diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 647ffe3e9..22c7b54b3 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -10,7 +10,7 @@ import csv
 
 from PIL import Image, PngImagePlugin
 
-from modules import shared, devices, sd_hijack, processing, sd_models
+from modules import shared, devices, sd_hijack, processing, sd_models, images
 import modules.textual_inversion.dataset
 from modules.textual_inversion.learn_schedule import LearnRateScheduler
 
@@ -247,6 +247,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
 
     last_saved_file = "<none>"
     last_saved_image = "<none>"
+    forced_filename = "<none>"
     embedding_yet_to_be_embedded = False
 
     ititial_step = embedding.step or 0
@@ -296,8 +297,8 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
         })
 
         if embedding.step > 0 and images_dir is not None and embedding.step % create_image_every == 0:
-            last_saved_image = os.path.join(images_dir, f'{embedding_name}-{embedding.step}.png')
-
+            forced_filename = f'{embedding_name}-{embedding.step}'
+            last_saved_image = os.path.join(images_dir, forced_filename)
             p = processing.StableDiffusionProcessingTxt2Img(
                 sd_model=shared.sd_model,
                 do_not_save_grid=True,
@@ -353,8 +354,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
                 captioned_image.save(last_saved_image_chunks, "PNG", pnginfo=info)
                 embedding_yet_to_be_embedded = False
 
-            image.save(last_saved_image)
-
+            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)
             last_saved_image += f", prompt: {preview_text}"
 
         shared.state.job_no = embedding.step