StableDiffusion/KohyaSS
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Merge pull request #317 from bmaltais/dev

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v21.1.5
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bmaltais 2023-03-06 12:47:44 -05:00 committed by GitHub
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6 changed files with 179 additions and 41 deletions
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5
README.md
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@ -176,6 +176,11 @@ This will store your a backup file with your current locally installed pip packa
## Change History
* 2023/03/05 (v21.1.5):
- Add replace underscore with space option to WD14 captioning. Thanks @sALTaccount!
- Improve how custom preset is set and handles.
- Add support for `--listen` argument. This allow gradio to listen for connections from other devices on the network (or internet). For example: `gui.ps1 --listen "0.0.0.0"` will allow anyone to connect to the gradio webui.
- Updated `Resize LoRA` tab to support LoCon resizing. Added new resize
* 2023/03/05 (v21.1.4):
- Removing legacy and confusing use 8bit adam chackbox. It is now configured using the Optimiser drop down list. It will be set properly based on legacy config files.
* 2023/03/04 (v21.1.3):

3
finetune/tag_images_by_wd14_tagger.py
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@ -125,7 +125,7 @@ def main(args):
tag_text = ""
for i, p in enumerate(prob[4:]): # numpyとか使うのが良いけど、まあそれほど数も多くないのでループで
if p >= args.thresh and i < len(tags):
tag_text += ", " + tags[i]
tag_text += ", " + (tags[i].replace("_", " ") if args.replace_underscores else tags[i])
if len(tag_text) > 0:
tag_text = tag_text[2:] # 最初の ", " を消す
@ -190,6 +190,7 @@ if __name__ == '__main__':
help="extension of caption file (for backward compatibility) / 出力されるキャプションファイルの拡張子（スペルミスしていたのを残してあります）")
parser.add_argument("--caption_extension", type=str, default=".txt", help="extension of caption file / 出力されるキャプションファイルの拡張子")
parser.add_argument("--debug", action="store_true", help="debug mode")
parser.add_argument("--replace_underscores", action="store_true", help="replace underscores in tags with spaces / タグのアンダースコアをスペースに置き換える")
args = parser.parse_args()

7
kohya_gui.py
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@ -52,6 +52,9 @@ def UI(**kwargs):
server_port = kwargs.get('server_port', 0)
inbrowser = kwargs.get('inbrowser', False)
share = kwargs.get('share', False)
server_name = kwargs.get('listen')
launch_kwargs['server_name'] = server_name
if username and password:
launch_kwargs['auth'] = (username, password)
if server_port > 0:
@ -66,6 +69,9 @@ def UI(**kwargs):
if __name__ == '__main__':
# torch.cuda.set_per_process_memory_fraction(0.48)
parser = argparse.ArgumentParser()
parser.add_argument(
'--listen', type=str, default='127.0.0.1', help='IP to listen on for connections to Gradio'
)
parser.add_argument(
'--username', type=str, default='', help='Username for authentication'
)
@ -93,4 +99,5 @@ if __name__ == '__main__':
inbrowser=args.inbrowser,
server_port=args.server_port,
share=args.share,
listen=args.listen,
)

