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Browse filesI 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 +21 -26
- requirements.txt +4 -4
app.py
CHANGED
@@ -5,22 +5,13 @@ 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://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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@@ -37,7 +28,7 @@ torch.hub.download_url_to_file(
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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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@@ -65,28 +56,32 @@ def inference(img, version, scale):
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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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if version == 'v1.2':
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face_enhancer = GFPGANer(
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elif version == 'v1.3':
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face_enhancer = GFPGANer(
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elif version == 'v1.4':
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face_enhancer = GFPGANer(
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elif version == 'RestoreFormer':
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face_enhancer = GFPGANer(
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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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_, _, 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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@@ -143,8 +138,8 @@ demo = gr.Interface(
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title=title,
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description=description,
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article=article,
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['10045.png', 'v1.4', 2]])
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demo.queue().launch()
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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 snapshot_download, 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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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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# 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 = 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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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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face_enhancer = None
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snapshot_folder = snapshot_download(repo_id=GFPGAN_REPO_ID)
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if version == 'v1.2':
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path = os.path.join(snapshot_folder, 'GFPGANv1.2.pth')
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face_enhancer = GFPGANer(
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model_path=path, upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
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elif version == 'v1.3':
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path = os.path.join(snapshot_folder, 'GFPGANv1.3.pth')
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face_enhancer = GFPGANer(
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model_path=path, upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
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elif version == 'v1.4':
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path = os.path.join(snapshot_folder, 'GFPGANv1.4.pth')
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face_enhancer = GFPGANer(
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model_path=path, upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
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elif version == 'RestoreFormer':
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path = os.path.join(snapshot_folder, 'RestoreFormer.pth')
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face_enhancer = GFPGANer(
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model_path=path, upscale=2, arch='RestoreFormer', channel_multiplier=2,
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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)
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except RuntimeError as error:
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print('Error', error)
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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],
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['lincoln.jpg', 'v1.4', 2],
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['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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requirements.txt
CHANGED
@@ -1,8 +1,8 @@
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torch>=1
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basicsr>=1.4.2
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facexlib>=0.
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gfpgan>=1.3.
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realesrgan>=0.
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numpy
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opencv-python
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torchvision
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torch>=2.0.1
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basicsr>=1.4.2
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facexlib>=0.3.0
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gfpgan>=1.3.8
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realesrgan>=0.3.0
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numpy
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opencv-python
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torchvision
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