Autofix Ruff W (not W605) (mostly whitespace)

3 files changed, 11 insertions, 11 deletions

diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py
index 41610e03..b9621fc9 100644
--- a/modules/textual_inversion/dataset.py
+++ b/modules/textual_inversion/dataset.py
@@ -118,7 +118,7 @@ class PersonalizedBase(Dataset):
                 weight = torch.ones(latent_sample.shape)

             else:

                 weight = None

-            

+

             if latent_sampling_method == "random":

                 entry = DatasetEntry(filename=path, filename_text=filename_text, latent_dist=latent_dist, weight=weight)

             else:

@@ -243,4 +243,4 @@ class BatchLoaderRandom(BatchLoader):
         return self

 

 def collate_wrapper_random(batch):

-    return BatchLoaderRandom(batch)
\ No newline at end of file
+    return BatchLoaderRandom(batch)

diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py
index d0cad09e..a009d8e8 100644
--- a/modules/textual_inversion/preprocess.py
+++ b/modules/textual_inversion/preprocess.py
@@ -125,7 +125,7 @@ def multicrop_pic(image: Image, mindim, maxdim, minarea, maxarea, objective, thr
         default=None

     )

     return wh and center_crop(image, *wh)

-    

+

 

 def preprocess_work(process_src, process_dst, process_width, process_height, preprocess_txt_action, process_keep_original_size, process_flip, process_split, process_caption, process_caption_deepbooru=False, split_threshold=0.5, overlap_ratio=0.2, process_focal_crop=False, process_focal_crop_face_weight=0.9, process_focal_crop_entropy_weight=0.3, process_focal_crop_edges_weight=0.5, process_focal_crop_debug=False, process_multicrop=None, process_multicrop_mindim=None, process_multicrop_maxdim=None, process_multicrop_minarea=None, process_multicrop_maxarea=None, process_multicrop_objective=None, process_multicrop_threshold=None):

     width = process_width

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 9e1b2b9a..d489ed1e 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -323,16 +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"):

@@ -402,7 +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 \

@@ -412,7 +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)

 

@@ -439,7 +439,7 @@ def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_st
             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")

@@ -485,7 +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:

@@ -513,7 +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)