143
library/common_gui.py
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@ -9,18 +9,47 @@ refresh_symbol = '\U0001f504' # 🔄
save_style_symbol = '\U0001f4be' # 💾
document_symbol = '\U0001F4C4' # 📄
# define a list of substrings to search for v2 base models
V2_BASE_MODELS = [
'stabilityai/stable-diffusion-2-1-base',
'stabilityai/stable-diffusion-2-base',
]
# define a list of substrings to search for v_parameterization models
V_PARAMETERIZATION_MODELS = [
'stabilityai/stable-diffusion-2-1',
'stabilityai/stable-diffusion-2',
]
# define a list of substrings to v1.x models
V1_MODELS = [
'CompVis/stable-diffusion-v1-4',
'runwayml/stable-diffusion-v1-5',
]
# define a list of substrings to search for
ALL_PRESET_MODELS = V2_BASE_MODELS + V_PARAMETERIZATION_MODELS + V1_MODELS
def update_my_data(my_data):
if my_data.get('use_8bit_adam', False) == True:
my_data['optimizer'] = 'AdamW8bit'
# my_data['use_8bit_adam'] = False
if my_data.get('optimizer', 'missing') == 'missing' and my_data.get('use_8bit_adam', False) == False:
if (
my_data.get('optimizer', 'missing') == 'missing'
and my_data.get('use_8bit_adam', False) == False
):
my_data['optimizer'] = 'AdamW'
if my_data.get('model_list', 'custom') == []:
print('Old config with empty model list. Setting to custom...')
my_data['model_list'] = 'custom'
# If Pretrained model name or path is not one of the preset models then set the preset_model to custom
if not my_data.get('pretrained_model_name_or_path', '') in ALL_PRESET_MODELS:
my_data['model_list'] = 'custom'
return my_data
@ -282,63 +311,76 @@ def save_inference_file(output_dir, v2, v_parameterization, output_name):
def set_pretrained_model_name_or_path_input(
model_list, pretrained_model_name_or_path, v2, v_parameterization
):
# define a list of substrings to search for
substrings_v2 = [
'stabilityai/stable-diffusion-2-1-base',
'stabilityai/stable-diffusion-2-base',
]
# check if $v2 and $v_parameterization are empty and if $pretrained_model_name_or_path contains any of the substrings in the v2 list
if str(model_list) in substrings_v2:
if str(model_list) in V2_BASE_MODELS:
print('SD v2 model detected. Setting --v2 parameter')
v2 = True
v_parameterization = False
return model_list, v2, v_parameterization
# define a list of substrings to search for v-objective
substrings_v_parameterization = [
'stabilityai/stable-diffusion-2-1',
'stabilityai/stable-diffusion-2',
]
pretrained_model_name_or_path = str(model_list)
# check if $v2 and $v_parameterization are empty and if $pretrained_model_name_or_path contains any of the substrings in the v_parameterization list
if str(model_list) in substrings_v_parameterization:
if str(model_list) in V_PARAMETERIZATION_MODELS:
print(
'SD v2 v_parameterization detected. Setting --v2 parameter and --v_parameterization'
)
v2 = True
v_parameterization = True
pretrained_model_name_or_path = str(model_list)
return model_list, v2, v_parameterization
# define a list of substrings to v1.x
substrings_v1_model = [
'CompVis/stable-diffusion-v1-4',
'runwayml/stable-diffusion-v1-5',
]
if str(model_list) in substrings_v1_model:
if str(model_list) in V1_MODELS:
v2 = False
v_parameterization = False
return model_list, v2, v_parameterization
pretrained_model_name_or_path = str(model_list)
if model_list == 'custom':
if (
str(pretrained_model_name_or_path) in substrings_v1_model
or str(pretrained_model_name_or_path) in substrings_v2
str(pretrained_model_name_or_path) in V1_MODELS
or str(pretrained_model_name_or_path) in V2_BASE_MODELS
or str(pretrained_model_name_or_path)
in substrings_v_parameterization
in V_PARAMETERIZATION_MODELS
):
pretrained_model_name_or_path = ''
v2 = False
v_parameterization = False
return pretrained_model_name_or_path, v2, v_parameterization
return model_list, pretrained_model_name_or_path, v2, v_parameterization
###
### Gradio common GUI section
###
def set_v2_checkbox(
model_list, v2, v_parameterization
):
# check if $v2 and $v_parameterization are empty and if $pretrained_model_name_or_path contains any of the substrings in the v2 list
if str(model_list) in V2_BASE_MODELS:
v2 = True
v_parameterization = False
# check if $v2 and $v_parameterization are empty and if $pretrained_model_name_or_path contains any of the substrings in the v_parameterization list
if str(model_list) in V_PARAMETERIZATION_MODELS:
v2 = True
v_parameterization = True
if str(model_list) in V1_MODELS:
v2 = False
v_parameterization = False
return v2, v_parameterization
def set_model_list(
model_list,
pretrained_model_name_or_path,
v2,
v_parameterization,
):
if not pretrained_model_name_or_path in ALL_PRESET_MODELS:
model_list = 'custom'
else:
model_list = pretrained_model_name_or_path
return model_list, v2, v_parameterization
###
### Gradio common GUI section
###
def gradio_config():
@ -362,6 +404,15 @@ def gradio_config():
)
def get_pretrained_model_name_or_path_file(
model_list, pretrained_model_name_or_path
):
pretrained_model_name_or_path = get_any_file_path(
pretrained_model_name_or_path
)
set_model_list(model_list, pretrained_model_name_or_path)
def gradio_source_model():
with gr.Tab('Source model'):
# Define the input elements
@ -419,6 +470,8 @@ def gradio_source_model():
v_parameterization = gr.Checkbox(
label='v_parameterization', value=False
)
v2.change(set_v2_checkbox, inputs=[model_list, v2, v_parameterization], outputs=[v2, v_parameterization],show_progress=False)
v_parameterization.change(set_v2_checkbox, inputs=[model_list, v2, v_parameterization], outputs=[v2, v_parameterization],show_progress=False)
model_list.change(
set_pretrained_model_name_or_path_input,
inputs=[
@ -428,10 +481,28 @@ def gradio_source_model():
v_parameterization,
],
outputs=[
model_list,
pretrained_model_name_or_path,
v2,
v_parameterization,
],
show_progress=False,
)
# Update the model list and parameters when user click outside the button or field
pretrained_model_name_or_path.change(
set_model_list,
inputs=[
model_list,
pretrained_model_name_or_path,
v2,
v_parameterization,
],
outputs=[
model_list,
v2,
v_parameterization,
],
show_progress=False,
)
return (
pretrained_model_name_or_path,

