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Flow2GAN: Hybrid Flow Matching and GAN with Multi-Resolution Network for One-/Two-step High-Fidelity Audio Generation

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 5.5Download Report PDF
Flow2GANaudio generationFlow MatchingGANmulti-resolution
Abstract:

Existing dominant methods for audio generation include Generative Adversarial Networks (GANs) and diffusion-based methods like Flow Matching. GANs suffer from slow convergence and potential mode collapse during training, while diffusion methods require multi-step inference that introduces considerable computational overhead. In this work, we introduce Flow2GAN, a two-stage framework that combines Flow Matching training for learning generative capabilities with GAN fine-tuning for efficient few-step inference. Specifically, given audio's unique properties, we first improve Flow Matching for audio modeling through: 1) reformulating the objective as endpoint estimation, avoiding velocity estimation difficulties when involving empty regions; 2) applying spectral energy-based loss scaling to emphasize perceptually salient quieter regions. Building on these Flow Matching adaptations, we demonstrate that a further stage of lightweight GAN fine-tuning enables us to obtain one-step generator that produces high-quality audio. In addition, we develop a multi-branch network architecture that processes Fourier coefficients at different time-frequency resolutions, which improves the modeling capabilities compared to prior single-resolution designs. Experimental results indicate that our Flow2GAN delivers high-fidelity audio generation from Mel-spectrograms or discrete audio tokens, achieving better quality-efficiency trade-offs than existing state-of-the-art GAN-based and Flow Matching-based methods. Online demo samples are available at \url{https://flow2gan.github.io}.

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This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
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Overview

Overall Novelty Assessment

The paper proposes Flow2GAN, a two-stage framework combining Flow Matching training with GAN fine-tuning for efficient audio generation from compressed representations. It resides in the 'Flow Matching and Rectified Flow Methods' leaf, which contains five papers including the original work. This leaf sits within the broader 'Few-Step Generative Models for Audio Synthesis' branch, indicating a moderately populated research direction focused on reducing inference steps while maintaining audio fidelity. The taxonomy reveals this is an active area with parallel efforts in consistency models, adversarial vocoders, and diffusion-based acceleration.

The taxonomy structure shows neighboring leaves exploring alternative acceleration strategies: 'Consistency Models and Latent Consistency Distillation' (three papers), 'GAN-Based and Adversarial Vocoders' (three papers), and 'Diffusion and Bridge Models for Few-Step Generation' (four papers). Flow2GAN bridges flow matching and adversarial training, positioning it at the intersection of these directions. The scope notes clarify that flow matching methods emphasize trajectory optimization, while GAN vocoders prioritize single-step synthesis. Flow2GAN's hybrid approach diverges from purely flow-based methods by incorporating adversarial refinement, and from pure GAN vocoders by retaining flow matching's generative learning phase.

Among twenty-one candidates examined, none clearly refute the three main contributions. The two-stage Flow2GAN framework was assessed against ten candidates with no refutations found. The improved Flow Matching adaptations (endpoint estimation, spectral energy-based loss scaling) were examined against one candidate without overlap. The multi-branch Fourier architecture was evaluated against ten candidates, again with no refutations. This limited search scope suggests the specific combination of flow matching improvements and GAN fine-tuning may be novel within the examined literature, though the analysis does not cover the full breadth of audio generation research.

Based on top-twenty-one semantic matches, the work appears to introduce a distinctive synthesis of existing techniques rather than entirely new primitives. The endpoint estimation and spectral loss scaling adapt flow matching to audio's perceptual properties, while the GAN fine-tuning stage addresses inference efficiency. The multi-branch architecture represents an architectural contribution for multi-resolution frequency modeling. However, the limited search scope means potentially relevant prior work in broader generative modeling or signal processing may not have been captured.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
21
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: Few-step high-fidelity audio generation from compressed representations. The field is organized around several complementary research directions. Neural Audio Compression and Codec Design focuses on developing efficient representations such as SoundStream[15] and Neural Audio Compression[2], which provide the compressed latent spaces that downstream synthesis methods rely upon. Few-Step Generative Models for Audio Synthesis explores rapid inference techniques including flow matching, rectified flows, and adversarial training strategies like Adversarial Flow Matching[7] and Data Efficient Reflow[40]. Latent Diffusion and Spectrogram-Based Text-to-Audio Generation investigates diffusion-based approaches operating on learned or spectral representations, exemplified by AudioLCM[5] and Latent Diffusion Audio[19]. Conditional and Controllable Audio Synthesis addresses user-guided generation through text, pitch, or other modalities, while Domain-Specific and Application-Oriented Audio Synthesis targets specialized tasks such as music, speech, or sound effects. Theoretical Foundations and Review Studies provide broader perspectives on generative audio modeling. Within the few-step synthesis branch, a central tension exists between achieving high fidelity and minimizing inference steps. Flow matching methods like FlashAudio[18] and Flow2GAN Few-step[35] pursue efficient sampling trajectories, while adversarial approaches such as Adversarial Schrodinger Bridge[14] combine flow-based training with discriminative refinement. Flow2GAN[0] sits squarely in this active area, emphasizing the integration of flow matching with adversarial objectives to enable rapid generation from codec latents. Compared to purely flow-based methods like FlashAudio[18], Flow2GAN[0] leverages adversarial training to sharpen sample quality in very few steps, while differing from distillation-focused approaches like AudioLCM[5] by directly optimizing flow trajectories with GAN losses. This positioning reflects ongoing exploration of hybrid architectures that balance sampling efficiency, perceptual quality, and training stability across compressed audio representations.

Claimed Contributions

Flow2GAN two-stage framework combining Flow Matching and GAN fine-tuning

The authors propose a two-stage training strategy that first trains a model using Flow Matching to learn robust generative capabilities, then applies lightweight GAN fine-tuning to enable high-quality one-step or few-step audio generation. This approach combines the stable training of diffusion methods with the efficiency of GANs.

10 retrieved papers
Improved Flow Matching for audio modeling with endpoint estimation and spectral energy-based loss scaling

The authors adapt Flow Matching specifically for audio by reformulating the training objective to predict the clean audio endpoint directly rather than velocity, and by incorporating spectral energy-based loss weighting to emphasize perceptually important low-energy regions. These modifications address unique challenges in audio data such as silent segments and loss-perception mismatch.

1 retrieved paper
Multi-branch network architecture processing multi-resolution Fourier coefficients

The authors design a multi-branch ConvNeXt-based network structure that operates on Fourier coefficients at multiple time-frequency resolutions. This architecture serves as a more powerful backbone with enhanced modeling capabilities compared to previous single-branch approaches.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Flow2GAN two-stage framework combining Flow Matching and GAN fine-tuning

The authors propose a two-stage training strategy that first trains a model using Flow Matching to learn robust generative capabilities, then applies lightweight GAN fine-tuning to enable high-quality one-step or few-step audio generation. This approach combines the stable training of diffusion methods with the efficiency of GANs.

Contribution

Improved Flow Matching for audio modeling with endpoint estimation and spectral energy-based loss scaling

The authors adapt Flow Matching specifically for audio by reformulating the training objective to predict the clean audio endpoint directly rather than velocity, and by incorporating spectral energy-based loss weighting to emphasize perceptually important low-energy regions. These modifications address unique challenges in audio data such as silent segments and loss-perception mismatch.

Contribution

Multi-branch network architecture processing multi-resolution Fourier coefficients

The authors design a multi-branch ConvNeXt-based network structure that operates on Fourier coefficients at multiple time-frequency resolutions. This architecture serves as a more powerful backbone with enhanced modeling capabilities compared to previous single-branch approaches.

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