Read, Allocate, Route: Role-Coupled Tokenization for Pixel-Space Generative Transformers
READ THE ORIGINAL SOURCEOpen original sourcehuggingface.co · Opens in a new tabSpecSlot is a pixel-space rectified-flow Transformer that expands each spatial anchor into multiple slots, each assigned a disjoint band of DCT coefficients. Only the lowest-frequency slot per anchor joins global attention, while all slots interact locally, so output detail can increase without enlarging the global attention sequence. A spatial head adds a local correction to the spectral prediction. Reported results include a 38.2% lower cost than JiT-B/16 on ImageNet-512 with an FID gap of 0.69, and a DPG-Bench gain over JiT-B/8 at 40.6% lower forward cost.
One primary source; other links are context, not independent corroboration.
- Primary sourceHugging Face Daily Papers
- Discovery linkHugging Face
- Linked in sourceView arXiv page
- Linked in sourceView PDF
- Linked in sourcehttps://github.com/JiongfanYang/SpecSlot
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SpecSlot is a pixel-space rectified-flow Transformer that expands each spatial anchor into multiple slots, each assigned a disjoint band of DCT coefficients. Only the lowest-frequency slot per anchor joins global attention, while all slots interact locally, so output detail can increase without enlarging the global attention sequence. A spatial head adds a local correction to the spectral prediction. Reported results include a 38.2% lower cost than JiT-B/16 on ImageNet-512 with an FID gap of 0.69, and a DPG-Bench gain over JiT-B/8 at 40.6% lower forward cost.
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Key findings from the stored brief
- Only the lowest-frequency slot from each anchor participates in global attention; all slots interact locally.
- On ImageNet-512, SpecSlot improves FID by 2.84 over a control with 3.9% fewer FLOPs.
- On ImageNet-512, it comes within 0.69 FID of JiT-B/16 at 38.2% lower cost.
- On text-to-image generation, SpecSlot surpasses JiT-B/8 on DPG-Bench by 1.06 at 40.6% lower forward cost.
Source claims not independently checked; comparisons are against specific baselines (JiT, FLOPs-matched controls) under the paper's own setup.
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Evidence & sources
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Why this may matter
Pixel-space generative Transformers normally need more tokens for finer detail, which makes global attention expensive. Decoupling spectral detail from global sequence length offers a route to higher detail at lower compute.
Limits and open questions
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