diff --git a/codes/models/archs/ChainedEmbeddingGen.py b/codes/models/archs/ChainedEmbeddingGen.py
index fd1e76fc..34e6b5b3 100644
--- a/codes/models/archs/ChainedEmbeddingGen.py
+++ b/codes/models/archs/ChainedEmbeddingGen.py
@@ -149,6 +149,8 @@ class StructuredChainedEmbeddingGenWithBypass(nn.Module):
         self.grad_extract = ImageGradientNoPadding()
         self.upsample = FinalUpsampleBlock2x(64)
         self.ref_join_std = 0
+        self.block_residual_means = [0 for _ in range(depth)]
+        self.block_residual_stds = [0 for _ in range(depth)]
         self.bypass_maps = []
 
     def forward(self, x, recurrent=None):
@@ -172,6 +174,8 @@ class StructuredChainedEmbeddingGenWithBypass(nn.Module):
             residual, bypass_map = checkpoint(self.bypasses[i], residual, context)
             fea = fea + residual
             self.bypass_maps.append(bypass_map.detach())
+            self.block_residual_means[i] = residual.mean().item()
+            self.block_residual_stds[i] = residual.std().item()
             if i < 3:
                 structure_br = checkpoint(self.structure_joins[i], grad, fea)
                 grad = grad + checkpoint(self.structure_blocks[i], structure_br)
@@ -184,4 +188,9 @@ class StructuredChainedEmbeddingGenWithBypass(nn.Module):
 
     def get_debug_values(self, step, net_name):
         biases = [b.bias.item() for b in self.bypasses]
-        return { 'ref_join_std': self.ref_join_std, 'bypass_biases': sum(biases) / len(biases) }
+        blk_stds, blk_means = {}, {}
+        for i, (s, m) in enumerate(zip(self.block_residual_stds, self.block_residual_means)):
+            blk_stds['block_%i' % (i+1,)] = s
+            blk_means['block_%i' % (i+1,)] = m
+        return {'ref_join_std': self.ref_join_std, 'bypass_biases': sum(biases) / len(biases),
+                'blocks_std': blk_stds, 'blocks_mean': blk_means}
diff --git a/codes/train.py b/codes/train.py
index 122ce55f..931a4c95 100644
--- a/codes/train.py
+++ b/codes/train.py
@@ -204,7 +204,7 @@ def main():
                 _t = time()
 
             #### log
-            if current_step % opt['logger']['print_freq'] == 0:
+            if current_step % opt['logger']['print_freq'] == 0 and rank <= 0:
                 logs = model.get_current_log(current_step)
                 message = '[epoch:{:3d}, iter:{:8,d}, lr:('.format(epoch, current_step)
                 for v in model.get_current_learning_rate():
@@ -212,16 +212,15 @@ def main():
                 message += ')] '
                 for k, v in logs.items():
                     if 'histogram' in k:
-                        if rank <= 0:
-                            tb_logger.add_histogram(k, v, current_step)
+                        tb_logger.add_histogram(k, v, current_step)
+                    elif isinstance(v,  dict):
+                        tb_logger.add_scalars(k, v, current_step)
                     else:
                         message += '{:s}: {:.4e} '.format(k, v)
                         # tensorboard logger
                         if opt['use_tb_logger'] and 'debug' not in opt['name']:
-                            if rank <= 0:
                                 tb_logger.add_scalar(k, v, current_step)
-                if rank <= 0:
-                    logger.info(message)
+                logger.info(message)
 
             #### save models and training states
             if current_step % opt['logger']['save_checkpoint_freq'] == 0:
diff --git a/codes/train2.py b/codes/train2.py
index 71517fa4..e6348a72 100644
--- a/codes/train2.py
+++ b/codes/train2.py
@@ -204,7 +204,7 @@ def main():
                 _t = time()
 
             #### log
-            if current_step % opt['logger']['print_freq'] == 0:
+            if current_step % opt['logger']['print_freq'] == 0 and rank <= 0:
                 logs = model.get_current_log(current_step)
                 message = '[epoch:{:3d}, iter:{:8,d}, lr:('.format(epoch, current_step)
                 for v in model.get_current_learning_rate():
@@ -212,16 +212,15 @@ def main():
                 message += ')] '
                 for k, v in logs.items():
                     if 'histogram' in k:
-                        if rank <= 0:
-                            tb_logger.add_histogram(k, v, current_step)
+                        tb_logger.add_histogram(k, v, current_step)
+                    elif isinstance(v,  dict):
+                        tb_logger.add_scalars(k, v, current_step)
                     else:
                         message += '{:s}: {:.4e} '.format(k, v)
                         # tensorboard logger
                         if opt['use_tb_logger'] and 'debug' not in opt['name']:
-                            if rank <= 0:
                                 tb_logger.add_scalar(k, v, current_step)
-                if rank <= 0:
-                    logger.info(message)
+                logger.info(message)
 
             #### save models and training states
             if current_step % opt['logger']['save_checkpoint_freq'] == 0: