NoiseCLR: A Contrastive Learning Approach for Unsupervised Discovery of
  Interpretable Directions in Diffusion Models
Yusuf Dalva Pinar Yanardag
Generative models have been very popular in the recent years for their image
generation capabilities. GAN-based models are highly regarded for their
disentangled latent space, which is a key feature contributing to their success
in controlled image editing. On the other hand, diffusion models have emerged
as powerful tools for generating high-quality images. However, the latent space
of diffusion models is not as thoroughly explored or understood. Existing
methods that aim to explore the latent space of diffusion models usually relies
on text prompts to pinpoint specific semantics. However, this approach may be
restrictive in areas such as art, fashion, or specialized fields like medicine,
where suitable text prompts might not be available or easy to conceive thus
limiting the scope of existing work. In this paper, we propose an unsupervised
method to discover latent semantics in text-to-image diffusion models without
relying on text prompts. Our method takes a small set of unlabeled images from
specific domains, such as faces or cats, and a pre-trained diffusion model, and
discovers diverse semantics in unsupervised fashion using a contrastive
learning objective. Moreover, the learned directions can be applied
simultaneously, either within the same domain (such as various types of facial
edits) or across different domains (such as applying cat and face edits within
the same image) without interfering with each other. Our extensive experiments
show that our method achieves highly disentangled edits, outperforming existing
approaches in both diffusion-based and GAN-based latent space editing methods.