---
language:
- ru
- en
tags:
- PyTorch
thumbnail: "https://github.com/ai-forever/KandiSuperRes/"
---

# KandiSuperRes - diffusion model for 4K super resolution

[Habr Post](https://habr.com/ru/companies/sberbank/articles/775590/) | [Github](https://github.com/ai-forever/KandiSuperRes/) | [Telegram-bot](https://t.me/kandinsky21_bot) | [Technical Report](https://arxiv.org/pdf/2312.03511.pdf)| [Our text-to-image model](https://github.com/ai-forever/Kandinsky-3/tree/main)

![](title.png)

## Description

KandiSuperRes is an open-source diffusion model for x4 super resolution. This model is based on the [Kandinsky 3.0](https://github.com/ai-forever/Kandinsky-3/tree/main) architecture with some modifications. For generation in 4K, the [MultiDiffusion](https://arxiv.org/pdf/2302.08113.pdf) algorithm was used, which allows to generate panoramic images. For more information: details of architecture and training, example of generations check out our [Habr post]().

## Installing

To install repo first one need to create conda environment:

```
conda create -n kandisuperres -y python=3.8;
source activate kandisuperres;
pip install -r requirements.txt;
```

## How to use

```python
from KandiSuperRes import get_SR_pipeline
from PIL import Image

sr_pipe = get_SR_pipeline(device='cuda', fp16=True)

lr_image = Image.open('')
sr_image = sr_pipe(lr_image)
```

## Authors
+ Anastasia Maltseva [Github](https://github.com/NastyaMittseva)
+ Vladimir Arkhipkin: [Github](https://github.com/oriBetelgeuse)
+ Andrey Kuznetsov: [Github](https://github.com/kuznetsoffandrey), [Blog](https://t.me/complete_ai)
+ Denis Dimitrov: [Github](https://github.com/denndimitrov), [Blog](https://t.me/dendi_math_ai)