diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index bbd1f673c..c963fc404 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -561,6 +561,7 @@ def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradi
     _loss_step = 0 #internal
     # size = len(ds.indexes)
     # loss_dict = defaultdict(lambda : deque(maxlen = 1024))
+    loss_logging = deque(maxlen=len(ds) * 3)  # this should be configurable parameter, this is 3 * epoch(dataset size)
     # losses = torch.zeros((size,))
     # previous_mean_losses = [0]
     # previous_mean_loss = 0
@@ -610,7 +611,7 @@ def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradi
                 # go back until we reach gradient accumulation steps
                 if (j + 1) % gradient_step != 0:
                     continue
-
+                loss_logging.append(_loss_step)
                 if clip_grad:
                     clip_grad(weights, clip_grad_sched.learn_rate)
                 
@@ -644,7 +645,7 @@ def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradi
                 if shared.opts.training_enable_tensorboard:
                     epoch_num = hypernetwork.step // len(ds)
                     epoch_step = hypernetwork.step - (epoch_num * len(ds)) + 1
-                    mean_loss = sum(sum(x) for x in loss_dict.values()) / sum(len(x) for x in loss_dict.values())
+                    mean_loss = sum(loss_logging) / len(loss_logging)
                     textual_inversion.tensorboard_add(tensorboard_writer, loss=mean_loss, global_step=hypernetwork.step, step=epoch_step, learn_rate=scheduler.learn_rate, epoch_num=epoch_num)
 
                 textual_inversion.write_loss(log_directory, "hypernetwork_loss.csv", hypernetwork.step, steps_per_epoch, {
@@ -688,9 +689,6 @@ def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradi
 
                     processed = processing.process_images(p)
                     image = processed.images[0] if len(processed.images) > 0 else None
-                    
-                    if shared.opts.training_enable_tensorboard and shared.opts.training_tensorboard_save_images:
-                        textual_inversion.tensorboard_add_image(tensorboard_writer, f"Validation at epoch {epoch_num}", image, hypernetwork.step)
 
                     if unload:
                         shared.sd_model.cond_stage_model.to(devices.cpu)
@@ -701,7 +699,10 @@ def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradi
                     hypernetwork.train()
                     if image is not None:
                         shared.state.assign_current_image(image)
-
+                        if shared.opts.training_enable_tensorboard and shared.opts.training_tensorboard_save_images:
+                            textual_inversion.tensorboard_add_image(tensorboard_writer,
+                                                                    f"Validation at epoch {epoch_num}", image,
+                                                                    hypernetwork.step)
                         last_saved_image, last_text_info = images.save_image(image, images_dir, "", p.seed, p.prompt, shared.opts.samples_format, processed.infotexts[0], p=p, forced_filename=forced_filename, save_to_dirs=False)
                         last_saved_image += f", prompt: {preview_text}"