v20.6.0

- Increase max LoRA rank (dim) size to 1024.
    - Update finetune preprocessing scripts.
        - ``.bmp`` and ``.jpeg`` are supported. Thanks to breakcore2 and p1atdev!
        - The default weights of ``tag_images_by_wd14_tagger.py`` is now ``SmilingWolf/wd-v1-4-convnext-tagger-v2``. You can specify another model id from ``SmilingWolf`` by ``--repo_id`` option. Thanks to SmilingWolf for the great work.
        - To change the weight, remove ``wd14_tagger_model`` folder, and run the script again.
        - ``--max_data_loader_n_workers`` option is added to each script. This option uses the DataLoader for data loading to speed up loading, 20%~30% faster.
        - Please specify 2 or 4, depends on the number of CPU cores.
        - ``--recursive`` option is added to ``merge_dd_tags_to_metadata.py`` and ``merge_captions_to_metadata.py``, only works with ``--full_path``.
        - ``make_captions_by_git.py`` is added. It uses [GIT microsoft/git-large-textcaps](https://huggingface.co/microsoft/git-large-textcaps) for captioning.
        - ``requirements.txt`` is updated. If you use this script, [please update the libraries](https://github.com/kohya-ss/sd-scripts#upgrade).
        - Usage is almost the same as ``make_captions.py``, but batch size should be smaller.
        - ``--remove_words`` option removes as much text as possible (such as ``the word "XXXX" on it``).
        - ``--skip_existing`` option is added to ``prepare_buckets_latents.py``. Images with existing npz files are ignored by this option.
        - ``clean_captions_and_tags.py`` is updated to remove duplicated or conflicting tags, e.g. ``shirt`` is removed when ``white shirt`` exists. if ``black hair`` is with ``red hair``, both are removed.
    - Tag frequency is added to the metadata in ``train_network.py``. Thanks to space-nuko!
        - __All tags and number of occurrences of the tag are recorded.__ If you do not want it, disable metadata storing with ``--no_metadata`` option.

kohya_gui.py

@@ -19,7 +19,7 @@ def UI(username, password):
             print('Load CSS...')
             css += file.read() + '\n'
 
-    interface = gr.Blocks(css=css)
+    interface = gr.Blocks(css=css, title="Kohya_ss GUI")
 
     with interface:
         with gr.Tab('Dreambooth'):

library/extract_lora_gui.py

@@ -109,11 +109,11 @@ def gradio_extract_lora_tab():
             )
         with gr.Row():
             dim = gr.Slider(
-                minimum=1,
-                maximum=128,
+                minimum=4,
+                maximum=1024,
                 label='Network Dimension',
-                value=8,
-                step=1,
+                value=128,
+                step=4,
                 interactive=True,
             )
             v2 = gr.Checkbox(label='v2', value=False, interactive=True)

library/git_caption_gui.py

@@ -0,0 +1,126 @@
+import gradio as gr
+from easygui import msgbox
+import subprocess
+import os
+from .common_gui import get_folder_path, add_pre_postfix
+
+
+def caption_images(
+    train_data_dir,
+    caption_ext,
+    batch_size,
+    max_data_loader_n_workers,
+    max_length,
+    model_id,
+    prefix,
+    postfix,
+):
+        # Check for images_dir_input
+    if train_data_dir == '':
+        msgbox('Image folder is missing...')
+        return
+    
+    if caption_ext == '':
+        msgbox('Please provide an extension for the caption files.')
+        return
+
+    print(f'GIT captioning files in {train_data_dir}...')
+    run_cmd = f'.\\venv\\Scripts\\python.exe "finetune/make_captions.py"'
+    if not model_id == '':
+        run_cmd += f' --model_id="{model_id}"'
+    run_cmd += f' --batch_size="{int(batch_size)}"'
+    run_cmd += f' --max_data_loader_n_workers="{int(max_data_loader_n_workers)}"'
+    run_cmd += f' --max_length="{int(max_length)}"'
+    if caption_ext != '':
+        run_cmd += f' --caption_extension="{caption_ext}"'
+    run_cmd += f' "{train_data_dir}"'
+
+    print(run_cmd)
+
+    # Run the command
+    subprocess.run(run_cmd)
+
+    # Add prefix and postfix
+    add_pre_postfix(
+        folder=train_data_dir,
+        caption_file_ext=caption_ext,
+        prefix=prefix,
+        postfix=postfix,
+    )
+
+    print('...captioning done')
+
+
+###
+# Gradio UI
+###
+
+
+def gradio_git_caption_gui_tab():
+    with gr.Tab('GIT Captioning'):
+        gr.Markdown(
+            'This utility will use GIT to caption files for each images in a folder.'
+        )
+        with gr.Row():
+            train_data_dir = gr.Textbox(
+                label='Image folder to caption',
+                placeholder='Directory containing the images to caption',
+                interactive=True,
+            )
+            button_train_data_dir_input = gr.Button(
+                '📂', elem_id='open_folder_small'
+            )
+            button_train_data_dir_input.click(
+                get_folder_path, outputs=train_data_dir
+            )
+        with gr.Row():
+            caption_ext = gr.Textbox(
+                label='Caption file extension',
+                placeholder='Extention for caption file. eg: .caption, .txt',
+                value='.txt',
+                interactive=True,
+            )
+
+            prefix = gr.Textbox(
+                label='Prefix to add to BLIP caption',
+                placeholder='(Optional)',
+                interactive=True,
+            )
+
+            postfix = gr.Textbox(
+                label='Postfix to add to BLIP caption',
+                placeholder='(Optional)',
+                interactive=True,
+            )
+
+            batch_size = gr.Number(
+                value=1, label='Batch size', interactive=True
+            )
+
+        with gr.Row():
+            max_data_loader_n_workers = gr.Number(
+                value=2, label='Number of workers', interactive=True
+            )
+            max_length = gr.Number(
+                value=75, label='Max length', interactive=True
+            )
+            model_id = gr.Textbox(
+                label="Model",
+                placeholder="(Optional) model id for GIT in Hugging Face", interactive=True
+            )
+
+        caption_button = gr.Button('Caption images')
+
+        caption_button.click(
+            caption_images,
+            inputs=[
+                train_data_dir,
+                caption_ext,
+                batch_size,
+                max_data_loader_n_workers,
+                max_length,
+                model_id,
+                prefix,
+                postfix,
+            ],
+        )

