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README.md

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----
-license: apache-2.0
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+# PID: Physics-Informed Diffusion Model for Infrared Image Generation
+
+<img src="PID.png" alt="PID" style="zoom:50%;" />
+
+## Update 
+
+* 2025/05 The paper is accepted by Pattern Recognition: https://doi.org/10.1016/j.patcog.2025.111816
+* We have released our code.
+
+## Environment
+
+It is recommended to install the environment with environment.yaml. 
+
+```bash
+conda env create --file=environment.yaml
+```
+
+## Datasets
+
+Download **KAIST** dataset from https://github.com/SoonminHwang/rgbt-ped-detection
+
+Download **FLIRv1** dataset from https://www.flir.com/oem/adas/adas-dataset-form/
+
+We adopt the official dataset split in our experiments.
+
+## Checkpoint
+
+VQGAN can be downloaded from https://ommer-lab.com/files/latent-diffusion/vq-f8.zip (Other GAN models can be downloaded from https://github.com/CompVis/latent-diffusion).
+
+TeVNet and PID heckpoints can be found in [HuggingFace](https://huggingface.co/FerrisMao/PID).
+
+## Evaluation
+
+Use the shellscript to evaluate. `indir` is the input directory of visible RGB images, `outdir` is the output directory of translated infrared images, `config` is the chosen config in `configs/latent-diffusion/config.yaml`.  We prepare some RGB images in `dataset/KAIST` for quick evaluation.
+
+```sh
+bash run_test_kaist512_vqf8.sh
+```
+
+## Train
+
+### Dataset preparation
+
+Prepare corresponding RGB and infrared images with same names in two directories.
+
+### Stage 1: Train TeVNet
+
+```bash
+cd TeVNet
+bash shell/train.sh
+```
+
+### Stage 2: Train PID
+
+To accelerate training, we recommend using our pretrained model. 
+
+```bash
+bash shell/run_train_kaist512_vqf8.sh
+```
+
+## Acknowledgements
+
+Our code is built upon [LDM](https://github.com/CompVis/latent-diffusion) and [HADAR](https://github.com/FanglinBao/HADAR). We thank the authors for their excellent work.
+
+## Citation
+
+If you find this work is helpful in your research, please consider citing our paper:
+
+```
+@article{mao2026pid,
+  title={PID: physics-informed diffusion model for infrared image generation},
+  author={Mao, Fangyuan and Mei, Jilin and Lu, Shun and Liu, Fuyang and Chen, Liang and Zhao, Fangzhou and Hu, Yu},
+  journal={Pattern Recognition},
+  volume={169},
+  pages={111816},
+  year={2026},
+  publisher={Elsevier}
+}
+```
+
+If you have any question, feel free to contact [email protected].