fix(ti-trainer): clamp token norm to CLIP manifold to prevent buzz artifacts

Diagnosis: learned tokens grew to norm ~3.2 while real CLIP content tokens
sit at ~1.0. Model never trained on embeddings that large — activates buzz
artifact instead of semantic style shift.

Fix: measure mean token norm from content positions (1–20) of dataset CLIP
embeddings at startup, clamp learned_tokens per-token after every optimizer
step to max 1.5× that reference (50% headroom). Token norm is now logged
as current/limit for easy monitoring.

ti_sweep_1.json: rebuild around norm_clamp group — n4_clamped (primary
diagnostic), prefix_clamped, n8_prefix_clamped, warm_clamped.

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

nodes/selva_textual_inversion_trainer.py

@@ -288,6 +288,19 @@ class SelvaTextualInversionTrainer:
             )
             print(f"[TI Trainer] Init: random N(0, 0.02)", flush=True)
 
+        # --- Measure CLIP token norm from the dataset (content positions 1–20) ---
+        # Learned tokens must stay within this range or the model treats them as
+        # out-of-distribution and produces buzz artifacts instead of style shift.
+        with torch.no_grad():
+            sample_norms = []
+            for item in dataset[:min(len(dataset), 20)]:
+                tc = item[3].squeeze(0)          # [77, 1024]
+                sample_norms.append(tc[1:20].norm(dim=-1))  # skip BOS/EOS
+            clip_norm_ref   = torch.cat(sample_norms).mean().item()
+            clip_norm_limit = clip_norm_ref * 1.5   # 50% headroom above real tokens
+        print(f"[TI Trainer] CLIP token norm ref={clip_norm_ref:.4f}  "
+              f"limit={clip_norm_limit:.4f}", flush=True)
+
         # --- Optimizer + scheduler ---
         optimizer = torch.optim.AdamW([learned_tokens], lr=lr, weight_decay=1e-2)
 
@@ -356,6 +369,13 @@ class SelvaTextualInversionTrainer:
             scheduler.step()
             optimizer.zero_grad()
 
+            # Clamp token norm to CLIP manifold — prevents out-of-distribution
+            # embeddings that cause buzz artifacts instead of style shift.
+            with torch.no_grad():
+                norms = learned_tokens.norm(dim=-1, keepdim=True).clamp(min=1e-8)
+                scale = (clip_norm_limit / norms).clamp(max=1.0)
+                learned_tokens.data.mul_(scale)
+
             running_loss += loss.item()
             pbar.update(1)
 
@@ -364,10 +384,10 @@ class SelvaTextualInversionTrainer:
                 loss_history.append(round(avg, 6))
                 running_loss = 0.0
                 lr_now = scheduler.get_last_lr()[0]
-                norm = learned_tokens.norm().item()
+                norm = learned_tokens.norm(dim=-1).mean().item()
                 print(f"[TI Trainer] step {step:5d}/{steps}  "
                       f"loss={avg:.4f}  lr={lr_now:.2e}  "
-                      f"token_norm={norm:.4f}", flush=True)
+                      f"token_norm={norm:.4f}/{clip_norm_limit:.4f}", flush=True)
 
             if step % save_every == 0 or step == steps:
                 # Save checkpoint