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

2 files changed, 14 insertions, 14 deletions

diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py
index 2dc64c3c..88d68c76 100644
--- a/modules/textual_inversion/dataset.py
+++ b/modules/textual_inversion/dataset.py
@@ -28,9 +28,9 @@ class DatasetEntry:
 

 

 class PersonalizedBase(Dataset):

-    def __init__(self, data_root, width, height, repeats, flip_p=0.5, placeholder_token="*", model=None, cond_model=None, device=None, template_file=None, include_cond=False, batch_size=1, gradient_step=1, shuffle_tags=False, tag_drop_out=0, latent_sampling_method='once'):        

+    def __init__(self, data_root, width, height, repeats, flip_p=0.5, placeholder_token="*", model=None, cond_model=None, device=None, template_file=None, include_cond=False, batch_size=1, gradient_step=1, shuffle_tags=False, tag_drop_out=0, latent_sampling_method='once'):

         re_word = re.compile(shared.opts.dataset_filename_word_regex) if len(shared.opts.dataset_filename_word_regex) > 0 else None

-        

+

         self.placeholder_token = placeholder_token

 

         self.width = width

@@ -50,14 +50,14 @@ class PersonalizedBase(Dataset):
 

         self.image_paths = [os.path.join(data_root, file_path) for file_path in os.listdir(data_root)]

 

-        

+

         self.shuffle_tags = shuffle_tags

         self.tag_drop_out = tag_drop_out

 

         print("Preparing dataset...")

         for path in tqdm.tqdm(self.image_paths):

             if shared.state.interrupted:

-                raise Exception("inturrupted")

+                raise Exception("interrupted")

             try:

                 image = Image.open(path).convert('RGB').resize((self.width, self.height), PIL.Image.BICUBIC)

             except Exception:

@@ -144,7 +144,7 @@ class PersonalizedDataLoader(DataLoader):
             self.collate_fn = collate_wrapper_random

         else:

             self.collate_fn = collate_wrapper

-        

+

 

 class BatchLoader:

     def __init__(self, data):

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