MixTok: Adaptive Visual Tokenization with Global 1D Tokens and Sparse 2D Refinement
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
The Flow has not summarised this story yet — read it at arXiv Computer Vision.
One-dimensional (1D) variable-length visual tokenizers enable adaptive compression by varying the number of tokens, allowing downstream autoregressive (AR) models to flexibly trade off generation qual...
arXiv:2609.36756v1 Announce Type: cross Abstract: One-dimensional (1D) variable-length visual tokenizers enable adaptive compression by varying the number of tokens, allowing downstream autoregressiv...
QuadTok introduces a quadtree-based visual tokenizer that replaces traditional 2D grids or 1D token sequences with a hierarchical structure, allowing dynamic allocation of tokens to visually complex regions while using coarser resolution for homogeneous areas. The tokenizer, trained on ImageNet, reduces token usage by about 10% compared to a fixed 256-token grid and retains comparable reconstruction fidelity. Leveraging the tree’s natural causality, a 947 M GPT-style model conditioned on a quadtree topology achieves a 2.08 gFID on ImageNet 256×256 and supports zero‑shot spatially controlled image generation.
BudgetPix introduces a compute‑adaptive tokenization framework for pixel‑space image diffusion, dynamically allocating computational resources based on visual complexity and spatial layout. It comprises an adaptive encoder using an entropy‑guided quadtree and multi‑scale patch embedder, a scale‑aware decoder, and a flexible training schedule that allows pixel‑space denoisers to operate across variable token counts. The method integrates with existing diffusion architectures, achieving comparable fidelity to larger models while using only a fraction of the compute budget, and demonstrates improved quality‑efficiency tradeoffs in both text‑to‑image and class‑conditional generation.
arXiv:2606.06158v2 Announce Type: replace Abstract: Adaptive video tokenisation seeks to dynamically allocate token budgets based on the underlying visual complexity of a sequence. Current continuous...
arXiv:2606. 05552v1 Announce Type: new Abstract: Despite progress in image tokenization, standard methods encode redundant information by mixing all granularities within each token, thus redundancy persists between tokens.