Papers with flow matching
Flow Matching for Conditional Text Generation in a Few Sampling Steps (2024.eacl-short)
Copied to clipboard
| Challenge: | Current diffusion models face multiple drawbacks including slow sampling, noise schedule sensitivity, and misalignment between training and sampling stages. |
| Approach: | They propose a method which leverages flow matching for conditional text generation. |
| Outcome: | The proposed method can generate text in a few steps by training with a novel anchor loss, alleviating the need for expensive hyperparameter optimization of the noise schedule prevalent in diffusion models. |
One-step Nonautoregressive Natural Language Generation with Shortcut Flow Matching Models (2026.acl-short)
Copied to clipboard
| Challenge: | Recent diffusion-based approaches to text generation are inefficient due to the need for multiple denoising steps. |
| Approach: | They propose a shortcut flow-matching model that learns to directly predict multi-step denoising outcomes in a single step. |
| Outcome: | The proposed model improves on three datasets and can predict multi-step denoising outcomes in a single step. |
WaveFM: A High-Fidelity and Efficient Vocoder Based on Flow Matching (2025.naacl-long)
Copied to clipboard
| Challenge: | Flow matching is a robust and stable approach to training diffusion models, but it can result in subpar audio quality. |
| Approach: | They propose a reparameterized flow matching model for mel-spectrogram conditioned speech synthesis that uses a mel prior instead of a standard Gaussian prior to minimize unnecessary transportation costs. |
| Outcome: | The proposed model improves sample quality and generation speed for speech vocoders while reducing transportation costs. |
F5-TTS: A Fairytaler that Fakes Fluent and Faithful Speech with Flow Matching (2025.acl-long)
Copied to clipboard
| Challenge: | Recent research in Text-to-Speech (TTS) has experienced great advancement . current models can synthesize speech for any given text and mimic the speaker of audio prompt. |
| Approach: | They propose a fully non-autoregressive text-to-speech system based on flow matching with Diffusion Transformer (DiT) without complex designs such as duration model, text encoder, and phoneme alignment, the text input is simply padded with filler tokens to the same length as input speech, and then denoising is performed for speech generation. |
| Outcome: | The proposed system achieves an inference RTF of 0.15, which is greatly improved compared to state-of-the-art diffusion-based models. |
Enable Fast Sampling for Seq2Seq Text Diffusion (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing text generation methods use autoregressive (AR) methods, which generate tokens one by one, but are time-consuming. |
| Approach: | They propose an efficient model FMSeq which utilizes flow matching to straighten the generation path, thereby enabling fast sampling for diffusion-based seq2seq text generation. |
| Outcome: | The proposed model generates comparable quality to the SOTA diffusion-based DiffuSeq in just 10 steps, achieving a 200-fold speedup. |