Self-supervised h-space directions enable responsible diffusion generation

Li et al. target the same additive h-space bottleneck as Asyrp but replace its CLIP-guided optimization with a self-supervised reconstruction technique [li-etal-2024-self-discovering-diffusion-directions] A single vector c is optimized so the frozen U-Net reconstructs a concept-present image when conditioned instead on a concept-stripped prompt plus c added to h-space [li-etal-2024-self-discovering-diffusion-directions] Because the model is frozen and the prompt omits the concept, gradient descent forces c to absorb exactly the missing semantic content, needing no labels or CLIP [li-etal-2024-self-discovering-diffusion-directions] A negative-prompt variant learns an anti-concept direction for safety mitigation; linearity and additive composability are demonstrated qualitatively and imposed by design [li-etal-2024-self-discovering-diffusion-directions]