feat: add inject_mode (suffix/prefix) to TI pipeline

Observation: n4_baseline loss barely moved (1.025→0.965 over 3000 steps),
token_norm grew linearly without plateau — generator likely ignores last-K
CLIP positions (EOS/padding zone) where suffix injects.

Fix: add inject_mode parameter throughout the pipeline:
- "suffix": replace last K positions (original behavior, model may ignore)
- "prefix": replace positions 1:1+K right after BOS — highest attention
  weight in CLIP, much stronger gradient signal expected

Changes:
- selva_textual_inversion_trainer.py: _inject_tokens() helper centralises
  the torch.cat construction for both modes; used in training loop and eval;
  inject_mode stored in checkpoint files
- selva_textual_inversion_loader.py: reads inject_mode from checkpoint,
  includes in TEXTUAL_INVERSION bundle
- selva_sampler.py: uses _inject_tokens() via bundle's inject_mode field
- selva_ti_scheduler.py: inject_mode in _PARAM_DEFAULTS, config, and
  _train_inner call
- ti_sweep_1.json: updated with prefix_inject group (n4, n8, n4+warm);
  n4_baseline marked completed; suffix experiments retained for comparison

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

nodes/selva_ti_scheduler.py

@@ -82,6 +82,7 @@ _PARAM_DEFAULTS = {
     "seed":         42,
     "save_every":   1000,
     "init_text":    "",
+    "inject_mode":  "suffix",
 }
 
 _PALETTE = [
@@ -356,6 +357,7 @@ class SelvaTiScheduler:
             seed        = int(cfg["seed"])
             save_every  = int(cfg["save_every"])
             init_text   = str(cfg["init_text"])
+            inject_mode = str(cfg["inject_mode"])
 
             output_dir = output_root / exp_id
             output_dir.mkdir(parents=True, exist_ok=True)
@@ -365,7 +367,8 @@ class SelvaTiScheduler:
             if exp_desc:
                 print(f"[TI Scheduler] {exp_desc}", flush=True)
             print(f"[TI Scheduler] n_tokens={n_tokens}  lr={lr:.2e}  steps={steps}  "
-                  f"batch_size={batch_size}  warmup={warmup}  seed={seed}", flush=True)
+                  f"batch_size={batch_size}  warmup={warmup}  seed={seed}  "
+                  f"inject_mode={inject_mode}", flush=True)
             if init_text:
                 print(f"[TI Scheduler] init_text='{init_text}'", flush=True)
 
@@ -381,6 +384,7 @@ class SelvaTiScheduler:
                     "seed":         seed,
                     "save_every":   save_every,
                     "init_text":    init_text,
+                    "inject_mode":  inject_mode,
                 },
                 "results":        {"status": "running"},
                 "embeddings_path": None,
@@ -397,7 +401,7 @@ class SelvaTiScheduler:
                         device, dtype, mode,
                         data_dir, out_path,
                         n_tokens, steps, lr, batch_size,
-                        warmup, seed, save_every, init_text,
+                        warmup, seed, save_every, init_text, inject_mode,
                     )
 
                 duration     = time.monotonic() - t_start