library/utilities.py

@@ -9,6 +9,7 @@ import argparse
 from library.basic_caption_gui import gradio_basic_caption_gui_tab
 from library.convert_model_gui import gradio_convert_model_tab
 from library.blip_caption_gui import gradio_blip_caption_gui_tab
+from library.git_caption_gui import gradio_git_caption_gui_tab
 from library.wd14_caption_gui import gradio_wd14_caption_gui_tab
 
 
@@ -23,6 +24,7 @@ def utilities_tab(
     with gr.Tab('Captioning'):
         gradio_basic_caption_gui_tab()
         gradio_blip_caption_gui_tab()
+        gradio_git_caption_gui_tab()
         gradio_wd14_caption_gui_tab()
     gradio_convert_model_tab()
 

lora_gui.py

@@ -291,11 +291,11 @@ def train_model(
     if unet_lr == '':
         unet_lr = 0
 
-    if (float(text_encoder_lr) == 0) and (float(unet_lr) == 0):
-        msgbox(
-            'At least one Learning Rate value for "Text encoder" or "Unet" need to be provided'
-        )
-        return
+    # if (float(text_encoder_lr) == 0) and (float(unet_lr) == 0):
+    #     msgbox(
+    #         'At least one Learning Rate value for "Text encoder" or "Unet" need to be provided'
+    #     )
+    #     return
 
     # Get a list of all subfolders in train_data_dir
     subfolders = [
@@ -383,15 +383,26 @@ def train_model(
     if not float(prior_loss_weight) == 1.0:
         run_cmd += f' --prior_loss_weight={prior_loss_weight}'
     run_cmd += f' --network_module=networks.lora'
-    if not float(text_encoder_lr) == 0:
-        run_cmd += f' --text_encoder_lr={text_encoder_lr}'
+    
+    if not (float(text_encoder_lr) == 0) or not (float(unet_lr) == 0):
+        if not (float(text_encoder_lr) == 0) and not (float(unet_lr) == 0):
+            run_cmd += f' --text_encoder_lr={text_encoder_lr}'
+            run_cmd += f' --unet_lr={unet_lr}'
+        elif not (float(text_encoder_lr) == 0):
+            run_cmd += f' --text_encoder_lr={text_encoder_lr}'
+            run_cmd += f' --network_train_text_encoder_only'
+        else:
+            run_cmd += f' --unet_lr={unet_lr}'
+            run_cmd += f' --network_train_unet_only'
     else:
-        run_cmd += f' --network_train_unet_only'
-    if not float(unet_lr) == 0:
-        run_cmd += f' --unet_lr={unet_lr}'
-    else:
-        run_cmd += f' --network_train_text_encoder_only'
+        if float(text_encoder_lr) == 0: 
+            msgbox(
+                'Please input learning rate values.'
+            )
+            return
+        
     run_cmd += f' --network_dim={network_dim}'
+    
     if not lora_network_weights == '':
         run_cmd += f' --network_weights="{lora_network_weights}"'
     if int(gradient_accumulation_steps) > 1:
@@ -400,6 +411,8 @@ def train_model(
         run_cmd += f' --output_name="{output_name}"'
     if not lr_scheduler_num_cycles == '':
         run_cmd += f' --lr_scheduler_num_cycles="{lr_scheduler_num_cycles}"'
+    else:
+        run_cmd += f' --lr_scheduler_num_cycles="{epoch}"'
     if not lr_scheduler_power == '':
         run_cmd += f' --output_name="{lr_scheduler_power}"'
 
@@ -612,19 +625,19 @@ def lora_tab(
                 placeholder='Optional',
             )
             network_dim = gr.Slider(
-                minimum=1,
-                maximum=128,
+                minimum=4,
+                maximum=1024,
                 label='Network Rank (Dimension)',
                 value=8,
-                step=1,
+                step=4,
                 interactive=True,
             )
             network_alpha = gr.Slider(
-                minimum=1,
-                maximum=128,
+                minimum=4,
+                maximum=1024,
                 label='Network Alpha',
                 value=1,
-                step=1,
+                step=4,
                 interactive=True,
             )
         with gr.Row():

requirements.txt

@@ -9,13 +9,14 @@ pytorch_lightning
 bitsandbytes==0.35.0
 tensorboard
 safetensors==0.2.6
-gradio
+gradio==3.16.2
 altair
 easygui
+tk
 # for BLIP captioning
 requests
-timm==0.4.12
-fairscale==0.4.4
+timm
+fairscale
 # for WD14 captioning
 tensorflow<2.11
 huggingface-hub