fix

docs/MATCHA_FLOW.md

@@ -18,7 +18,38 @@ Relevant settings:
 - `model.matcha_sigma_min`: minimum flow path noise.
 - `model.matcha_n_timesteps`: Euler steps at inference; more steps trade speed
   for refinement quality.
+- `model.matcha_use_diff_attention`: enables Differential Attention V2 in the
+  U-Net transformer blocks. It uses explicit PyTorch matrix multiplication and
+  softmax, with no FlashAttention, CUDA-only kernel, or extra dependency.
 
 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.
+
+## Matcha-only fine-tuning
+
+Set `train.matcha_only` to `true` to train only `net_g.matcha`. All legacy
+generator parameters are frozen, and adversarial, duration-discriminator,
+WavLM-discriminator, mel, duration, and KL optimization steps are skipped. The
+existing VITS normalizing-flow latent `z_p` remains the detached CFM target.
+
+This mode requires `model.use_matcha: true`. Optimizer state from a full-model
+checkpoint is intentionally not restored because its parameter groups differ;
+model weights are still loaded. After the Matcha loss has converged, set
+`train.matcha_only` back to `false` and use a lower learning rate for joint
+fine-tuning.
+
+For an already preprocessed dataset (including Google Colab), the mode can be
+configured without rerunning preprocessing:
+
+```bash
+python configure_matcha_ft.py --config Data/MyModel/config.json \
+  --learning-rate 0.0001 --epochs 20 --save-every-steps 100
+```
+
+To switch to the second-stage joint fine-tuning:
+
+```bash
+python configure_matcha_ft.py --config Data/MyModel/config.json \
+  --joint --learning-rate 0.00002
+```