license: other
license_name: flux-1-dev-non-commercial-license
tags:
- image-to-image
- SVDQuant
- INT4
- FLUX.1
- Diffusion
- Quantization
- inpainting
- image-generation
- text-to-image
- ICLR2025
- FLUX.1-Fill-dev
language:
- en
base_model:
- black-forest-labs/FLUX.1-Fill-dev
base_model_relation: quantized
pipeline_tag: image-to-image
datasets:
- mit-han-lab/svdquant-datasets
library_name: diffusers
Quantization Library: DeepCompressor Inference Engine: Nunchaku
svdq-int4-flux.1-fill-dev is an INT4-quantized version of FLUX.1-Fill-dev, which can fill areas in existing images based on a text description. It offers approximately 4× memory savings while also running 2–3× faster than the original BF16 model.
Method
Quantization Method -- SVDQuant
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
(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 to set up the environment. Then you can run the model with
import torch
from diffusers import FluxFillPipeline
from diffusers.utils import load_image
from nunchaku.models.transformer_flux import NunchakuFluxTransformer2dModel
image = load_image("https://huggingface.co/mit-han-lab/svdq-int4-flux.1-fill-dev/resolve/main/example.png")
mask = load_image("https://huggingface.co/mit-han-lab/svdq-int4-flux.1-fill-dev/resolve/main/mask.png")
transformer = NunchakuFluxTransformer2dModel.from_pretrained("mit-han-lab/svdq-int4-flux.1-fill-dev")
pipe = FluxFillPipeline.from_pretrained(
"black-forest-labs/FLUX.1-Fill-dev", transformer=transformer, torch_dtype=torch.bfloat16
).to("cuda")
image = pipe(
prompt="A wooden basket of a cat.",
image=image,
mask_image=mask,
height=1024,
width=1024,
guidance_scale=30,
num_inference_steps=50,
max_sequence_length=512,
).images[0]
image.save("flux.1-fill-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 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
@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}
}