State-of-the-art generative text-to-image models are known to exhibit social biases and over-represent certain groups like people of perceived lighter skin tones and men in their outcomes. In this work, we propose a method to mitigate such biases and ensure that the outcomes are fair across different groups of people. We do this by finetuning text-to-image models on synthetic data that varies in perceived skin tones and genders constructed from diverse text prompts. These text prompts are constructed from multiplicative combinations of ethnicities, genders, professions, age groups, and so on, resulting in diverse synthetic data. Our diversity finetuned (DFT) model improves the group fairness metric by 150% for perceived skin tone and 97.7% for perceived gender. Compared to baselines, DFT models generate more people with perceived darker skin tone and more women. To foster open research, we will release all text prompts and code to generate training images.

Figure 1: We mitigate stereotypical biases by finetuning Stable Diffusion-1.5 (26) and Stable Diffusion-XL (22) on synthetic data that varies across perceived skin tones, genders, professions, and age groups. For the same prompt and seed, notice that our diversity finetuned (DFT) models generate more inclusive results..

Table 1: Effect of prompt qualifiers on group fairness. Given lighter skin tone and perceived male gender are the sub-groups TTI models default to, we measure disparate impact (Eqn. 1) relative to these categories (i.e., X2 = light, male). L/M/D refers to distribution of the predicted skin tones into light, medium and dark categories and F/M refers to distribution into predicted female and male categories.

Table 2: Effect of data composition. Note that a balanced distribution of perceived skin tones yields better performance. L/M/D refers to distribution of the predicted skin tones into light, medium and dark categories.