Composable Visual Tokenizers with Generator-Free Diagnostics of Learnability

TL;DR

This paper presents CompTok, a novel training framework for visual tokenizers that enhances compositionality and learnability, enabling high-quality image generation and semantic editing through a generator-free diagnostic approach.

Contribution

CompTok introduces a diffusion-based tokenizer training method with a recognition model and manifold constraints, improving compositionality and enabling diagnostics of learnability without generators.

Findings

Achieves state-of-the-art performance on image class-conditioned generation.

Enables semantic editing by swapping tokens between images.

Provides metrics to measure token space compositionality and learnability.

Abstract

We introduce CompTok, a training framework for learning visual tokenizers whose tokens are enhanced for compositionality. CompTok uses a token-conditioned diffusion decoder. By employing an InfoGAN-style objective, where we train a recognition model to predict the tokens used to condition the diffusion decoder using the decoded images, we enforce the decoder to not ignore any of the tokens. To promote compositional control, besides the original images, CompTok also trains on tokens formed by swapping token subsets between images, enabling more compositional control of the token over the decoder. As the swapped tokens between images do not have ground truth image targets, we apply a manifold constraint via an adversarial flow regularizer to keep unpaired swap generations on the natural-image distribution. The resulting tokenizer not only achieves state-of-the-art performance on image class-conditioned generation, but also demonstrates properties such as swapping tokens between images to achieve high level semantic editing of an image. Additionally, we propose two metrics that measures the landscape of the token space that can be useful to describe not only the compositionality of the tokens, but also how easy to learn the landscape is for a generator to be trained on this space. We show in experiments that CompTok can improve on both of the metrics as well as supporting state-of-the-art generators for class conditioned generation.