Adapting Self-Supervised Representations as a Latent Space for Efficient Generation

MCML Authors

Johannes Schusterbauer

→ Group Björn Ommer
 Computer Vision & Learning

Felix Krause

→ Group Björn Ommer
 Computer Vision & Learning

Björn Ommer

Prof. Dr.

Principal Investigator

Computer Vision & Learning

Abstract

We introduce Representation Tokenizer (RepTok), a generative modeling framework that represents an image using a single continuous latent token obtained from self-supervised vision transformers. Building on a pre-trained SSL encoder, we fine-tune only the semantic token embedding and pair it with a generative decoder trained jointly using a standard flow matching objective. This adaptation enriches the token with low-level, reconstruction-relevant details, enabling faithful image reconstruction. To preserve the favorable geometry of the original SSL space, we add a cosine-similarity loss that regularizes the adapted token, ensuring the latent space remains smooth and suitable for generation. Our single-token formulation resolves spatial redundancies of 2D latent spaces and significantly reduces training costs. Despite its simplicity and efficiency, RepTok achieves competitive results on class-conditional ImageNet generation and naturally extends to text-to-image synthesis, reaching competitive zero-shot performance on MS-COCO under extremely limited training budgets. Our findings highlight the potential of fine-tuned SSL representations as compact and effective latent spaces for efficient generative modeling.

misc

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Preprint

Oct. 2025

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Authors

M. Gui • J. Schusterbauer • T. Phan • F. Krause • J. Susskind • M. A. Bautista • B. Ommer

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Links

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Research Area

 B1 | Computer Vision