diff --git a/ginka/train_vq.py b/ginka/train_vq.py
index 364858a..f67e70f 100644
--- a/ginka/train_vq.py
+++ b/ginka/train_vq.py
@@ -42,6 +42,7 @@ GENERATE_STEP    = 18    # 推理时 MaskGIT 迭代步数
 MAP_SIZE         = 13 * 13
 MAP_H = MAP_W    = 13
 LABEL_SMOOTHING  = 0.0
+WALL_MASK_RATIO  = 0.8
 
 # VQ-VAE 超参
 VQ_L      = 16   # summary token 数量（即 z 的序列长度）
@@ -288,6 +289,21 @@ def make_random_wall_seed(ratio_min: float = 0.02, ratio_max: float = 0.08) -> t
     seed[0, idx] = 1   # wall
     return seed
 
+
+def make_random_struct_cond() -> torch.Tensor:
+    """
+    生成一个随机结构条件，所有标签均取合法非-null 值。
+
+    Returns:
+        [1, 4] LongTensor，顺序 [cond_sym, cond_room, cond_branch, cond_outer]
+    """
+    from .maskGIT.model import SYM_VOCAB, ROOM_VOCAB, BRANCH_VOCAB, OUTER_VOCAB
+    sym    = random.randint(0, SYM_VOCAB    - 2)   # 0-6
+    room   = random.randint(0, ROOM_VOCAB   - 2)   # 0-2
+    branch = random.randint(0, BRANCH_VOCAB - 2)   # 0-2
+    outer  = random.randint(0, OUTER_VOCAB  - 2)   # 0-1
+    return torch.tensor([[sym, room, branch, outer]], dtype=torch.long, device=device)
+
 @torch.no_grad()
 def validate(
     model_vq: GinkaVQVAE,
@@ -356,7 +372,7 @@ def validate(
         if all(v is not None for v in captured.values()):
             break
 
-    # ── 公共辅助：对给定条件地图随机采样 n 次 z 并迭代生成 ──────────────────
+    # ── 公共辅助：对给定条件地图随机采样 n 次 z 并迭代生成（无条件）──────────────
     def _rand_gens(cond_map, n):
         imgs = []
         for i in range(n):
@@ -365,6 +381,18 @@ def validate(
             imgs.append(label_image(make_map_image(gen[0], tile_dict), f"z_rand_{i + 1}"))
         return imgs
 
+    # ── 公共辅助：对给定条件地图随机采样 n 次 z 并迭代生成（随机结构标签）────────
+    def _rand_gens_with_struct(cond_map, n):
+        imgs = []
+        for i in range(n):
+            z_r = model_vq.sample(1, device)
+            sc_r = make_random_struct_cond()           # [1, 4] 随机合法标签
+            gen  = maskgit_generate(model_mg, z_r, init_map=cond_map, struct_cond=sc_r)
+            img  = label_image(make_map_image(gen[0], tile_dict), f"z_rand_{i + 1}")
+            img  = annotate_struct(img, sc_r[0])
+            imgs.append(img)
+        return imgs
+
     # ── 场景1：标准掩码补全（子集 A）─────────────────────────────────────────
     if captured['A'] is not None:
         cap = captured['A']
@@ -446,11 +474,17 @@ def validate(
         cv2.imwrite(f"{epoch_dir}/scene4_entrance.png", grid_images(row))
 
     # ── 场景5：完全随机生成（无数据集参照）──────────────────────────────────
-    # 随机稀疏墙壁种子，对应推理时"不提供任何条件，直接生成"的场景
-    rand_seed = make_random_wall_seed()                         # [1, MAP_SIZE]
-    seed_img  = label_image(make_map_image(rand_seed[0], tile_dict), "random seed")
-    row = [seed_img] + _rand_gens(rand_seed, N_Z_SAMPLES + 1)  # 多采一个 z 展示多样性
-    cv2.imwrite(f"{epoch_dir}/scene5_random.png", grid_images(row))
+    # 5a：随机结构标签 — 验证结构导向能力
+    rand_seed_a = make_random_wall_seed()
+    seed_img_a  = label_image(make_map_image(rand_seed_a[0], tile_dict), "random seed")
+    row_a = [seed_img_a] + _rand_gens_with_struct(rand_seed_a, N_Z_SAMPLES + 1)
+    cv2.imwrite(f"{epoch_dir}/scene5a_random_cond.png", grid_images(row_a))
+
+    # 5b：无条件（struct_cond=None）— 验证基线生成质量
+    rand_seed_b = make_random_wall_seed()
+    seed_img_b  = label_image(make_map_image(rand_seed_b[0], tile_dict), "random seed")
+    row_b = [seed_img_b] + _rand_gens(rand_seed_b, N_Z_SAMPLES + 1)
+    cv2.imwrite(f"{epoch_dir}/scene5b_random_uncond.png", grid_images(row_b))
 
     avg_val_loss = val_loss_total / max(val_steps, 1)
     return avg_val_loss
@@ -492,12 +526,14 @@ def train():
     dataset_train = GinkaVQDataset(
         args.train,
         subset_weights=SUBSET_WEIGHTS,
+        wall_mask_ratio=WALL_MASK_RATIO,
     )
     dataset_val = GinkaVQDataset(
         args.validate,
         subset_weights=SUBSET_WEIGHTS,
         room_thresholds=dataset_train.room_th,
         branch_thresholds=dataset_train.branch_th,
+        wall_mask_ratio=WALL_MASK_RATIO,
     )
     dataloader_train = DataLoader(
         dataset_train, batch_size=BATCH_SIZE, shuffle=True,