feat: SelVA Skip Experiment node + save partial scalars on skip

- New node: SelVA Skip Experiment — writes skip_current.flag from UI,
  queue in a second workflow tab while scheduler is running
- SkipExperiment now attaches partial loss/grad/spectral data to the
  exception so the scheduler saves all collected scalars in the summary

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

nodes/__init__.py

@@ -9,6 +9,7 @@ _NODES = {
     "SelvaLoraTrainer":      (".selva_lora_trainer",      "SelvaLoraTrainer",      "SelVA LoRA Trainer"),
     "SelvaLoraScheduler":    (".selva_lora_scheduler",    "SelvaLoraScheduler",    "SelVA LoRA Scheduler"),
     "SelvaDatasetBrowser":   (".selva_dataset_browser",   "SelvaDatasetBrowser",   "SelVA Dataset Browser"),
+    "SelvaSkipExperiment":   (".selva_skip_experiment",   "SelvaSkipExperiment",   "SelVA Skip Experiment"),
 }
 
 for key, (module_path, class_name, display_name) in _NODES.items():

nodes/selva_lora_scheduler.py

@@ -478,10 +478,17 @@ class SelvaLoraScheduler:
             except SkipExperiment as e:
                 duration = time.monotonic() - t_start
                 print(f"[LoRA Scheduler] Experiment '{exp_id}' skipped: {e}", flush=True)
+                partial   = getattr(e, "partial", {})
+                lh        = partial.get("loss_history", [])
+                smoothed  = _smooth_losses(lh) if lh else []
                 exp_record["results"] = {
-                    "status":           "skipped",
-                    "error":            str(e),
-                    "duration_seconds": round(duration, 1),
+                    "status":            "skipped",
+                    "stopped_at_step":   partial.get("stopped_at_step"),
+                    "final_loss":        round(smoothed[-1], 6) if smoothed else None,
+                    "loss_history":      [round(v, 6) for v in lh],
+                    "grad_norm_history": partial.get("grad_norm_history", []),
+                    "spectral_metrics":  {str(k): v for k, v in partial.get("spectral_metrics", {}).items()},
+                    "duration_seconds":  round(duration, 1),
                 }
                 _write_summary()
                 pbar_outer.update(1)

nodes/selva_lora_trainer.py

@@ -758,7 +758,14 @@ class SelvaLoraTrainer:
                     skip_flag = output_dir.parent / "skip_current.flag"
                     if skip_flag.exists():
                         skip_flag.unlink()
-                        raise SkipExperiment(f"skip_current.flag detected at step {step} — skipping to next experiment")
+                        exc = SkipExperiment(f"skip_current.flag detected at step {step} — skipping to next experiment")
+                        exc.partial = {
+                            "loss_history":      list(loss_history),
+                            "grad_norm_history": list(grad_norm_history),
+                            "spectral_metrics":  dict(spectral_metrics),
+                            "stopped_at_step":   step,
+                        }
+                        raise exc
 
                     avg = running_loss / log_interval
                     loss_history.append(avg)

nodes/selva_skip_experiment.py

@@ -0,0 +1,50 @@
+from pathlib import Path
+
+import folder_paths
+
+from .utils import SELVA_CATEGORY
+
+
+class SelvaSkipExperiment:
+    """Writes skip_current.flag into a sweep output_root.
+
+    Queue this node while a SelVA LoRA Scheduler sweep is running to skip
+    the current experiment and move to the next one. The trainer picks up
+    the flag within 50 steps (~a few seconds).
+    """
+
+    OUTPUT_NODE = True
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "output_root": ("STRING", {
+                    "default": "",
+                    "tooltip": "output_root of the running sweep — same value as in your experiments JSON.",
+                }),
+            },
+        }
+
+    RETURN_TYPES = ("STRING",)
+    RETURN_NAMES = ("flag_path",)
+    OUTPUT_TOOLTIPS = ("Path where the flag was written.",)
+    FUNCTION = "skip"
+    CATEGORY = SELVA_CATEGORY
+    DESCRIPTION = (
+        "Signals the running SelVA LoRA Scheduler to skip the current experiment "
+        "and move to the next one. Queue this node while the scheduler is running. "
+        "Partial scalars collected so far are saved in the summary."
+    )
+
+    def skip(self, output_root: str):
+        p = Path(output_root.strip())
+        if not p.is_absolute():
+            p = Path(folder_paths.get_output_directory()) / p
+        if not p.exists():
+            raise FileNotFoundError(f"[SelVA Skip] output_root not found: {p}")
+
+        flag = p / "skip_current.flag"
+        flag.touch()
+        print(f"[SelVA Skip] Flag written: {flag}", flush=True)
+        return (str(flag),)