# Matcha flow integration

Style-Bert-VITS2 can optionally generate its aligned prior latent with a
Matcha-TTS-inspired conditional flow matching (CFM) decoder. The existing
BERT/style encoder, duration predictors, VITS normalizing flow, and waveform
generator are retained.

Set `model.use_matcha` to `true` for a newly trained or fine-tuned model. The
template configurations enable it. Models whose configuration does not contain
this key continue to use the original Gaussian prior sampling path.

Relevant settings:

- `train.c_matcha`: weight of the CFM velocity loss.
- `model.matcha_channels`: U-Net hidden width.
- `model.matcha_num_heads`: attention head count.
- `model.matcha_dropout`: transformer dropout.
- `model.matcha_sigma_min`: minimum flow path noise.
- `model.matcha_n_timesteps`: Euler steps at inference; more steps trade speed
  for refinement quality.

The Matcha branch has new parameters, so enabling it on an existing checkpoint
requires fine-tuning before inference. It is trained on the same random latent
segments used by the waveform generator to keep memory use bounded.