---
license: bsd-3-clause
language:
- en
tags:
  - super-resolution
  - image-to-image
  - real-esrgan
  - esrgan
  - anime
  - pytorch
paper:
- https://arxiv.org/abs/2107.10833
pipeline_tag: image-to-image
---

# Real-ESRGAN x4plus Anime 6B

This repository hosts the **`RealESRGAN_x4plus_anime_6B.pth`** pre-trained generator weights from the [xinntao/Real-ESRGAN](https://github.com/xinntao/Real-ESRGAN) project. The file is a 1:1 mirror of the asset originally released by Xintao Wang on **August 31, 2021** as part of [Real-ESRGAN v0.2.2.4](https://github.com/xinntao/Real-ESRGAN/releases/tag/v0.2.2.4).

Per the [v0.2.2.4 release notes](https://github.com/xinntao/Real-ESRGAN/releases/tag/v0.2.2.4), this checkpoint is *"optimized for **anime** images with much smaller model size."* It is a 6-block RRDBNet — significantly smaller (~18 MB) than the standard 23-block [`x4plus`](https://huggingface.co/amd/realesrgan-x4plus) model (~67 MB) — and is intended for upscaling anime / illustration content. Visual comparisons with [waifu2x](https://github.com/nihui/waifu2x-ncnn-vulkan) are documented in the upstream [`docs/anime_model.md`](https://github.com/xinntao/Real-ESRGAN/blob/master/docs/anime_model.md).

## 📋 Model Details

| Field | Value |
| --- | --- |
| Original release | [v0.2.2.4](https://github.com/xinntao/Real-ESRGAN/releases/tag/v0.2.2.4), 31 Aug 2021 |
| Authors | Xintao Wang, Liangbin Xie, Chao Dong, Ying Shan — Tencent ARC Lab; Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences ([paper](https://arxiv.org/abs/2107.10833)) |
| Architecture | `RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=6, num_grow_ch=32, scale=4)` ([source](https://github.com/xinntao/Real-ESRGAN/blob/master/inference_realesrgan.py)). The `_6B` suffix denotes **6 RRDB blocks**, vs. 23 in the standard `x4plus`. |
| Upscale factor | 4× |
| Weight file | `RealESRGAN_x4plus_anime_6B.pth` (~18 MB) |
| Domain | Anime / illustration imagery |
| Paper | [Wang et al., 2021 — *Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data*](https://arxiv.org/abs/2107.10833) (ICCVW 2021) |
| License | [BSD 3-Clause](LICENSE), Copyright (c) 2021 Xintao Wang |
| Source repository | [github.com/xinntao/Real-ESRGAN](https://github.com/xinntao/Real-ESRGAN) |
| Original asset URL | [github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth) |
| Reference doc | [`docs/anime_model.md`](https://github.com/xinntao/Real-ESRGAN/blob/master/docs/anime_model.md) |

## ⚡ Intended Use

This model is purpose-built for upscaling **anime / line-art / illustration** content at 4×. Its compact 6-block architecture makes it roughly 4× smaller than the standard `x4plus` checkpoint and correspondingly faster at inference. For natural photographs and general scenes the larger [`amd/realesrgan-x4plus`](https://huggingface.co/amd/realesrgan-x4plus) (23-block) checkpoint typically gives better results.

## 🛠️ How to Use

The canonical entry point is the upstream [Real-ESRGAN repository](https://github.com/xinntao/Real-ESRGAN). The workflow below mirrors the [PyTorch Inference](https://github.com/xinntao/Real-ESRGAN/blob/master/docs/anime_model.md#pytorch-inference) section of the upstream `anime_model.md`:

```bash
# 1. Clone Real-ESRGAN
git clone https://github.com/xinntao/Real-ESRGAN.git
cd Real-ESRGAN

# 2. Install dependencies
pip install basicsr facexlib gfpgan
pip install -r requirements.txt
python setup.py develop

# 3. Download the weights from this Hugging Face repo
huggingface-cli download amd/realesrgan-x4plus-anime-6b RealESRGAN_x4plus_anime_6B.pth --local-dir weights

# 4. Run inference
python inference_realesrgan.py -n RealESRGAN_x4plus_anime_6B -i inputs
```

A portable NCNN executable variant is also available via the `realesrgan-x4plus-anime` model name in [Real-ESRGAN-ncnn-vulkan](https://github.com/xinntao/Real-ESRGAN/releases) — see the upstream [README.md](https://github.com/xinntao/Real-ESRGAN/blob/master/README.md) for the full set of options.

## ⚠️ Caveats and Recommendations

This is a domain-specific model tuned for anime / illustration content. Quality on natural photos will typically be lower than with the standard 23-block [`x4plus`](https://huggingface.co/amd/realesrgan-x4plus) checkpoint. [Wang et al. (2021)](https://arxiv.org/abs/2107.10833) generally note that Real-ESRGAN can still introduce aliasing or artifacts on difficult inputs; see the upstream [`anime_model.md`](https://github.com/xinntao/Real-ESRGAN/blob/master/docs/anime_model.md) for qualitative comparisons against waifu2x.

## 📌 Citation

If you use this model, please cite the original Real-ESRGAN paper:

```bibtex
@InProceedings{wang2021realesrgan,
    author    = {Xintao Wang and Liangbin Xie and Chao Dong and Ying Shan},
    title     = {Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data},
    booktitle = {International Conference on Computer Vision Workshops (ICCVW)},
    date      = {2021}
}
```

## 📜 License

These weights are distributed under the **[BSD 3-Clause License](LICENSE)**, Copyright (c) 2021 Xintao Wang ([upstream LICENSE](https://github.com/xinntao/Real-ESRGAN/blob/master/LICENSE)). This repository re-hosts the original artifact unchanged; please attribute the original authors when using or redistributing the weights.

## 🤗 Acknowledgments

All credit for the model architecture, training methodology, and weights goes to **[Xintao Wang](https://github.com/xinntao)** and the Real-ESRGAN authors at Tencent ARC Lab and the Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences. This Hugging Face repository exists only as a convenient mirror of the pre-trained weight file alongside its license and citation context.