I've been reading a research paper titled High-Resolution Image Synthesis with Latent Diffusion Models by Robin Rombach et al. and came across an a concept related to diffusion models (DMs). In the abstract, the authors state:
By decomposing the image formation process into a sequential application of denoising autoencoders, diffusion models (DMs) achieve state-of-the-art synthesis results on image data and beyond. Additionally, their formulation allows for a guiding mechanism to control the image generation process without retraining. However, since these models typically operate directly in pixel space, optimization of powerful DMs often consumes hundreds of GPU days and inference is expensive due to sequential evaluations.
I'm trying to gain a deeper understanding of this highlighted part, particularly how the formulation of DMs allows for a guidance mechanism that controls the image generation process without the need for retraining.
Can anyone suggest detailed resources, or perhaps elaborate on the mechanism through which this control is achieved in DMs?