Residualizing adjacent denoising timesteps yields directionally-stable SD feature trajectories

Yeung et al. fit a ridge residualization between adjacent normalized timestep activations in Stable Diffusion 1.5's U-Net mid-block cross-attention, then train a BatchTopK SAE on the residualized trajectory [yeung-etal-2026-residualized-temporal-saes-diffusion] Each latent's decoder direction maps to a feature trajectory across denoising timesteps, and residualized variants beat non-residualized, timestep-wise, and Matryoshka SAE baselines on reconstruction, strongest at later timesteps [yeung-etal-2026-residualized-temporal-saes-diffusion] Spatial cosine-similarity and cross-timestep decoder self-similarity maps quantify directional stability [yeung-etal-2026-residualized-temporal-saes-diffusion] Single-feature and feature-transfer steering during generation produces semantically meaningful effects, reported qualitatively rather than benchmark-quantified [yeung-etal-2026-residualized-temporal-saes-diffusion]