Self-Guided Diffusion Models

Understand

Diffusion models have demonstrated remarkable progress in image generation quality, especially when guidance is used to control the generative process.

• However, guidance requires a large amount of image-annotation pairs for training and is thus dependent on their availability, correctness and unbiasedness.
• In this paper, we eliminate the need for such annotation by instead leveraging the flexibility of self-supervision signals to design a framework for self-guided diffusion models.
• By leveraging a feature extraction function and a self-annotation function, our method provides guidance signals at various image granularities: from the level of holistic images to object boxes and even segmentation masks.