_version=11,
+        export_params=True
+    )
+    print('GFPGAN exported to ONNX')
 
 
 def convert_diffuser():
@@ -70,18 +117,27 @@ def main() -> int:
     parser = ArgumentParser(
         prog='onnx-web model converter',
         description='convert checkpoint models to ONNX')
-    parser.add_argument('--diffusers', type=str, nargs='+',
+    parser.add_argument('--diffusers', type=str, nargs='*',
                         help='models using the diffusers pipeline')
     parser.add_argument('--gfpgan', action='store_true')
     parser.add_argument('--resrgan', action='store_true')
     args = parser.parse_args()
     print(args)
 
-    for model in args.diffusers:
-        print('convert ' + model)
+    if args.diffusers:
+        for source in args.diffusers:
+            print('converting Diffusers model: %s' % source[0])
+            convert_diffuser(*source)
 
     if args.resrgan:
-        convert_real_esrgan()
+        for source in sources.get('real_esrgan'):
+            print('converting Real ESRGAN model: %s' % source[0])
+            convert_real_esrgan(*source)
+
+    if args.gfpgan:
+        for source in sources.get('gfpgan'):
+            print('converting GFPGAN model: %s' % source[0])
+            convert_gfpgan(*source)
 
     return 0
 
diff --git a/api/onnx_web/serve.py b/api/onnx_web/serve.py
index d9afbfd9..43fce6ad 100644
--- a/api/onnx_web/serve.py
+++ b/api/onnx_web/serve.py
@@ -99,7 +99,6 @@ mask_filters = {
 upscale_models = [
     'RealESRGAN_x4plus',
     'GFPGANv1.3',
-    # 'https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth',
 ]