1 files changed, 19 insertions, 15 deletions

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index d2e62e58..9e1b2b9a 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -1,7 +1,6 @@
 import os

 import sys

 import traceback

-import inspect

 from collections import namedtuple

 

 import torch

@@ -30,7 +29,7 @@ textual_inversion_templates = {}
 def list_textual_inversion_templates():

     textual_inversion_templates.clear()

 

-    for root, dirs, fns in os.walk(shared.cmd_opts.textual_inversion_templates_dir):

+    for root, _, fns in os.walk(shared.cmd_opts.textual_inversion_templates_dir):

         for fn in fns:

             path = os.path.join(root, fn)

 

@@ -69,7 +68,7 @@ class Embedding:
                 'hash': self.checksum(),

                 'optimizer_state_dict': self.optimizer_state_dict,

             }

-            torch.save(optimizer_saved_dict, filename + '.optim')

+            torch.save(optimizer_saved_dict, f"{filename}.optim")

 

     def checksum(self):

         if self.cached_checksum is not None:

@@ -167,8 +166,7 @@ class EmbeddingDatabase:
         # textual inversion embeddings

         if 'string_to_param' in data:

             param_dict = data['string_to_param']

-            if hasattr(param_dict, '_parameters'):

-                param_dict = getattr(param_dict, '_parameters')  # fix for torch 1.12.1 loading saved file from torch 1.11

+            param_dict = getattr(param_dict, '_parameters', param_dict)  # fix for torch 1.12.1 loading saved file from torch 1.11

             assert len(param_dict) == 1, 'embedding file has multiple terms in it'

             emb = next(iter(param_dict.items()))[1]

         # diffuser concepts

@@ -199,7 +197,7 @@ class EmbeddingDatabase:
         if not os.path.isdir(embdir.path):

             return

 

-        for root, dirs, fns in os.walk(embdir.path, followlinks=True):

+        for root, _, fns in os.walk(embdir.path, followlinks=True):

             for fn in fns:

                 try:

                     fullfn = os.path.join(root, fn)

@@ -216,7 +214,7 @@ class EmbeddingDatabase:
     def load_textual_inversion_embeddings(self, force_reload=False):

         if not force_reload:

             need_reload = False

-            for path, embdir in self.embedding_dirs.items():

+            for embdir in self.embedding_dirs.values():

                 if embdir.has_changed():

                     need_reload = True

                     break

@@ -229,10 +227,16 @@ class EmbeddingDatabase:
         self.skipped_embeddings.clear()

         self.expected_shape = self.get_expected_shape()

 

-        for path, embdir in self.embedding_dirs.items():

+        for embdir in self.embedding_dirs.values():

             self.load_from_dir(embdir)

             embdir.update()

 

+        # re-sort word_embeddings because load_from_dir may not load in alphabetic order.

+        # using a temporary copy so we don't reinitialize self.word_embeddings in case other objects have a reference to it.

+        sorted_word_embeddings = {e.name: e for e in sorted(self.word_embeddings.values(), key=lambda e: e.name.lower())}

+        self.word_embeddings.clear()

+        self.word_embeddings.update(sorted_word_embeddings)

+

         displayed_embeddings = (tuple(self.word_embeddings.keys()), tuple(self.skipped_embeddings.keys()))

         if self.previously_displayed_embeddings != displayed_embeddings:

             self.previously_displayed_embeddings = displayed_embeddings

@@ -431,8 +435,8 @@ def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_st
     optimizer = torch.optim.AdamW([embedding.vec], lr=scheduler.learn_rate, weight_decay=0.0)

     if shared.opts.save_optimizer_state:

         optimizer_state_dict = None

-        if os.path.exists(filename + '.optim'):

-            optimizer_saved_dict = torch.load(filename + '.optim', map_location='cpu')

+        if os.path.exists(f"{filename}.optim"):

+            optimizer_saved_dict = torch.load(f"{filename}.optim", map_location='cpu')

             if embedding.checksum() == optimizer_saved_dict.get('hash', None):

                 optimizer_state_dict = optimizer_saved_dict.get('optimizer_state_dict', None)

     

@@ -464,7 +468,7 @@ def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_st
     try:

         sd_hijack_checkpoint.add()

 

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

+        for _ in range((steps-initial_step) * gradient_step):

             if scheduler.finished:

                 break

             if shared.state.interrupted:

@@ -593,17 +597,17 @@ def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_st
                         data = torch.load(last_saved_file)

                         info.add_text("sd-ti-embedding", embedding_to_b64(data))

 

-                        title = "<{}>".format(data.get('name', '???'))

+                        title = f"<{data.get('name', '???')}>"

 

                         try:

                             vectorSize = list(data['string_to_param'].values())[0].shape[0]

-                        except Exception as e:

+                        except Exception:

                             vectorSize = '?'

 

                         checkpoint = sd_models.select_checkpoint()

                         footer_left = checkpoint.model_name

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

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

+                        footer_mid = f'[{checkpoint.shorthash}]'

+                        footer_right = f'{vectorSize}v {steps_done}s'

 

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

                         captioned_image = insert_image_data_embed(captioned_image, data)