Newest papers

Spatially Gated Demographic Steering for Debiasing Multi-Person Text-to-Image Generation

Venkatesh Thirugnana Sambandham, Torsten Schön · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.29651 · cited by 0 works (OpenAlex) · open-access PDF

Abstract

Text-to-Image models reproduce societal stereotypes, and existing mitigation applies one demographic correction to the whole scene. In multi-person scene generation this creates a trade-off: global steering improves aggregate balance only by homogenizing the people within an image. Steering-based methods like Embedding Arithmetic drive the intra-image diversity below the unsteered baseline. Text- and latent-projection approaches shift the aggregate skew with only marginal gains in intra-image diversity. We present Spatially Gated Steering (SGS), which steers the demographics of each person independently by extracting per-person masks from the Diffusion Transformer's (DiT) internal attention maps at a critical denoising timestep and applying a distinct demographic vector within each mask for the remaining steps. SGS needs no fine-tuning, inversion or layout specifications, and uses a lightweight segmentor over frozen DiT attention features to localize subjects at early timesteps. On skewed occupational prompts, SGS reaches high intra-image racial and gender diversity while keeping aggregate balance competitive and image quality near baseline, a result we validate on two architecturally distinct diffusion transformers, FLUX.1-Dev and SD3.5-Large. Codebase and model weights are available at https://github.com/cvims/spatially-gated-steering

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Topics: Debiasing, Skew, Computer science, Glyph (data visualization), Aggregate (composite), Offset (computer science)

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