Our method is free of costly human labeling. However, it demonstrates significantly stronger generalization performance than existing image contrast enhancement methods across and within different tasks, even outperforming manually created ground-truths in multiple no-reference image quality metrics

A visual comparison with manually edited ground truth (GT) images across several datasets. The regions marked with red boxes indicate areas of local under- or over-exposure, resulting in detail loss in the GT, which may be inferior to the enhanced results produced by our UNICE model.

The core idea of this method is to use a multi-exposure fusion sequence as supervision signals, generate a sequence from a single 8-bit image, and then perform multi-exposure fusion.

Our UNICE Dataset is available at huggingface.

This repository contains the exposure control branch. For the fusion functionality, please switch to the fusion branch.

To set up the environment, use the provided environment.yaml file:

conda env create -f environment.yaml

To train the model, run the following command:

CUDA_VISIBLE_DEVICES=1 ../miniconda3/envs/img2img-turbo/bin/python src/train_pix2pix_turbo.py \
  --pretrained_model_name_or_path="stabilityai/sd-turbo" \
  --output_dir="output/pix2pix_turbo/exposure" \
  --dataset_folder="data/exposure" \
  --resolution=512 \
  --train_batch_size=2 \
  --enable_xformers_memory_efficient_attention \
  --viz_freq 50 \
  --report_to "wandb" \
  --tracker_project_name "pix2pix_turbo_exposure"

GPU Memory requirements: On a Tesla A100 40GB GPU:

• Batch size 1 requires ~19561MiB
• Batch size 2 requires ~34853MiB

You can also check Colab for a convenient test.

🔗 Pre-trained weights are available at huggingface.

To test the model with different exposure values, use the following script:

#!/bin/bash

# Define the exposure value
exposure=0.5
output_dir="output/$exposure"

CUDA_VISIBLE_DEVICES=5 ../miniconda3/envs/img2img-turbo/bin/python src/inference.py \
--model_path "checkpoints/exposure.pkl" \
--input_dir /local/mnt/workspace/ruodcui/code/adaptive_3dlut/data/BAID512/input/ \
--output_dir $output_dir \
--prompt "exposure control" \
--exposure $exposure

This project borrows code from img2img-turbo. We sincerely thank the authors for their contributions to the community.

If you have any questions, please feel free to contact me at [email protected].

If our code helps your research or work, please consider citing our paper. The following are BibTeX references:

@misc{ruodai2025UNICE,
      title={UNICE: Training A Universal Image Contrast Enhancer},
      author={Ruodai Cui and Lei Zhang},
      year={2025},
      eprint={2507.17157},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2507.17157},
}