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Running on Zero
| import os | |
| import sys | |
| import types | |
| import cv2 | |
| import torch | |
| import torchvision.transforms as _tv_transforms | |
| # --------------------------------------------------------------------------- | |
| # torchvision compatibility shim | |
| # | |
| # basicsr (pulled in by gfpgan / realesrgan) does: | |
| # from torchvision.transforms.functional_tensor import rgb_to_grayscale | |
| # torchvision removed the `functional_tensor` module in 0.17 (Jan 2024). Since | |
| # ZeroGPU now requires torch >= 2.8 (=> torchvision >= 0.23), that import would | |
| # raise ModuleNotFoundError and crash the Space at startup. We register a tiny | |
| # stand-in module that forwards rgb_to_grayscale, BEFORE basicsr is imported. | |
| # --------------------------------------------------------------------------- | |
| try: | |
| from torchvision.transforms.functional import rgb_to_grayscale as _rgb_to_grayscale | |
| except Exception: # very new torchvision may only expose it under v2 | |
| from torchvision.transforms.v2.functional import rgb_to_grayscale as _rgb_to_grayscale | |
| if 'torchvision.transforms.functional_tensor' not in sys.modules: | |
| _shim = types.ModuleType('torchvision.transforms.functional_tensor') | |
| _shim.rgb_to_grayscale = _rgb_to_grayscale | |
| sys.modules['torchvision.transforms.functional_tensor'] = _shim | |
| setattr(_tv_transforms, 'functional_tensor', _shim) | |
| import gradio as gr | |
| from basicsr.archs.srvgg_arch import SRVGGNetCompact | |
| from gfpgan.utils import GFPGANer | |
| from realesrgan.utils import RealESRGANer | |
| # Try to import spaces, but handle gracefully if not available | |
| try: | |
| import spaces | |
| HAS_SPACES = True | |
| except ImportError: | |
| HAS_SPACES = False | |
| print("spaces library not available, GPU decorator will be skipped") | |
| os.system("pip freeze") | |
| # download weights | |
| if not os.path.exists('realesr-general-x4v3.pth'): | |
| os.system("wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth -P .") | |
| if not os.path.exists('GFPGANv1.2.pth'): | |
| os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.2.pth -P .") | |
| if not os.path.exists('GFPGANv1.3.pth'): | |
| os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth -P .") | |
| if not os.path.exists('GFPGANv1.4.pth'): | |
| os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth -P .") | |
| if not os.path.exists('RestoreFormer.pth'): | |
| os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/RestoreFormer.pth -P .") | |
| if not os.path.exists('CodeFormer.pth'): | |
| os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/CodeFormer.pth -P .") | |
| def safe_download(url, dst): | |
| """Download a file if it isn't already present. | |
| Returns True on success, False on failure. Never raises, so a single | |
| broken/blocked URL (e.g. an external host changing its rules) can't take | |
| down the whole Space at import time. | |
| """ | |
| if os.path.exists(dst): | |
| return True | |
| try: | |
| torch.hub.download_url_to_file(url, dst) | |
| return True | |
| except Exception as error: | |
| print(f'Could not download {dst}: {error}') | |
| return False | |
| # Example images. Note the Lincoln URL uses 960px, a "standard" Wikimedia | |
| # thumbnail size. Wikimedia's CDN now rejects non-standard sizes such as the | |
| # old 1024px (HTTP 400: "Use thumbnail sizes listed on https://w.wiki/GHai"). | |
| # Allowed sizes: 20, 40, 60, 120, 250, 330, 500, 960, 1280, 1920, 3840. | |
| EXAMPLE_DOWNLOADS = [ | |
| ('https://upload.wikimedia.org/wikipedia/commons/thumb/a/ab/Abraham_Lincoln_O-77_matte_collodion_print.jpg/960px-Abraham_Lincoln_O-77_matte_collodion_print.jpg', | |
| 'lincoln.jpg'), | |
| ('https://user-images.githubusercontent.com/17445847/187400315-87a90ac9-d231-45d6-b377-38702bd1838f.jpg', | |
| 'AI-generate.jpg'), | |
| ('https://user-images.githubusercontent.com/17445847/187400981-8a58f7a4-ef61-42d9-af80-bc6234cef860.jpg', | |
| 'Blake_Lively.jpg'), | |
| ('https://user-images.githubusercontent.com/17445847/187401133-8a3bf269-5b4d-4432-b2f0-6d26ee1d3307.png', | |
| '10045.png'), | |
| ] | |
| for _url, _dst in EXAMPLE_DOWNLOADS: | |
