---
id: realesrgan
version: "0.3.0"
license: BSD-3-Clause License
license_treatment: permissive
maintenance: dormant
---

# realesrgan — Real-ESRGAN aims at developing Practical Algorithms for General Image Restoration

License: permissive · Maintenance: dormant · Downloads: 184.2K/mo

## What it is and what it does

Real-ESRGAN is a practical image and video restoration tool that enlarges low-resolution content using deep neural networks. It extends ESRGAN with models trained purely on synthetic data, making it robust to real-world degradation without requiring paired high/low-resolution training data. The package includes general-purpose upscaling models and specialized anime video models, plus integration with GFPGAN for face enhancement.

You use it by instantiating an upsampler with a chosen model and calling enhance() on input images or frames. It handles grayscale, 16-bit, and alpha-channel images, supports arbitrary output scales via LANCZOS4 resizing, and offers a tile option for processing large images. The package depends on torch, torchvision, basicsr, gfpgan, facexlib, numpy, opencv-python, Pillow, and tqdm.

Use it for:

- Upscale low-resolution photographs or screenshots by 2x to 4x without training custom models.
- Restore anime images and videos using the specialized anime models.
- Enhance faces in portraits or video frames by combining Real-ESRGAN with integrated GFPGAN.
- Process archived or degraded video footage frame-by-frame to improve visual quality.
- Batch upscale images with tile mode to handle memory constraints on large files.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Real-ESRGAN upscales images and videos using deep learning models trained on synthetic data, with specialized support for anime content and face enhancement through integrated GFPGAN.

Yes, if you need practical image or video upscaling. Real-ESRGAN is well-established with no known vulnerabilities, permissively licensed, and requires only standard Python dependencies. The dormant maintenance status is not a blocker—the package is feature-complete and stable. Install it if you have a GPU or can tolerate CPU inference; expect to download model weights on first use.

## Install

pip install realesrgan
uv add realesrgan
poetry add realesrgan

## Installing realesrgan

Before you install: Low friction: pure Python wheel with no compiled dependencies. Maintenance is dormant (last commit 2024-08-06, 1424 days since release), but the repo remains active with no known vulnerabilities. Nine runtime dependencies include torch, torchvision, and opencv-python, which are substantial but standard for computer vision work.

License in practice: BSD-3-Clause is permissive: you can use, modify, and distribute this package freely in commercial or private projects, provided you include the license notice and disclaim liability.

Quickstart:

pip install realesrgan

from realesrgan import RealESRGANer

upsampler = RealESRGANer(scale=4, model_name='RealESRGAN_x4plus')
output, _ = upsampler.enhance(input_img, outscale=4)

Requires PyTorch >= 1.7; model weights are downloaded on first use and can be several hundred MB.

Verify before relying:

- Whether pre-trained model weights are automatically cached or must be manually downloaded.
- Exact memory and disk requirements for different model variants (x2plus, x4plus, anime_6B, etc.).
- Performance characteristics (inference time, VRAM usage) on typical hardware.
- Supported input and output formats beyond what the fact sheet explicitly documents.

## Package facts

- License: BSD-3-Clause License (permissive)
- Python support: unspecified
- Install friction: low
- Maintenance: dormant
- Downloads: 184.2K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

image upscaling super-resolution, video restoration enhancement, anime image upscaler, face enhancement restoration, blind super-resolution, esrgan pytorch, image restoration deep learning, image-upscaling, video-restoration, deep-learning

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