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base_model: black-forest-labs/FLUX.1-schnell
base_model_relation: quantized
datasets:
- mit-han-lab/svdquant-datasets
language:
- en
library_name: diffusers
license: apache-2.0
pipeline_tag: text-to-image
tags:
- text-to-image
- SVDQuant
- FLUX.1-schnell
- FLUX.1
- Diffusion
- Quantization
- ICLR2025
---
**This repository has been migrated to https://huggingface.co/nunchaku-tech/nunchaku-flux.1-schnell 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-schnell
This repository contains Nunchaku-quantized versions of [FLUX.1-schnell](https://huggingface.co/black-forest-labs/FLUX.1-schnell), 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:** apache-2.0
- **Quantized from model:** [FLUX.1-schnell](https://huggingface.co/black-forest-labs/FLUX.1-schnell)
### Model Files
- [`svdq-int4_r32-flux.1-schnell.safetensors`](./svdq-int4_r32-flux.1-schnell.safetensors): SVDQuant quantized INT4 FLUX.1-schnell model. For users with non-Blackwell GPUs (pre-50-series).
- [`svdq-fp4_r32-flux.1-schnell.safetensors`](./svdq-fp4_r32-flux.1-schnell.safetensors): SVDQuant quantized NVFP4 FLUX.1-schnell 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-schnell.py](https://github.com/nunchaku-tech/nunchaku/blob/main/examples/flux.1-schnell.py). Check our [tutorial](https://nunchaku.tech/docs/nunchaku/usage/basic_usage.html) for more advanced usage.
- ComfyUI Usage: See [nunchaku-flux.1-schnell.json](https://nunchaku.tech/docs/ComfyUI-nunchaku/workflows/t2i.html#nunchaku-flux-1-schnell-json).
## Performance
## 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}
}
``` |