Update app.py

app.py

@@ -23,17 +23,17 @@ 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',
-    'lincoln.jpg')
+    'https://thumbs.dreamstime.com/b/tower-bridge-traditional-red-bus-black-white-colors-view-to-tower-bridge-london-black-white-colors-108478942.jpg',
+    'a1.jpg')
 torch.hub.download_url_to_file(
-    'https://user-images.githubusercontent.com/17445847/187400315-87a90ac9-d231-45d6-b377-38702bd1838f.jpg',
-    'AI-generate.jpg')
+    'https://media.istockphoto.com/id/523514029/photo/london-skyline-b-w.jpg?s=612x612&w=0&k=20&c=kJS1BAtfqYeUDaORupj0sBPc1hpzJhBUUqEFfRnHzZ0=',
+    'a2.jpg')
 torch.hub.download_url_to_file(
-    'https://user-images.githubusercontent.com/17445847/187400981-8a58f7a4-ef61-42d9-af80-bc6234cef860.jpg',
-    'Blake_Lively.jpg')
+    'https://i.guim.co.uk/img/media/06f614065ed82ca0e917b149a32493c791619854/0_0_3648_2789/master/3648.jpg?width=700&quality=85&auto=format&fit=max&s=05764b507c18a38590090d987c8b6202',
+    'a3.jpg')
 torch.hub.download_url_to_file(
-    'https://user-images.githubusercontent.com/17445847/187401133-8a3bf269-5b4d-4432-b2f0-6d26ee1d3307.png',
-    '10045.png')
+    'https://i.pinimg.com/736x/46/96/9e/46969eb94aec2437323464804d27706d--victorian-london-victorian-era.jpg',
+    'a4.jpg')
 
 # 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')
@@ -48,8 +48,6 @@ os.makedirs('output', exist_ok=True)
 def inference(img, version, scale):
     # weight /= 100
     print(img, version, scale)
-    if scale > 100:
-        scale = 100  # avoid too large scale value
     try:
         extension = os.path.splitext(os.path.basename(str(img)))[1]
         img = cv2.imread(img, cv2.IMREAD_UNCHANGED)
@@ -62,10 +60,6 @@ def inference(img, version, scale):
             img_mode = None
 
         h, w = img.shape[0:2]
-        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)
 
@@ -81,9 +75,12 @@ def inference(img, version, scale):
         elif version == 'RestoreFormer':
             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)
+        elif version == 'CodeFormer':
+             face_enhancer = GFPGANer(
+             model_path='CodeFormer.pth', upscale=2, arch='CodeFormer', channel_multiplier=2, bg_upsampler=upsampler)
+        elif version == 'RealESR-General-x4v3':
+             face_enhancer = GFPGANer(
+             model_path='realesr-general-x4v3.pth', upscale=2, arch='realesr-general', channel_multiplier=2, bg_upsampler=upsampler)
 
         try:
             # _, _, output = face_enhancer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True, weight=weight)
@@ -101,7 +98,7 @@ def inference(img, version, scale):
         if img_mode == 'RGBA':  # RGBA images should be saved in png format
             extension = 'png'
         else:
-            extension = 'png'
+            extension = 'jpg'
         save_path = f'output/out.{extension}'
         cv2.imwrite(save_path, output)
 
@@ -112,39 +109,34 @@ def inference(img, version, scale):
         return None, None
 
 
-title = "GFPGAN: Practical Face Restoration Algorithm"
-description = r"""Gradio demo for <a href='https://github.com/TencentARC/GFPGAN' target='_blank'><b>GFPGAN: Towards Real-World Blind Face Restoration with Generative Facial Prior</b></a>.<br>
-It can be used to restore your **old photos** or improve **AI-generated faces**.<br>
-To use it, simply upload your image.<br>
-If GFPGAN is helpful, please help to ⭐ the <a href='https://github.com/TencentARC/GFPGAN' target='_blank'>Github Repo</a> and recommend it to your friends 😊
+title = "Image Upscaling & Restoration(esp. Face) using GFPGAN Algorithm"
+description = r"""Gradio demo for <a href='https://github.com/TencentARC/GFPGAN' target='_blank'><b>GFPGAN: Towards Real-World Blind Face Restoration and Upscalling of the image with a Generative Facial Prior</b></a>.<br>
+Practically the algorithm is used to restore your **old photos** or improve **AI-generated faces**.<br>
+To use it, simply just upload the concerned image.<br>
 """
 article = r"""
-
 [![download](https://img.shields.io/github/downloads/TencentARC/GFPGAN/total.svg)](https://github.com/TencentARC/GFPGAN/releases)
 [![GitHub Stars](https://img.shields.io/github/stars/TencentARC/GFPGAN?style=social)](https://github.com/TencentARC/GFPGAN)
 [![arXiv](https://img.shields.io/badge/arXiv-Paper-<COLOR>.svg)](https://arxiv.org/abs/2101.04061)
-
-If you have any question, please email 📧 `[email protected]` or `[email protected]`.
-
-<center><img src='https://visitor-badge.glitch.me/badge?page_id=akhaliq_GFPGAN' alt='visitor badge'></center>
-<center><img src='https://visitor-badge.glitch.me/badge?page_id=Gradio_Xintao_GFPGAN' alt='visitor badge'></center>
+<center><img src='https://visitor-badge.glitch.me/badge?page_id=dj_face_restoration_GFPGAN' alt='visitor badge'></center>
 """
 demo = gr.Interface(
     inference, [
-        gr.Image(type="filepath", label="Input"),
-        # gr.Radio(['v1.2', 'v1.3', 'v1.4', 'RestoreFormer', 'CodeFormer'], type="value", value='v1.4', label='version'),
-        gr.Radio(['v1.2', 'v1.3', 'v1.4', 'RestoreFormer'], type="value", value='v1.4', label='version'),
-        gr.Number(label="Rescaling factor", value=2),
-        # gr.Slider(0, 100, label='Weight, only for CodeFormer. 0 for better quality, 100 for better identity', value=50)
+        gr.inputs.Image(type="filepath", label="Input"),
+        # gr.inputs.Radio(['v1.2', 'v1.3', 'v1.4', 'RestoreFormer', 'CodeFormer'], type="value", default='v1.4', label='version'),
+        gr.inputs.Radio(['v1.2', 'v1.3', 'v1.4', 'RestoreFormer','CodeFormer','RealESR-General-x4v3'], type="value", default='v1.4', label='version'),
+        gr.inputs.Number(label="Rescaling factor", default=2),
+        # gr.Slider(0, 100, label='Weight, only for CodeFormer. 0 for better quality, 100 for better identity', default=50)
     ], [
-        gr.Image(type="numpy", label="Output (The whole image)"),
-        gr.File(label="Download the output image")
+        gr.outputs.Image(type="numpy", label="Output (The whole image)"),
+        gr.outputs.File(label="Download the output image")
     ],
     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],
-              ['10045.png', 'v1.4', 2]])
-demo.queue().launch()
+    examples=[['a1.jpg', 'v1.4', 2], ['a2.jpg', 'v1.4', 2], ['a3.jpg', 'v1.4', 2],['a4.jpg', 'v1.4', 2]])
+    
+demo.queue(concurrency_count=4)
+demo.launch()