Merge branch 'master' of github.com:AUTOMATIC1111/stable-diffusion-webui

1 files changed, 9 insertions, 9 deletions

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index e28c357a..f6112578 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -133,7 +133,7 @@ class EmbeddingDatabase:
 

                 process_file(fullfn, fn)

             except Exception:

-                print(f"Error loading emedding {fn}:", file=sys.stderr)

+                print(f"Error loading embedding {fn}:", file=sys.stderr)

                 print(traceback.format_exc(), file=sys.stderr)

                 continue

 

@@ -194,7 +194,7 @@ def write_loss(log_directory, filename, step, epoch_len, values):
             csv_writer.writeheader()

 

         epoch = (step - 1) // epoch_len

-        epoch_step = (step - 1) % epoch_len 

+        epoch_step = (step - 1) % epoch_len

 

         csv_writer.writerow({

             "step": step,

@@ -263,16 +263,16 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_
 

     initial_step = embedding.step or 0

     if initial_step >= steps:

-        shared.state.textinfo = f"Model has already been trained beyond specified max steps"

+        shared.state.textinfo = "Model has already been trained beyond specified max steps"

         return embedding, filename

     scheduler = LearnRateScheduler(learn_rate, steps, initial_step)

 

    # dataset loading may take a while, so input validations and early returns should be done before this

     shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."

     old_parallel_processing_allowed = shared.parallel_processing_allowed

-    

+

     pin_memory = shared.opts.pin_memory

-    

+

     ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=embedding_name, model=shared.sd_model, cond_model=shared.sd_model.cond_stage_model, device=devices.device, template_file=template_file, batch_size=batch_size, gradient_step=gradient_step, shuffle_tags=shuffle_tags, tag_drop_out=tag_drop_out, latent_sampling_method=latent_sampling_method)

 

     latent_sampling_method = ds.latent_sampling_method

@@ -295,12 +295,12 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_
     loss_step = 0

     _loss_step = 0 #internal

 

-    

+

     last_saved_file = "<none>"

     last_saved_image = "<none>"

     forced_filename = "<none>"

     embedding_yet_to_be_embedded = False

-    

+

     pbar = tqdm.tqdm(total=steps - initial_step)

     try:

         for i in range((steps-initial_step) * gradient_step):

@@ -327,10 +327,10 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_
                     c = shared.sd_model.cond_stage_model(batch.cond_text)

                     loss = shared.sd_model(x, c)[0] / gradient_step

                     del x

-                    

+

                     _loss_step += loss.item()

                 scaler.scale(loss).backward()

-                

+

                 # go back until we reach gradient accumulation steps

                 if (j + 1) % gradient_step != 0:

                     continue