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FLUX that Plays Music
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This paper explores a simple extension of diffusion-based rectified flow Transformers for text-to-music generation, termed as FluxMusic. Generally, along with design in advanced Flux\footnote{https://github.com/black-forest-labs/flux} model, we transfers it into a latent VAE space of mel-spectrum. It involves first applying a sequence of independent attention to the double text-music stream, followed by a stacked single music stream for denoised patch prediction. We employ multiple pre-trained text encoders to sufficiently capture caption semantic information as well as inference flexibility. In between, coarse textual information, in conjunction with time step embeddings, is utilized in a modulation mechanism, while fine-grained textual details are concatenated with the music patch sequence as inputs. Through an in-depth study, we demonstrate that rectified flow training with an optimized architecture significantly outperforms established diffusion methods for the text-to-music task, as evidenced by various automatic metrics and human preference evaluations. Our experimental data, code, and model weights are made publicly available at: \url{https://github.com/feizc/FluxMusic}.
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Cited by 13 Pith papers
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Academic Text-to-Music Grand Challenge: Datasets, Baselines, and Evaluation Methods
Presents the ATTM grand challenge with efficiency and performance tracks for text-to-music generation using a public instrumental music dataset, evaluated via FAD, CLAP, a new CCS metric, and subjective tests.
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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.
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Unified Audio Intelligence Without Regressing on Text Intelligence
Audex unifies audio understanding and generation on a strong text MoE backbone with multi-stage SFT plus text-only Cascade RL, matching open SOTA audio scores while mostly retaining text capability.
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Persian MusicGen: A Large-Scale Dataset and Culturally-Aware Generative Model for Persian Music
Introduces the first large-scale Persian music dataset and shows fine-tuned MusicGen produces compositions more aligned with Persian stylistic conventions via tag-based evaluation.
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SonicMaster: Towards Controllable All-in-One Music Restoration and Mastering
SonicMaster is a text-conditioned flow-matching generative model for unified music restoration and mastering, trained on a dataset of simulated degradations across equalization, dynamics, reverb, amplitude, and stereo.
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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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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...
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Academic Text-to-Music Grand Challenge: Datasets, Baselines, and Evaluation Methods
The paper introduces the ATTM Grand Challenge with a CC-licensed instrumental subset of MTG-Jamendo, two tracks, and evaluation via FAD, CLAP, and a new Concept Coverage Score to support academic text-to-music research.
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F5-TTS: A Fairytaler that Fakes Fluent and Faithful Speech with Flow Matching
F5-TTS generates natural speech from text via flow matching on DiT with simple text padding, ConvNeXt refinement, and sway sampling, trained on 100K hours multilingual data.
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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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TinyMusician: On-Device Music Generation with Knowledge Distillation and Mixed Precision Quantization
TinyMusician distills MusicGen and applies hand-picked mixed-precision quantization to make a 1.04 GB on-device music generator, but the headline '93% quality, 55% smaller' claims conflict with the paper's own tables.
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UT-AISTimprt submission for ICME 2026 Grand Challenge on Academic Text-to-Music Generation
Text-embedding clustering for batch sampling outperforms audio-embedding clustering on objective metrics in low-data text-to-music generation, with moderate cluster counts best on metrics and larger counts better for ...
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Improving Text-to-Music Generation with Human Preference Rewards
A text-to-music model is improved by conditioning on and selecting with a human preference reward, where expert iteration on top outputs contributes the largest measured gains on 100 Song Describer prompts.
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