---
base_model: black-forest-labs/FLUX.1-dev
base_model_relation: quantized
datasets:
- mit-han-lab/svdquant-datasets
language:
- en
library_name: diffusers
license: other
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
license_name: flux-1-dev-non-commercial-license
pipeline_tag: text-to-image
tags:
- text-to-image
- SVDQuant
- FLUX.1-dev
- FLUX.1
- Diffusion
- Quantization
- ICLR2025

---
**This repository has been migrated to https://huggingface.co/nunchaku-tech/nunchaku-flux.1-dev and will be hidden in December 2025.**

<p align="center" style="border-radius: 10px">
  <img src="https://huggingface.co/datasets/nunchaku-tech/cdn/resolve/main/nunchaku/assets/nunchaku.svg" width="30%" alt="Nunchaku Logo"/>
</p>

# Model Card for nunchaku-flux.1-dev

![visual](https://huggingface.co/datasets/nunchaku-tech/cdn/resolve/main/nunchaku/assets/teaser.jpg)
This repository contains Nunchaku-quantized versions of [FLUX.1-dev](https://huggingface.co/black-forest-labs/FLUX.1-dev), designed to generate high-quality images from text prompts. It is optimized for efficient inference while maintaining minimal loss in performance.

## Model Details

### Model Description

- **Developed by:** Nunchaku Team
- **Model type:** text-to-image
- **License:** [flux-1-dev-non-commercial-license](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md)
- **Quantized from model:** [FLUX.1-dev](https://huggingface.co/black-forest-labs/FLUX.1-dev)

### Model Files

- [`svdq-int4_r32-flux.1-dev.safetensors`](./svdq-int4_r32-flux.1-dev.safetensors): SVDQuant quantized INT4 FLUX.1-dev model. For users with non-Blackwell GPUs (pre-50-series).
- [`svdq-fp4_r32-flux.1-dev.safetensors`](./svdq-fp4_r32-flux.1-dev.safetensors): SVDQuant quantized NVFP4 FLUX.1-dev model. For users with Blackwell GPUs (50-series).


### Model Sources

- **Inference Engine:** [nunchaku](https://github.com/nunchaku-tech/nunchaku)
- **Quantization Library:** [deepcompressor](https://github.com/nunchaku-tech/deepcompressor)
- **Paper:** [SVDQuant: Absorbing Outliers by Low-Rank Components for 4-Bit Diffusion Models](http://arxiv.org/abs/2411.05007)
- **Demo:** [svdquant.mit.edu](https://svdquant.mit.edu)

## Usage

- Diffusers Usage: See [flux.1-dev.py](https://github.com/nunchaku-tech/nunchaku/blob/main/examples/flux.1-dev.py). Check our [tutorial](https://nunchaku.tech/docs/nunchaku/usage/basic_usage.html) for more advanced usage.
- ComfyUI Usage: See [nunchaku-flux.1-dev.json](https://nunchaku.tech/docs/ComfyUI-nunchaku/workflows/t2i.html#nunchaku-flux-1-dev-json).

## Performance

![performance](https://huggingface.co/datasets/nunchaku-tech/cdn/resolve/main/nunchaku/assets/efficiency.jpg)

## Citation

```bibtex
@inproceedings{
  li2024svdquant,
  title={SVDQuant: Absorbing Outliers by Low-Rank Components for 4-Bit Diffusion Models},
  author={Li*, Muyang and Lin*, Yujun and Zhang*, Zhekai and Cai, Tianle and Li, Xiuyu and Guo, Junxian and Xie, Enze and Meng, Chenlin and Zhu, Jun-Yan and Han, Song},
  booktitle={The Thirteenth International Conference on Learning Representations},
  year={2025}
}
```

## Attribution Notice

The FLUX.1 [dev] Model is licensed by Black Forest Labs Inc. under the FLUX.1 [dev] Non-Commercial License. Copyright Black Forest Labs Inc. IN NO EVENT SHALL BLACK FOREST LABS INC. BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH USE OF THIS MODEL.