Diffusion Loss Scales Linearly with Structured Language in Prompts
July 30, 2026
Diffusion loss does not scale with total token count but instead follows a linear relationship with white-box likelihood (GPG) and a power law with black-box attributes (ED). Using these scaling laws, prompts can be optimized via semantic and geometric annotations to improve image generation quality.
HOW THIS AFFECTS YOU
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builderYou can improve generation quality by constructing prompts with specific structured annotations instead of just adding more tokens.
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researcherYou can use GPG and ED metrics to predict diffusion loss behavior during training.