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---
base_model: black-forest-labs/FLUX.1-Canny-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-Canny-dev/blob/main/LICENSE.md
license_name: flux-1-dev-non-commercial-license
pipeline_tag: image-to-image
tags:
- image-to-image
- SVDQuant
- FLUX.1-Canny-dev
- FLUX.1
- Diffusion
- Quantization
- ICLR2025
---
<p align="center" style="border-radius: 10px">
<img src="https://huggingface.co/datasets/nunchaku-tech/cdn/resolve/main/nunchaku/assets/nunchaku_v2.png" width="30%" alt="Nunchaku Logo"/>
</p>
<div align="center">
<a href=https://discord.gg/Wk6PnwX9Sm target="_blank"><img src=https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fdiscord.com%2Fapi%2Finvites%2FWk6PnwX9Sm%3Fwith_counts%3Dtrue&query=%24.approximate_member_count&logo=discord&logoColor=white&label=Discord&color=green&suffix=%20total height=22px></a>
<a href=https://huggingface.co/datasets/nunchaku-tech/cdn/resolve/main/nunchaku/assets/wechat.jpg target="_blank"><img src=https://img.shields.io/badge/WeChat-07C160?logo=wechat&logoColor=white height=22px></a>
</div>
# Model Card for nunchaku-flux.1-canny-dev
This repository contains Nunchaku-quantized versions of [FLUX.1-Canny-dev](https://huggingface.co/black-forest-labs/FLUX.1-Canny-dev), capable of generating an image based on a text description while following the structure of a given input image. It is optimized for efficient inference while maintaining minimal loss in performance.
## Model Details
### Model Description
- **Developed by:** Nunchaku Team
- **Model type:** image-to-image
- **License:** [flux-1-dev-non-commercial-license](https://huggingface.co/black-forest-labs/FLUX.1-Canny-dev/blob/main/LICENSE.md)
- **Quantized from model:** [FLUX.1-Canny-dev](https://huggingface.co/black-forest-labs/FLUX.1-Canny-dev)
### Model Files
- [`svdq-int4_r32-flux.1-canny-dev.safetensors`](./svdq-int4_r32-flux.1-canny-dev.safetensors): SVDQuant quantized INT4 FLUX.1-Canny-dev model. For users with non-Blackwell GPUs (pre-50-series).
- [`svdq-fp4_r32-flux.1-canny-dev.safetensors`](./svdq-fp4_r32-flux.1-canny-dev.safetensors): SVDQuant quantized NVFP4 FLUX.1-Canny-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-canny-dev.py](https://github.com/nunchaku-tech/nunchaku/blob/main/examples/flux.1-canny-dev.py). Check our [tutorial](https://nunchaku.tech/docs/nunchaku/usage/controlnet.html#flux-1-tools) for more advanced usage.
- ComfyUI Usage: See [nunchaku-flux.1-canny-dev.json](https://nunchaku.tech/docs/ComfyUI-nunchaku/workflows/canny.html#nunchaku-flux-1-canny-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}
}
```
## 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. |