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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 [stable-diffusion 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={2026}
}
``` |