Add model card for RecTok

README.md

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+---
+license: apache-2.0
+pipeline_tag: text-to-image
+---
+
+# RecTok: Reconstruction Distillation along Rectified Flow
+
+<div align="center">
+
+[![arXiv](https://img.shields.io/badge/arXiv-RecTok-b31b1b.svg?style=flat-square)](https://arxiv.org/abs/2512.13421)
+[![Project Page](https://img.shields.io/badge/Project-Page-blue?style=flat-square)](https://shi-qingyu.github.io/rectok.github.io/)
+[![License](https://img.shields.io/badge/License-Apache_2.0-green.svg?style=flat-square)](https://github.com/Shi-qingyu/RecTok/blob/main/LICENSE)
+
+</div>
+
+This repository contains the official PyTorch implementation for the paper [RecTok: Reconstruction Distillation along Rectified Flow](https://huggingface.co/papers/2512.13421).
+
+RecTok addresses the fundamental trade-off between latent space dimensionality and generation quality in visual tokenizers for diffusion models. It proposes two key innovations: flow semantic distillation and reconstruction-alignment distillation. This approach enriches the semantic information in forward flow trajectories, which serve as the training space for diffusion transformers, rather than focusing solely on the latent space. As a result, RecTok achieves superior image reconstruction, generation quality, and discriminative performance, setting state-of-the-art results on gFID-50K and demonstrating consistent improvements with increasing latent dimensionality.
+
+-   **Paper**: [RecTok: Reconstruction Distillation along Rectified Flow](https://huggingface.co/papers/2512.13421)
+-   **Project Page**: [https://shi-qingyu.github.io/rectok.github.io/](https://shi-qingyu.github.io/rectok.github.io/)
+-   **Code**: [https://github.com/Shi-qingyu/RecTok](https://github.com/Shi-qingyu/RecTok)
+
+<p align="center">
+  <img src="https://github.com/Shi-qingyu/RecTok/raw/main/assets/pipeline.png" width="720" alt="RecTok Pipeline">
+</p>
+
+## Usage
+
+For detailed instructions on setting up the environment, downloading models, and performing evaluation or training, please refer to the [official GitHub repository](https://github.com/Shi-qingyu/RecTok).
+
+### Installation
+
+Set up the environment and install dependencies:
+
+```bash
+# Clone the repository
+git clone https://github.com/Shi-qingyu/RecTok.git
+cd RecTok
+
+# Create and activate conda environment
+conda create -n rectok python=3.10 -y
+conda activate rectok
+
+# Install requirements
+pip install -r requirements.txt
+```
+
+### Download Models
+
+Download pretrained models and necessary data assets:
+```bash
+# Download from HuggingFace
+huggingface-cli download QingyuShi/RecTok --local-dir ./pretrained_models
+# Organize data assets and offline models
+mv ./pretrained_models/data ./data
+mv ./pretrained_models/offline_models.zip ./offline_models.zip
+unzip offline_models.zip && rm offline_models.zip
+```
+
+### Generative Model Evaluation
+
+Evaluate the generation quality (FID, etc.). You can find the evaluation results in directory `./work_dirs/gen_model_training/RecTok_eval`:
+
+```bash
+bash run_eval_diffusion.sh \
+    pretrained_models/RecTok_decft.pth \        # path to RecTok checkpoint
+    pretrained_models/ditdhxl_epoch_0599.pth \  # path to DiTDH-XL checkpoint
+    pretrained_models/ditdhs_epoch_0029.pth     # path to autoguidance model checkpoint
+```
+
+Selected examples of class-conditional generation results on ImageNet-1K 256x256:
+<p align="center">
+  <img src="https://github.com/Shi-qingyu/RecTok/raw/main/assets/qualitative.png" width="1080" alt="RecTok Qualitative Results">
+</p>
+
+FID-50k and Inception Score without CFG and with CFG:
+|cfg| MAR Model                    | Epochs | FID-50K | Inception Score | #params | 
+|---|------------------------------|---------|---------|-----------------|---------|
+|1.0| $\text{DiT}^{\text{DH}}\text{-XL}$ + RecTok      | 80      | 2.09    | 198.6           | 839M    |
+|1.29| $\text{DiT}^{\text{DH}}\text{-XL}$ + RecTok | 80      | 1.48    | 223.8           | 839M    |
+|1.0| $\text{DiT}^{\text{DH}}\text{-XL}$ + RecTok      | 600      | 1.34    | 254.6           | 839M    |
+|1.29| $\text{DiT}^{\text{DH}}\text{-XL}$ + RecTok | 600      | 1.13    | 289.2           | 839M    |
+
+## Citation
+
+If you find this work useful for your research, please consider citing:
+
+```bibtex
+@article{shi2025rectok,
+  title={RecTok: Reconstruction Distillation along Rectified Flow},
+  author={Shi, Qingyu and Wu, Size and Bai, Jinbin and Yu, Kaidong and Wang, Yujing and Tong, Yunhai and Li, Xiangtai and Li, Xuelong},
+  journal={arXiv preprint arXiv:2512.13421},
+  year={2025}
+}
+```
+
+## Acknowledgements
+
+We thank the authors of [lDeTok](https://github.com/Jiawei-Yang/DeTok), [RAE](https://github.com/bytetriper/RAE), [MAE](https://github.com/facebookresearch/mae), [DiT](https://github.com/facebookresearch/DiT), and [LightningDiT](https://github.com/hustvl/LightningDiT) for their foundational work.
+
+Our codebase builds upon several excellent open-source projects, including [lDeTok](https://github.com/Jiawei-Yang/DeTok), [RAE](https://github.com/bytetriper/RAE), and [torch_fidelity](https://github.com/toshas/torch-fidelity). We are grateful to the communities behind them.
+
+We sincerely thank [Jiawei Yang](https://jiawei-yang.github.io/) and [Boyang Zheng](https://bytetriper.github.io/) for providing insightful feedback.