AHPA adaptively aligns diffusion transformers to hierarchical VAE priors via a dynamic router that matches supervision granularity to the current noise level, improving convergence and quality.
Representation alignment for generation: Training diffusion transformers is easier than you think
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CV 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
AHPA: Adaptive Hierarchical Prior Alignment for Diffusion Transformers
AHPA adaptively aligns diffusion transformers to hierarchical VAE priors via a dynamic router that matches supervision granularity to the current noise level, improving convergence and quality.