arXiv Computer Vision
Oct 16

Image Tokenizers Should Be Trained Between Images

The paper proposes InterpTok, a method that fine‑tunes the MAETok image tokenizer to better handle codes that lie between training samples. By training the decoder on mixtures of image codes and using a discriminator plus a mixing‑ratio loss, InterpTok preserves reconstruction quality while dramatically improving interpolation and generation metrics. The approach yields sharper, more natural image compositions and significantly lowers FID scores for diffusion models trained on the new tokenizer.

By Ole Bei{\ss}wenger, Jan-Niklas Dihlmann, Hendrik Lensch
arXiv Computer Vision
Oct 9

BudgetPix: Compute-Adaptive Tokenization for Pixel-Space Image Diffusion

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.

By Ozgur Kara, Yujia Chen, Daniel Watson, David Forsyth, James Matthew Rehg, Wen-Sheng Chu, Du Tran
Hugging Face Trending Papers
Oct 13

PixelMoE: Scaling Pixel-Space Diffusion with LLM-Style Mixture-of-Experts

PixelMoE introduces a pixel-space generative framework that applies LLM-style Mixture-of-Experts (MoE) to diffusion models, enabling sparse scaling of model capacity without diffusion-specific routing. The approach combines Token-Choice routing, fine-grained routed experts, a shared expert, and bias-based load balancing, while also employing Pixel-Space Hierarchical Flow Matching to adjust spatial resolution during denoising. PixelMoE outperforms dense counterparts at comparable active parameter counts, achieving strong FID scores on class-conditional ImageNet-1K.