1 files changed, 21 insertions, 23 deletions

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 379df243..d489ed1e 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

@@ -229,7 +227,7 @@ 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()

 

@@ -325,16 +323,16 @@ def tensorboard_add(tensorboard_writer, loss, global_step, step, learn_rate, epo
     tensorboard_add_scaler(tensorboard_writer, f"Learn rate/train/epoch-{epoch_num}", learn_rate, step)

 

 def tensorboard_add_scaler(tensorboard_writer, tag, value, step):

-    tensorboard_writer.add_scalar(tag=tag, 

+    tensorboard_writer.add_scalar(tag=tag,

         scalar_value=value, global_step=step)

 

 def tensorboard_add_image(tensorboard_writer, tag, pil_image, step):

     # Convert a pil image to a torch tensor

     img_tensor = torch.as_tensor(np.array(pil_image, copy=True))

-    img_tensor = img_tensor.view(pil_image.size[1], pil_image.size[0], 

+    img_tensor = img_tensor.view(pil_image.size[1], pil_image.size[0],

         len(pil_image.getbands()))

     img_tensor = img_tensor.permute((2, 0, 1))

-                

+

     tensorboard_writer.add_image(tag, img_tensor, global_step=step)

 

 def validate_train_inputs(model_name, learn_rate, batch_size, gradient_step, data_root, template_file, template_filename, steps, save_model_every, create_image_every, log_directory, name="embedding"):

@@ -404,7 +402,7 @@ def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_st
     if initial_step >= steps:

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

         return embedding, filename

-    

+

     scheduler = LearnRateScheduler(learn_rate, steps, initial_step)

     clip_grad = torch.nn.utils.clip_grad_value_ if clip_grad_mode == "value" else \

         torch.nn.utils.clip_grad_norm_ if clip_grad_mode == "norm" else \

@@ -414,7 +412,7 @@ def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_st
     # 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

-    

+

     if shared.opts.training_enable_tensorboard:

         tensorboard_writer = tensorboard_setup(log_directory)

 

@@ -437,11 +435,11 @@ 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)

-    

+

         if optimizer_state_dict is not None:

             optimizer.load_state_dict(optimizer_state_dict)

             print("Loaded existing optimizer from checkpoint")

@@ -470,7 +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:

@@ -487,7 +485,7 @@ def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_st
 

                 if clip_grad:

                     clip_grad_sched.step(embedding.step)

-            

+

                 with devices.autocast():

                     x = batch.latent_sample.to(devices.device, non_blocking=pin_memory)

                     if use_weight:

@@ -515,7 +513,7 @@ def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_st
                 # go back until we reach gradient accumulation steps

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

                     continue

-                

+

                 if clip_grad:

                     clip_grad(embedding.vec, clip_grad_sched.learn_rate)

 

@@ -599,17 +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)