Alibaba's Tongyi-MAI: Pixel-Space Diffusion Up to 4.75x Faster
Crazy-Repeat-2006 · reddit · 2026-08-20
Researchers from Alibaba's Tongyi team published a paper on training pixel-space text-to-image diffusion models. Observing that direct large-scale pre-training in pixel space converges slowly, they propose a latent-to-pixel strategy. This approach acquires generative priors in latent space before transitioning to pixel space during post-training. By optimizing key factors like weight initialization and noise schedule, the resulting models match or outperform latent-space counterparts while achieving 3.18x to 4.75x end-to-end inference speedups.
Related event: Tongyi Proposes Latent-to-Pixel Strategy to Speed Up Diffusion Models(2 posts)→
More from Research
- MIT, Stanford Launch Public AI Observatory to Measure Real-World Assistant Usage — ShayneRedford · 2026-08-20
- Stanford releases quickstart guide for ENCODE GRAMMAR genomics AI tool — anshulkundaje · 2026-08-20
- Peking U & MSRA: BCP optimizes robot replanning timing via lightweight policy — 机器之心 · 2026-08-20
- AI agent stacks blocks in browser physics sim as open embodied-AI arena debuts — NovaCoding · 2026-08-20
- Agent Arena: Million-task leaderboard for real-world AI agents — arena · 2026-08-20
- Robot Achieves One-Shot Imitation with Zero Fine-Tuning — Scobleizer · 2026-08-20