feat: add SelVA LoRA Scheduler node for automated experiment sweeps

- Extract _prepare_dataset() from SelvaLoraTrainer.train() as a module-level
  function so the dataset can be encoded once and reused across experiments
- Change _train_inner() return value from tuple to dict (adds loss_history,
  meta, completed; train() unpacks for ComfyUI — no change to node outputs)
- New SelvaLoraScheduler node: reads a JSON sweep file, runs N experiments
  sequentially, writes experiment_summary.json (updated after each run) and
  loss_comparison.png with all smoothed curves overlaid on the same axes
- Register SelvaLoraScheduler in nodes/__init__.py

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

nodes/__init__.py

@@ -7,6 +7,7 @@ _NODES = {
     "SelvaSampler":          (".selva_sampler",           "SelvaSampler",          "SelVA Sampler"),
     "SelvaLoraLoader":       (".selva_lora_loader",       "SelvaLoraLoader",       "SelVA LoRA Loader"),
     "SelvaLoraTrainer":      (".selva_lora_trainer",      "SelvaLoraTrainer",      "SelVA LoRA Trainer"),
+    "SelvaLoraScheduler":    (".selva_lora_scheduler",    "SelvaLoraScheduler",    "SelVA LoRA Scheduler"),
 }
 
 for key, (module_path, class_name, display_name) in _NODES.items():