metadata
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
Quickstart
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,
)