[Submitted on 21 Jul 2026]
Title:VQ-Transplant: Efficient VQ-Module Integration for Pre-trained Visual Tokenizers
View a PDF of the paper titled VQ-Transplant: Efficient VQ-Module Integration for Pre-trained Visual Tokenizers, by Xianghong Fang and Yuan Yuan and Dehan Kong and Tim G. J. Rudner
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Abstract:Vector Quantization (VQ) underpins modern discrete visual tokenization. However, training quantization modules for state-of-the-art VQ-based models requires significant computational resources which, in practice, all but prevents the development of novel, cutting-edge VQ techniques under resource constraints. To address this limitation, we propose {\bf VQ-Transplant}, a simple framework that enables plug-and-play integration of new VQ modules into frozen, pre-trained tokenizers by replacing their native VQ modules. Crucially, the proposed transplantation process preserves all encoder-decoder parameters, obviating the need for costly end-to-end retraining when modifying the quantization method. To mitigate decoder-quantization mismatch, we introduce a lightweight decoder adaptation strategy (trained for only 5 epochs on ImageNet-1k) to align feature priors with the new quantization space. In our empirical evaluation, we find that VQ-Transplant allows obtaining near state-of-the-art reconstruction fidelity for industry-level models like VAR while reducing the training cost by 95\%. VQ-Transplant democratizes quantization research by enabling resource-efficient integration of novel VQ techniques while matching industry-level reconstruction performance.
Comments: 20 pages, 9 figures and 16 tables
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2607.19575 [cs.CV]
(or arXiv:2607.19575v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2607.19575
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Xianghong Fang [view email] [v1] Tue, 21 Jul 2026 21:01:49 UTC (26,641 KB)
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