Diffusion Probabilistic Models (DPMs) have shown a powerful capacity of generating high-quality image samples. Recently, diffusion autoencoders (Diff-AE) have been proposed to explore DPMs for representation learning via autoencoding. Their key idea is to jointly train an encoder for discovering meaningful representations from images and a conditional DPM as the decoder for reconstructing images. Considering that training DPMs from scratch will take a long time and there have existed numerous pre-trained DPMs, we propose \textbf{P}re-trained \textbf{D}PM \textbf{A}uto\textbf{E}ncoding (\textbf{PDAE}), a general method to adapt existing pre-trained DPMs to the decoders for image reconstruction, with better training efficiency and performance than Diff-AE. Specifically, we find that the reason that pre-trained DPMs fail to reconstruct an image from its latent variables is due to the information loss of forward process, which causes a gap between their predicted posterior mean and the true one. From this perspective, the classifier-guided sampling method can be explained as computing an extra mean shift to fill the gap, reconstructing the lost class information in samples. These imply that the gap corresponds to the lost information of the image, and we can reconstruct the image by filling the gap. Drawing inspiration from this, we employ a trainable model to predict a mean shift according to encoded representation and train it to fill as much gap as possible, in this way, the encoder is forced to learn as much information as possible from images to help the filling. By reusing a part of network of pre-trained DPMs and redesigning the weighting scheme of diffusion loss, PDAE can learn meaningful representations from images efficiently. Extensive experiments demonstrate the effectiveness, efficiency and flexibility of PDAE.
翻译:集成概率模型(DPMs) 显示了生成高质量图像样本的强大能力。 最近, 已经提议推广自动编码器( Diff- AE) 来探索 DPM, 以便通过自动编码学习 。 它们的关键想法是共同训练一个编码器, 以便从图像中发现有意义的表达方式, 并使用一个有条件的 DPM 来重建图像。 考虑到从头到尾培训DPM 将花费很长的时间, 并且已经有许多经过培训的DPM 。 我们提议 传播自动编码器( Diff- AE), 以便探索 DPM (Diff- AE) 以通过自动编码来进行演示 。 将现有的经培训过的DPM 用于图像重建, 其培训效率和性能比 Diff- AE 还要好。 具体地说, 我们发现, 预先培训过的DPMs 无法重建图像, 其潜在变异性变异性, 是因为信息过程会弥补, 导致预测的 DPMscarf{Dr>, 的变异性, 。