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.
Improved denoising diffusion probabilistic models, 2021
3 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 3representative citing papers
JFDL allows pre-trained Consistency Models to perform guided image generation post-hoc by aligning flow distributions, reducing FID scores on CIFAR-10 and ImageNet without needing a teacher model.
Optimizing the noise schedule, preparing a balanced bucketed dataset, and aligning outputs with human preferences enables Playground v2.5 to reach state-of-the-art aesthetic quality across aspect ratios.
citing papers explorer
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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.
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Post-Hoc Guidance for Consistency Models by Joint Flow Distribution Learning
JFDL allows pre-trained Consistency Models to perform guided image generation post-hoc by aligning flow distributions, reducing FID scores on CIFAR-10 and ImageNet without needing a teacher model.
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Playground v2.5: Three Insights towards Enhancing Aesthetic Quality in Text-to-Image Generation
Optimizing the noise schedule, preparing a balanced bucketed dataset, and aligning outputs with human preferences enables Playground v2.5 to reach state-of-the-art aesthetic quality across aspect ratios.