Dreamguider: Tuning Free Conditional Generation

Diffusion models have emerged as a formidable tool for training-free conditional generation. However, a key hurdle in inference-time guidance techniques is the need for compute-heavy backpropagation through the diffusion network for estimating the guidance direction. Moreover, these techniques often require handcrafted parameter tuning on a case-by-case basis. Although some recent works have introduced minimal compute methods for linear inverse problems, a generic lightweight guidance solution to both linear and non-linear guidance problems is still missing.

To this end, we propose Dreamguider, a method that enables inference-time guidance without the compute-heavy backpropagation through the diffusion network. The key idea is to approximate the guidance direction with respect to the current sample, thereby removing the backpropagation operation. Moreover, we propose an empirical guidance scale that works for a wide variety of tasks, hence removing the need for handcrafted parameter tuning. We further introduce an effective lightweight augmentation strategy that significantly boosts the performance during inference-time guidance. We present experiments using Dreamguider on multiple linear and non-linear tasks across multiple datasets and models to show the effectiveness of the proposed modules.