**Nunchaku** is an efficient inference engine designed for 4-bit diffusion models, as demonstrated in our paper [SVDQuant](http://arxiv.org/abs/2411.05007). Please check [DeepCompressor](https://github.com/mit-han-lab/deepcompressor) for the quantization library.
**Nunchaku** is a high-performance inference engine optimized for 4-bit diffusion models, as introduced in our paper [SVDQuant](http://arxiv.org/abs/2411.05007). For the underlying quantization library, check out [DeepCompressor](https://github.com/mit-han-lab/deepcompressor).
Check [here](https://github.com/mit-han-lab/nunchaku/issues/149) to join our user groups on [**Slack**](https://join.slack.com/t/nunchaku/shared_invite/zt-3170agzoz-NgZzWaTrEj~n2KEV3Hpl5Q) and [**WeChat**](./assets/wechat.jpg)for discussions! If you have any questions, encounter issues, or are interested in contributing to the codebase, feel free to share your thoughts there!
Join our user groups on [**Slack**](https://join.slack.com/t/nunchaku/shared_invite/zt-3170agzoz-NgZzWaTrEj~n2KEV3Hpl5Q) and [**WeChat**](./assets/wechat.jpg)to engage in discussions with the community! More details can be found [here](https://github.com/mit-han-lab/nunchaku/issues/149). If you have any questions, run into issues, or are interested in contributing, don’t hesitate to reach out!
## News
-**[2025-03-11]****🚀 Release [4-bit Shuttle-Jaguar](https://huggingface.co/mit-han-lab/svdq-int4-shuttle-jaguar)!** Check the INT4 models in our [HuggingFace](https://huggingface.co/collections/mit-han-lab/svdquant-67493c2c2e62a1fc6e93f45c) or [ModelScope](https://modelscope.cn/collections/svdquant-468e8f780c2641) collections! FP4 models are coming soon!
-**[2025-03-13]**📦 Seperate the ComfyUI node into a [standalone repository](https://github.com/mit-han-lab/ComfyUI-nunchaku) for easier installation! Plus, [4-bit Shuttle-Jaguar](https://huggingface.co/mit-han-lab/svdq-int4-shuttle-jaguar) is now fully supported!
-**[2025-03-07]** 🚀 **Nunchaku v0.1.4 Released!** We've supported [4-bit text encoder and per-layer CPU offloading](#Low-Memory-Inference), reducing FLUX's minimum memory requirement to just **4 GiB** while maintaining a **2–3× speedup**. This update also fixes various issues related to resolution, LoRA, pin memory, and runtime stability. Check out the release notes for full details!
-**[2025-02-20]** 🚀 We release the [pre-built wheels](https://huggingface.co/mit-han-lab/nunchaku) to simplify installation! Check [here](#Installation) for the guidance!
-**[2025-02-20]** 🚀 **Support NVFP4 precision on NVIDIA RTX 5090!** NVFP4 delivers superior image quality compared to INT4, offering **~3× speedup** on the RTX 5090 over BF16. Learn more in our [blog](https://hanlab.mit.edu/blog/svdquant-nvfp4), checkout [`examples`](./examples) for usage and try [our demo](https://svdquant.mit.edu/flux1-schnell/) online!
...
...
@@ -19,11 +19,17 @@ Check [here](https://github.com/mit-han-lab/nunchaku/issues/149) to join our use
-**[2025-02-14]** 🔥 **[LoRA conversion script](nunchaku/convert_lora.py)** is now available! [ComfyUI FLUX.1-tools workflows](./comfyui) is released!
-**[2025-02-11]** 🎉 **[SVDQuant](http://arxiv.org/abs/2411.05007) has been selected as a ICLR 2025 Spotlight! FLUX.1-tools Gradio demos are now available!** Check [here](#gradio-demos) for the usage details! Our new [depth-to-image demo](https://svdquant.mit.edu/flux1-depth-dev/) is also online—try it out!
-**[2025-02-04]****🚀 4-bit [FLUX.1-tools](https://blackforestlabs.ai/flux-1-tools/) is here!** Enjoy a **2-3× speedup** over the original models. Check out the [examples](./examples) for usage. **ComfyUI integration is coming soon!**
<details>
<summary>More</summary>
-**[2025-01-23]** 🚀 **4-bit [SANA](https://nvlabs.github.io/Sana/) support is here!** Experience a 2-3× speedup compared to the 16-bit model. Check out the [usage example](./examples/int4-sana_1600m_pag.py) and the [deployment guide](app/sana/t2i) for more details. Explore our live demo at [svdquant.mit.edu](https://svdquant.mit.edu)!
-**[2025-01-22]** 🎉 [**SVDQuant**](http://arxiv.org/abs/2411.05007) has been accepted to **ICLR 2025**!
-**[2024-12-08]** Support [ComfyUI](https://github.com/comfyanonymous/ComfyUI). Please check [comfyui/README.md](comfyui/README.md) for the usage.
-**[2024-12-08]** Support [ComfyUI](https://github.com/comfyanonymous/ComfyUI). Please check [mit-han-lab/ComfyUI-nunchaku](https://github.com/mit-han-lab/ComfyUI-nunchaku) for the usage.
-**[2024-11-07]** 🔥 Our latest **W4A4** Diffusion model quantization work [**SVDQuant**](https://hanlab.mit.edu/projects/svdquant) is publicly released! Check [**DeepCompressor**](https://github.com/mit-han-lab/deepcompressor) for the quantization library.
</details>
## Overview
...
...
@@ -233,11 +239,11 @@ image = pipeline(
image.save("flux.1-dev-ghibsky.png")
```
**For ComfyUI users, we have implemented a node to convert the LoRA weights on the fly. All you need to do is specify the correct LoRA format. Please refer to [comfyui/README.md](comfyui/README.md) for more details.**
**For ComfyUI users, we have implemented a node to convert the LoRA weights on the fly. All you need to do is specify the correct LoRA format. Please refer to [mit-han-lab/ComfyUI-nunchaku](https://github.com/mit-han-lab/ComfyUI-nunchaku) for more details.**
## ComfyUI
Please refer to [comfyui/README.md](comfyui/README.md) for the usage in [ComfyUI](https://github.com/comfyanonymous/ComfyUI).
Please refer to [mit-han-lab/ComfyUI-nunchaku](https://github.com/mit-han-lab/ComfyUI-nunchaku) for the usage in [ComfyUI](https://github.com/comfyanonymous/ComfyUI).
**Note**: This node is **deprecated**! Please check **[ComfyUI-nunchaku/](https://github.com/mit-han-lab/ComfyUI-nunchaku/)** for the latest version.
## Installation
Please first install `nunchaku` following the instructions in [README.md](https://github.com/mit-han-lab/nunchaku?tab=readme-ov-file#installation). Then just install `image_gen_aux` with
