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.
Sara: Structural and adversarial representa- tion alignment for training-efficient diffusion models, 2025
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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.