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HomeLLMsnunchaku flux.1 dev

nunchaku flux.1 dev

by nunchaku-ai

Open source · 7k downloads · 49 likes

2.1
(49 reviews)ImageAPI & Local
About

Nunchaku Flux.1 dev is an AI model specialized in generating images from text descriptions, offering an optimized and quantized alternative to the FLUX.1-dev model. It stands out for its increased efficiency, enabling fast inference even on less powerful hardware configurations while maintaining high visual quality. This model is particularly suited for creators, developers, and artists looking to integrate a high-performance image generation solution into their projects. Its advanced quantization approach makes it accessible on a broader range of GPUs, including pre-Blackwell models. Nunchaku Flux.1 dev thus positions itself as a versatile tool for various applications, from artistic creation to the automation of visual content.

Documentation

Nunchaku Logo

Model Card for nunchaku-flux.1-dev

visual This repository contains Nunchaku-quantized versions of 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
  • Quantized from model: FLUX.1-dev

Model Files

  • 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: SVDQuant quantized NVFP4 FLUX.1-dev model. For users with Blackwell GPUs (50-series).

Model Sources

  • Inference Engine: nunchaku
  • Quantization Library: deepcompressor
  • Paper: SVDQuant: Absorbing Outliers by Low-Rank Components for 4-Bit Diffusion Models
  • Demo: demo.nunchaku.tech

Usage

  • Diffusers Usage: See flux.1-dev.py. Check our tutorial for more advanced usage.
  • ComfyUI Usage: See nunchaku-flux.1-dev.json.

Performance

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}
}

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.

Capabilities & Tags
diffuserstext-to-imageSVDQuantFLUX.1-devFLUX.1DiffusionQuantizationICLR2025en
Links & Resources
Specifications
CategoryImage
AccessAPI & Local
LicenseOpen Source
PricingOpen Source
Rating
2.1

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