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High Fidelity Text-Guided Music Editing via Single-Stage Flow Matching
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We introduce MelodyFlow, an efficient text-controllable high-fidelity music generation and editing model. It operates on continuous latent representations from a low frame rate 48 kHz stereo variational auto encoder codec. Based on a diffusion transformer architecture trained on a flow-matching objective the model can edit diverse high quality stereo samples of variable duration, with simple text descriptions. We adapt the ReNoise latent inversion method to flow matching and compare it with the original implementation and naive denoising diffusion implicit model (DDIM) inversion on a variety of music editing prompts. Our results indicate that our latent inversion outperforms both ReNoise and DDIM for zero-shot test-time text-guided editing on several objective metrics. Subjective evaluations exhibit a substantial improvement over previous state of the art for music editing. Code and model weights will be publicly made available. Samples are available at https://melodyflow.github.io.
Forward citations
Cited by 4 Pith papers
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Unified Audio Intelligence Without Regressing on Text Intelligence
A unified 30B MoE audio-text LLM achieves state-of-the-art audio understanding, generation, and speech tasks while preserving text reasoning comparable to its text-only backbone.
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Auto-Regressive vs Flow-Matching: a Comparative Study of Modeling Paradigms for Text-to-Music Generation
Under matched training conditions, auto-regressive models slightly outperform flow-matching on music quality and temporal control, while flow-matching offers faster inference and better inpainting flexibility.
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FlowSonic: Stable Zero-Shot Music Editing via High-Order Trajectory Integration
FlowSonic combines deterministic rectified-flow inversion, cached cross-attention injection, and a 'seeded' third-order Adams-Bashforth solver to report better timbre and genre edits on small datasets.
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WhisQ: Cross-Modal Representation Learning for Text-to-Music MOS Prediction
WhisQ uses Whisper and Qwen with co-attention and optimal transport to predict music quality and text-alignment scores, but its reported improvements do not match its own data.
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