Ace Step Sae Scores Guitar Electronic

--- library_name: audio-interv tags: - ace-step - audio - feature-selection - guitar-electronic - interpretability - music - sae - sparse-autoencoder - steering --- # SAE Feature-Selection Scores — `guitar_electronic` (ACE-Step) Per-concept feature-importance scores for the ACE-Step SAEs at `transformer_blocks.6.cross_attn` and `transformer_blocks.7.cross_attn`. Consumed at inference time by `SAESteeringController` via `load_features_from_score_cache` (top-k features per diffusion step). ## Files - `tf7_scores.pkl` — scores for the tf7 SAE. - `tf6_scores.pkl` — scores for the tf6 SAE. Each pickle is a dict keyed by selection method (`tfidf`, `diff`, `mean_pos`, ...); values are tensors of shape `(num_timesteps, num_features)`. ## Paper TADA! Tuning Audio Diffusion Models through Activation Steering — [https://huggingface.co/papers/2602.11910](https://huggingface.co/papers/2602.11910) ## Quickstart ```python from src.steering.methods.sae import load_features_from_score_cache top20_tf7 = load_features_from_score_cache( "lukasz-staniszewski/ace-step-sae-scores-guitar-electronic", score_filename="tf7_scores.pkl", top_k=20, ) top20_tf6 = load_features_from_score_cache( "lukasz-staniszewski/ace-step-sae-scores-guitar-electronic", score_filename="tf6_scores.pkl", top_k=20, ) ```