Изображения
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Free local upscaler — 8K photos and 4K video with Real-ESRGAN, no cloud
★ 65 звёздMIT
Смотреть руководство →Real-ESRGAN — проект с открытым исходным кодом из категории «Изображения» от xinntao (xinntao/Real-ESRGAN на GitHub). У него 37 147 звёзд на GitHub и 4 495 форков, он распространяется по лицензии BSD-3-Clause и последний раз обновлялся на GitHub 6 августа 2024.
Real-ESRGAN — practical blind super-resolution for real-world photos and video: restores and upscales degraded images with models trained on pure synthetic data, BSD-3-Clause
Clone the repo and install the requirements, or install the realesrgan package from PyPI (with basicsr, facexlib and gfpgan) per the README dependencies section.
Fetch the pretrained weights for your job — RealESRGAN_x4plus for general photos, the anime_6B model for illustrations, or the general-x4v3 model when you want denoising control with -dn.
Run inference_realesrgan.py with your input folder and chosen model; use --outscale for an arbitrary final scale and --face_enhance to chain GFPGAN face restoration.
Trained on synthetic degradations that mimic real damage, so it restores photos — not just clean downscales — convincingly.
General x4plus, a compact anime model, anime-video models and the denoise-tunable realesr-general-x4v3, all in the model zoo.
Integrates GFPGAN so portraits get face-aware restoration in the same pass with --face_enhance.
Official ncnn-vulkan executables for Windows, Linux and macOS run upscaling without any Python environment.
The package route from the README ecosystem.
pip install basicsr facexlib gfpgan
pip install realesrganThe repo route: install requirements, then run the inference script on a folder of images.
git clone https://github.com/xinntao/Real-ESRGAN.git
cd Real-ESRGAN
pip install -r requirements.txt
python inference_realesrgan.py -n RealESRGAN_x4plus -i inputs -o resultsDownload realesrgan-ncnn-vulkan for your OS from the v0.2.5.0 release and run it directly — no Python needed.
https://github.com/xinntao/Real-ESRGAN/releasesRestoration is one branch of the wider image-to-image translation family — the branch that starts with the classic CycleGAN and pix2pix implementations, which taught models to turn sketches into photos and day scenes into night ones.
Xintao Wang and co-authors at Tencent ARC Lab and the Shenzhen Institutes of Advanced Technology (xinntao/Real-ESRGAN on GitHub); the paper is arXiv 2107.10833.
Yes — BSD-3-Clause licensed, including for commercial use, subject to the licence terms. Check the licences of any model weights you add.
Honestly: it is a mature, stable codebase — its last push was 2024-08-06 — rather than an actively evolving one. It remains the reference implementation and is embedded in many other tools.
Against GFPGAN from the same lab family, Real-ESRGAN is the general image/video restorer and GFPGAN is the face specialist — they are designed to chain together. Against newer diffusion upscalers, Real-ESRGAN is far faster and more predictable, with less invented detail.
pip install realesrgan (plus basicsr, facexlib, gfpgan), download the x4plus weights, and run inference_realesrgan.py on a test folder — or grab the portable ncnn build if you want zero setup.
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