Update LoRA GUI

Various improvements

Author: bmaltais

README.md

@@ -12,18 +12,38 @@ You can find the dreambooth solution spercific [Dreambooth README](README_dreamb
 
 You can find the finetune solution spercific [Finetune README](README_finetune.md)
 
+## LoRA
+
+You can create LoRA network by running the dedicated GUI with:
+
+```
+python lora_gui.py
+```
+
+or via the all in one GUI:
+
+```
+python kahya_gui.py
+```
+
+Once you have created the LoRA network you can generate images via auto1111 by installing the extension found here: https://github.com/kohya-ss/sd-webui-additional-networks
+
 ## Change history
 
-* 12/30 (v19) update:
+* 2023/01/01 (v19.1) update:
+    - merge kohys_ss upstream code  updates
+    - rework Dreambooth LoRA GUI
+    - fix bug where LoRA network weights were not loaded to properly resume training
+* 2022/12/30 (v19) update:
     - support for LoRA network training in kohya_gui.py.
-* 12/23 (v18.8) update:
+* 2022/12/23 (v18.8) update:
     - Fix for conversion tool issue when the source was an sd1.x diffuser model
     - Other minor code and GUI fix
-* 12/22 (v18.7) update:
+* 2022/12/22 (v18.7) update:
     - Merge dreambooth and finetune is a common GUI
     - General bug fixes and code improvements
-* 12/21 (v18.6.1) update:
+* 2022/12/21 (v18.6.1) update:
     - fix issue with dataset balancing when the number of detected images in the folder is 0
 
-* 12/21 (v18.6) update:
+* 2022/12/21 (v18.6) update:
     - add optional GUI authentication support via: `python fine_tune.py --username=<name> --password=<password>`

dreambooth_gui.py

@@ -15,6 +15,7 @@
     get_folder_path,
     remove_doublequote,
     get_file_path,
+    get_any_file_path,
     get_saveasfile_path,
 )
 from library.dreambooth_folder_creation_gui import (
@@ -236,7 +237,7 @@ def train_model(
     seed,
     num_cpu_threads_per_process,
     cache_latent,
-    caption_extention,
+    caption_extension,
     enable_bucket,
     gradient_checkpointing,
     full_fp16,
@@ -396,7 +397,8 @@ def save_inference_file(output_dir, v2, v_parameterization):
     run_cmd += f' --seed={seed}'
     run_cmd += f' --save_precision={save_precision}'
     run_cmd += f' --logging_dir={logging_dir}'
-    run_cmd += f' --caption_extention={caption_extention}'
+    if not caption_extension == '':
+        run_cmd += f' --caption_extension={caption_extension}'
     if not stop_text_encoder_training == 0:
         run_cmd += (
             f' --stop_text_encoder_training={stop_text_encoder_training}'
@@ -542,7 +544,7 @@ def dreambooth_tab(
                 document_symbol, elem_id='open_folder_small'
             )
             pretrained_model_name_or_path_fille.click(
-                get_file_path,
+                get_any_file_path,
                 inputs=[pretrained_model_name_or_path_input],
                 outputs=pretrained_model_name_or_path_input,
             )

examples/word_frequency.ps1

@@ -1,12 +1,12 @@
-$txt_files_folder = "D:\dreambooth\training_twq\mad_hatter\all"
-$txt_prefix_to_ignore = "asd"
-$txt_postfix_ti_ignore = "asd"
+$txt_files_folder = "D:\dataset\metart_g1\img\100_asd girl"
+$txt_prefix_to_ignore = "asds"
+$txt_postfix_ti_ignore = "asds"
 
 # Should not need to touch anything below
 
 # (Get-Content $txt_files_folder"\*.txt" ).Replace(",", "") -Split '\W' | Group-Object -NoElement | Sort-Object -Descending -Property Count
 
-$combined_txt = Get-Content $txt_files_folder"\*.txt"
+$combined_txt = Get-Content $txt_files_folder"\*.cap"
 $combined_txt = $combined_txt.Replace(",", "")
 $combined_txt = $combined_txt.Replace("$txt_prefix_to_ignore", "")
 $combined_txt = $combined_txt.Replace("$txt_postfix_ti_ignore", "") -Split '\W' | Group-Object -NoElement | Sort-Object -Descending -Property Count

finetune_gui.py

@@ -9,6 +9,7 @@
 from library.common_gui import (
     get_folder_path,
     get_file_path,
+    get_any_file_path,
     get_saveasfile_path,
 )
 from library.utilities import utilities_tab
@@ -436,7 +437,7 @@ def finetune_tab():
                 document_symbol, elem_id='open_folder_small'
             )
             pretrained_model_name_or_path_file.click(
-                get_file_path,
+                get_any_file_path,
                 inputs=pretrained_model_name_or_path_input,
                 outputs=pretrained_model_name_or_path_input,
             )