51
library/resize_lora_gui.py
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@ -17,6 +17,9 @@ def resize_lora(
save_to,
save_precision,
device,
dynamic_method,
dynamic_param,
verbose,
):
# Check for caption_text_input
if model == '':
@ -27,16 +30,35 @@ def resize_lora(
if not os.path.isfile(model):
msgbox('The provided model is not a file')
return
if dynamic_method == 'sv_ratio':
if float(dynamic_param) < 2:
msgbox(f'Dynamic parameter for {dynamic_method} need to be 2 or greater...')
return
if dynamic_method == 'sv_fro' or dynamic_method == 'sv_cumulative':
if float(dynamic_param) < 0 or float(dynamic_param) > 1:
msgbox(f'Dynamic parameter for {dynamic_method} need to be between 0 and 1...')
return
# Check if save_to end with one of the defines extension. If not add .safetensors.
if not save_to.endswith(('.pt', '.safetensors')):
save_to += '.safetensors'
if device == '':
device = 'cuda'
run_cmd = f'{PYTHON} "{os.path.join("networks","resize_lora.py")}"'
run_cmd = f'{PYTHON} "{os.path.join("tools","resize_lora.py")}"'
run_cmd += f' --save_precision {save_precision}'
run_cmd += f' --save_to {save_to}'
run_cmd += f' --model {model}'
run_cmd += f' --new_rank {new_rank}'
run_cmd += f' --device {device}'
if not dynamic_method == 'None':
run_cmd += f' --dynamic_method {dynamic_method}'
run_cmd += f' --dynamic_param {dynamic_param}'
if verbose:
run_cmd += f' --verbose'
print(run_cmd)
@ -56,7 +78,7 @@ def gradio_resize_lora_tab():
with gr.Tab('Resize LoRA'):
gr.Markdown('This utility can resize a LoRA.')
lora_ext = gr.Textbox(value='*.pt *.safetensors', visible=False)
lora_ext = gr.Textbox(value='*.safetensors *.pt', visible=False)
lora_ext_name = gr.Textbox(value='LoRA model types', visible=False)
with gr.Row():
@ -84,6 +106,27 @@ def gradio_resize_lora_tab():
interactive=True,
)
with gr.Row():
dynamic_method = gr.Dropdown(
choices=['None',
'sv_ratio',
'sv_fro',
'sv_cumulative'
],
value='sv_fro',
label='Dynamic method',
interactive=True
)
dynamic_param = gr.Textbox(
label='Dynamic parameter',
value='0.9',
interactive=True,
placeholder='Value for the dynamic method selected.'
)
verbose = gr.Checkbox(
label='Verbose',
value=False
)
with gr.Row():
save_to = gr.Textbox(
label='Save to',
@ -109,6 +152,7 @@ def gradio_resize_lora_tab():
label='Device',
placeholder='{Optional) device to use, cuda for GPU. Default: cuda',
interactive=True,
value='cuda',
)
convert_button = gr.Button('Resize model')
@ -121,6 +165,9 @@ def gradio_resize_lora_tab():
save_to,
save_precision,
device,
dynamic_method,
dynamic_param,
verbose,
],
show_progress=False,
)

11
library/wd14_caption_gui.py
View File

@ -5,7 +5,7 @@ from .common_gui import get_folder_path
import os
def caption_images(train_data_dir, caption_extension, batch_size, thresh):
def caption_images(train_data_dir, caption_extension, batch_size, thresh, replace_underscores):
# Check for caption_text_input
# if caption_text_input == "":
# msgbox("Caption text is missing...")
@ -24,6 +24,7 @@ def caption_images(train_data_dir, caption_extension, batch_size, thresh):
run_cmd = f'accelerate launch "./finetune/tag_images_by_wd14_tagger.py"'
run_cmd += f' --batch_size="{int(batch_size)}"'
run_cmd += f' --thresh="{thresh}"'
run_cmd += f' --replace_underscores' if replace_underscores else ''
if caption_extension != '':
run_cmd += f' --caption_extension="{caption_extension}"'
run_cmd += f' "{train_data_dir}"'
@ -75,11 +76,17 @@ def gradio_wd14_caption_gui_tab():
batch_size = gr.Number(
value=1, label='Batch size', interactive=True
)
replace_underscores = gr.Checkbox(
label='Replace underscores in filenames with spaces',
value=False,
interactive=True,
)
caption_button = gr.Button('Caption images')
caption_button.click(
caption_images,
inputs=[train_data_dir, caption_extension, batch_size, thresh],
inputs=[train_data_dir, caption_extension, batch_size, thresh, replace_underscores],
show_progress=False,
)