arXiv Machine Learning
2d ago

Purify Before You Align: Improving Representation Alignment for Diffusion Models

The paper proposes Local Appearance Purification (LAP), a method that refines the alignment target for diffusion models by removing locally predictable content and focusing on the residual. LAP improves generation quality across multiple pretrained encoders, notably reducing FID from 53.1 to 42.3 on ImageNet-256 with MAE. The authors provide a theoretical framework linking alignment effectiveness to the information emphasized by the target and introduce a constant‑readout test to predict purification benefits.

By Yingheng Wang, Yaoqiang Li, Yaqin Wu, Junwen Bai, Jiatao Gu, Christopher De Sa, Volodymyr Kuleshov
arXiv Machine Learning
3d ago

Are We Overthinking One-Step Generation?

The paper introduces Priming Models, a two‑stage framework for training one‑step generative models. First, the model is trained on a reconstruction objective such as pixel‑space denoising at a fixed noise level, then it undergoes feature‑space distributional post‑training. This simple reconstruction stage alone is shown to eliminate the difficulty of distributional training, leading to significant improvements in training compute versus generation quality across tasks like class‑conditional image generation, text‑to‑image generation, and world modeling, with a state‑of‑the‑art FID of 1.19 on ImageNet 256×256 using about one‑tenth the compute of previous methods.

By Woobin Park, Jaeyeon Kim, Yilun Du, Sitan Chen, Bohyung Han
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
arXiv Computer Vision
Sep 22

What Makes a Good Medical Image Tokenizer? Rethinking Reconstruction and Generation in Medical Image Tokenization

arXiv:2609.24691v1 Announce Type: new Abstract: Latent diffusion models now dominate medical image generation, and every such pipeline rests on a \emph{tokenizer} that compresses images into the late...

By Niklas Bubeck, Yundi Zhang, Vasiliki Sideri-Lampretsa, Julian McGinnis, Jiancheng Yang, Daniel Rueckert, Jiazhen Pan