Test-Time Registers as Global Priors for Tokenized Image Generation

• URL: http://arxiv.org/abs/2607.16824v2
• Date: Tue, 21 Jul 2026 09:16:54 GMT
• Title: Test-Time Registers as Global Priors for Tokenized Image Generation
• Abstract summary: We show that testtime register features exhibit stronger low-frequency concentration than both pixel-space readouts and patch-mean features.<n>Motivated by these diagnostics, we introduce RegToken, a training procedure that converts register structure into a small set of global prior tokens.
• Score: 25.626464690706484
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Attention-based models often develop attention sinks, where a small number of tokens repeatedly attract attention and accumulate unusually large activations. In vision transformers, these outliers are closely related to registers, which have been diagnostically linked to global, low-frequency image structure. Existing work has largely studied registers through interpretability analyses and linear probes, leaving open whether they can be operationalized as plug-and-play signals for generation without retraining. We revisit this question in tokenized image generation. Using OpenCLIP and DINOv2 on ImageNet, we find that test-time register features exhibit stronger low-frequency concentration than both [CLS] readouts and patch-mean features, and show a consistent (albeit moderate) correlation with pixel-space DCT low-frequency energy. Motivated by these diagnostics, we introduce RegToken, a training-free procedure that converts register structure into a small set of global prior tokens by (i) NFN-based layer localization, (ii) TokenRank-guided subspace extraction, and (iii) a projection-and-conservation update on the register subspace. Inserted into a frozen compact 1D token generation pipeline, RegToken improves ImageNet generation and alignment metrics (e.g., FID-5k 20.5 to 20.1, SigLIP 3.6 to 3.9) without modifying pretrained weights, and accelerates test-time optimization (Steps@$τ$ 74 to 52). Overall, our results suggest that structures often viewed as attention artifacts can be repurposed as lightweight global priors for tokenized generation.

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