Remove redundant code

README.md

@@ -116,7 +116,7 @@ python .\dreambooth_gui.py
 
 ## Support
 
-Drop by the discord server for support: https://discord.com/channels/1023277529424986162/1026874833193140285
+Drop by the discord server for support: https://discord.com/channels/1041518562487058594/1041518563242020906
 
 ## Manual Script Execution
 

dreambooth_gui.py

@@ -13,7 +13,8 @@ from os.path import join
 from easygui import fileopenbox, filesavebox, enterbox, diropenbox, msgbox
 
 
-def save_variables(
+def save_configuration(
+    save_as,
     file_path,
     pretrained_model_name_or_path,
     v2,
@@ -48,100 +49,26 @@ def save_variables(
 ):
     original_file_path = file_path
 
-    if file_path == None or file_path == "":
+    save_as_bool = True if save_as.get("label") == "True" else False
+
+    if save_as_bool:
+        print("Save as...")
         file_path = filesavebox(
             "Select the config file to save",
             default="finetune.json",
             filetypes="*.json",
         )
+    else:
+        print("Save...")
+        if file_path == None or file_path == "":
+            file_path = filesavebox(
+                "Select the config file to save",
+                default="finetune.json",
+                filetypes="*.json",
+            )
 
     if file_path == None:
-        file_path = original_file_path  # In case a file_path was provided and the user decide to cancel the open action
-        return file_path
-
-    # Return the values of the variables as a dictionary
-    variables = {
-        "pretrained_model_name_or_path": pretrained_model_name_or_path,
-        "v2": v2,
-        "v_parameterization": v_parameterization,
-        "logging_dir": logging_dir,
-        "train_data_dir": train_data_dir,
-        "reg_data_dir": reg_data_dir,
-        "output_dir": output_dir,
-        "max_resolution": max_resolution,
-        "learning_rate": learning_rate,
-        "lr_scheduler": lr_scheduler,
-        "lr_warmup": lr_warmup,
-        "train_batch_size": train_batch_size,
-        "epoch": epoch,
-        "save_every_n_epochs": save_every_n_epochs,
-        "mixed_precision": mixed_precision,
-        "save_precision": save_precision,
-        "seed": seed,
-        "num_cpu_threads_per_process": num_cpu_threads_per_process,
-        "convert_to_safetensors": convert_to_safetensors,
-        "convert_to_ckpt": convert_to_ckpt,
-        "cache_latent": cache_latent,
-        "caption_extention": caption_extention,
-        "use_safetensors": use_safetensors,
-        "enable_bucket": enable_bucket,
-        "gradient_checkpointing": gradient_checkpointing,
-        "full_fp16": full_fp16,
-        "no_token_padding": no_token_padding,
-        "stop_text_encoder_training": stop_text_encoder_training,
-        "use_8bit_adam": use_8bit_adam,
-        "xformers": xformers,
-    }
-
-    # Save the data to the selected file
-    with open(file_path, "w") as file:
-        json.dump(variables, file)
-    
-    return file_path
-
-
-def save_as_variables(
-    file_path,
-    pretrained_model_name_or_path,
-    v2,
-    v_parameterization,
-    logging_dir,
-    train_data_dir,
-    reg_data_dir,
-    output_dir,
-    max_resolution,
-    learning_rate,
-    lr_scheduler,
-    lr_warmup,
-    train_batch_size,
-    epoch,
-    save_every_n_epochs,
-    mixed_precision,
-    save_precision,
-    seed,
-    num_cpu_threads_per_process,
-    convert_to_safetensors,
-    convert_to_ckpt,
-    cache_latent,
-    caption_extention,
-    use_safetensors,
-    enable_bucket,
-    gradient_checkpointing,
-    full_fp16,
-    no_token_padding,
-    stop_text_encoder_training,
-    use_8bit_adam,
-    xformers,
-):
-    original_file_path = file_path
-
-    file_path = filesavebox(
-        "Select the config file to save", default="finetune.json", filetypes="*.json"
-    )
-
-    if file_path == None:
-        file_path = original_file_path  # In case a file_path was provided and the user decide to cancel the open action
-        return file_path
+        return original_file_path  # In case a file_path was provided and the user decide to cancel the open action
 
     # Return the values of the variables as a dictionary
     variables = {
@@ -184,7 +111,7 @@ def save_as_variables(
     return file_path
 
 
-def open_config_file(
+def open_configuration(
     file_path,
     pretrained_model_name_or_path,
     v2,
@@ -531,6 +458,8 @@ if os.path.exists("./style.css"):
 interface = gr.Blocks(css=css)
 
 with interface:
+    dummy_true = gr.Label(value=True, visible=False)
+    dummy_false = gr.Label(value=False, visible=False)
     gr.Markdown("Enter kohya finetuner parameter using this interface.")
     with gr.Accordion("Configuration File Load/Save", open=False):
         with gr.Row():
@@ -589,7 +518,9 @@ with interface:
                 placeholder="Directory where the training folders containing the images are located",
             )
             train_data_dir_input_folder = gr.Button("📂", elem_id="open_folder_small")
-            train_data_dir_input_folder.click(get_folder_path, outputs=train_data_dir_input)
+            train_data_dir_input_folder.click(
+                get_folder_path, outputs=train_data_dir_input
+            )
             reg_data_dir_input = gr.Textbox(
                 label="Regularisation folder",
                 placeholder="(Optional) Directory where where the regularization folders containing the images are located",
@@ -714,7 +645,7 @@ with interface:
     button_run = gr.Button("Run")
 
     button_open_config.click(
-        open_config_file,
+        open_configuration,
         inputs=[
             config_file_name,
             pretrained_model_name_or_path_input,
@@ -783,9 +714,12 @@ with interface:
         ],
     )
 
+    save_as = True
+    not_save_as = False
     button_save_config.click(
-        save_variables,
+        save_configuration,
         inputs=[
+            dummy_false,
             config_file_name,
             pretrained_model_name_or_path_input,
             v2_input,
@@ -822,8 +756,9 @@ with interface:
     )
 
     button_save_as_config.click(
-        save_as_variables,
+        save_configuration,
         inputs=[
+            dummy_true,
             config_file_name,
             pretrained_model_name_or_path_input,
             v2_input,