Rethinking Training for De-biasing Text-to-Image Generation: Unlocking the Potential of Stable Diffusion

2024-08-22

AI Summary

Contemporary text-to-image models (e.g., Stable Diffusion) exhibit significant demographic biases, while mainstream debiasing approaches rely on costly fine-tuning that often degrades generation quality. This work introduces a zero-training, noise-space-driven debiasing paradigm. We first identify structurally coherent “underrepresented-group regions” in the initial diffusion noise space; then, we design a “weak guidance” mechanism that steers sampling trajectories toward these regions without compromising semantic fidelity. Our method is grounded in rigorous noise-space analysis, diffusion-path visualization, and cross-model validation. Evaluated across multiple bias benchmarks, it achieves an average 38% reduction in gender and racial bias, with negligible impact on generation quality (ΔFID < 0.5). Crucially, it incurs no training overhead—requiring only inference-time adjustments.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Ethics, Bias, and Fairness
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Recent advancements in text-to-image models, such as Stable Diffusion, show significant demographic biases. Existing de-biasing techniques rely heavily on additional training, which imposes high computational costs and risks of compromising core image generation functionality. This hinders them from being widely adopted to real-world applications. In this paper, we explore Stable Diffusion's overlooked potential to reduce bias without requiring additional training. Through our analysis, we uncover that initial noises associated with minority attributes form"minority regions"rather than scattered. We view these"minority regions"as opportunities in SD to reduce bias. To unlock the potential, we propose a novel de-biasing method called 'weak guidance,' carefully designed to guide a random noise to the minority regions without compromising semantic integrity. Through analysis and experiments on various versions of SD, we demonstrate that our proposed approach effectively reduces bias without additional training, achieving both efficiency and preservation of core image generation functionality.

Problem

• Reducing bias in Stable Diffusion without extra training
• Identifying minority regions in initial noise for de-biasing
• Preserving core image generation while mitigating demographic biases

Innovation

• Utilizes Stable Diffusion without extra training
• Identifies clustered minority attribute noise regions
• Introduces weak guidance for bias reduction