| --- |
| license: apache-2.0 |
| pipeline_tag: image-to-image |
| --- |
| |
| # DecQ: Detail-Condensing Queries for Enhanced Reconstruction and Generation in Representation Autoencoders |
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| This repository contains the weights for **DecQ**, a framework that introduces lightweight detail-condensing queries into Representation Autoencoders (RAEs). DecQ improves spatial reconstruction capacity while preserving the pretrained semantic space of vision foundation models (VFMs), facilitating high-quality image reconstruction and faster convergence in latent diffusion models. |
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| - **Paper:** [DecQ: Detail-Condensing Queries for Enhanced Reconstruction and Generation in Representation Autoencoders](https://huggingface.co/papers/2605.22777) |
| - **Repository:** [GitHub - Tianhang-Wang/DecQ](https://github.com/Tianhang-Wang/DecQ) |
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| ## Overview |
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| DecQ addresses the reconstruction–generation trade-off in RAEs by using detail-condensing queries to extract fine-grained information from intermediate VFM features through condenser modules. These queries are incorporated into the decoder to support reconstruction and are jointly generated with patch tokens during generative modeling. |
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| Key features: |
| - **Lightweight:** Only 8 additional queries and 3.9% extra computation. |
| - **Improved Reconstruction:** Significant PSNR improvement over frozen DINOv2-based RAEs. |
| - **Faster Convergence:** Achieves 3.3× faster convergence in generative modeling. |
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| This repository currently contains the **Stage 1 tokenizer weights**. |
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|
| ## Sample Usage |
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| To perform image reconstruction using the Stage 1 autoencoder, you can use the sampling script provided in the [official repository](https://github.com/Tianhang-Wang/DecQ): |
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|
| ```bash |
| python src/stage1_sample.py \ |
| --config <config_path> \ |
| --image <input_image_path> |
| ``` |
|
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| Refer to the GitHub repository for environment setup and configuration files. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @article{wang2026decq, |
| title={DecQ: Detail-Condensing Queries for Enhanced Reconstruction and Generation in Representation Autoencoders}, |
| author={Wang, Tianhang and Chen, Yitong and Song, Wei and Wu, Zuxuan and Li, Min and Wang, Jiaqi}, |
| journal={arXiv preprint arXiv:2605.22777}, |
| year={2026} |
| } |
| ``` |
