1 files changed, 25 insertions, 1 deletions
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 0a059044..e9cf432f 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -1,6 +1,7 @@
 import os
 import sys
 import traceback
+import inspect
 
 import torch
 import tqdm
@@ -230,6 +231,26 @@ def write_loss(log_directory, filename, step, epoch_len, values):
             **values,
         })
 
+# Note: hypernetwork.py has a nearly identical function of the same name. 
+def save_settings_to_file(model_name, model_hash, initial_step, num_of_dataset_images, embedding_name, vectors_per_token, learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
+    # Starting index of preview-related arguments.
+    border_index = 18
+    # Get a list of the argument names.
+    arg_names = inspect.getfullargspec(save_settings_to_file).args    
+    # Create a list of the argument names to include in the settings string.
+    names = arg_names[:border_index]  # Include all arguments up until the preview-related ones.
+    if preview_from_txt2img:
+        names.extend(arg_names[border_index:])  # Include preview-related arguments if applicable.
+    # Build the settings string.
+    settings_str = "datetime : " + datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S") + "\n"
+    for name in names:
+        if name != 'log_directory': # It's useless and redundant to save log_directory.
+            value = locals()[name]
+            settings_str += f"{name}: {value}\n"
+    # Create or append to the file.
+    with open(os.path.join(log_directory, 'settings.txt'), "a+") as fout:
+        fout.write(settings_str + "\n\n")
+
 def validate_train_inputs(model_name, learn_rate, batch_size, gradient_step, data_root, template_file, steps, save_model_every, create_image_every, log_directory, name="embedding"):
     assert model_name, f"{name} not selected"
     assert learn_rate, "Learning rate is empty or 0"
@@ -293,8 +314,8 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_
     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 \
         None
@@ -308,6 +329,9 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_
 
     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)
 
+    if shared.opts.save_training_settings_to_txt:
+            save_settings_to_file(checkpoint.model_name, '[{}]'.format(checkpoint.hash), initial_step, len(ds), embedding_name, len(embedding.vec), learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height)
+
     latent_sampling_method = ds.latent_sampling_method
 
     dl = modules.textual_inversion.dataset.PersonalizedDataLoader(ds, latent_sampling_method=latent_sampling_method, batch_size=ds.batch_size, pin_memory=pin_memory)