feat: add activation steering pipeline (extractor, loader, sampler injection)

Implements per-block DiT activation steering as an alternative to textual
inversion. Extractor runs frozen generator on dataset with BJ vs empty
conditions, records mean hidden-state delta per block, saves [hidden_dim]
vectors (seq-averaged so they broadcast to any inference duration). Loader
reads the bundle. Sampler registers forward hooks during the ODE that add
strength × vec to each block output, cleaned up in a finally block.

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

nodes/selva_activation_steering_loader.py

@@ -0,0 +1,62 @@
+"""SelVA Activation Steering Loader.
+
+Loads a steering_vectors.pt bundle produced by SelVA Activation Steering Extractor
+and returns a STEERING_VECTORS dict for use by SelVA Sampler.
+"""
+
+from pathlib import Path
+
+import torch
+import folder_paths
+
+from .utils import SELVA_CATEGORY
+
+
+class SelvaActivationSteeringLoader:
+    CATEGORY    = SELVA_CATEGORY
+    FUNCTION    = "load"
+    RETURN_TYPES  = ("STEERING_VECTORS",)
+    RETURN_NAMES  = ("steering_vectors",)
+    OUTPUT_TOOLTIPS = ("Steering vectors bundle — connect to SelVA Sampler's steering_vectors input.",)
+    DESCRIPTION = (
+        "Loads activation steering vectors from a .pt file produced by "
+        "SelVA Activation Steering Extractor. Connect to SelVA Sampler to nudge "
+        "denoising toward the target activation patterns."
+    )
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "path": ("STRING", {
+                    "default": "steering_vectors.pt",
+                    "tooltip": "Path to steering_vectors.pt. Relative paths resolve to ComfyUI output directory.",
+                }),
+            },
+        }
+
+    def load(self, path):
+        p = Path(path.strip())
+        if not p.is_absolute():
+            p = Path(folder_paths.get_output_directory()) / p
+        if not p.exists():
+            raise FileNotFoundError(f"[Steering] File not found: {p}")
+
+        payload = torch.load(str(p), map_location="cpu", weights_only=False)
+
+        n_blocks = payload["n_blocks"]
+        n_joint  = payload["n_joint"]
+        n_fused  = payload["n_fused"]
+        n_vecs   = len(payload["steering_vectors"])
+
+        print(f"[Steering] Loaded: {p}", flush=True)
+        print(f"[Steering] blocks={n_blocks} (joint={n_joint} fused={n_fused})  "
+              f"latent_seq_len={payload['latent_seq_len']}  "
+              f"n_samples={payload['n_samples']}", flush=True)
+        print(f"[Steering] mode={payload.get('mode')}  variant={payload.get('variant')}", flush=True)
+
+        norms = [payload["steering_vectors"][i].norm().item() for i in range(n_vecs)]
+        mean_norm = sum(norms) / len(norms)
+        print(f"[Steering] Mean steering norm across {n_vecs} blocks: {mean_norm:.4f}", flush=True)
+
+        return (payload,)