Pixel-Space Diffusion Training Slower, Ideal for Distillation with 3x Speedup
bdsqlsz · x · 2026-08-20
A new paper, "An Empirical Study of Training Pixel-Space Text-to-Image Diffusion Models," investigates training efficiency in pixel versus latent spaces. It finds that direct large-scale pre-training in pixel space converges significantly slower than in latent space.
Key Findings & Strategy:
- Proposes a "latent-to-pixel" strategy: efficiently acquire generative priors in latent space, then transition to pixel space during post-training.
- Systematically examines key design choices like weight initialization, data composition, and prediction targets.
- Results: The resulting pixel-space models match or outperform latent-space counterparts while achieving 3.18x to 4.75x end-to-end inference speedups.
- Tip: Using a noise scale of 2 can reduce color drift.
Related event: Study: Pixel-Space Diffusion Training Better for Distillation(2 posts)→
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