**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).
**Nunchaku** is a high-performance inference engine optimized for 4-bit neural networks, 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).
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!
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@@ -23,9 +22,9 @@ Join our user groups on [**Slack**](https://join.slack.com/t/nunchaku/shared_inv
<details>
<summary>More</summary>
-**[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!**
-**[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)!
-**[2025-01-23]** 🚀 **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/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 [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.
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@@ -88,7 +87,7 @@ Once PyTorch is installed, you can directly install `nunchaku` from our [Hugging
**Note**: NVFP4 wheels are not currently available because PyTorch has not officially supported CUDA 11.8. To use NVFP4, you will need **Blackwell GPUs (e.g., 50-series GPUs)** and must **build from source**.
**Note**: NVFP4 wheels are not currently available because PyTorch has not officially supported CUDA 12.8. To use NVFP4, you will need **Blackwell GPUs (e.g., 50-series GPUs)** and must **build from source**.
**[Optional]** You can verify your installation by running: `python -m nunchaku.test`. This command will download and run our 4-bit FLUX.1-schnell model.
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@@ -156,7 +155,7 @@ Specifically, `nunchaku` shares the same APIs as [diffusers](https://github.com/
### Low Memory Inference
To further reduce GPU memory usage, you can use our 4-bit T5 encoder along with CPU offloading, requiring a minimum of just 4GiB of memory. The usage is also simple in the diffusers' way. For example, the [script](examples/int4-flux.1-dev-qencoder.py) for FLUX.1-dev is as follows:
To further reduce GPU memory usage, you can use our 4-bit T5 encoder along with CPU offloading, requiring a minimum of just 4GiB of memory. The usage is also simple in the diffusers' way. For example, the [script](examples/flux.1-dev-qencoder.py) for FLUX.1-dev is as follows:
[SVDQuant](http://arxiv.org/abs/2411.05007) seamlessly integrates with off-the-shelf LoRAs without requiring requantization. To convert your LoRA safetensors to our format, use the following command:
-`--quant-path`: The path to the quantized base model. It can be a local path or a remote Hugging Face model. For example, you can use [`mit-han-lab/svdq-int4-flux.1-dev/transformer_blocks.safetensors`](https://huggingface.co/mit-han-lab/svdq-int4-flux.1-dev/blob/main/transformer_blocks.safetensors) for FLUX.1-dev.
-`--lora-path`: The path to your LoRA safetensors, which can also be a local or remote Hugging Face model.
-`--lora-format`: Specifies the LoRA format. Supported formats include:
-`auto`: The default option. Automatically detects the appropriate LoRA format.
-`--output-root`: Specifies the output directory for the converted LoRA.
-`--lora-name`: Sets the name of the converted LoRA file (without `.safetensors` extension).
After converting your LoRA, you can use your converted weight with:
[SVDQuant](http://arxiv.org/abs/2411.05007) seamlessly integrates with off-the-shelf LoRAs without requiring requantization. You can simply use your LoRA with:
`path_to_your_lora` can also be a remote HuggingFace path. In [examples/int4-flux.1-dev-lora.py](examples/int4-flux.1-dev-lora.py), we provide a minimal example script for running [Ghibsky](https://huggingface.co/aleksa-codes/flux-ghibsky-illustration) LoRA with SVDQuant's INT4 FLUX.1-dev:
`path_to_your_lora` can also be a remote HuggingFace path. In [examples/flux.1-dev-lora.py](examples/flux.1-dev-lora.py), we provide a minimal example script for running [Ghibsky](https://huggingface.co/aleksa-codes/flux-ghibsky-illustration) LoRA with SVDQuant's 4-bit FLUX.1-dev:
)# Path to your LoRA safetensors, can also be a remote HuggingFace path
transformer.set_lora_strength(1)# Your LoRA strength here
### End of LoRA Related Code ###
image=pipeline(
"GHIBSKY style, cozy mountain cabin covered in snow, with smoke curling from the chimney and a warm, inviting light spilling through the windows",
"GHIBSKY style, cozy mountain cabin covered in snow, with smoke curling from the chimney and a warm, inviting light spilling through the windows",# noqa: E501
num_inference_steps=25,
guidance_scale=3.5,
).images[0]
image.save("flux.1-dev-ghibsky.png")
image.save(f"flux.1-dev-ghibsky-{precision}.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 [mit-han-lab/ComfyUI-nunchaku](https://github.com/mit-han-lab/ComfyUI-nunchaku) for more details.**
