We introduce DC-SSDAE, a novel autoencoder framework for efficient high-resolution image generation. By integrating a deep compression encoder for high-ratio spatial reduction, a single-step diffusion decoder for fast reconstruction, and equilibrium matching for stable generative training, DC-SSDAE achieves compact latent representations while preserving perceptual quality. Trained on ImageNet-1K, it replaces flow matching with a time-invariant equilibrium gradient, enabling flexible gradient-descent sampling. This combination addresses optimization challenges in high-compression settings, offering potential speedups in diffusion model pipelines without adversarial losses. The purpose of this project is to prove this architecture can work well among s-o-t-a VAE models, and offers a strong & stable codebase for other VAE researchers to build upon.

It is obvious that the increasing size of LLMs has created enormous model deployment and energy consumption problems.

From now on, when trying diffusion model, use Equilibrium Matching (EqM) to learn the equilibrium (static) gradient of an implicit energy landscape instead of using Flow Matching learns non-equilibrium velocity field that varies over time

AIs today are still so Dense! I mean it metaphorically and literally.