Read, Allocate, Route: Role-Coupled Tokenization for Pixel-Space Generative Transformers

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.

Image tokenizers convert pixels into compact latent sequences for efficient generative modeling. Most approaches arrange latents on a downsampled two-dimensional grid, tying sequence length to image resolution. We compare two variable-length strategies: computationally efficient patch-derived tokens, which inherit explicit spatial organization, and more demanding register tokens, which have no predefined spatial layout. Our reconstruction results reveal that patch-derived tokens perform substantially better under extreme compression, whereas register tokens achieve slightly better reconstruction quality at intermediate budgets. At high token budgets, both approaches achieve similar reconstruction quality. For downstream generative tasks, register tokens outperform patch-derived tokens. Adding representation alignment with external pretrained visual encoders substantially improves the generative performance of patch-derived tokens, allowing them to match or outperform register tokens depending on the token budget. We further find that both approaches learn spatially localized representations. Patch-derived tokens largely retain their explicitly imposed spatial organization, while register tokens develop an implicit two-dimensional spatial structure despite having no predefined layout. Representation alignment reduces this spatial organizational constraint, enabling individual tokens to exert a stronger non-local influence.

Diffusion Transformers achieve strong image generation performance, but most operate in compressed latent spaces. Pixel-space diffusion avoids this information loss, yet existing approaches map each raw image patch to a single token, forcing one representation to handle both global communication and fine-grained details. We address this issue by proposing a new architecture, DuSPiT, a Dual-branch SubPatch Pixel Transformer. This model separates global structural reasoning from local appearance modeling. DuSPiT uses a compact base branch for efficient global reasoning and a parallel, high-capacity pixel branch, organized into subpatch groups, to preserve detailed appearance, with the two branches interacting through cross-attention. Our results show that DuSPiT generates images with richer details and stronger fine-grained structures, while also achieving a better quality--efficiency trade-off than prior pixel-space diffusion transformers.