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Duplicate from clem/Image_Face_Upscale_Restoration-GFPGAN
Browse filesCo-authored-by: 🤗 <clem@users.noreply.huggingface.co>
- .gitattributes +31 -0
- README.md +14 -0
- app.py +142 -0
- packages.txt +3 -0
- requirements.txt +13 -0
.gitattributes
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README.md
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---
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title: Image Face Upscale Restoration-GFPGAN
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emoji: 📈
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colorFrom: blue
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colorTo: gray
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sdk: gradio
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sdk_version: 3.1.7
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app_file: app.py
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pinned: false
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license: apache-2.0
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duplicated_from: clem/Image_Face_Upscale_Restoration-GFPGAN
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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app.py
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import os
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import cv2
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import gradio as gr
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import torch
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from basicsr.archs.srvgg_arch import SRVGGNetCompact
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from gfpgan.utils import GFPGANer
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from realesrgan.utils import RealESRGANer
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os.system("pip freeze")
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# download weights
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if not os.path.exists('realesr-general-x4v3.pth'):
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os.system("wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth -P .")
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if not os.path.exists('GFPGANv1.2.pth'):
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os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.2.pth -P .")
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if not os.path.exists('GFPGANv1.3.pth'):
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os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth -P .")
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if not os.path.exists('GFPGANv1.4.pth'):
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os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth -P .")
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if not os.path.exists('RestoreFormer.pth'):
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os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/RestoreFormer.pth -P .")
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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://thumbs.dreamstime.com/b/tower-bridge-traditional-red-bus-black-white-colors-view-to-tower-bridge-london-black-white-colors-108478942.jpg',
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'a1.jpg')
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torch.hub.download_url_to_file(
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'https://media.istockphoto.com/id/523514029/photo/london-skyline-b-w.jpg?s=612x612&w=0&k=20&c=kJS1BAtfqYeUDaORupj0sBPc1hpzJhBUUqEFfRnHzZ0=',
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'a2.jpg')
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torch.hub.download_url_to_file(
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'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',
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'a3.jpg')
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torch.hub.download_url_to_file(
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'https://i.pinimg.com/736x/46/96/9e/46969eb94aec2437323464804d27706d--victorian-london-victorian-era.jpg',
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'a4.jpg')
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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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model_path = 'realesr-general-x4v3.pth'
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half = True if torch.cuda.is_available() else False
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upsampler = RealESRGANer(scale=4, model_path=model_path, model=model, tile=0, tile_pad=10, pre_pad=0, half=half)
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os.makedirs('output', exist_ok=True)
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# def inference(img, version, scale, weight):
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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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if len(img.shape) == 3 and img.shape[2] == 4:
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img_mode = 'RGBA'
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elif len(img.shape) == 2: # for gray inputs
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img_mode = None
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img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
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else:
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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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if version == 'v1.2':
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face_enhancer = GFPGANer(
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model_path='GFPGANv1.2.pth', upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
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elif version == 'v1.3':
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face_enhancer = GFPGANer(
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model_path='GFPGANv1.3.pth', upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
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elif version == 'v1.4':
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face_enhancer = GFPGANer(
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model_path='GFPGANv1.4.pth', upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
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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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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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_, _, output = face_enhancer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True)
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except RuntimeError as error:
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print('Error', error)
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try:
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if scale != 2:
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interpolation = cv2.INTER_AREA if scale < 2 else cv2.INTER_LANCZOS4
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h, w = img.shape[0:2]
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output = cv2.resize(output, (int(w * scale / 2), int(h * scale / 2)), interpolation=interpolation)
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except Exception as error:
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print('wrong scale input.', error)
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if img_mode == 'RGBA': # RGBA images should be saved in png format
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extension = 'png'
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else:
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extension = 'jpg'
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save_path = f'output/out.{extension}'
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cv2.imwrite(save_path, output)
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output = cv2.cvtColor(output, cv2.COLOR_BGR2RGB)
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return output, save_path
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except Exception as error:
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print('global exception', error)
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return None, None
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title = "Image Upscaling & Restoration(esp. Face) using GFPGAN 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 and Upscalling of the image with a Generative Facial Prior</b></a>.<br>
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Practically the algorithm is used to restore your **old photos** or improve **AI-generated faces**.<br>
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To use it, simply just upload the concerned image.<br>
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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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<center><img src='https://visitor-badge.glitch.me/badge?page_id=dj_face_restoration_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.inputs.Image(type="filepath", label="Input"),
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# gr.inputs.Radio(['v1.2', 'v1.3', 'v1.4', 'RestoreFormer', 'CodeFormer'], type="value", default='v1.4', label='version'),
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gr.inputs.Radio(['v1.2', 'v1.3', 'v1.4', 'RestoreFormer','CodeFormer','RealESR-General-x4v3'], type="value", default='v1.4', label='version'),
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gr.inputs.Number(label="Rescaling factor", default=2),
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# gr.Slider(0, 100, label='Weight, only for CodeFormer. 0 for better quality, 100 for better identity', default=50)
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], [
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gr.outputs.Image(type="numpy", label="Output (The whole image)"),
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gr.outputs.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=[['a1.jpg', 'v1.4', 2], ['a2.jpg', 'v1.4', 2], ['a3.jpg', 'v1.4', 2],['a4.jpg', 'v1.4', 2]])
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demo.queue(concurrency_count=4)
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demo.launch()
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packages.txt
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ffmpeg
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libsm6
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libxext6
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requirements.txt
ADDED
@@ -0,0 +1,13 @@
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torch>=1.7
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basicsr>=1.4.2
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facexlib>=0.2.5
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gfpgan>=1.3.7
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realesrgan>=0.2.5
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numpy
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opencv-python
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torchvision
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scipy
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tqdm
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lmdb
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pyyaml
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yapf
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