LUCID

LUCID: Learning Unified Control for Image Deflaring and Exposure Mastery in Nighttime Photography

LUCID is a unified framework for nighttime image restoration. It targets the coupled degradations that often appear together in night photography: underexposure, intense flare, ghosting artifacts, and visible light sources.

Photography is the art of painting with light, yet nighttime scenes are shaped by competing degradations: intense flares obscure scene structure, while photon-limited regions collapse into noise. Conventional approaches address these factors in isolation, overlooking the fact that these degradations are fundamentally entangled. LUCID reframes nighttime restoration as a continuous and controllable process rather than a fixed correction, restoring challenging nighttime images with flexible control over exposure, light sources, flare, and ghosting artifacts.

Checkpoints

This repository contains the pretrained checkpoints used by the LUCID codebase:

File	Description
LUCID_main/model_40000.pkl	Main LUCID restoration checkpoint
Flare_Disentangle/latest.pth	Flare disentanglement network checkpoint

The base diffusion model is SD-Turbo and should be downloaded separately:

https://huggingface.co/stabilityai/sd-turbo

Usage

Clone the code repository:

git clone https://github.com/frakenation/LUCID.git
cd LUCID

Download these checkpoints:

git lfs install
git clone https://huggingface.co/Unswear/LUCID lucid_weights

Example inference:

python -m src.inference \
  --input_dir ./data/test/input \
  --pretrained_model_name_or_path stabilityai/sd-turbo \
  --model_path ./lucid_weights/LUCID_main/model_40000.pkl \
  --flare_disentanglement_path ./lucid_weights/Flare_Disentangle/latest.pth \
  --output_dir ./results/lucid \
  --resolution 512 \
  --timestep 199 \
  --ms_unet \
  --inference_mode cfg_guidance \
  --cfg_scale 1.05 \
  --device cuda

For multi-scale exposure control, use src.inference_cfg_index or the provided shell scripts in the GitHub repository.

Links

• Project page: https://xiaoyunyuan.net/index.html?project=lucid
• Paper: https://arxiv.org/abs/2606.06901
• Code: https://github.com/frakenation/LUCID
• Video: https://www.youtube.com/watch?v=AGPLSiZcK_I

Citation

If you find this project useful, please cite the paper from the project page or arXiv.