Stay Fair! Ensuring Group Fairness in Diffusion Models Across Guidance Scales
StayFair ensures group fairness in diffusion models across guidance scales, improving fairness by 8.6%.
Key Findings
Methodology
StayFair extends Strong Demographic Parity to guidance, designing fair guidance algorithms. For classifier guidance, it equalizes classifier output distributions; for classifier-free guidance, it adjusts the null embedding. This method modifies only the guidance step, orthogonal to model debiasing.
Key Results
- StayFair improves fairness by 8.6% in occupational generation tasks, maintaining image quality across guidance scales.
- On CelebA, StayFair reduces bias range from 13.3% to 1.6%.
- On SD1.5, StayFair reduces bias range from 9.0% to 5.2%.
Significance
StayFair addresses fairness issues in diffusion models across varying guidance scales, ensuring stable group proportions when users adjust guidance scales. This has significant implications for academia and industry, particularly in model fairness and diversity.
Technical Contribution
StayFair introduces the concept of guidance bias, proposes new fairness conditions, and implements them in both classifier and classifier-free guidance. It complements existing debiasing methods, offering new engineering possibilities.
Novelty
StayFair is the first to extend Strong Demographic Parity to guidance, addressing the issue of guidance bias increasing with scale, providing more comprehensive fairness assurance compared to existing methods.
Limitations
- In extreme guidance scales, some bias may still persist.
- Additional computational resources are required to estimate and adjust guidance bias.
Future Work
Future research could explore further optimization of guidance bias estimation and applications on larger datasets.
AI Executive Summary
Diffusion models often exhibit biases related to sensitive attributes like gender and race during image generation. Existing debiasing methods typically optimize at a fixed guidance scale, failing to accommodate user adjustments.
The StayFair method introduces the concept of guidance bias, extending Strong Demographic Parity to the guidance phase and designing new fair guidance algorithms. It balances classifier outputs in classifier guidance and adjusts null embeddings in classifier-free guidance.
Experimental results demonstrate that StayFair maintains group fairness across guidance scales and validates its effectiveness on multiple datasets. This method offers a new solution for ensuring fairness in generative models, with broad application prospects.
Deep Analysis
Background
Diffusion models have recently excelled in conditional generation tasks, particularly in text-to-image synthesis. However, these models often exhibit biases in sensitive attributes like gender and race, potentially amplifying biases present in training data. Existing debiasing methods focus on model training, failing to address fairness issues arising from changes in guidance scales.
Core Problem
Existing debiasing methods are typically optimized at a fixed guidance scale, unable to accommodate user adjustments during generation. This results in unstable group proportions across different scales, affecting model fairness and diversity.
Innovation
The StayFair method introduces the concept of guidance bias, extends Strong Demographic Parity to the guidance phase, and designs new fair guidance algorithms. It balances classifier outputs in classifier guidance and adjusts null embeddings in classifier-free guidance, ensuring fairness across varying guidance scales.
Methodology
- �� Extend Strong Demographic Parity to the guidance phase, defining fairness conditions.
- �� Balance classifier output distributions in classifier guidance.
- �� Adjust null embeddings in classifier-free guidance to counteract bias.
- �� Modify only the guidance step, orthogonal to model debiasing.
Experiments
Experiments were conducted on the CelebA dataset using ADM-G for classifier guidance, evaluating gender bias. SD1.5 and SD3 models were used for classifier-free guidance, assessing gender bias in occupational generation tasks. Baseline methods include RW and GDRO.
Results
StayFair significantly reduces bias range across guidance scales while maintaining image quality. On CelebA, the bias range is reduced from 13.3% to 1.6%. On SD1.5, the bias range is reduced from 9.0% to 5.2%.
Applications
StayFair can be applied to generative tasks requiring fairness assurance, such as advertising generation and virtual character design. Its stability across guidance scales makes it suitable for diverse application scenarios.
Limitations & Outlook
In extreme guidance scales, some bias may still persist. Additional computational resources are required to estimate and adjust guidance bias. Future research could explore further optimization of guidance bias estimation.
Plain Language Accessible to non-experts
Imagine a factory producing balls of different colors. The factory has a color palette that adjusts the color intensity. Existing factory settings work only at a fixed color intensity, but users want to adjust the intensity as needed. StayFair acts like a smart palette, ensuring that the balls produced maintain color uniformity, regardless of how the user adjusts the palette.
ELI14 Explained like you're 14
Imagine you're playing a game where you can design your character's appearance. Usually, the game gives you default looks, but you want to tweak the character's appearance, like skin color or hairstyle. StayFair is like a super helper that ensures no matter how you change the character's appearance, the game fairly shows all kinds of characters without favoring one particular look. Isn't that cool?
Glossary
Diffusion Model
A generative model that creates data by reversing a forward corruption process.
Used as the foundational model for image generation.
Guidance Scale
A parameter that controls how conditioning information influences generation.
Key parameter for users adjusting the generation process.
Strong Demographic Parity
Requires prediction distributions to match across groups.
Used to define fairness conditions.
Classifier Guidance
Uses classifier outputs to guide the generation process.
A common guidance mechanism.
Classifier-Free Guidance
An implicit guidance mechanism that does not rely on classifiers.
Implemented using Bayes' rule.
Open Questions Unanswered questions from this research
- 1 How to further reduce bias at extreme guidance scales?
- 2 How to validate StayFair's effectiveness on larger datasets?
Applications
Immediate Applications
Advertising Generation
Advertising companies can use StayFair to generate fair ad content, ensuring balanced representation of different groups.
Long-term Vision
Virtual Character Design
Game developers can use StayFair to design diverse virtual characters, meeting the personalized needs of different players.
Abstract
Diffusion models steer conditional generation with a tunable guidance scale to trade off prompt alignment and diversity. However, existing debiasing techniques are optimized for a single scale, degrading fairness when users adjust this parameter. We trace this behavior to a previously overlooked source by decomposing total bias into two components: a model bias and a guidance bias. While prior work primarily targets the former, we show that the guidance bias grows monotonically with the guidance scale, eventually dominating the high-guidance regimes users prefer. To address this, we extend Strong Demographic Parity to guidance and derive a condition under which the target distribution retains its group ratio across guidance scales. We propose StayFair, which leverages this condition to design fair guidance algorithms in both regimes. For classifier guidance, it equalizes the classifier's output distributions across groups; for classifier-free guidance, it shifts the null embedding by a prompt-dependent offset. Because StayFair modifies only the guidance step, it is orthogonal to model debiasing and can be layered onto existing fair diffusion models to extend their fairness across guidance scales. Across class-conditional and text-to-image generation, StayFair decouples fairness from the guidance scale without sacrificing image quality.