Update paper link and add usage instructions

README.md

@@ -1,28 +1,30 @@
 ---
 base_model:
-  - stabilityai/stable-diffusion-2
-pipeline_tag: image-to-image
+- stabilityai/stable-diffusion-2
 library_name: diffusers
+pipeline_tag: image-to-image
 tags:
-  - reflection-removal
-  - reflection-separation
-  - image-restoration
-  - diffusion
-  - stable-diffusion
-  - cvpr2026
+- reflection-removal
+- reflection-separation
+- image-restoration
+- diffusion
+- stable-diffusion
+- cvpr2026
 ---
 
 # Reflection Separation from a Single Image via Joint Latent Diffusion
 
-Pre-trained checkpoints for the CVPR 2026 paper. Given a single photo taken
-through glass, the model jointly generates the **transmission** and
-**reflection** layers.
+This repository contains pre-trained checkpoints for the CVPR 2026 paper [Reflection Separation from a Single Image via Joint Latent Diffusion](https://huggingface.co/papers/2606.04107).
+
+Given a single photo taken through glass, the model jointly generates the **transmission** (reflection-free) and **reflection** layers using a fine-tuned Stable Diffusion 2 architecture.
 
-- 🌐 Project page: https://brian90709.github.io/diff-reflection-separation/
-- 💻 Code: https://github.com/Brian90709/diff-reflection-separation-code
-- 📄 Paper: arXiv (coming soon)
+- **Authors:** Zheng-Hui Huang, Zhixiang Wang, Yu-Lun Liu, and Yung-Yu Chuang
+- 🌐 **Project page:** [https://brian90709.github.io/diff-reflection-separation/](https://brian90709.github.io/diff-reflection-separation/)
+- 💻 **Code:** [https://github.com/Brian90709/diff-reflection-separation-code](https://github.com/Brian90709/diff-reflection-separation-code)
+- 📄 **Paper:** [arXiv:2606.04107](https://arxiv.org/abs/2606.04107)
 
-[Zheng-Hui Huang](https://github.com/Brian90709), [Zhixiang Wang](https://lightchaserx.github.io), [Yu-Lun Liu](https://yulunalexliu.github.io), [Yung-Yu Chuang](https://www.csie.ntu.edu.tw/~cyy/)
+## Method Overview
+Single-image reflection separation is challenging under conditions like glare or weak reflections. This method leverages generative diffusion priors to simultaneously generate transmission and reflection layers through a unified diffusion model, incorporating a novel cross-layer self-attention mechanism for better feature disentanglement and a disjoint sampling strategy to reduce interference between layers.
 
 ## Files
 
@@ -34,13 +36,29 @@ through glass, the model jointly generates the **transmission** and
 
 ## Usage
 
-Download the weights into `./checkpoints`, then follow the
-[code repository](https://github.com/Brian90709/diff-reflection-separation-code):
+### Installation
+Clone the [official repository](https://github.com/Brian90709/diff-reflection-separation-code) and set up the environment:
+
+```bash
+conda create -y -n diffrs python=3.10 && conda activate diffrs
+pip install torch==2.5.1 torchvision==0.20.1 --index-url https://download.pytorch.org/whl/cu121
+pip install -r requirements.txt
+```
+
+### Inference
+Download the weights into `./checkpoints`:
 
 ```bash
 huggingface-cli download Brian9999/diff-reflection-separation --repo-type model --local-dir ./checkpoints
 ```
 
+Run the inference script on a directory of images:
+
+```bash
+python infer_layersep.py --input_dir ./samples --save_to_dir ./outputs
+```
+Each input yields three files: `*_transmission.png` (reflection-free result), `*_reflection.png`, and `*_ori_transmission.png` (transmission before CFW refinement).
+
 ## Citation
 
 ```bibtex
@@ -50,4 +68,4 @@ huggingface-cli download Brian9999/diff-reflection-separation --repo-type model
   booktitle = {CVPR},
   year      = {2026}
 }
-```
+```