Update README.md

README.md

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-license: apache-2.0
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+# 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)
+
+![](assets/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)