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.
The paper revisits pixel-space diffusion models, showing that a vanilla diffusion transformer can generate directly in pixel space by swapping velocity prediction for clean image prediction. It introduces MFlow, an adaptive parameterization that interpolates between these two prediction modes based on data representation and model capacity, and demonstrates competitive performance on ImageNet, UCF‑101, and text‑to‑image tasks. Additionally, the authors add ConvRefiner, a lightweight convolutional module trained post‑hoc to restore high‑frequency details while preserving FID.
By Soumik Mukhopadhyay, Long Mai, Abhinav Shrivastava, Aniruddha Mahapatra
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:2610.09450v1 Announce Type: new
Abstract: Pixel-space diffusion models avoid the lossy VAE of latent models, which suggests an advantage on downstream tasks where fine-grained detail matters. W...
By Hanqiu Li Cai (SperidLabs), Chema Garabito (SperidLabs)
The paper investigates how visual representations contribute to pixel diffusion transformers and finds that higher representation quality does not always improve generative performance, and that separating architecture into specialized decoders mainly redistributes modeling rather than enhancing generative suitability. Based on these findings, the authors propose Representation Guidance (RG), a method that compacts generation-relevant information into a low-dimensional bottleneck and explicitly uses it to steer decoder generation. RG achieves state‑of‑the‑art results on ImageNet 256×256 with fewer parameters, scales efficiently, and extends to text‑to‑image tasks with strong benchmark performance.
By Zihao Pan, Mengting Chen, Cheng Shi, Taihang Hu, Zhengrong Yue, Zikang Wang, Yuan Wang, Wei Li, Sibei Yang
arXiv:2603.15132v3 Announce Type: replace
Abstract: While recent Flow Matching models avoid the reconstruction bottlenecks of latent autoencoders by operating directly in pixel space, the raw pixel m...
By Hainuo Wang, Mingjia Li, Xiaojie Guo
Unified image restoration (UIR) aims to recover high-quality (HQ) content from low-quality (LQ) images with different degradations using a single model. Most recent methods adapt large pretrained text-to-image (T2I) latent diffusion models for their strong capacity and generative priors.
arXiv:2609.00798v1 Announce Type: new
Abstract: Pixel-space diffusion has recently emerged as a promising direction for high-fidelity image generation by modeling images directly in the original pixe...
By Weiyi You, Jinhua Zhang, Xingyu Zhou, Wei Long, Junyu Lou, Shuhang Gu
arXiv:2609.39222v1 Announce Type: new
Abstract: High-compression tokenizers are essential for scaling latent image generative models. However, aggressive compression creates a fundamental tradeoff be...
By Xu Huang, Ye Huang, Zijun Liao, Yuwei Niu, Xiaojie Li, Menghan Zhou, De Wen Soh, Xiaotong Li, Daquan Zhou
arXiv:2609.24919v1 Announce Type: new
Abstract: Recent advances in pixel-space diffusion models have narrowed the image quality gap with latent-space diffusion, but still converge more slowly and lag...
By Yongsheng Yu, Wei Xiong, Yichen Sheng, Shiqiu Liu, Jiebo Luo
arXiv:2610.28086v1 Announce Type: cross
Abstract: Real-time video generation, interactive world models, and on-device image synthesis are almost a reality. Diffusion Transformers (DiT) achieve excell...
By Jarek Liesen, Matthew T. Jackson, Benjamin Ellis, Shimon Whiteson, Jakob N. Foerster
arXiv:2606. 29400v1 Announce Type: cross Abstract: In computer graphics, visual content is continuously warped, zoomed and resampled.
By Giulio Federico, Giuseppe Amato, Claudio Gennaro, Fabio Carrara, Marco Di Benedetto