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methods / Direction Extraction / CLIP-guided latent direction optimization

CLIP-guided latent direction optimization

Techniqueadvanced

Finds an edit direction (or small per-timestep function) in a frozen generative model's bottleneck by gradient-optimizing a directional CLIP loss between source and target text prompts, plus a reconstruction/regularization term, rather than contrasting labeled activation classes.

Used in (1 observation)

Diffusion h-space supports linear, timestep-consistent edit directionsmeasured
structure: Linear Direction · models: DDPM++ (CelebA-HQ 256x256), DDPM++ (LSUN-Church 256x256), DDPM++ (LSUN-Bedroom 256x256), iDDPM (AFHQ-Dog 256x256), ADM P2-weighted (METFACES 256x256) · paper: Diffusion Models already have a Semantic Latent Space