REVIEW 32 cited by
ACE-Step: A Step Towards Music Generation Foundation Model
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
ACE-Step: A Step Towards Music Generation Foundation Model
read the original abstract
We introduce ACE-Step, a novel open-source foundation model for music generation that overcomes key limitations of existing approaches and achieves state-of-the-art performance through a holistic architectural design. Current methods face inherent trade-offs between generation speed, musical coherence, and controllability. For example, LLM-based models (e.g. Yue, SongGen) excel at lyric alignment but suffer from slow inference and structural artifacts. Diffusion models (e.g. DiffRhythm), on the other hand, enable faster synthesis but often lack long-range structural coherence. ACE-Step bridges this gap by integrating diffusion-based generation with Sana's Deep Compression AutoEncoder (DCAE) and a lightweight linear transformer. It also leverages MERT and m-hubert to align semantic representations (REPA) during training, allowing rapid convergence. As a result, our model synthesizes up to 4 minutes of music in just 20 seconds on an A100 GPU-15x faster than LLM-based baselines-while achieving superior musical coherence and lyric alignment across melody, harmony, and rhythm metrics. Moreover, ACE-Step preserves fine-grained acoustic details, enabling advanced control mechanisms such as voice cloning, lyric editing, remixing, and track generation (e.g. lyric2vocal, singing2accompaniment). Rather than building yet another end-to-end text-to-music pipeline, our vision is to establish a foundation model for music AI: a fast, general-purpose, efficient yet flexible architecture that makes it easy to train subtasks on top of it. This paves the way for the development of powerful tools that seamlessly integrate into the creative workflows of music artists, producers, and content creators. In short, our goal is to build a stable diffusion moment for music. The code, the model weights and the demo are available at: https://ace-step.github.io/.
Forward citations
Cited by 32 Pith papers
-
Towards Unified Song Generation and Singing Voice Conversion with Accompaniment Co-Generation
UniSinger unifies speaker-cloned song generation and accompaniment co-generation SVC in one multimodal diffusion transformer model trained with curriculum learning via task-specific modality masking.
-
OmniNFT: Modality-wise Omni Diffusion Reinforcement for Joint Audio-Video Generation
OmniNFT introduces modality-wise advantage routing, layer-wise gradient surgery, and region-wise loss reweighting in an online diffusion RL framework to improve audio-video quality, alignment, and synchronization.
-
TMD-Bench: A Multi-Level Evaluation Paradigm for Music-Dance Co-Generation
TMD-Bench is a multi-level benchmark that measures music-dance co-generation quality including beat-level rhythmic synchronization, supported by a new dataset and Music Captioner, and shows commercial models lag in rh...
-
MIDI-Informed Singing Accompaniment Generation in a Compositional Song Pipeline
MIDI-SAG generates consistent long-form singing accompaniments by feeding symbolic MIDI timing, chords, and structure labels into a compositional pipeline built from pre-trained modules.
-
MusicMark: A Robust Generative Watermarking Framework for Music Generation
Embedding watermark bits into diffusion semantic latents via a frozen-backbone adapter yields far more robust music provenance than post-hoc watermarking under codecs and cover-song attacks.
-
TORUS: A Test of Rendering-Understanding Self-Coherence for Unified Audio Models
A new audio benchmark, TORUS, shows unified audio models are not self-coherent: the best model answers 50.5% of questions about its own generations, below a 63.2% cascaded specialist baseline.
-
MPEcho: A Melody and Phoneme-Aware Generative Framework for Controllable Cover Song Generation
Adding SVS-style phoneme conditioning and a length regulator to melody-guided cover generation cuts phoneme error rate from 45.6% to 18.7% while holding melody metrics.
-
A Diagnostic Evaluation Framework for AI-Generated Cover Songs Using Music-Theoretic and Acoustic Features
AI-generated covers most often fail on harmonic progression (53% severe) and arrangement (47%), while key consistency is better preserved; nine low-level features and a threshold rule could not reliably detect those failures.
-
Genre Bias or Aesthetic Perception? Identifying and Mitigating Shortcut Learning in Music Evaluation
SongEval's aesthetic scores are shortcut by genre (pop-centric), and a focal-loss plus group-regularized training objective measurably reduces that genre bias.
-
Qwen-Music Technical Report
Qwen-Music generates full songs from text using a 25 Hz semantic-token LLM with melody chain-of-thought planning and a diffusion renderer, claiming state-of-the-art quality vs. Suno, Mureka, and MiniMax.
-
Qwen-Music Technical Report
Qwen-Music generates high-fidelity vocal songs via 25 Hz semantic tokens, Melody-CoT planning, and DiT rendering, claiming SOTA on 13/16 metrics and expert preference over proprietary systems.
-
Anysynth:Zero-Shot Instrument Cloning via In-Context Learning and Asymmetric Hierarchical Guidance
An embedding-free DiT flow-matching synthesizer clones instrument timbre from uncompressed reference audio via in-context attention and asymmetric hierarchical CFG, beating embedding and token baselines with prompt-le...
