Update README.md

README.md

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-license: apache-2.0
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+---
+license: apache-2.0
+---
+
+<div align="center">
+
+## Controllable Layer Decomposition for Reversible Multi-Layer Image Generation
+
+🏠 [Homepage](https://monkek123King.github.io/CLD_page)      📄 [Paper](http://arxiv.org/abs/2511.16249)      🤗 [HuggingFace](https://huggingface.co/papers/2511.16249)
+
+
+</div>
+
+
+### 📢 News
+
+  * **`Dec 2025`:** Experiment checkpoints are released [here](https://huggingface.co/thuteam/CLD)\! 🎉
+  * **`Nov 2025`:** The paper is now available on [arXiv](https://arxiv.org/abs/2511.16249). ☕️
+
+-----
+
+## 🚀 Getting Started
+
+### 🔧 Installation
+
+**a. Create a conda virtual environment and activate it.**
+```shell
+conda env create -f environment.yml
+conda activate CLD
+```
+
+**b. Clone CLD.**
+```
+git clone https://github.com/monkek123King/CLD.git
+```
+
+### 📦 Prepare model ckpt
+**a. Download FLUX.1-dev weights**
+```
+from huggingface_hub import snapshot_download
+
+repo_id = "black-forest-labs/FLUX.1-dev"
+snapshot_download(repo_id, local_dir=Path_to_pretrained_FLUX_model)
+```
+
+**b.Download adapter pre-trained weights**
+```
+from huggingface_hub import snapshot_download
+
+repo_id = "alimama-creative/FLUX.1-dev-Controlnet-Inpainting-Alpha"
+snapshot_download(repo_id, local_dir=Path_to_pretrained_FLUX_adapter)
+```
+
+**c. Download LoRA weights for CLD from https://huggingface.co/thuteam/CLD**
+```
+ckpt
+├── decouple_LoRA
+│   ├── adapter
+│   │   └── pytorch_lora_weights.safetensors
+│   ├── layer_pe.pth
+│   └── transformer
+│       └── pytorch_lora_weights.safetensors
+├── pre_trained_LoRA
+│   └── pytorch_lora_weights.safetensors
+├── prism_ft_LoRA
+│   └── pytorch_lora_weights.safetensors
+└── trans_vae
+    └── 0008000.pt
+```
+
+**d. YAML configuration file**
+```
+pretrained_model_name_or_path: Path_to_pretrained_FLUX_model
+pretrained_adapter_path: Path_to_pretrained_FLUX_adapter
+transp_vae_path: "ckpt/trans_vae/0008000.pt"
+pretrained_lora_dir: "ckpt/pre_trained_LoRA"
+artplus_lora_dir: "ckpt/prism_ft_LoRA"
+lora_ckpt: "ckpt/decouple_LoRA/transformer"
+layer_ckpt: "ckpt/decouple_LoRA"
+adapter_lora_dir: "ckpt/decouple_LoRA/adapter"
+```
+
+
+### 🏋️ Train and Evaluate
+
+**Train**
+
+```
+python -m train.train -c train/train.yaml
+```
+
+**Infer**
+```
+python -m infer.infer -c infer/infer.yaml
+```
+
+**Eval**
+
+Prepare the ground-truth samples.
+```
+python -m eval.prepare_gt
+```
+
+Evaluate to obtain the metric results.
+```
+python evaluate.py --pred-dir "Path_to_predict_results" --gt-dir "Path_to_gt_samples" --output-dir "Path_to_save_eval_results"
+```
+
+-----
+
+## ✍️ Citation
+
+If you find our work useful for your research, please consider citing our paper and giving this repository a star 🌟.
+
+```bibtex
+@article{liu2025controllable,
+  title={Controllable Layer Decomposition for Reversible Multi-Layer Image Generation},
+  author={Liu, Zihao and Xu, Zunnan and Shu, Shi and Zhou, Jun and Zhang, Ruicheng and Tang, Zhenchao and Li, Xiu},
+  journal={arXiv preprint arXiv:2511.16249},
+  year={2025}
+}
+```