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KohyaSS
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Update GUI and Readme

This commit is contained in:
bmaltais 2022-12-22 13:14:46 -05:00
parent f60c8addd5
commit 8a99af9269
8 changed files with 79 additions and 69 deletions
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20
README_dreambooth.md
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@ -71,7 +71,7 @@ When a new release comes out you can upgrade your repo with the following comman
.\upgrade.bat
```
or you can do it manually with
alternatively you can do it manually with
```powershell
cd kohya_ss
@ -84,15 +84,27 @@ Once the commands have completed successfully you should be ready to use the new
## GUI
There is now support for GUI based training using gradio. You can start the GUI interface by running:
There is now support for GUI based training using gradio. You can start the complete kohya training GUI interface by running:
```powershell
.\dreambooth.bat
.\kohya.cmd
```
and select the Dreambooth tab.
Alternativelly you can use the Dreambooth focus GUI with
```powershell
.\dreambooth.cmd
```
## CLI
You can find various examples of how to leverage the fine_tune.py in this folder: https://github.com/bmaltais/kohya_ss/tree/master/examples
You can find various examples of how to leverage the `train_db.py` in this folder: https://github.com/bmaltais/kohya_ss/tree/master/examples
## Support
Drop by the discord server for support: https://discord.com/channels/1041518562487058594/1041518563242020906
## Quickstart screencast

14
README_finetune.md
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@ -107,15 +107,23 @@ You can also use the `Captioning` tool found under the `Utilities` tab in the GU
## GUI
Support for GUI based training using gradio. You can start the GUI interface by running:
There is now support for GUI based training using gradio. You can start the complete kohya training GUI interface by running:
```powershell
.\finetune.bat
.\kohya.cmd
```
and select the Finetune tab.
Alternativelly you can use the Finetune focus GUI with
```powershell
.\finetune.cmd
```
## CLI
You can find various examples of how to leverage the fine_tune.py in this folder: https://github.com/bmaltais/kohya_ss/tree/master/examples
You can find various examples of how to leverage the `fine_tune.py` in this folder: https://github.com/bmaltais/kohya_ss/tree/master/examples
## Support

0
dreambooth.bat → dreambooth.cmd
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2
dreambooth_gui.py
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@ -509,7 +509,7 @@ def dreambooth_tab(
):
dummy_db_true = gr.Label(value=True, visible=False)
dummy_db_false = gr.Label(value=False, visible=False)
gr.Markdown('Enter kohya finetuner parameter using this interface.')
gr.Markdown('Enter kohya dreambooth training parameter using this interface.')
with gr.Accordion('Configuration file', open=False):
with gr.Row():
button_open_config = gr.Button('Open 📂', elem_id='open_folder')

