| library_name: audio-interv | |
| tags: | |
| - ace-step | |
| - audio | |
| - electronic-music | |
| - feature-selection | |
| - interpretability | |
| - music | |
| - sae | |
| - sparse-autoencoder | |
| - steering | |
| # SAE Feature-Selection Scores — `electronic_music` (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-electronic-music", score_filename="tf7_scores.pkl", top_k=20, | |
| ) | |
| top20_tf6 = load_features_from_score_cache( | |
| "lukasz-staniszewski/ace-step-sae-scores-electronic-music", score_filename="tf6_scores.pkl", top_k=20, | |
| ) | |
| ``` | |