Latent Diffusion-GAN: Adversarial Learning in the Autoencoded Latent Space
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Date
2026
Authors
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Journal ISSN
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Publisher
The Eurographics Association and John Wiley & Sons Ltd.
Abstract
Diffusion models are powerful generative frameworks for producing high-quality images by denoising latent variables from random noise. However, training with likelihood-based objectives can lead to oversmoothed high-frequency details such as textures and sharp edges. Adversarial training with GANs enhances sharpness but usually requires additional discriminator networks. We propose Latent Diffusion Generative Adversarial Networks (LD-GAN), a framework that integrates adversarial learning into diffusion models without modifying their pipeline. LD-GAN leverages the pretrained variational autoencoder as an energy-based discriminator, enabling adversarial training without extra parameters while preserving the latent priors learned from large datasets. We also introduce a structural consistency energy aligning encoder and decoder representations, improving perceptual quality. Experiments show improved sample fidelity, sharpness, and diversity across multiple generation tasks while maintaining efficient training dynamics.
Description
@article{10.1111:cgf.70409,
journal = {Computer Graphics Forum},
title = {{Latent Diffusion-GAN: Adversarial Learning in the Autoencoded Latent Space}},
author = {Jun, U-Chae and Ko, Jaeeun and Kang, Jiwoo},
year = {2026},
publisher = {The Eurographics Association and John Wiley & Sons Ltd.},
ISSN = {1467-8659},
DOI = {10.1111/cgf.70409}
}