Fix log off by 1

1 files changed, 12 insertions, 12 deletions

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index ff002d3e..17dfb223 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -184,9 +184,8 @@ def write_loss(log_directory, filename, step, epoch_len, values):
     if shared.opts.training_write_csv_every == 0:

         return

 

-    if step % shared.opts.training_write_csv_every != 0:

+    if (step + 1) % shared.opts.training_write_csv_every != 0:

         return

-

     write_csv_header = False if os.path.exists(os.path.join(log_directory, filename)) else True

 

     with open(os.path.join(log_directory, filename), "a+", newline='') as fout:

@@ -196,11 +195,11 @@ def write_loss(log_directory, filename, step, epoch_len, values):
             csv_writer.writeheader()

 

         epoch = step // epoch_len

-        epoch_step = step - epoch * epoch_len

+        epoch_step = step % epoch_len 

 

         csv_writer.writerow({

             "step": step + 1,

-            "epoch": epoch + 1,

+            "epoch": epoch,

             "epoch_step": epoch_step + 1,

             **values,

         })

@@ -282,15 +281,16 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
             loss.backward()

             optimizer.step()

 

+        steps_done = embedding.step + 1

 

         epoch_num = embedding.step // len(ds)

-        epoch_step = embedding.step - (epoch_num * len(ds)) + 1

+        epoch_step = embedding.step % len(ds)

 

-        pbar.set_description(f"[Epoch {epoch_num}: {epoch_step}/{len(ds)}]loss: {losses.mean():.7f}")

+        pbar.set_description(f"[Epoch {epoch_num}: {epoch_step+1}/{len(ds)}]loss: {losses.mean():.7f}")

 

-        if embedding.step > 0 and embedding_dir is not None and embedding.step % save_embedding_every == 0:

+        if embedding_dir is not None and steps_done % save_embedding_every == 0:

             # Before saving, change name to match current checkpoint.

-            embedding.name = f'{embedding_name}-{embedding.step}'

+            embedding.name = f'{embedding_name}-{steps_done}'

             last_saved_file = os.path.join(embedding_dir, f'{embedding.name}.pt')

             embedding.save(last_saved_file)

             embedding_yet_to_be_embedded = True

@@ -300,8 +300,8 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
             "learn_rate": scheduler.learn_rate

         })

 

-        if embedding.step > 0 and images_dir is not None and embedding.step % create_image_every == 0:

-            forced_filename = f'{embedding_name}-{embedding.step}'

+        if images_dir is not None and steps_done % create_image_every == 0:

+            forced_filename = f'{embedding_name}-{steps_done}'

             last_saved_image = os.path.join(images_dir, forced_filename)

             p = processing.StableDiffusionProcessingTxt2Img(

                 sd_model=shared.sd_model,

@@ -334,7 +334,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
 

             if save_image_with_stored_embedding and os.path.exists(last_saved_file) and embedding_yet_to_be_embedded:

 

-                last_saved_image_chunks = os.path.join(images_embeds_dir, f'{embedding_name}-{embedding.step}.png')

+                last_saved_image_chunks = os.path.join(images_embeds_dir, f'{embedding_name}-{steps_done}.png')

 

                 info = PngImagePlugin.PngInfo()

                 data = torch.load(last_saved_file)

@@ -350,7 +350,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
                 checkpoint = sd_models.select_checkpoint()

                 footer_left = checkpoint.model_name

                 footer_mid = '[{}]'.format(checkpoint.hash)

-                footer_right = '{}v {}s'.format(vectorSize, embedding.step)

+                footer_right = '{}v {}s'.format(vectorSize, steps_done)

 

                 captioned_image = caption_image_overlay(image, title, footer_left, footer_mid, footer_right)

                 captioned_image = insert_image_data_embed(captioned_image, data)