InsightTok: Improving Text and Face Fidelity in Discrete Tokenization for Autoregressive Image Generation

Hugging Face Daily Papers Papers
discrete-tokenization autoregressive-image-generation text-fidelity face-fidelity perceptual-losses visual-tokenization

Summary

InsightTok introduces content-aware perceptual losses to improve discrete visual tokenization for better text and face reconstruction, enhancing autoregressive image generation quality.

Text and faces are among the most perceptually salient and practically important patterns in visual generation, yet they remain challenging for autoregressive generators built on discrete tokenization. A central bottleneck is the tokenizer: aggressive downsampling and quantization often discard the fine-grained structures needed to preserve readable glyphs and distinctive facial features. We attribute this gap to standard discrete-tokenizer objectives being weakly aligned with text legibility and facial fidelity, as these objectives typically optimize generic reconstruction while compressing diverse content uniformly. To address this, we propose InsightTok, a simple yet effective discrete visual tokenization framework that enhances text and face fidelity through localized, content-aware perceptual losses. With a compact 16k codebook and a 16x downsampling rate, InsightTok significantly outperforms prior tokenizers in text and face reconstruction without compromising general reconstruction quality. These gains consistently transfer to autoregressive image generation in InsightAR, producing images with clearer text and more faithful facial details. Overall, our results highlight the potential of specialized supervision in tokenizer training for advancing discrete image generation.
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Source: https://huggingface.co/papers/2605.14333 Published on May 14

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Abstract

InsightTok improves discrete visual tokenization for better text and face reconstruction through content-aware perceptual losses, enhancing autoregressive image generation quality.

Text and faces are among the most perceptually salient and practically important patterns in visual generation, yet they remain challenging forautoregressive generatorsbuilt on discrete tokenization. A central bottleneck is thetokenizer: aggressivedownsamplingand quantization often discard the fine-grained structures needed to preserve readable glyphs and distinctive facial features. We attribute this gap to standarddiscrete-tokenizer objectivesbeing weakly aligned withtext legibilityandfacial fidelity, as these objectives typically optimize generic reconstruction while compressing diverse content uniformly. To address this, we propose InsightTok, a simple yet effectivediscrete visual tokenizationframework that enhances text and face fidelity through localized, content-awareperceptual losses. With a compact 16kcodebookand a 16xdownsamplingrate, InsightTok significantly outperforms priortokenizers in text and face reconstruction without compromising general reconstruction quality. These gains consistently transfer toautoregressive image generationin InsightAR, producing images with clearer text and more faithful facial details. Overall, our results highlight the potential of specialized supervision intokenizertraining for advancing discrete image generation.

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