feat(ti-trainer): add loss curve IMAGE output

Reuses _draw_loss_curve + _smooth_losses + _pil_to_tensor from the LoRA
trainer — raw loss in light blue, smoothed overlay in blue, matches the
LoRA trainer's visual style.

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

nodes/selva_textual_inversion_trainer.py

@@ -32,6 +32,9 @@ from .selva_lora_trainer import (
     _prepare_dataset,
     _spectral_metrics,
     _save_spectrogram,
+    _smooth_losses,
+    _draw_loss_curve,
+    _pil_to_tensor,
 )
 
 
@@ -113,9 +116,12 @@ class SelvaTextualInversionTrainer:
     OUTPUT_NODE = True
     CATEGORY    = SELVA_CATEGORY
     FUNCTION    = "train"
-    RETURN_TYPES  = ("STRING",)
-    RETURN_NAMES  = ("embeddings_path",)
-    OUTPUT_TOOLTIPS = ("Path to saved .pt embeddings — load with SelVA Textual Inversion Loader.",)
+    RETURN_TYPES  = ("STRING", "IMAGE")
+    RETURN_NAMES  = ("embeddings_path", "loss_curve")
+    OUTPUT_TOOLTIPS = (
+        "Path to saved .pt embeddings — load with SelVA Textual Inversion Loader.",
+        "Smoothed training loss curve.",
+    )
     DESCRIPTION = (
         "Trains K learnable CLIP token embeddings against your audio dataset "
         "with all model weights frozen. The tokens are then injected into the "
@@ -208,7 +214,9 @@ class SelvaTextualInversionTrainer:
                 n_tokens, steps, lr, batch_size,
                 warmup_steps, seed, save_every, init_text,
             )
-        return (r["embeddings_path"],)
+        smoothed = _smooth_losses(r["loss_history"]) if r["loss_history"] else []
+        curve_img = _draw_loss_curve(r["loss_history"], log_interval=50, smoothed=smoothed)
+        return (r["embeddings_path"], _pil_to_tensor(curve_img))
 
     def _train_inner(
         self, model, dataset, feature_utils_orig, seq_cfg,