---
license: apache-2.0
pipeline_tag: text-to-image
datasets:
- CaptionEmporium/midjourney-niji-1m-llavanext
---

# Simple Diffusion XS

*XS Size, Excess Quality*


At AiArtLab, we strive to create a free, compact and fast model that can be trained on consumer graphics cards.

- Model: 0.8b parameters [unet](https://huggingface.co/CompVis/stable-diffusion-v1-4)
- Text encoder: [LongCLIP with 248 tokens](https://huggingface.co/zer0int/CLIP-KO-LITE-TypoAttack-Attn-Dropout-ViT-L-14)
- VAE: 16x16ch, Simple VAE

### Example

```
import torch
from diffusers import DiffusionPipeline

device = "cuda" if torch.cuda.is_available() else "cpu"
dtype = torch.float16 if torch.cuda.is_available() else torch.float32

pipe_id = "AiArtLab/sdxs-08b"
pipe = DiffusionPipeline.from_pretrained(
    pipe_id,
    torch_dtype=dtype,
    trust_remote_code=True
).to(device)

prompt = "girl, smiling, red eyes, blue hair, white shirt"
negative_prompt="low quality, bad quality"
image = pipe(
    prompt=prompt,
    negative_prompt = negative_prompt,
).images[0]

image.show(image)
```

### Model Limitations:
- Limited concept coverage due to the small dataset (1kk).

## Acknowledgments
- **[Stan](https://t.me/Stangle)** — Key investor. Thank you for believing in us when others called it madness.
- **Captainsaturnus**
- **Love. Death. Transformers.**
- **TOPAPEC**

## Datasets
- **[CaptionEmporium](https://huggingface.co/CaptionEmporium)**

## Donations

Please contact with us if you may provide some GPU's or money on training

DOGE: DEw2DR8C7BnF8GgcrfTzUjSnGkuMeJhg83

BTC: 3JHv9Hb8kEW8zMAccdgCdZGfrHeMhH1rpN

## Contacts

[recoilme](https://t.me/recoilme) *prefered way


mail at aiartlab.org (slow response)

## Citation
```bibtex
@misc{sdxs,
  title={Simple Diffusion XS},
  author={recoilme with help of AiArtLab Team},
  url={https://huggingface.co/AiArtLab/sdxs-08b},
  year={2025}
}
```