feat: add SelVA Textual Inversion Trainer and Loader nodes

Learns K CLIP token embeddings ([K, 1024]) with all model weights frozen,
keeping generated latents on the decoder's natural manifold — avoids the
quality degradation that affects LoRA on BJ's audio dataset.

- selva_textual_inversion_trainer.py: trains learned_tokens via AdamW,
  injects into last K positions of 77-token CLIP embedding, checkpoints
  with eval audio + spectral metrics
- selva_textual_inversion_loader.py: loads .pt bundle, returns
  TEXTUAL_INVERSION dict for sampler
- selva_sampler.py: optional textual_inversion input; injects into both
  text_clip and neg_text_clip before preprocess_conditions
- __init__.py: registers both new nodes

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

nodes/__init__.py

@@ -14,6 +14,8 @@ _NODES = {
     "SelvaVaeRoundtrip":     (".selva_vae_roundtrip",     "SelvaVaeRoundtrip",     "SelVA VAE Roundtrip"),
     "SelvaHfSmoother":       (".selva_audio_preprocessors", "SelvaHfSmoother",       "SelVA HF Smoother"),
     "SelvaSpectralMatcher":  (".selva_audio_preprocessors", "SelvaSpectralMatcher",  "SelVA Spectral Matcher"),
+    "SelvaTextualInversionTrainer": (".selva_textual_inversion_trainer", "SelvaTextualInversionTrainer", "SelVA Textual Inversion Trainer"),
+    "SelvaTextualInversionLoader":  (".selva_textual_inversion_loader",  "SelvaTextualInversionLoader",  "SelVA Textual Inversion Loader"),
 }
 
 for key, (module_path, class_name, display_name) in _NODES.items():