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import os |
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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 huggingface_hub import hf_hub_download |
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from realesrgan.utils import RealESRGANer |
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REALESRGAN_REPO_ID = 'leonelhs/realesrgan' |
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GFPGAN_REPO_ID = 'leonelhs/gfpgan' |
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os.system("pip freeze") |
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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 = hf_hub_download(repo_id=REALESRGAN_REPO_ID, filename='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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def download_model(file): |
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return hf_hub_download(repo_id=GFPGAN_REPO_ID, filename=file) |
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def predict(image, version, scale): |
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scale = int(scale) |
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face_enhancer = None |
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if version == 'v1.2': |
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path = download_model('GFPGANv1.2.pth') |
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face_enhancer = GFPGANer( |
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model_path=path, upscale=scale, arch='clean', channel_multiplier=2, bg_upsampler=upsampler) |
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elif version == 'v1.3': |
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path = download_model('GFPGANv1.3.pth') |
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face_enhancer = GFPGANer( |
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model_path=path, upscale=scale, arch='clean', channel_multiplier=2, bg_upsampler=upsampler) |
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elif version == 'v1.4': |
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path = download_model('GFPGANv1.4.pth') |
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face_enhancer = GFPGANer( |
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model_path=path, upscale=scale, arch='clean', channel_multiplier=2, bg_upsampler=upsampler) |
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elif version == 'RestoreFormer': |
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path = download_model('RestoreFormer.pth') |
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face_enhancer = GFPGANer( |
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model_path=path, upscale=scale, arch='RestoreFormer', channel_multiplier=2, bg_upsampler=upsampler) |
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_, _, output = face_enhancer.enhance(image, has_aligned=False, only_center_face=False, paste_back=True) |
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return output |
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title = "GFPGAN" |
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description = r""" |
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<b>Practical Face Restoration Algorithm</b> |
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""" |
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article = r""" |
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<center><span>[email protected] or [email protected]</span></center> |
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</br> |
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<center><a href='https://github.com/TencentARC/GFPGAN' target='_blank'>Github Repo ⭐ </a> are welcome</center> |
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""" |
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demo = gr.Interface( |
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predict, [ |
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gr.Image(type="numpy", label="Input"), |
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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.Dropdown(["1", "2", "3", "4"], value="2", label="Rescaling factor") |
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], [ |
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gr.Image(type="numpy", label="Output", interactive=False) |
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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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demo.queue().launch() |
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