diff --git a/patches/external_pr/dataset.py b/patches/external_pr/dataset.py
index 5e06d3d..34a1018 100644
--- a/patches/external_pr/dataset.py
+++ b/patches/external_pr/dataset.py
@@ -169,6 +169,7 @@ class BatchLoader:
         self.cond_text = [entry.cond_text for entry in data]
         self.cond = [entry.cond for entry in data]
         self.latent_sample = torch.stack([entry.latent_sample for entry in data]).squeeze(1)
+        self.filename = [entry.filename for entry in data]
         # self.emb_index = [entry.emb_index for entry in data]
         # print(self.latent_sample.device)
 
diff --git a/patches/external_pr/hypernetwork.py b/patches/external_pr/hypernetwork.py
index 57c13dd..78b609e 100644
--- a/patches/external_pr/hypernetwork.py
+++ b/patches/external_pr/hypernetwork.py
@@ -7,13 +7,14 @@ import os
 import sys
 import traceback
 import inspect
+from collections import defaultdict, deque
 
 import torch
 import tqdm
 
 
 from modules import shared, sd_models, devices, processing, sd_samplers
-from modules.hypernetworks.hypernetwork import optimizer_dict, stack_conds, save_hypernetwork
+from modules.hypernetworks.hypernetwork import optimizer_dict, stack_conds, save_hypernetwork, report_statistics
 from modules.textual_inversion.learn_schedule import LearnRateScheduler
 from .textual_inversion import validate_train_inputs, write_loss
 from ..hypernetwork import Hypernetwork, load_hypernetwork
@@ -155,7 +156,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, gradient_step,
     loss_step = 0
     _loss_step = 0  # internal
     # size = len(ds.indexes)
-    # loss_dict = defaultdict(lambda : deque(maxlen = 1024))
+    loss_dict = defaultdict(lambda: deque(maxlen=1024))
     # losses = torch.zeros((size,))
     # previous_mean_losses = [0]
     # previous_mean_loss = 0
@@ -196,7 +197,10 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, gradient_step,
                         shared.sd_model.cond_stage_model.to(devices.cpu)
                     else:
                         c = stack_conds(batch.cond).to(devices.device, non_blocking=pin_memory)
-                    loss = shared.sd_model(x, c)[0] / gradient_step
+                    loss = shared.sd_model(x, c)[0]
+                    for filenames in batch.filename:
+                        loss_dict[filenames].append(loss.item())
+                    loss /= gradient_step
                     del x
                     del c
 
@@ -314,8 +318,7 @@ Last saved image: {html.escape(last_saved_image)}<br/>
         pbar.leave = False
         pbar.close()
         hypernetwork.eval()
-        # report_statistics(loss_dict)
-
+    report_statistics(loss_dict)
     filename = os.path.join(shared.cmd_opts.hypernetwork_dir, f'{hypernetwork_name}.pt')
     hypernetwork.optimizer_name = optimizer_name
     if shared.opts.save_optimizer_state: