Update app.py
Browse files
app.py
CHANGED
@@ -23,17 +23,17 @@ if not os.path.exists('CodeFormer.pth'):
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os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/CodeFormer.pth -P .")
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torch.hub.download_url_to_file(
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'https://
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'
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torch.hub.download_url_to_file(
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'https://
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'
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torch.hub.download_url_to_file(
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'https://
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'
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torch.hub.download_url_to_file(
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'https://
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'
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# background enhancer with RealESRGAN
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model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu')
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@@ -48,6 +48,8 @@ os.makedirs('output', exist_ok=True)
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def inference(img, version, scale):
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# weight /= 100
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print(img, version, scale)
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try:
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extension = os.path.splitext(os.path.basename(str(img)))[1]
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img = cv2.imread(img, cv2.IMREAD_UNCHANGED)
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@@ -60,6 +62,10 @@ def inference(img, version, scale):
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img_mode = None
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h, w = img.shape[0:2]
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if h < 300:
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img = cv2.resize(img, (w * 2, h * 2), interpolation=cv2.INTER_LANCZOS4)
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@@ -75,12 +81,9 @@ def inference(img, version, scale):
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elif version == 'RestoreFormer':
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face_enhancer = GFPGANer(
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model_path='RestoreFormer.pth', upscale=2, arch='RestoreFormer', channel_multiplier=2, bg_upsampler=upsampler)
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elif version == 'CodeFormer':
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elif version == 'RealESR-General-x4v3':
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face_enhancer = GFPGANer(
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model_path='realesr-general-x4v3.pth', upscale=2, arch='realesr-general', channel_multiplier=2, bg_upsampler=upsampler)
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try:
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# _, _, output = face_enhancer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True, weight=weight)
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@@ -109,34 +112,39 @@ def inference(img, version, scale):
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return None, None
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title = "
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description = r"""Gradio demo for <a href='https://github.com/TencentARC/GFPGAN' target='_blank'><b>GFPGAN: Towards Real-World Blind Face Restoration
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To use it, simply
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"""
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article = r"""
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[![download](https://img.shields.io/github/downloads/TencentARC/GFPGAN/total.svg)](https://github.com/TencentARC/GFPGAN/releases)
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[![GitHub Stars](https://img.shields.io/github/stars/TencentARC/GFPGAN?style=social)](https://github.com/TencentARC/GFPGAN)
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[![arXiv](https://img.shields.io/badge/arXiv-Paper-<COLOR>.svg)](https://arxiv.org/abs/2101.04061)
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"""
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demo = gr.Interface(
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inference, [
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gr.
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# gr.
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gr.
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gr.
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# gr.Slider(0, 100, label='Weight, only for CodeFormer. 0 for better quality, 100 for better identity',
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], [
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gr.
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gr.
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],
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title=title,
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description=description,
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article=article,
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# examples=[['AI-generate.jpg', 'v1.4', 2, 50], ['lincoln.jpg', 'v1.4', 2, 50], ['Blake_Lively.jpg', 'v1.4', 2, 50],
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# ['10045.png', 'v1.4', 2, 50]]).launch()
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examples=[['
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demo.queue(
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demo.launch()
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os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/CodeFormer.pth -P .")
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torch.hub.download_url_to_file(
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'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',
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'lincoln.jpg')
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torch.hub.download_url_to_file(
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'https://user-images.githubusercontent.com/17445847/187400315-87a90ac9-d231-45d6-b377-38702bd1838f.jpg',
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'AI-generate.jpg')
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torch.hub.download_url_to_file(
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'https://user-images.githubusercontent.com/17445847/187400981-8a58f7a4-ef61-42d9-af80-bc6234cef860.jpg',
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'Blake_Lively.jpg')
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torch.hub.download_url_to_file(
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'https://user-images.githubusercontent.com/17445847/187401133-8a3bf269-5b4d-4432-b2f0-6d26ee1d3307.png',
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'10045.png')
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# background enhancer with RealESRGAN
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model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu')
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def inference(img, version, scale):
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# weight /= 100
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print(img, version, scale)
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if scale > 4:
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scale = 4 # avoid too large scale value
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try:
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extension = os.path.splitext(os.path.basename(str(img)))[1]
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img = cv2.imread(img, cv2.IMREAD_UNCHANGED)
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img_mode = None
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h, w = img.shape[0:2]
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if h > 3500 or w > 3500:
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print('too large size')
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return None, None
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if h < 300:
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img = cv2.resize(img, (w * 2, h * 2), interpolation=cv2.INTER_LANCZOS4)
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elif version == 'RestoreFormer':
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face_enhancer = GFPGANer(
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model_path='RestoreFormer.pth', upscale=2, arch='RestoreFormer', channel_multiplier=2, bg_upsampler=upsampler)
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# elif version == 'CodeFormer':
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# face_enhancer = GFPGANer(
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# model_path='CodeFormer.pth', upscale=2, arch='CodeFormer', channel_multiplier=2, bg_upsampler=upsampler)
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try:
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# _, _, output = face_enhancer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True, weight=weight)
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return None, None
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title = "GFPGAN: Practical Face Restoration Algorithm"
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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>
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It can be used to restore your **old photos** or improve **AI-generated faces**.<br>
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To use it, simply upload your image.<br>
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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 😊
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"""
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article = r"""
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[![download](https://img.shields.io/github/downloads/TencentARC/GFPGAN/total.svg)](https://github.com/TencentARC/GFPGAN/releases)
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[![GitHub Stars](https://img.shields.io/github/stars/TencentARC/GFPGAN?style=social)](https://github.com/TencentARC/GFPGAN)
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[![arXiv](https://img.shields.io/badge/arXiv-Paper-<COLOR>.svg)](https://arxiv.org/abs/2101.04061)
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If you have any question, please email 📧 `[email protected]` or `[email protected]`.
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<center><img src='https://visitor-badge.glitch.me/badge?page_id=akhaliq_GFPGAN' alt='visitor badge'></center>
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<center><img src='https://visitor-badge.glitch.me/badge?page_id=Gradio_Xintao_GFPGAN' alt='visitor badge'></center>
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"""
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demo = gr.Interface(
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inference, [
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gr.Image(type="filepath", label="Input"),
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# gr.Radio(['v1.2', 'v1.3', 'v1.4', 'RestoreFormer', 'CodeFormer'], type="value", value='v1.4', label='version'),
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gr.Radio(['v1.2', 'v1.3', 'v1.4', 'RestoreFormer'], type="value", value='v1.4', label='version'),
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gr.Number(label="Rescaling factor", value=2),
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# gr.Slider(0, 100, label='Weight, only for CodeFormer. 0 for better quality, 100 for better identity', value=50)
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], [
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gr.Image(type="numpy", label="Output (The whole image)"),
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gr.File(label="Download the output image")
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],
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title=title,
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description=description,
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article=article,
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# examples=[['AI-generate.jpg', 'v1.4', 2, 50], ['lincoln.jpg', 'v1.4', 2, 50], ['Blake_Lively.jpg', 'v1.4', 2, 50],
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# ['10045.png', 'v1.4', 2, 50]]).launch()
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examples=[['AI-generate.jpg', 'v1.4', 2], ['lincoln.jpg', 'v1.4', 2], ['Blake_Lively.jpg', 'v1.4', 2],
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['10045.png', 'v1.4', 2]])
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demo.queue().launch()
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