-
Anysynth:Zero-Shot Instrument Cloning via In-Context Learning and Asymmetric Hierarchical Guidance
A zero-shot instrument cloning system feeds raw reference audio into a flow-matching DiT and uses asymmetric CFG to keep melody and timbre control separate.
-
An Empirical Analysis of AI Slop in Music Streaming
Empirical study finds 93% of AI music on Spotify gets negligible plays, distributors have inconsistent unenforced AI policies, and detection methods are unreliable, suggesting slop may become self-sustaining.
-
S2Accompanist: A Semantic-Aware and Structure-Guided Diffusion Model for Music Accompaniment Generation
S2Accompanist is a 402M-parameter semantic-aware diffusion model that achieves SOTA on the ATTM Grand Challenge benchmark for music accompaniment generation via automated data processing and structure-guided VAE fine-tuning.
-
Cutting rules in strong field QED with application to trident pair production
Cutting rules for strong-field QED are formulated and used to relate higher-loop corrections to trident pair production, yielding a spin-resolved analytical rate expression in constant crossed fields.
-
Cutting rules in strong field QED with application to trident pair production
A Veltman-style cutting equation for plane-wave QED is formulated and used to relate two-loop elastic electron scattering corrections to the trident rate in a constant crossed field, with a spin-resolved analytical ra...
-
APEX: Large-scale Multi-task Aesthetic-Informed Popularity Prediction for AI-Generated Music
APEX jointly predicts engagement-based popularity and five aesthetic quality dimensions for AI-generated music, improving human preference prediction on out-of-distribution generative systems.
-
APEX: Large-scale Multi-task Aesthetic-Informed Popularity Prediction for AI-Generated Music
APEX jointly predicts popularity and aesthetic quality for AI-generated music from MERT embeddings and shows that aesthetic features improve human preference prediction on unseen generative systems.
-
SongBench: A Fine-Grained Multi-Aspect Benchmark for Song Quality Assessment
SongBench is a new fine-grained benchmark for song quality assessment with seven dimensions and an expert-annotated dataset of 11,717 samples showing high correlation with professional ratings.
-
LaDA-Band: Language Diffusion Models for Vocal-to-Accompaniment Generation
LaDA-Band applies discrete masked diffusion with dual-track conditioning and progressive training to generate vocal-to-accompaniment tracks that improve acoustic authenticity, global coherence, and dynamic orchestrati...
-
Echoes: A semantically-aligned music deepfake detection dataset
A 110-hour, 10-provider, semantically aligned music deepfake dataset is released and shown to be harder and more transferable than prior benchmarks.
-
TADA! Tuning Audio Diffusion Models through Activation Steering
Activation steering at a semantic bottleneck in audio diffusion models achieves state-of-the-art control over musical attributes such as instruments, vocals, and genres.
-
Ovi: Twin Backbone Cross-Modal Fusion for Audio-Video Generation
A single generative model uses twin DiT backbones with blockwise cross-attention and scaled-RoPE timing exchange to synthesize synchronized audio-video directly.
-
Echoes: A semantically-aligned music deepfake detection dataset
A semantically aligned, multi-provider music deepfake dataset is harder for detectors and trains models that transfer better than prior AI-music datasets.
-
Pushing the Frontier of Full-Song Generation: Hierarchical Autoregressive Planning Meets Flow-Matching Rendering
A unified hierarchical-LM-plus-flow-matching system reports top-tier full-song vocal generation, ranking 2–3 on an external blind leaderboard, but releases neither code nor evaluation data.
-
LeVo 2: Stable and Melodious Song Generation via Hierarchical Representation Modeling and Progressive Post-Training
LeVo 2 presents a hierarchical LLM-Diffusion model with progressive post-training stages to generate full-length songs that balance semantic planning, track-specific acoustics, and musicality.
-
ImmersiveTTS: Environment-Aware Text-to-Speech with Multimodal Diffusion Transformer and Domain-Specific Representation Alignment
ImmersiveTTS proposes an environment-aware TTS system that integrates speech with environmental audio via multimodal diffusion transformer, joint attention, and domain-specific representation alignment, claiming super...
-
Instrumental Text-to-Music Generation with Auxiliary Conditioning Branches
Auxiliary lyric and timbre branches improve instrumental text-to-music generation quality in a controlled DiT setting even with degenerate inputs, outperforming parameter-reallocated depth variants and external baseli...
-
LaDA-Band: Language Diffusion Models for Vocal-to-Accompaniment Generation
LaDA-Band generates complete vocal-aligned instrumental accompaniments with discrete masked diffusion, claiming simultaneous gains in audio fidelity, long-range coherence, and orchestration quality.
-
JoVA: Unified Multimodal Learning for Joint Video-Audio Generation and Editing
A dual-branch diffusion transformer with joint video-audio self-attention and a keypoint-based mouth-area loss reports top lip-sync and speech metrics on two benchmarks.
-
SongFormer: Scaling Music Structure Analysis with Heterogeneous Supervision
SongFormer achieves state-of-the-art strict boundary detection and functional label accuracy in music structure analysis by fusing SSL representations and using learned source embeddings on a new 14k-song corpus and e...
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.