An Empirical Study of Training Pixel-Space Text-to-Image Diffusion Models

Dengyang Jiang; Ruoyi Du; Zhennan Chen; Dongyang Liu; Zanyi Wang; Mingzhe Zheng; Xiangpeng Yang; Huanqia Cai; Aiming Hao; Yuming Jiang; Peng Gao; Harry Yang; Steven Hoi

arXiv · 2608.16887

An Empirical Study of Training Pixel-Space Text-to-Image Diffusion Models

Dengyang Jiang·Ruoyi Du·Zhennan Chen·Dongyang Liu·Zanyi Wang·Mingzhe Zheng·Xiangpeng Yang·Huanqia Cai·Aiming Hao·Yuming Jiang·Peng Gao·Harry Yang·Steven Hoi

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Abstract

This paper investigates an increasingly important topic in generative modeling: pixel-space diffusion models. Although numerous studies have explored this topic, most focus on small-scale or class-conditional settings. Consequently, a practical recipe for training pixel-space models that rival or exceed well-established latent-space counterparts remains elusive. Through a comprehensive empirical study, we first observe that direct large-scale pre-training in pixel space converges substantially more slowly than in latent space. This observation motivates a latent-to-pixel strategy that acquires generative priors efficiently in latent space and transitions to pixel space during post-training. We then systematically investigate the key design choices governing this transition, including weight initialization, data composition, prediction target, decoder architecture, and noise schedule, and identify a practical recipe that makes the resulting pixel-space models match or outperform their latent-space counterparts while delivering 3.18 to 4.75 times end-to-end inference speedups. We hope that our findings provide useful empirical insights and practical guidelines for future research on pixel-space generation.

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Dengyang Jiang, Ruoyi Du, Zhennan Chen, Dongyang Liu, Zanyi Wang, Mingzhe Zheng, Xiangpeng Yang, Huanqia Cai, Aiming Hao, Yuming Jiang, Peng Gao, Harry Yang, Steven Hoi. 2026-08-17. An Empirical Study of Training Pixel-Space Text-to-Image Diffusion Models. https://arxiv.org/abs/2608.16887

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