Consequently, image datasets depicting these groups have limited capacity to fully represent these demographics and intersectional identities. While we recognize that assigning a bias score based on these limited resources might not be entirely accurate, it is a vital first step in the right direction. Moreover, the bias effect size (Eq 8) may sometimes be unreliable [Meade et al., As described in Sec. 5, we manually compare our best HardNeg Stable Diffusion with vanilla Stable A storefront with'Hello W orld' written on it. This leaves us with 6 categories and 104 prompts:Category Prompts Colours A brown bird and a blue bear. An elephant is behind a tree.

U-Net layers: Randomly selected within the range of 7 to 15. Learning rate for prompt optimization:: A random value between 0.01 and 5e-4 was chosen for Noise level: Randomly chosen within the range t =1 to t = 10, where T = 50. Number of optimization steps: Randomly chosen in the range of 100 to 300. Learning rate for prompt optimization: 2. 37 10 Image crop size: Crop size as a percentage of the original image is 93. The architecture in Figure 3 is based on the stable diffusion model version 1.4 [ Correct correspondences are indicated in blue, while incorrect ones are depicted in orange. Correct correspondences are indicated in blue, while incorrect ones are depicted in orange.

This holds even for "out of distribution" target images (LEGO cats).

Fair representation learning provides an effective way of enforcing fairness constraints without compromising utility for downstream users.