Upgrade Gradio

library/common_gui.py

@@ -355,6 +355,7 @@ def gradio_source_model():
             pretrained_model_name_or_path = gr.Textbox(
                 label='Pretrained model name or path',
                 placeholder='enter the path to custom model or name of pretrained model',
+                value='runwayml/stable-diffusion-v1-5'
             )
             pretrained_model_name_or_path_file = gr.Button(
                 document_symbol, elem_id='open_folder_small'
@@ -373,7 +374,7 @@ def gradio_source_model():
                 outputs=pretrained_model_name_or_path,
             )
             model_list = gr.Dropdown(
-                label='(Optional) Model Quick Pick',
+                label='Model Quick Pick',
                 choices=[
                     'custom',
                     'stabilityai/stable-diffusion-2-1-base',
@@ -383,6 +384,7 @@ def gradio_source_model():
                     'runwayml/stable-diffusion-v1-5',
                     'CompVis/stable-diffusion-v1-4',
                 ],
+                value='runwayml/stable-diffusion-v1-5'
             )
             save_model_as = gr.Dropdown(
                 label='Save trained model as',
@@ -397,7 +399,7 @@ def gradio_source_model():
             )
 
         with gr.Row():
-            v2 = gr.Checkbox(label='v2', value=True)
+            v2 = gr.Checkbox(label='v2', value=False)
             v_parameterization = gr.Checkbox(
                 label='v_parameterization', value=False
             )

lora_gui.py

@@ -123,7 +123,7 @@ def save_configuration(
     caption_dropout_rate,
     optimizer,
     optimizer_args,noise_offset, 
-    locon=0, conv_dim=0, conv_alpha=0,
+    LoRA_type='Standard', conv_dim=0, conv_alpha=0,
 ):
     # Get list of function parameters and values
     parameters = list(locals().items())
@@ -231,7 +231,7 @@ def open_configuration(
     caption_dropout_rate,
     optimizer,
     optimizer_args,noise_offset, 
-    locon=0, conv_dim=0, conv_alpha=0,
+    LoRA_type='Standard', conv_dim=0, conv_alpha=0,
 ):
     # Get list of function parameters and values
     parameters = list(locals().items())
@@ -256,6 +256,12 @@ def open_configuration(
         if not key in ['file_path']:
             values.append(my_data.get(key, value))
 
+    # This next section is about making the LoCon parameters visible if LoRA_type = 'Standard'
+    if my_data.get('LoRA_type', 'Standard') == 'LoCon':
+        values.append(gr.Group.update(visible=True))
+    else:
+        values.append(gr.Group.update(visible=False))
+
     return tuple(values)
 
