fix bucketing doesn't work in controlnet training

networks/control_net_lllite.py

@@ -120,6 +120,7 @@ class LLLiteModule(torch.nn.Module):
         / call the model inside, so if necessary, surround it with torch.no_grad()
         """
         # timestepごとに呼ばれないので、あらかじめ計算しておく / it is not called for each timestep, so calculate it in advance
+        # print(f"C {self.lllite_name}, cond_image.shape={cond_image.shape}")
         cx = self.conditioning1(cond_image)
         if not self.is_conv2d:
             # reshape / b,c,h,w -> b,h*w,c
@@ -146,7 +147,7 @@ class LLLiteModule(torch.nn.Module):
             cx = cx.repeat(2, 1, 1, 1) if self.is_conv2d else cx.repeat(2, 1, 1)
             if self.use_zeros_for_batch_uncond:
                 cx[0::2] = 0.0  # uncond is zero
-        # print(f"C {self.lllite_name}, lx.shape={lx.shape}, cx.shape={cx.shape}")
+        # print(f"C {self.lllite_name}, x.shape={x.shape}, cx.shape={cx.shape}")
 
         # downで入力の次元数を削減し、conditioning image embeddingと結合する
         # 加算ではなくchannel方向に結合することで、うまいこと混ぜてくれることを期待している