Spatially Gated Demographic Steering for Debiasing Multi-Person Text-to-Image Generation
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.
ProofPaper ↗
Key points
- 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.
- 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.
Sources (1)
- [1]Spatially Gated Demographic Steering for Debiasing Multi-Person Text-to-Image GenerationarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 21, 12:32 PM
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.
Text-to-Image models reproduce societal stereotypes, and existing mitigation applies one demographic correction to the whole scene.
Extractive summary: sentences quoted from the sources.
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