  Diffusion probabilistic models (DPMs) have shown remarkable results on
various image synthesis tasks such as text-to-image generation and image
inpainting. However, compared to other generative methods like VAEs and GANs,
DPMs lack a low-dimensional, interpretable, and well-decoupled latent code.
Recently, diffusion autoencoders (Diff-AE) were proposed to explore the
potential of DPMs for representation learning via autoencoding. Diff-AE
provides an accessible latent space that exhibits remarkable interpretability,
allowing us to manipulate image attributes based on latent codes from the
space. However, previous works are not generic as they only operated on a few
limited attributes. To further explore the latent space of Diff-AE and achieve
a generic editing pipeline, we proposed a module called Group-supervised
AutoEncoder(dubbed GAE) for Diff-AE to achieve better disentanglement on the
latent code. Our proposed GAE has trained via an attribute-swap strategy to
acquire the latent codes for multi-attribute image manipulation based on
examples. We empirically demonstrate that our method enables
multiple-attributes manipulation and achieves convincing sample quality and
attribute alignments, while significantly reducing computational requirements
compared to pixel-based approaches for representational decoupling. Code will
be released soon.
