Study: Pixel-Space Training Suited for Diffusion Distillation
bdsqlsz · x · 2026-08-20
A new empirical study on training text-to-image diffusion models in pixel space reveals:
- Convergence: Pixel-space pre-training converges more slowly compared to latent space training.
- Use Case: This slower convergence makes it more suitable for the distillation stage of model development.
- Color Drift: Using a Noise Scale of 2 helps mitigate color drift issues.
The research provides data-backed insights for optimizing diffusion model training pipelines.
Related event: Study: Pixel-Space Diffusion Training Better for Distillation(2 posts)→
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