62 citations · 74 across the 5 of their papers we have counts for
4 papers · 1 filter
Optimizing Hierarchical Image VAEs for Sample Quality
Eric Luhman, Troy Luhman
While hierarchical variational autoencoders (VAEs) have achieved great density estimation on image modeling tasks, samples from their prior tend to look less convincing than models…
Improving Diffusion Model Efficiency Through Patching
Troy Luhman, Eric Luhman
Diffusion models are a powerful class of generative models that iteratively denoise samples to produce data. While many works have focused on the number of iterations in this sampl…
Knowledge Distillation in Iterative Generative Models for Improved Sampling Speed
Eric Luhman, Troy Luhman
Iterative generative models, such as noise conditional score networks and denoising diffusion probabilistic models, produce high quality samples by gradually denoising an initial n…
Diffusion models for Handwriting Generation
Troy Luhman, Eric Luhman
In this paper, we propose a diffusion probabilistic model for handwriting generation. Diffusion models are a class of generative models where samples start from Gaussian noise and…