perf(agent): batch SigLIP crop embeds per image + load truncated images

Two issues surfaced by the live logs (GPU pegged at ~0% util, 0.5 jobs/s,
truncated-image failures):

- BATCH the SigLIP embeds: collect all of an image's crops (figure + booru_yolo
  components + panels) and embed them in ONE forward pass instead of one
  forward+lock per crop. The per-crop path serialised every crop through the
  inference lock and starved the GPU (≈0% util, autoscaler stuck oscillating);
  batching gives a real GPU-bound workload + far higher throughput. CCIP still
  runs per figure inline.
- LOAD_TRUNCATED_IMAGES in the agent (matches the server embedder): slightly-
  truncated scraped images now load instead of failing the job 3× then erroring
  ("image file is truncated (N bytes not processed)").

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa

agent/fc_agent/embedder.py

@@ -58,12 +58,20 @@ class CropEmbedder:
 
     def embed(self, image: Image.Image) -> list[float]:
         """A crop → its embedding as a plain float list, ready to POST."""
+        return self.embed_batch([image])[0]
+
+    def embed_batch(self, images: list) -> list[list[float]]:
+        """Embed many crops in ONE forward pass — far better GPU utilisation +
+        only one lock acquisition than embedding each crop separately (which
+        starved the GPU and serialised the whole pool)."""
+        if not images:
+            return []
         self.load()
         torch = self._torch
-        enc = self._processor(images=image, return_tensors="pt")
+        enc = self._processor(images=images, return_tensors="pt")
         pixel_values = enc["pixel_values"].to(self._device, self._dt)
         with self._infer_lock, torch.no_grad():
             out = self._model.get_image_features(pixel_values=pixel_values)
         pooled = out.pooler_output if hasattr(out, "pooler_output") else out
-        vec = pooled[0].float().cpu().numpy().astype(np.float32).reshape(-1)
-        return vec.tolist()
+        arr = pooled.float().cpu().numpy().astype(np.float32)
+        return [row.reshape(-1).tolist() for row in arr]