| safe_download(_url, _dst) | |
| # 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') | |
| model_path = 'realesr-general-x4v3.pth' | |
| half = True if torch.cuda.is_available() else False | |
| upsampler = RealESRGANer(scale=4, model_path=model_path, model=model, tile=0, tile_pad=10, pre_pad=0, half=half) | |
| os.makedirs('output', exist_ok=True) | |
| def inference_internal(img, version, scale): | |
| print(img, version, scale) | |
| if scale > 4: | |
| scale = 4 # avoid too large scale value | |
| try: | |
| extension = os.path.splitext(os.path.basename(str(img)))[1] | |
| img = cv2.imread(img, cv2.IMREAD_UNCHANGED) | |
| if len(img.shape) == 3 and img.shape[2] == 4: | |
| img_mode = 'RGBA' | |
| elif len(img.shape) == 2: # for gray inputs | |
| img_mode = None | |
| img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) | |
| else: | |
| 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) | |
| if version == 'v1.2': | |
| face_enhancer = GFPGANer( | |
| model_path='GFPGANv1.2.pth', upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler) | |
| elif version == 'v1.3': | |
| face_enhancer = GFPGANer( | |
| model_path='GFPGANv1.3.pth', upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler) | |
| elif version == 'v1.4': | |
| face_enhancer = GFPGANer( | |
| model_path='GFPGANv1.4.pth', upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler) | |
| elif version == 'RestoreFormer': | |
| face_enhancer = GFPGANer( | |
| model_path='RestoreFormer.pth', upscale=2, arch='RestoreFormer', channel_multiplier=2, bg_upsampler=upsampler) | |
| try: | |
| _, _, output = face_enhancer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True) | |
| except RuntimeError as error: | |
| print('Error', error) | |
| try: | |
| if scale != 2: | |
| interpolation = cv2.INTER_AREA if scale < 2 else cv2.INTER_LANCZOS4 | |
| h, w = img.shape[0:2] | |
| output = cv2.resize(output, (int(w * scale / 2), int(h * scale / 2)), interpolation=interpolation) | |
| except Exception as error: | |
| print('wrong scale input.', error) | |
| if img_mode == 'RGBA': # RGBA images should be saved in png format | |
| extension = 'png' | |
| else: | |
| extension = 'jpg' | |
| save_path = f'output/out.{extension}' | |
| cv2.imwrite(save_path, output) | |
| output = cv2.cvtColor(output, cv2.COLOR_BGR2RGB) | |
| return output, save_path | |
| except Exception as error: | |
| print('global exception', error) | |
| return None, None | |
| # Conditionally apply GPU decorator if available | |
| if HAS_SPACES: | |
| def inference(img, version, scale): | |
| return inference_internal(img, version, scale) | |
| else: | |
| def inference(img, version, scale): | |
| return inference_internal(img, version, scale) | |
| title = "GFPGAN: Practical Face Restoration Algorithm" | |
| description = r"""Gradio demo Fix 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 star the <a href='https://github.com/Nick088Official/GFPGAN-Fix' target='_blank'>Github Repo</a> and recommend it to your friends. | |
| """ | |
| article = r""" | |
| [](https://github.com/TencentARC/GFPGAN/releases) | |
| [](https://github.com/TencentARC/GFPGAN) | |
| [](https://arxiv.org/abs/2101.04061) | |
| If you have any question, please email `xintao.wang@outlook.com` or `xintaowang@tencent.com` (original creators). | |
| """ | |
| # Only offer examples for images that actually downloaded. | |
| candidate_examples = [ | |
| ['AI-generate.jpg', 'v1.4', 2], | |
| ['lincoln.jpg', 'v1.4', 2], | |
| ['Blake_Lively.jpg', 'v1.4', 2], | |
| ['10045.png', 'v1.4', 2], | |
| ] | |
| examples = [ex for ex in candidate_examples if os.path.exists(ex[0])] | |
| demo = gr.Interface( | |
| fn=inference, | |
| inputs=[ | |
| gr.Image(type="filepath", label="Input"), | |
| gr.Radio(['v1.2', 'v1.3', 'v1.4', 'RestoreFormer'], type="value", value='v1.4', label='version'), | |
| gr.Number(label="Rescaling factor", value=2), | |
| ], | |
| outputs=[ | |
| gr.Image(type="numpy", label="Output (The whole image)"), | |
| gr.File(label="Download the output image") | |
| ], | |
| title=title, | |
| description=description, | |
| article=article, | |
| examples=examples if examples else None) | |
| if __name__ == "__main__": | |
| demo.queue().launch() |