0
finetune.bat → finetune.cmd
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110
finetune_gui.py
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@ -44,7 +44,6 @@ def save_configuration(
train_text_encoder,
create_buckets,
create_caption,
train,
save_model_as,
caption_extension,
):
@ -89,7 +88,6 @@ def save_configuration(
'train_text_encoder': train_text_encoder,
'create_buckets': create_buckets,
'create_caption': create_caption,
'train': train,
'save_model_as': save_model_as,
'caption_extension': caption_extension,
}
@ -125,7 +123,6 @@ def open_config_file(
train_text_encoder,
create_buckets,
create_caption,
train,
save_model_as,
caption_extension,
):
@ -170,7 +167,6 @@ def open_config_file(
my_data.get('train_text_encoder', train_text_encoder),
my_data.get('create_buckets', create_buckets),
my_data.get('create_caption', create_caption),
my_data.get('train', train),
my_data.get('save_model_as', save_model_as),
my_data.get('caption_extension', caption_extension),
)
@ -179,7 +175,6 @@ def open_config_file(
def train_model(
generate_caption_database,
generate_image_buckets,
train,
pretrained_model_name_or_path,
v2,
v_parameterization,
@ -262,59 +257,58 @@ def train_model(
# Run the command
subprocess.run(command)
if train:
image_num = len(
[f for f in os.listdir(image_folder) if f.endswith('.npz')]
)
print(f'image_num = {image_num}')
image_num = len(
[f for f in os.listdir(image_folder) if f.endswith('.npz')]
)
print(f'image_num = {image_num}')
repeats = int(image_num) * int(dataset_repeats)
print(f'repeats = {str(repeats)}')
repeats = int(image_num) * int(dataset_repeats)
print(f'repeats = {str(repeats)}')
# calculate max_train_steps
max_train_steps = int(
math.ceil(float(repeats) / int(train_batch_size) * int(epoch))
)
print(f'max_train_steps = {max_train_steps}')
# calculate max_train_steps
max_train_steps = int(
math.ceil(float(repeats) / int(train_batch_size) * int(epoch))
)
print(f'max_train_steps = {max_train_steps}')
lr_warmup_steps = round(
float(int(lr_warmup) * int(max_train_steps) / 100)
)
print(f'lr_warmup_steps = {lr_warmup_steps}')
lr_warmup_steps = round(
float(int(lr_warmup) * int(max_train_steps) / 100)
)
print(f'lr_warmup_steps = {lr_warmup_steps}')
run_cmd = f'accelerate launch --num_cpu_threads_per_process={num_cpu_threads_per_process} "./fine_tune.py"'
if v2:
run_cmd += ' --v2'
if v_parameterization:
run_cmd += ' --v_parameterization'
if train_text_encoder:
run_cmd += ' --train_text_encoder'
run_cmd += (
f' --pretrained_model_name_or_path={pretrained_model_name_or_path}'
)
run_cmd += f' --in_json={train_dir}/meta_lat.json'
run_cmd += f' --train_data_dir={image_folder}'
run_cmd += f' --output_dir={output_dir}'
if not logging_dir == '':
run_cmd += f' --logging_dir={logging_dir}'
run_cmd += f' --train_batch_size={train_batch_size}'
run_cmd += f' --dataset_repeats={dataset_repeats}'
run_cmd += f' --learning_rate={learning_rate}'
run_cmd += f' --lr_scheduler={lr_scheduler}'
run_cmd += f' --lr_warmup_steps={lr_warmup_steps}'
run_cmd += f' --max_train_steps={max_train_steps}'
run_cmd += f' --use_8bit_adam'
run_cmd += f' --xformers'
run_cmd += f' --mixed_precision={mixed_precision}'
run_cmd += f' --save_every_n_epochs={save_every_n_epochs}'
run_cmd += f' --seed={seed}'
run_cmd += f' --save_precision={save_precision}'
if not save_model_as == 'same as source model':
run_cmd += f' --save_model_as={save_model_as}'
run_cmd = f'accelerate launch --num_cpu_threads_per_process={num_cpu_threads_per_process} "./fine_tune.py"'
if v2:
run_cmd += ' --v2'
if v_parameterization:
run_cmd += ' --v_parameterization'
if train_text_encoder:
run_cmd += ' --train_text_encoder'
run_cmd += (
f' --pretrained_model_name_or_path={pretrained_model_name_or_path}'
)
run_cmd += f' --in_json={train_dir}/meta_lat.json'
run_cmd += f' --train_data_dir={image_folder}'
run_cmd += f' --output_dir={output_dir}'
if not logging_dir == '':
run_cmd += f' --logging_dir={logging_dir}'
run_cmd += f' --train_batch_size={train_batch_size}'
run_cmd += f' --dataset_repeats={dataset_repeats}'
run_cmd += f' --learning_rate={learning_rate}'
run_cmd += f' --lr_scheduler={lr_scheduler}'
run_cmd += f' --lr_warmup_steps={lr_warmup_steps}'
run_cmd += f' --max_train_steps={max_train_steps}'
run_cmd += f' --use_8bit_adam'
run_cmd += f' --xformers'
run_cmd += f' --mixed_precision={mixed_precision}'
run_cmd += f' --save_every_n_epochs={save_every_n_epochs}'
run_cmd += f' --seed={seed}'
run_cmd += f' --save_precision={save_precision}'
if not save_model_as == 'same as source model':
run_cmd += f' --save_model_as={save_model_as}'
print(run_cmd)
# Run the command
subprocess.run(run_cmd)
print(run_cmd)
# Run the command
subprocess.run(run_cmd)
# check if output_dir/last is a folder... therefore it is a diffuser model
last_dir = pathlib.Path(f'{output_dir}/last')
@ -409,7 +403,7 @@ def finetune_tab():
dummy_ft_true = gr.Label(value=True, visible=False)
dummy_ft_false = gr.Label(value=False, visible=False)
gr.Markdown(
'Enter kohya finetuner parameter using this interface.'
'Enter kohya finetune training parameter using this interface.'
)
with gr.Accordion('Configuration File Load/Save', open=False):
with gr.Row():
@ -632,16 +626,14 @@ def finetune_tab():
create_buckets = gr.Checkbox(
label='Generate image buckets', value=True
)
train = gr.Checkbox(label='Train model', value=True)
button_run = gr.Button('Run')
button_run = gr.Button('Train model')
button_run.click(
train_model,
inputs=[
create_caption,
create_buckets,
train,
pretrained_model_name_or_path_input,
v2_input,
v_parameterization_input,
@ -693,7 +685,6 @@ def finetune_tab():
train_text_encoder_input,
create_buckets,
create_caption,
train,
save_model_as_dropdown,
caption_extention_input,
],
@ -721,7 +712,6 @@ def finetune_tab():
train_text_encoder_input,
create_buckets,
create_caption,
train,
save_model_as_dropdown,
caption_extention_input,
],
@ -754,7 +744,6 @@ def finetune_tab():
train_text_encoder_input,
create_buckets,
create_caption,
train,
save_model_as_dropdown,
caption_extention_input,
],
@ -788,7 +777,6 @@ def finetune_tab():
train_text_encoder_input,
create_buckets,
create_caption,
train,
save_model_as_dropdown,
caption_extention_input,
],

1
kohya.cmd Normal file
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@ -0,0 +1 @@
.\venv\Scripts\python.exe .\kohya_gui.py

1
utilities.cmd Normal file
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@ -0,0 +1 @@
.\venv\Scripts\python.exe library\utilities.py