| --- |
| license: other |
| pipeline_tag: image-to-image |
| tags: |
| - image-restoration |
| - low-light-enhancement |
| - deflare |
| - diffusion |
| - nighttime-photography |
| - siggraph-2026 |
| --- |
| |
| # 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: |
|
|
| ```bash |
| git clone https://github.com/frakenation/LUCID.git |
| cd LUCID |
| ``` |
|
|
| Download these checkpoints: |
|
|
| ```bash |
| git lfs install |
| git clone https://huggingface.co/Unswear/LUCID lucid_weights |
| ``` |
|
|
| Example inference: |
|
|
| ```bash |
| 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. |
| |
