---
title: AuraSR
canonical_url: "https://www.modelscope.cn/models/AI-ModelScope/AuraSR"
md_url: "https://www.modelscope.cn/models/AI-ModelScope/AuraSR.md"
repository: AI-ModelScope/AuraSR
last_updated: 2024-07-26
license: cc
pipeline_tag: text-generation
tasks:
  - text-generation
parameters: 617.6M
tensor_type:
  - F32
library_name:
  - safetensors
  - pytorch
frameworks:
  - pytorch
downloads: 2535
stars: 1
tags:
  - art
  - pytorch
  - super-resolution
---

# AuraSR

> AuraSR - AI-ModelScope 在 ModelScope 开源的模型。GAN-based Super-Resolution for upscaling generated images, a variation of the GigaGAN paper for image-conditioned upscaling. Torch implementation is based on the unofficial lucidrains/gigagan-pytorch repository.

AI-ModelScope/AuraSR 是 ModelScope 魔搭社区上的 617.6M 参数text-generation模型，采用 cc 许可。

- **Repository**: AI-ModelScope/AuraSR
- **License**: cc
- **Tasks**: text-generation
- **Parameters**: 617.6M
- **Tags**: art, pytorch, super-resolution
- **Downloads**: 2535
- **Stars**: 1
- **Last updated**: 2024-07-26

Source: https://www.modelscope.cn/models/AI-ModelScope/AuraSR

---

# AuraSR
![aurasr example](https://storage.googleapis.com/falserverless/gallery/aurasr-animated.webp)

GAN-based Super-Resolution for upscaling generated images, a variation of the [GigaGAN](https://mingukkang.github.io/GigaGAN/) paper for image-conditioned upscaling. Torch implementation is based on the unofficial [lucidrains/gigagan-pytorch](https://github.com/lucidrains/gigagan-pytorch) repository.

## Usage

```bash
$ pip install aura-sr
```

```python
from aura_sr import AuraSR

aura_sr = AuraSR.from_pretrained("fal-ai/AuraSR")
```

```python
import requests
from io import BytesIO
from PIL import Image

def load_image_from_url(url):
    response = requests.get(url)
    image_data = BytesIO(response.content)
    return Image.open(image_data)

image = load_image_from_url("https://mingukkang.github.io/GigaGAN/static/images/iguana_output.jpg").resize((256, 256))
upscaled_image = aura_sr.upscale_4x(image)
```