Diffusion models are emerging as powerful solutions for generating
high-fidelity and diverse images, often surpassing GANs under many
circumstances. However, their slow inference speed hinders their potential for
real-time applications. To address this, DiffusionGAN leveraged a conditional
GAN to drastically reduce the denoising steps and speed up inference. Its
advancement, Wavelet Diffusion, further accelerated the process by converting
data into wavelet space, thus enhancing efficiency. Nonetheless, these models
still fall short of GANs in terms of speed and image quality. To bridge these
gaps, this paper introduces the Latent Denoising Diffusion GAN, which employs
pre-trained autoencoders to compress images into a compact latent space,
significantly improving inference speed and image quality. Furthermore, we
propose a Weighted Learning strategy to enhance diversity and image quality.
Experimental results on the CIFAR-10, CelebA-HQ, and LSUN-Church datasets prove
that our model achieves state-of-the-art running speed among diffusion models.
Compared to its predecessors, DiffusionGAN and Wavelet Diffusion, our model
shows remarkable improvements in all evaluation metrics. Code and pre-trained
checkpoints: https://github.com/thanhluantrinh/LDDGAN.git

该论文介绍了一种使用预训练自编码器将图像压缩为紧凑的潜在空间的潜隐去噪扩散生成对抗网络（LDDGAN），以显著提高推断速度和图像质量，并提出了一种加权学习策略来增强多样性和图像质量。该模型在 CIFAR-10，CelebA-HQ 和 LSUN-Church 数据集上的实验结果证明了其在扩散模型中达到了最先进的运行速度。与其前身 DiffusionGAN 和 Wavelet Diffusion 相比，我们的模型在所有评估指标上都表现出了显著的改进。