Embedding an Ethical Mind: Aligning Text-to-Image Synthesis via Lightweight Value Optimization

TL;DR

This paper introduces LiVO, a lightweight method for aligning text-to-image models with human values by optimizing a plug-and-play value encoder, reducing harmful outputs and improving ethical content generation.

Contribution

LiVO is a novel, lightweight approach that aligns T2I models with human values without extensive model fine-tuning, using a preference optimization loss and a large preference dataset.

Findings

LiVO significantly reduces harmful and biased outputs.

LiVO achieves faster convergence compared to baselines.

LiVO effectively balances image quality and ethical alignment.

Abstract

Recent advancements in diffusion models trained on large-scale data have enabled the generation of indistinguishable human-level images, yet they often produce harmful content misaligned with human values, e.g., social bias, and offensive content. Despite extensive research on Large Language Models (LLMs), the challenge of Text-to-Image (T2I) model alignment remains largely unexplored. Addressing this problem, we propose LiVO (Lightweight Value Optimization), a novel lightweight method for aligning T2I models with human values. LiVO only optimizes a plug-and-play value encoder to integrate a specified value principle with the input prompt, allowing the control of generated images over both semantics and values. Specifically, we design a diffusion model-tailored preference optimization loss, which theoretically approximates the Bradley-Terry model used in LLM alignment but provides a more flexible trade-off between image quality and value conformity. To optimize the value encoder, we also develop a framework to automatically construct a text-image preference dataset of 86k (prompt, aligned image, violating image, value principle) samples. Without updating most model parameters and through adaptive value selection from the input prompt, LiVO significantly reduces harmful outputs and achieves faster convergence, surpassing several strong baselines and taking an initial step towards ethically aligned T2I models.