 
@@ -319,7 +325,7 @@ def train_model(
     caption_dropout_rate,
     optimizer,
     optimizer_args,noise_offset, 
-    locon, conv_dim, conv_alpha,
+    LoRA_type, conv_dim, conv_alpha,
 ):  
     if pretrained_model_name_or_path == '':
         msgbox('Source model information is missing')
@@ -455,7 +461,7 @@ def train_model(
         run_cmd += f' --save_model_as={save_model_as}'
     if not float(prior_loss_weight) == 1.0:
         run_cmd += f' --prior_loss_weight={prior_loss_weight}'
-    if locon:
+    if LoRA_type == 'LoCon':
         getlocon(os.path.exists(os.path.join(path_of_this_folder, 'locon')))
         run_cmd += f' --network_module=locon.locon.locon_kohya'
         run_cmd += f' --network_args "conv_dim={conv_dim}" "conv_alpha={conv_alpha}"'
@@ -634,6 +640,14 @@ def lora_tab(
         )
     with gr.Tab('Training parameters'):
         with gr.Row():
+            LoRA_type = gr.Dropdown(
+                label='LoRA type',
+                choices=[
+                    'Standard',
+                    'LoCon',
+                ],
+                value='Standard'
+            )
             lora_network_weights = gr.Textbox(
                 label='LoRA network weights',
                 placeholder='{Optional) Path to existing LoRA network weights to resume training',
@@ -666,6 +680,7 @@ def lora_tab(
             lr_scheduler_value='cosine',
             lr_warmup_value='10',
         )
+
         with gr.Row():
             text_encoder_lr = gr.Textbox(
                 label='Text Encoder learning rate',
@@ -693,23 +708,17 @@ def lora_tab(
                 step=1,
                 interactive=True,
             )
-        with gr.Row():
-            max_resolution = gr.Textbox(
-                label='Max resolution',
-                value='512,512',
-                placeholder='512,512',
-            )
-            stop_text_encoder_training = gr.Slider(
-                minimum=0,
-                maximum=100,
-                value=0,
-                step=1,
-                label='Stop text encoder training',
-            )
-            enable_bucket = gr.Checkbox(label='Enable buckets', value=True)
-        with gr.Accordion('Advanced Configuration', open=False):
+
+        with gr.Group(visible=False) as LoCon_group:
+            def LoRA_type_change(LoRA_type):
+                if LoRA_type == "LoCon":
+                    return gr.Group.update(visible=True)
+                else:
+                    return gr.Group.update(visible=False)
+
             with gr.Row():
-                locon= gr.Checkbox(label='Train a LoCon instead of a general LoRA (does not support v2 base models) (may not be able to some utilities now)', value=False)
+
+                # locon= gr.Checkbox(label='Train a LoCon instead of a general LoRA (does not support v2 base models) (may not be able to some utilities now)', value=False)
                 conv_dim = gr.Slider(
                     minimum=1,
                     maximum=512,
@@ -724,6 +733,23 @@ def lora_tab(
                     step=1,
                     label='LoCon Convolution Alpha',
                 )
+            # Show of hide LoCon conv settings depending on LoRA type selection
+            LoRA_type.change(LoRA_type_change, inputs=[LoRA_type], outputs=[LoCon_group])
+        with gr.Row():
+            max_resolution = gr.Textbox(
+                label='Max resolution',
+                value='512,512',
+                placeholder='512,512',
+            )
+            stop_text_encoder_training = gr.Slider(
+                minimum=0,
+                maximum=100,
+                value=0,
+                step=1,
+                label='Stop text encoder training',
+            )
+            enable_bucket = gr.Checkbox(label='Enable buckets', value=True)
+        with gr.Accordion('Advanced Configuration', open=False):
             with gr.Row():
                 no_token_padding = gr.Checkbox(
                     label='No token padding', value=False
@@ -869,13 +895,13 @@ def lora_tab(
         caption_dropout_rate,
         optimizer,
         optimizer_args,noise_offset, 
-        locon, conv_dim, conv_alpha,
+        LoRA_type, conv_dim, conv_alpha,
     ]
 
     button_open_config.click(
         open_configuration,
         inputs=[config_file_name] + settings_list,
-        outputs=[config_file_name] + settings_list,
+        outputs=[config_file_name] + settings_list + [LoCon_group],
     )
 
     button_save_config.click(

requirements.txt

@@ -1,28 +1,28 @@
 accelerate==0.15.0
-transformers==4.26.0
-ftfy==6.1.1
 albumentations==1.3.0
-opencv-python==4.7.0.68
-einops==0.6.0
+altair==4.2.2
+bitsandbytes==0.35.0
+dadaptation==1.5
 diffusers[torch]==0.10.2
+easygui==0.98.3
+einops==0.6.0
+ftfy==6.1.1
+gradio==3.19.1
+lion-pytorch==0.0.6
+opencv-python==4.7.0.68
 pytorch-lightning==1.9.0
-bitsandbytes==0.35.0
-tensorboard==2.10.1
 safetensors==0.2.6
-gradio==3.16.2
-altair==4.2.2
-easygui==0.98.3
+tensorboard==2.10.1
 tk==0.1.0
-lion-pytorch==0.0.6
-dadaptation==1.5
+transformers==4.26.0
 # for BLIP captioning
+fairscale==0.4.13
 requests==2.28.2
 timm==0.6.12
-fairscale==0.4.13
 # for WD14 captioning
 # tensorflow<2.11
-tensorflow==2.10.1
 huggingface-hub==0.12.0
+tensorflow==2.10.1
 # xformers @ https://github.com/C43H66N12O12S2/stable-diffusion-webui/releases/download/f/xformers-0.0.14.dev0-cp310-cp310-win_amd64.whl
 # for kohya_ss library
 .