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While diffusion models excel at generating high-quality samples, their latentvariables typically lack semantic meaning and are not suitable forrepresentation learning. Here, we propose InfoDiffusion, an algorithm thataugments diffusion models with low-dimensional latent variables that capturehigh-level factors of variation in the data. InfoDiffusion relies on a learningobjective regularized with the mutual information between observed and hiddenvariables, which improves latent space quality and prevents the latents frombeing ignored by expressive diffusion-based decoders. Empirically, we find thatInfoDiffusion learns disentangled and human-interpretable latentrepresentations that are competitive with state-of-the-art generative andcontrastive methods, while retaining the high sample quality of diffusionmodels. Our method enables manipulating the attributes of generated images andhas the potential to assist tasks that require exploring a learned latent spaceto generate quality samples, e.g., generative design.

InfoDiffusion: Representation Learning Using Information Maximizing Diffusion Models