Latent Diffusion Model without Variational Autoencoder
Authors: name: Minglei Shi, name: Haolin Wang, name: Wenzhao Zheng, name: Ziyang Yuan, name: Xiaoshi Wu, name: Xintao Wang, name: Pengfei Wan, name: Jie Zhou, name: Jiwen Lu
Organization: Kling Team

Recent progress in diffusion-based visual generation has largely relied on
latent diffusion models with variational autoencoders (VAEs). While effective
for high-fidelity synthesis, this VAE+diffusion paradigm suffers from limited
training efficiency, slow inference, and poor transferability to broader vision
tasks. These issues stem from a key limitation of VAE latent spaces: the lack
of clear semantic separation and strong discriminative structure. Our analysis
confirms that these properties are crucial not only for perception and
understanding tasks, but also for the stable and efficient training of latent
diffusion models. Motivated by this insight, we introduce SVG, a novel latent
diffusion model without variational autoencoders, which leverages
self-supervised representations for visual generation. SVG constructs a feature
space with clear semantic discriminability by leveraging frozen DINO features,
while a lightweight residual branch captures fine-grained details for
high-fidelity reconstruction. Diffusion models are trained directly on this
semantically structured latent space to facilitate more efficient learning. As
a result, SVG enables accelerated diffusion training, supports few-step
sampling, and improves generative quality. Experimental results further show
that SVG preserves the semantic and discriminative capabilities of the
underlying self-supervised representations, providing a principled pathway
toward task-general, high-quality visual representations.