feat: add DITTO optimizer, upgrade BigVGAN trainer, document all nodes

BigVGAN trainer (selva_bigvgan_trainer.py):
- Add snake_alpha_only train mode: tunes only ~27K per-channel α params
  (0.024% of 112M) — physically cannot cause harmonic smearing
- Add lambda_l2sp: L2-SP anchor regularization toward pretrained weights
- Add optional discriminator_path: frozen MPD+MRD feature matching loss
  replaces mel L1 when a BigVGAN discriminator checkpoint is provided
- Inline MPD + MRD discriminator implementations (no extra dependencies)

DITTO optimizer (selva_ditto_optimizer.py):
- New node: inference-time noise optimization (arXiv:2401.12179)
- Optimizes x₀ via mel Gram matrix style loss against BJ reference clips
- All model weights frozen — zero quality degradation risk
- Truncated BPTT through last n_grad_steps of the ODE (configurable)
- Gradient checkpointing on each differentiated step

Docs:
- README: document all 20 nodes (was 3), add workflow diagrams
- STYLE_TRANSFER.md: new guide — DITTO, vocoder fine-tuning tiers,
  why LoRA/TI fail, combined approach, dataset prep

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

nodes/__init__.py

@@ -21,6 +21,7 @@ _NODES = {
     "SelvaActivationSteeringLoader":         (".selva_activation_steering_loader",         "SelvaActivationSteeringLoader",         "SelVA Activation Steering Loader"),
     "SelvaBigvganTrainer":                   (".selva_bigvgan_trainer",                    "SelvaBigvganTrainer",                   "SelVA BigVGAN Trainer"),
     "SelvaBigvganLoader":                    (".selva_bigvgan_loader",                     "SelvaBigvganLoader",                    "SelVA BigVGAN Loader"),
+    "SelvaDittoOptimizer":                   (".selva_ditto_optimizer",                    "SelvaDittoOptimizer",                   "SelVA DITTO Optimizer"),
 }
 
 for key, (module_path, class_name, display_name) in _NODES.items():