DEFT: Efficient Fine-Tuning of Diffusion Models by Learning the Generalised $h$-transform

Understand

Generative modelling paradigms based on denoising diffusion processes have emerged as a leading candidate for conditional sampling in inverse problems.

• In many real-world applications, we often have access to large, expensively trained unconditional diffusion models, which we aim to exploit for improving conditional sampling.
• Most recent approaches are motivated heuristically and lack a unifying framework, obscuring connections between them.
• Further, they often suffer from issues such as being very sensitive to hyperparameters, being expensive to train or needing access to weights hidden behind a closed API.