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I have updated this demo for load models from Hugging Face, also I have updated the requirements. Here is my demo to testing purposes https://huggingface.co/spaces/leonelhs/GFPGAN

app.py

@@ -5,22 +5,13 @@ import gradio as gr
 import torch
 from basicsr.archs.srvgg_arch import SRVGGNetCompact
 from gfpgan.utils import GFPGANer
+from huggingface_hub import snapshot_download, hf_hub_download
 from realesrgan.utils import RealESRGANer
 
+REALESRGAN_REPO_ID = 'leonelhs/realesrgan'
+GFPGAN_REPO_ID = 'leonelhs/gfpgan'
+
 os.system("pip freeze")
-# download weights
-if not os.path.exists('realesr-general-x4v3.pth'):
-    os.system("wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth -P .")
-if not os.path.exists('GFPGANv1.2.pth'):
-    os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.2.pth -P .")
-if not os.path.exists('GFPGANv1.3.pth'):
-    os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth -P .")
-if not os.path.exists('GFPGANv1.4.pth'):
-    os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth -P .")
-if not os.path.exists('RestoreFormer.pth'):
-    os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/RestoreFormer.pth -P .")
-if not os.path.exists('CodeFormer.pth'):
-    os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/CodeFormer.pth -P .")
 
 torch.hub.download_url_to_file(
     'https://upload.wikimedia.org/wikipedia/commons/thumb/a/ab/Abraham_Lincoln_O-77_matte_collodion_print.jpg/1024px-Abraham_Lincoln_O-77_matte_collodion_print.jpg',
@@ -37,7 +28,7 @@ torch.hub.download_url_to_file(
 
 # background enhancer with RealESRGAN
 model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu')
-model_path = 'realesr-general-x4v3.pth'
+model_path = hf_hub_download(repo_id=REALESRGAN_REPO_ID, filename='realesr-general-x4v3.pth')
 half = True if torch.cuda.is_available() else False
 upsampler = RealESRGANer(scale=4, model_path=model_path, model=model, tile=0, tile_pad=10, pre_pad=0, half=half)
 
@@ -65,28 +56,32 @@ def inference(img, version, scale):
         if h > 3500 or w > 3500:
             print('too large size')
             return None, None
-        
+
         if h < 300:
             img = cv2.resize(img, (w * 2, h * 2), interpolation=cv2.INTER_LANCZOS4)
 
+        face_enhancer = None
+        snapshot_folder = snapshot_download(repo_id=GFPGAN_REPO_ID)
+
         if version == 'v1.2':
+            path = os.path.join(snapshot_folder, 'GFPGANv1.2.pth')
             face_enhancer = GFPGANer(
-            model_path='GFPGANv1.2.pth', upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
+                model_path=path, upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
         elif version == 'v1.3':
+            path = os.path.join(snapshot_folder, 'GFPGANv1.3.pth')
             face_enhancer = GFPGANer(
-            model_path='GFPGANv1.3.pth', upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
+                model_path=path, upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
         elif version == 'v1.4':
+            path = os.path.join(snapshot_folder, 'GFPGANv1.4.pth')
             face_enhancer = GFPGANer(
-            model_path='GFPGANv1.4.pth', upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
+                model_path=path, upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
         elif version == 'RestoreFormer':
+            path = os.path.join(snapshot_folder, 'RestoreFormer.pth')
             face_enhancer = GFPGANer(
-            model_path='RestoreFormer.pth', upscale=2, arch='RestoreFormer', channel_multiplier=2, bg_upsampler=upsampler)
-        # elif version == 'CodeFormer':
-        #     face_enhancer = GFPGANer(
-        #     model_path='CodeFormer.pth', upscale=2, arch='CodeFormer', channel_multiplier=2, bg_upsampler=upsampler)
+                model_path=path, upscale=2, arch='RestoreFormer', channel_multiplier=2,
+                bg_upsampler=upsampler)
 
         try:
-            # _, _, output = face_enhancer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True, weight=weight)
             _, _, output = face_enhancer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True)
         except RuntimeError as error:
             print('Error', error)
@@ -143,8 +138,8 @@ demo = gr.Interface(
     title=title,
     description=description,
     article=article,
-    # examples=[['AI-generate.jpg', 'v1.4', 2, 50], ['lincoln.jpg', 'v1.4', 2, 50], ['Blake_Lively.jpg', 'v1.4', 2, 50],
-    #           ['10045.png', 'v1.4', 2, 50]]).launch()
-    examples=[['AI-generate.jpg', 'v1.4', 2], ['lincoln.jpg', 'v1.4', 2], ['Blake_Lively.jpg', 'v1.4', 2],
+    examples=[['AI-generate.jpg', 'v1.4', 2],
+              ['lincoln.jpg', 'v1.4', 2],
+              ['Blake_Lively.jpg', 'v1.4', 2],
               ['10045.png', 'v1.4', 2]])
 demo.queue().launch()

requirements.txt

@@ -1,8 +1,8 @@
-torch>=1.7
+torch>=2.0.1
 basicsr>=1.4.2
-facexlib>=0.2.5
-gfpgan>=1.3.7
-realesrgan>=0.2.5
+facexlib>=0.3.0
+gfpgan>=1.3.8
+realesrgan>=0.3.0
 numpy
 opencv-python
 torchvision