--- library_name: audio-interv tags: - activation-steering - all - audio - austeer - diffusion - electronic-music - interpretability - music - steering --- # AUSteer — `electronic_music` (ACE-Step) Per-(step, layer) sparse activation-momentum scores for the **electronic_music** concept on ACE-Step. At inference, `AUSteerSteeringController` adds `alpha` along the top-`k` most concept-discriminative bins. ## 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 import SteerableACEModel, AUSteerSteeringController model = SteerableACEModel(device="cuda") model.pipeline.load() ctrl = AUSteerSteeringController.from_pretrained( "lukasz-staniszewski/ace-step-austeer-rock-genre-all", alpha=15.0, k=256, mode="additive", ) with model.steer(ctrl): audio = model.generate( prompt="instrumental music", lyrics="[inst]", audio_duration=10.0, infer_step=30, manual_seed=0, ) ``` ## Generation config ```json { "method": "austeer", "concept": "electronic_music", "lyrics": "[inst]", "layers": "all", "layers_collected": [ "tf0", "tf1", "tf2", "tf3", "tf4", "tf5", "tf6", "tf7", "tf8", "tf9", "tf10", "tf11", "tf12", "tf13", "tf14", "tf15", "tf16", "tf17", "tf18", "tf19", "tf20", "tf21", "tf22", "tf23" ], "num_inference_steps": 30, "audio_duration": 30.0, "seed": 10, "guidance_scale": 5.0, "guidance_scale_text": 0.0, "guidance_scale_lyric": 0.0, "guidance_interval": 1.0, "guidance_interval_decay": 0.0 } ```