library/basic_caption_gui.py

@@ -75,7 +75,7 @@ def gradio_basic_caption_gui_tab():
             )
         with gr.Row():
             prefix = gr.Textbox(
-                label='Prefix to add to txt caption',
+                label='Prefix to add to caption',
                 placeholder='(Optional)',
                 interactive=True,
             )
@@ -85,7 +85,7 @@ def gradio_basic_caption_gui_tab():
                 interactive=True,
             )
             postfix = gr.Textbox(
-                label='Postfix to add to txt caption',
+                label='Postfix to add to caption',
                 placeholder='(Optional)',
                 interactive=True,
             )

lora_gui.py

@@ -64,7 +64,7 @@ def save_configuration(
     shuffle_caption,
     save_state,
     resume,
-    prior_loss_weight, text_encoder_lr, unet_lr, network_dim
+    prior_loss_weight, text_encoder_lr, unet_lr, network_dim, lora_network_weights
 ):
     original_file_path = file_path
 
@@ -118,7 +118,8 @@ def save_configuration(
         'prior_loss_weight': prior_loss_weight,
         'text_encoder_lr': text_encoder_lr,
         'unet_lr': unet_lr,
-        'network_dim': network_dim
+        'network_dim': network_dim,
+        'lora_network_weights': lora_network_weights,
     }
 
     # Save the data to the selected file
@@ -160,7 +161,7 @@ def open_configuration(
     shuffle_caption,
     save_state,
     resume,
-    prior_loss_weight, text_encoder_lr, unet_lr, network_dim
+    prior_loss_weight, text_encoder_lr, unet_lr, network_dim, lora_network_weights
 ):
 
     original_file_path = file_path
@@ -216,6 +217,7 @@ def open_configuration(
         my_data.get('text_encoder_lr', text_encoder_lr),
         my_data.get('unet_lr', unet_lr),
         my_data.get('network_dim', network_dim),
+        my_data.get('lora_network_weights', lora_network_weights),
     )
 
 
@@ -250,7 +252,7 @@ def train_model(
     shuffle_caption,
     save_state,
     resume,
-    prior_loss_weight, text_encoder_lr, unet_lr, network_dim
+    prior_loss_weight, text_encoder_lr, unet_lr, network_dim, lora_network_weights
 ):
     def save_inference_file(output_dir, v2, v_parameterization):
         # Copy inference model for v2 if required
@@ -432,6 +434,7 @@ def save_inference_file(output_dir, v2, v_parameterization):
     # elif network_train == 'Unet only':
     #     run_cmd += f' --network_train_unet_only'
     run_cmd += f' --network_dim={network_dim}'
+    run_cmd += f' --network_weights={lora_network_weights}'
 
 
     print(run_cmd)
@@ -568,7 +571,7 @@ def lora_tab(
                 document_symbol, elem_id='open_folder_small'
             )
             pretrained_model_name_or_path_file.click(
-                get_file_path,
+                get_any_file_path,
                 inputs=[pretrained_model_name_or_path_input],
                 outputs=pretrained_model_name_or_path_input,
             )
@@ -602,19 +605,7 @@ def lora_tab(
                 ],
                 value='same as source model',
             )
-        with gr.Row():
-            lora_network_weights = gr.Textbox(
-                label='LoRA network weights',
-                placeholder='{Optional) Path to existing LoRA network weights to resume training}',
-            )
-            lora_network_weights_file = gr.Button(
-                document_symbol, elem_id='open_folder_small'
-            )
-            lora_network_weights_file.click(
-                get_any_file_path,
-                inputs=[lora_network_weights],
-                outputs=lora_network_weights,
-            )
+            
         with gr.Row():
             v2_input = gr.Checkbox(label='v2', value=True)
             v_parameterization_input = gr.Checkbox(
@@ -699,6 +690,19 @@ def lora_tab(
             outputs=[logging_dir_input],
         )
     with gr.Tab('Training parameters'):
+        with gr.Row():
+            lora_network_weights = gr.Textbox(
+                label='LoRA network weights',
+                placeholder='{Optional) Path to existing LoRA network weights to resume training',
+            )
+            lora_network_weights_file = gr.Button(
+                document_symbol, elem_id='open_folder_small'
+            )
+            lora_network_weights_file.click(
+                get_any_file_path,
+                inputs=[lora_network_weights],
+                outputs=lora_network_weights,
+            )
         with gr.Row():
             # learning_rate_input = gr.Textbox(label='Learning rate', value=1e-4, visible=False)
             lr_scheduler_input = gr.Dropdown(
@@ -874,7 +878,7 @@ def lora_tab(
         shuffle_caption,
         save_state,
         resume,
-        prior_loss_weight, text_encoder_lr, unet_lr, network_dim
+        prior_loss_weight, text_encoder_lr, unet_lr, network_dim, lora_network_weights
     ]
 
     button_open_config.click(