fix(ti): lower default lr/batch, add lr_batch sweep group

n4_baseline showed token_norm growing linearly without plateau — classic
sign of lr too high relative to parameter count. With only K×1024 params,
gradient signal per param is already high-magnitude; high lr causes
overshoot rather than convergence.

- Default lr: 1e-3 → 2e-4 (matches LoRA working regime)
- Default batch_size: 16 → 4 (more diverse gradients, helps norm saturate)
- ti_sweep_1.json: add lr_batch group (lr_low_b4, lr_mid_b8,
  lr_low_b4_prefix, lr_2e3), restructure with clearer groups,
  annotate n4_baseline as completed with findings

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

experiments/ti_sweep_1.json

@@ -1,6 +1,6 @@
 {
   "name": "ti_sweep_1",
-  "description": "First TI sweep: inject position, token count, learning rate, and warm init. n4_baseline already completed (suffix, loss barely moved — model likely ignores last-K positions). Priority: prefix injection group.",
+  "description": "First TI sweep. n4_baseline (suffix, batch=16, lr=1e-3) completed — loss 1.025→0.963, plateau after step 1500, token_norm grew linearly without saturation (overshoot sign). Now testing: prefix injection, lower LR, smaller batch.",
   "data_dir": "/media/unraid/davinci/Selva/BJ/features",
   "output_root": "/media/unraid/davinci/Selva/BJ/experiment/ti_sweep_1",
   "base": {
@@ -18,55 +18,66 @@
 
     {
       "id": "n4_baseline",
-      "group": "suffix_token_count",
+      "group": "reference",
-      "description": "4 tokens, suffix, lr=1e-3, random init. COMPLETED — loss 1.025→0.965, nearly flat. Token norm grew linearly to 3.2 with no plateau. Model appears to ignore last-K positions."
+      "description": "COMPLETED. batch=16, lr=1e-3, suffix. Reference. Loss plateau ~0.963, token_norm linear growth to 3.2 — LR too high for the parameter count."
-    },
-    {
-      "id": "n8",
-      "group": "suffix_token_count",
-      "description": "8 tokens, suffix, lr=1e-3. More capacity — does it do better than n4_baseline?",
-      "n_tokens": 8
     },
 
     {
       "id": "n4_prefix",
       "group": "prefix_inject",
-      "description": "4 tokens at positions 1:5 (after BOS). Prefix positions carry the highest attention weight in CLIP — should produce much stronger loss signal than suffix.",
+      "description": "Same as baseline but prefix injection. Tests whether suffix positions are limiting signal — if prefix loss goes lower or converges faster, suffix was the bottleneck.",
       "inject_mode": "prefix"
     },
+
+    {
+      "id": "lr_low_b4",
+      "group": "lr_batch",
+      "description": "lr=2e-4, batch=4. Matches LoRA's working regime. Smaller batch = noisier but more diverse gradients; lower LR = smaller steps, token_norm should plateau rather than drift.",
+      "lr": 2e-4,
+      "batch_size": 4
+    },
+    {
+      "id": "lr_mid_b8",
+      "group": "lr_batch",
+      "description": "lr=5e-4, batch=8. Middle ground — half the baseline LR and batch. Token norm should grow slower and saturate.",
+      "lr": 5e-4,
+      "batch_size": 8
+    },
+    {
+      "id": "lr_low_b4_prefix",
+      "group": "lr_batch",
+      "description": "lr=2e-4, batch=4, prefix. Best LR/batch regime + best injection position combined.",
+      "lr": 2e-4,
+      "batch_size": 4,
+      "inject_mode": "prefix"
+    },
+
     {
       "id": "n8_prefix",
       "group": "prefix_inject",
-      "description": "8 tokens at prefix positions. More capacity + high-attention positions.",
+      "description": "8 tokens, prefix, baseline LR/batch. More capacity at the better injection position.",
       "n_tokens": 8,
       "inject_mode": "prefix"
     },
     {
       "id": "n4_prefix_warm",
       "group": "prefix_inject",
-      "description": "4 tokens, prefix, warm-started from 'mechanical impact sound design'. Best of both: semantically meaningful start + strong gradient signal.",
+      "description": "4 tokens, prefix, warm-started from 'mechanical impact sound design'.",
       "inject_mode": "prefix",
       "init_text": "mechanical impact sound design"
     },
 
     {
-      "id": "lr_5e4",
+      "id": "n8",
-      "group": "learning_rate",
+      "group": "suffix_token_count",
-      "description": "4 tokens, suffix, lr=5e-4. Slower convergence — mainly a baseline comparison for the prefix group.",
+      "description": "8 tokens, suffix, baseline LR/batch. Capacity ablation vs n4_baseline.",
-      "lr": 5e-4
+      "n_tokens": 8
     },
     {
       "id": "lr_2e3",
-      "group": "learning_rate",
+      "group": "lr_batch",
-      "description": "4 tokens, suffix, lr=2e-3. Faster early movement — does token norm plateau earlier?",
+      "description": "lr=2e-3, baseline batch. Expected to plateau earlier and higher than baseline — confirms LR is the issue.",
       "lr": 2e-3
-    },
-
-    {
-      "id": "n4_warm",
-      "group": "warm_init",
-      "description": "4 tokens, suffix, warm-started from 'mechanical impact sound design'.",
-      "init_text": "mechanical impact sound design"
     }
 
   ]

nodes/selva_textual_inversion_trainer.py

@@ -175,12 +175,12 @@ class SelvaTextualInversionTrainer:
                     "tooltip": "Training steps. 3000 is a reasonable starting point.",
                 }),
                 "lr": ("FLOAT", {
-                    "default": 1e-3, "min": 1e-5, "max": 1e-1, "step": 1e-5,
+                    "default": 2e-4, "min": 1e-5, "max": 1e-1, "step": 1e-5,
-                    "tooltip": "Learning rate. 1e-3 is a good default for textual inversion (higher than LoRA since there are far fewer parameters).",
+                    "tooltip": "Learning rate. 2e-4 matches the LoRA working regime. Higher LR (1e-3) causes token norm to drift without plateauing on small datasets.",
                 }),
                 "batch_size": ("INT", {
-                    "default": 16, "min": 1, "max": 64,
+                    "default": 4, "min": 1, "max": 64,
-                    "tooltip": "Clips sampled per training step.",
+                    "tooltip": "Clips sampled per training step. Smaller batch (4–8) gives more diverse gradients and helps token norm saturate rather than drift.",
                 }),
                 "seed": ("INT", {"default": 42, "min": 0, "max": 0xFFFFFFFF}),
                 "save_every": ("INT", {

nodes/selva_ti_scheduler.py

@@ -75,9 +75,9 @@ def _get_system_info() -> dict:
 
 _PARAM_DEFAULTS = {
     "n_tokens":     4,
-    "lr":           1e-3,
+    "lr":           2e-4,
     "steps":        3000,
-    "batch_size":   16,
+    "batch_size":   4,
     "warmup_steps": 100,
     "seed":         42,
     "save_every":   1000,