feat: sweep resume + 5 additional experiments (LR, target, extended)

Scheduler: on re-run, reads existing experiment_summary.json and skips
already-completed experiments — safe to stop and restart mid-sweep.

tier1_thorough: adds g5 (lr 3e-5/3e-4), g6 (full target attn.qkv+linear1
at r16 and r64), and g4_full_r64_6k (6000-step extended run) — 17 total.

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

experiments/tier1_thorough.json

@@ -98,6 +98,47 @@
       "lora_plus_ratio": 16.0,
       "lora_dropout": 0.05,
       "timestep_mode": "curriculum"
+    },
+
+    {
+      "id": "g5_lr_low",
+      "group": "lr",
+      "description": "LR=3e-5 — 3× lower than baseline. Tests if 1e-4 is overshooting.",
+      "lr": 3e-5
+    },
+    {
+      "id": "g5_lr_high",
+      "group": "lr",
+      "description": "LR=3e-4 — 3× higher than baseline. Tests if 1e-4 is too conservative.",
+      "lr": 3e-4
+    },
+
+    {
+      "id": "g6_target_full_r16",
+      "group": "target",
+      "description": "Rank 16 targeting attn.qkv + linear1 (FFN projections). Doubles LoRA coverage.",
+      "target": "attn.qkv linear1"
+    },
+    {
+      "id": "g6_target_full_r64",
+      "group": "target",
+      "description": "Rank 64 + alpha=32 targeting attn.qkv + linear1. Maximum coverage + expressiveness.",
+      "rank": 64,
+      "alpha": 32.0,
+      "target": "attn.qkv linear1"
+    },
+
+    {
+      "id": "g4_full_r64_6k",
+      "group": "combined",
+      "description": "All Tier 1 at rank 64 + alpha=32, extended to 6000 steps. Checks if convergence is done at 4000.",
+      "rank": 64,
+      "alpha": 32.0,
+      "lora_plus_ratio": 16.0,
+      "lora_dropout": 0.05,
+      "timestep_mode": "curriculum",
+      "steps": 6000,
+      "save_every": 1000
     }
   ]
 }

nodes/selva_lora_scheduler.py

@@ -296,8 +296,39 @@ class SelvaLoraScheduler:
         dataset = _prepare_dataset(model, data_dir, device)
 
         # ------------------------------------------------------------------
-        # 4. Build the summary skeleton (written incrementally)
+        # 4. Build or restore the summary (resume-aware)
         # ------------------------------------------------------------------
+        summary_path   = output_root / "experiment_summary.json"
+        completed_ids  = set()
+        all_curve_data = []   # collected for comparison image
+
+        if summary_path.exists():
+            try:
+                existing = json.loads(summary_path.read_text(encoding="utf-8"))
+                for rec in existing.get("experiments", []):
+                    if rec.get("results", {}).get("status") == "completed":
+                        completed_ids.add(rec["id"])
+                        lh = rec["results"].get("loss_history", [])
+                        all_curve_data.append({
+                            "id":           rec["id"],
+                            "loss_history": lh,
+                            "log_interval": rec["results"].get("log_interval", 50),
+                            "start_step":   0,
+                        })
+                # Restore the original summary, clear completed_at so it gets set again
+                summary = existing
+                summary["completed_at"] = None
+                if completed_ids:
+                    print(f"[LoRA Scheduler] Resuming — skipping {len(completed_ids)} "
+                          f"completed experiment(s): {sorted(completed_ids)}", flush=True)
+            except Exception as e:
+                print(f"[LoRA Scheduler] Could not read existing summary ({e}) — starting fresh",
+                      flush=True)
+                completed_ids = set()
+                all_curve_data = []
+                summary = None
+
+        if not completed_ids:
             summary = {
                 "sweep_name":   sweep_name,
                 "description":  description,
@@ -309,7 +340,6 @@ class SelvaLoraScheduler:
                 "n_clips":      n_clips,
                 "experiments":  [],
             }
-        summary_path = output_root / "experiment_summary.json"
 
         def _write_summary():
             summary_path.write_text(json.dumps(summary, indent=2), encoding="utf-8")
@@ -321,7 +351,6 @@ class SelvaLoraScheduler:
         # ------------------------------------------------------------------
         trainer      = SelvaLoraTrainer()
         pbar_outer   = comfy.utils.ProgressBar(len(spec["experiments"]))
-        all_curve_data = []   # collected for comparison image
         log_interval = 50   # matches _train_inner
 
         feature_utils_orig = model["feature_utils"]
@@ -332,6 +361,11 @@ class SelvaLoraScheduler:
         for exp in spec["experiments"]:
             exp_id  = exp["id"]
             exp_desc = exp.get("description", "")
+
+            if exp_id in completed_ids:
+                print(f"[LoRA Scheduler] Skipping '{exp_id}' (already completed)", flush=True)
+                pbar_outer.update(1)
+                continue
             cfg     = _merge_config(base_cfg, exp)
 
             # Required training params