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/selva_textual_inversion_loader.py

@@ -0,0 +1,66 @@
+"""SelVA Textual Inversion Loader.
+
+Loads a .pt file produced by SelvaTextualInversionTrainer and returns a
+TEXTUAL_INVERSION bundle that the SelVA Sampler can inject into text conditioning.
+"""
+
+from pathlib import Path
+
+import torch
+import folder_paths
+
+from .utils import SELVA_CATEGORY
+
+
+class SelvaTextualInversionLoader:
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "path": ("STRING", {
+                    "default": "textual_inversion.pt",
+                    "tooltip": "Path to a .pt file produced by SelVA Textual Inversion Trainer. "
+                               "Relative paths resolve to the ComfyUI output directory.",
+                }),
+            },
+        }
+
+    RETURN_TYPES  = ("TEXTUAL_INVERSION",)
+    RETURN_NAMES  = ("textual_inversion",)
+    OUTPUT_TOOLTIPS = ("Learned token embeddings — connect to SelVA Sampler's textual_inversion input.",)
+    FUNCTION  = "load"
+    CATEGORY  = SELVA_CATEGORY
+    DESCRIPTION = (
+        "Loads learned CLIP token embeddings produced by SelVA Textual Inversion Trainer. "
+        "Connect the output to the SelVA Sampler's optional textual_inversion input to guide "
+        "generation toward the training data style without degrading audio quality."
+    )
+
+    def load(self, path: str) -> tuple:
+        p = Path(path.strip())
+        if not p.is_absolute():
+            p = Path(folder_paths.get_output_directory()) / p
+        if not p.exists():
+            raise FileNotFoundError(f"[TI Loader] File not found: {p}")
+
+        data = torch.load(str(p), map_location="cpu", weights_only=False)
+
+        embeddings = data["embeddings"]   # [K, 1024]
+        n_tokens   = int(data.get("n_tokens", embeddings.shape[0]))
+
+        print(f"[TI Loader] Loaded '{p.name}'  n_tokens={n_tokens}  "
+              f"shape={tuple(embeddings.shape)}", flush=True)
+        if data.get("init_text"):
+            print(f"[TI Loader]   init_text='{data['init_text']}'", flush=True)
+        if data.get("step"):
+            print(f"[TI Loader]   trained {data['step']} / {data.get('steps', '?')} steps  "
+                  f"lr={data.get('lr', '?')}", flush=True)
+
+        bundle = {
+            "embeddings": embeddings,   # [K, 1024] float32 on CPU
+            "n_tokens":   n_tokens,
+            "path":       str(p),
+            "init_text":  data.get("init_text", ""),
+        }
+        return (bundle,)