diff --git a/src/remove_ai_watermarks/photomaker_restore.py b/src/remove_ai_watermarks/photomaker_restore.py
index d8c4bbd..3a0d610 100644
--- a/src/remove_ai_watermarks/photomaker_restore.py
+++ b/src/remove_ai_watermarks/photomaker_restore.py
@@ -319,11 +319,17 @@ def restore_faces_photomaker(
             continue
         # Get the ArcFace embedding for THIS face (V2's required ID branch). InsightFace
         # expects BGR; analyze_faces returns a list, take the first detection.
+        # Shape: upstream's inference_pmv2.py stacks per-image embeddings into a 2-D
+        # tensor (N_images, 512). The pipeline forward then calls `.unsqueeze(0)` ITSELF
+        # (line 705 of pipeline.py) to add a batch dim, so we must NOT pre-unsqueeze --
+        # giving `(1, 1, 512)` to the V2 forward made the id_encoder consume garbage and
+        # the pipeline output the training-time face collage (caught visually 2026-06-04).
+        # Dtype stays float32 here; the pipeline casts internally.
         faces = analyze_faces(face_analyser, id_crop_bgr)
         if not faces:
             logger.debug("photomaker_restore: InsightFace did not detect a face in the crop; skipping")
             continue
-        id_embed = torch.from_numpy(faces[0]["embedding"]).unsqueeze(0).to(pipeline.device, dtype=pipeline.dtype)
+        id_embeds = torch.stack([torch.from_numpy(faces[0]["embedding"])])
 
         id_crop_rgb = cv2.cvtColor(id_crop_bgr, cv2.COLOR_BGR2RGB)
         id_image_pil = Image.fromarray(id_crop_rgb)
@@ -338,7 +344,7 @@ def restore_faces_photomaker(
         out = pipeline(
             prompt=_PHOTOMAKER_PROMPT,
             input_id_images=[id_image_pil],
-            id_embeds=id_embed,
+            id_embeds=id_embeds,
             num_inference_steps=num_inference_steps,
             guidance_scale=guidance_scale,
             start_merge_step=style_strength,