README.md

---
license: other
license_name: flux-1-dev-non-commercial-license
tags:
- image-to-image
- SVDQuant
- INT4
- FLUX.1
- Diffusion
- Quantization
- ControlNet
- depth-to-image
- image-generation
- text-to-image
- FLUX.1-Depth-dev
- ICLR2025
language:
- en
base_model:
- black-forest-labs/FLUX.1-Depth-dev
base_model_relation: quantized
pipeline_tag: image-to-image
datasets:
- mit-han-lab/svdquant-datasets
library_name: diffusers
---

<p align="center" style="border-radius: 10px">
  <img src="https://github.com/mit-han-lab/nunchaku/raw/refs/heads/main/assets/logo.svg" width="50%" alt="logo"/>
</p>
<h4 style="display: flex; justify-content: center; align-items: center; text-align: center;">Quantization Library:&nbsp;<a href='https://github.com/mit-han-lab/deepcompressor'>DeepCompressor</a> &ensp; Inference Engine:&nbsp;<a href='https://github.com/mit-han-lab/nunchaku'>Nunchaku</a>
</h4>


<div style="display: flex; justify-content: center; align-items: center; text-align: center;">
  <a href="https://arxiv.org/abs/2411.05007">[Paper]</a>&ensp;
  <a href='https://github.com/mit-han-lab/nunchaku'>[Code]</a>&ensp;
  <a href='https://svdquant.mit.edu'>[Demo]</a>&ensp;
  <a href='https://hanlab.mit.edu/projects/svdquant'>[Website]</a>&ensp;
  <a href='https://hanlab.mit.edu/blog/svdquant'>[Blog]</a>
</div>

![teaser](https://huggingface.co/mit-han-lab/svdq-int4-flux.1-depth-dev/resolve/main/demo.jpg)
`svdq-int4-flux.1-depth-dev` is an INT4-quantized version of [`FLUX.1-Depth-dev`](https://huggingface.co/black-forest-labs/FLUX.1-Depth-dev), which can generate an image based on a text description while following the structure of a given input image. It offers approximately 4× memory savings while also running 2–3× faster than the original BF16 model.

## Method
#### Quantization Method -- SVDQuant

![intuition](https://github.com/mit-han-lab/nunchaku/raw/refs/heads/main/assets/intuition.gif)
Overview of SVDQuant. Stage1: Originally, both the activation ***X*** and weights ***W*** contain outliers, making 4-bit quantization challenging.  Stage 2: We migrate the outliers from activations to weights, resulting in the updated activation and weight. While the activation becomes easier to quantize, the weight now becomes more difficult. Stage 3: SVDQuant further decomposes the weight into a low-rank component and a residual with SVD. Thus, the quantization difficulty is alleviated by the low-rank branch, which runs at 16-bit precision. 

#### Nunchaku Engine Design

![engine](https://github.com/mit-han-lab/nunchaku/raw/refs/heads/main/assets/engine.jpg) (a) Naïvely running low-rank branch with rank 32 will introduce 57% latency overhead due to extra read of 16-bit inputs in *Down Projection* and extra write of 16-bit outputs in *Up Projection*. Nunchaku optimizes this overhead with kernel fusion. (b) *Down Projection* and *Quantize* kernels use the same input, while *Up Projection* and *4-Bit Compute* kernels share the same output. To reduce data movement overhead, we fuse the first two and the latter two kernels together.

## Model Description

- **Developed by:** MIT, NVIDIA, CMU, Princeton, UC Berkeley, SJTU and Pika Labs
- **Model type:** INT W4A4 model
- **Model size:** 6.64GB
- **Model resolution:** The number of pixels need to be a multiple of 65,536.
- **License:** Apache-2.0

## Usage

### Diffusers

Please follow the instructions in [mit-han-lab/nunchaku](https://github.com/mit-han-lab/nunchaku) to set up the environment. Also, install some ControlNet dependencies:

```shell
pip install git+https://github.com/asomoza/image_gen_aux.git
pip install controlnet_aux mediapipe
```

Then you can run the model with

```python
import torch
from diffusers import FluxControlPipeline
from diffusers.utils import load_image
from image_gen_aux import DepthPreprocessor

from nunchaku.models.transformer_flux import NunchakuFluxTransformer2dModel

transformer = NunchakuFluxTransformer2dModel.from_pretrained("mit-han-lab/svdq-int4-flux.1-depth-dev")

pipe = FluxControlPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-Depth-dev",
    transformer=transformer,
    torch_dtype=torch.bfloat16,
).to("cuda")

prompt = "A robot made of exotic candies and chocolates of different kinds. The background is filled with confetti and celebratory gifts."
control_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/robot.png")

processor = DepthPreprocessor.from_pretrained("LiheYoung/depth-anything-large-hf")
control_image = processor(control_image)[0].convert("RGB")

image = pipe(
    prompt=prompt, control_image=control_image, height=1024, width=1024, num_inference_steps=30, guidance_scale=10.0
).images[0]
image.save("flux.1-depth-dev.png")
```

### Comfy UI

Work in progress. Stay tuned!

## Limitations

- The model is only runnable on NVIDIA GPUs with architectures sm_86 (Ampere: RTX 3090, A6000), sm_89 (Ada: RTX 4090), and sm_80 (A100). See this [issue](https://github.com/mit-han-lab/nunchaku/issues/1) for more details.
- You may observe some slight differences from the BF16 models in detail.

### Citation

If you find this model useful or relevant to your research, please cite

```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}
}
```