Add support for max token

README.md

@@ -1,8 +1,6 @@
-# Kohya's dreambooth and finetuning
+# Kohya's GUI
 
-This repository now includes the solutions provided by Kohya_ss in a single location. I have combined both solutions under one repository to align with the new official Kohya repository where he will maintain his code from now on: https://github.com/kohya-ss/sd-scripts.
+This repository repository is providing a Gradio GUI for kohya's Stable Diffusion trainers found here: https://github.com/kohya-ss/sd-scripts. The GUI allow you to set the training parameters and generate and run the required CLI command to train the model.
-
-A note accompanying the release of his new repository can be found here: https://note.com/kohya_ss/n/nba4eceaa4594
 
 ## Required Dependencies
 
@@ -101,6 +99,8 @@ Once you have created the LoRA network you can generate images via auto1111 by i
 
 ## Change history
 
+* 2023/01/11 (v20.2.0):
+    - Add support for max token lenght
 * 2023/01/10 (v20.1.1):
     - Fix issue with LoRA config loading
 * 2023/01/10 (v20.1):

dreambooth_gui.py

@@ -73,6 +73,7 @@ def save_configuration(
     clip_skip,
     vae,
     output_name,
+    max_token_length,
 ):
     # Get list of function parameters and values
     parameters = list(locals().items())
@@ -151,6 +152,7 @@ def open_configuration(
     clip_skip,
     vae,
     output_name,
+    max_token_length,
 ):
     # Get list of function parameters and values
     parameters = list(locals().items())
@@ -213,6 +215,7 @@ def train_model(
     clip_skip,
     vae,
     output_name,
+    max_token_length,
 ):
     if pretrained_model_name_or_path == '':
         msgbox('Source model information is missing')
@@ -367,6 +370,8 @@ def train_model(
         run_cmd += f' --vae="{vae}"'
     if not output_name == '':
         run_cmd += f' --output_name="{output_name}"'
+    if (int(max_token_length) > 75):
+        run_cmd += f' --max_token_length={max_token_length}'
 
     print(run_cmd)
     # Run the command
@@ -694,6 +699,15 @@ def dreambooth_tab(
                 )
                 vae_button = gr.Button('📂', elem_id='open_folder_small')
                 vae_button.click(get_any_file_path, outputs=vae)
+                max_token_length = gr.Dropdown(
+                    label='Max Token Length',
+                    choices=[
+                        '75',
+                        '150',
+                        '225',
+                    ],
+                    value='75',
+                )
     with gr.Tab('Tools'):
         gr.Markdown(
             'This section provide Dreambooth tools to help setup your dataset...'
@@ -745,6 +759,7 @@ def dreambooth_tab(
         clip_skip,
         vae,
         output_name,
+        max_token_length,
     ]
 
     button_open_config.click(

finetune_gui.py

@@ -66,6 +66,7 @@ def save_configuration(
     mem_eff_attn,
     shuffle_caption,
     output_name,
+    max_token_length,
 ):
     # Get list of function parameters and values
     parameters = list(locals().items())
@@ -148,6 +149,7 @@ def open_config_file(
     mem_eff_attn,
     shuffle_caption,
     output_name,
+    max_token_length,
 ):
     # Get list of function parameters and values
     parameters = list(locals().items())
@@ -215,6 +217,7 @@ def train_model(
     mem_eff_attn,
     shuffle_caption,
     output_name,
+    max_token_length,
 ):
     # create caption json file
     if generate_caption_database:
@@ -331,6 +334,8 @@ def train_model(
         run_cmd += f' --resume={resume}'
     if not output_name == '':
         run_cmd += f' --output_name="{output_name}"'
+    if (int(max_token_length) > 75):
+        run_cmd += f' --max_token_length={max_token_length}'
 
     print(run_cmd)
     # Run the command
@@ -642,6 +647,15 @@ def finetune_tab():
                 gradient_accumulation_steps = gr.Number(
                     label='Gradient accumulate steps', value='1'
                 )
+                max_token_length = gr.Dropdown(
+                    label='Max Token Length',
+                    choices=[
+                        '75',
+                        '150',
+                        '225',
+                    ],
+                    value='75',
+                )
     with gr.Box():
         with gr.Row():
             create_caption = gr.Checkbox(
@@ -695,6 +709,7 @@ def finetune_tab():
         mem_eff_attn,
         shuffle_caption,
         output_name,
+        max_token_length,
     ]
 
     button_run.click(train_model, inputs=settings_list)

lora_gui.py

@@ -80,6 +80,7 @@ def save_configuration(
     mem_eff_attn,
     output_name,
     model_list,
+    max_token_length,
 ):
     # Get list of function parameters and values
     parameters = list(locals().items())
@@ -163,6 +164,7 @@ def open_configuration(
     mem_eff_attn,
     output_name,
     model_list,
+    max_token_length,
 ):
     # Get list of function parameters and values
     parameters = list(locals().items())
@@ -229,7 +231,8 @@ def train_model(
     gradient_accumulation_steps,
     mem_eff_attn,
     output_name,
-    model_list,
+    model_list, # Keep this. Yes, it is unused here but required given the common list used
+    max_token_length,
 ):
     if pretrained_model_name_or_path == '':
         msgbox('Source model information is missing')
@@ -405,6 +408,8 @@ def train_model(
     #     run_cmd += f' --vae="{vae}"'
     if not output_name == '':
         run_cmd += f' --output_name="{output_name}"'
+    if (int(max_token_length) > 75):
+        run_cmd += f' --max_token_length={max_token_length}'
 
     print(run_cmd)
     # Run the command
@@ -781,6 +786,16 @@ def lora_tab(
                 # )
                 # vae_button = gr.Button('📂', elem_id='open_folder_small')
                 # vae_button.click(get_any_file_path, outputs=vae)
+                max_token_length = gr.Dropdown(
+                    label='Max Token Length',
+                    choices=[
+                        '75',
+                        '150',
+                        '225',
+                    ],
+                    value='75',
+                )
+                
     with gr.Tab('Tools'):
         gr.Markdown(
             'This section provide Dreambooth tools to help setup your dataset...'
@@ -839,6 +854,7 @@ def lora_tab(
         mem_eff_attn,
         output_name,
         model_list,
+        max_token_length
     ]
 
     button_open_config.click(