InvDiff: Invariant Guidance for Bias Mitigation in Diffusion Models

Understand

As one of the most successful generative models, diffusion models have demonstrated remarkable efficacy in synthesizing high-quality images.

• These models learn the underlying high-dimensional data distribution in an unsupervised manner.
• Despite their success, diffusion models are highly data-driven and prone to inheriting the imbalances and biases present in real-world data.
• Some studies have attempted to address these issues by designing text prompts for known biases or using bias labels to construct unbiased data.