mirror of https://github.com/bmaltais/kohya_ss
Update SDXL tickbox features
bmaltais
parent
1703e40f85
commit
d96a278006
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@ -59,7 +59,8 @@ def save_configuration(
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file_path,
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pretrained_model_name_or_path,
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v2,
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v_parameterization, sdxl,
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v_parameterization,
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sdxl,
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logging_dir,
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train_data_dir,
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reg_data_dir,
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@ -176,7 +177,8 @@ def open_configuration(
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file_path,
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pretrained_model_name_or_path,
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v2,
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v_parameterization, sdxl,
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v_parameterization,
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sdxl,
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logging_dir,
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train_data_dir,
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reg_data_dir,
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@ -281,7 +283,8 @@ def train_model(
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print_only,
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pretrained_model_name_or_path,
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v2,
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v_parameterization, sdxl,
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v_parameterization,
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sdxl,
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logging_dir,
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train_data_dir,
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reg_data_dir,
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@ -374,7 +377,7 @@ def train_model(
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msg='Image folder does not exist', headless=headless_bool
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)
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return
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if not verify_image_folder_pattern(train_data_dir):
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return
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@ -385,7 +388,7 @@ def train_model(
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headless=headless_bool,
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)
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return
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if not verify_image_folder_pattern(reg_data_dir):
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return
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@ -399,9 +402,12 @@ def train_model(
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output_name, output_dir, save_model_as, headless=headless_bool
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):
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return
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if sdxl:
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output_message(msg='TI training is not compatible with an SDXL model.', headless=headless_bool)
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output_message(
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msg='TI training is not compatible with an SDXL model.',
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headless=headless_bool,
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)
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return
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if optimizer == 'Adafactor' and lr_warmup != '0':
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@ -51,7 +51,8 @@ def save_configuration(
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file_path,
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pretrained_model_name_or_path,
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v2,
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v_parameterization, sdxl,
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v_parameterization,
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sdxl,
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train_dir,
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image_folder,
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output_dir,
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@ -174,7 +175,8 @@ def open_configuration(
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file_path,
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pretrained_model_name_or_path,
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v2,
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v_parameterization, sdxl,
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v_parameterization,
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sdxl,
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train_dir,
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image_folder,
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output_dir,
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@ -285,7 +287,8 @@ def train_model(
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print_only,
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pretrained_model_name_or_path,
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v2,
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v_parameterization, sdxl,
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v_parameterization,
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sdxl,
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train_dir,
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image_folder,
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output_dir,
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@ -478,7 +481,7 @@ def train_model(
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run_cmd += f' "./sdxl_train.py"'
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else:
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run_cmd += f' "./fine_tune.py"'
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if v2:
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run_cmd += ' --v2'
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if v_parameterization:
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16
kohya_gui.py
16
kohya_gui.py
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@ -30,14 +30,14 @@ def UI(**kwargs):
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with open(os.path.join('./style.css'), 'r', encoding='utf8') as file:
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log.info('Load CSS...')
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css += file.read() + '\n'
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if os.path.exists('./.release'):
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with open(os.path.join('./.release'), 'r', encoding='utf8') as file:
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release= file.read()
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release = file.read()
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if os.path.exists('./README.md'):
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with open(os.path.join('./README.md'), 'r', encoding='utf8') as file:
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README= file.read()
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README = file.read()
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interface = gr.Blocks(
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css=css, title=f'Kohya_ss GUI {release}', theme=gr.themes.Default()
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@ -73,18 +73,18 @@ def UI(**kwargs):
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gradio_merge_lora_tab(headless=headless)
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gradio_merge_lycoris_tab(headless=headless)
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gradio_resize_lora_tab(headless=headless)
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with gr.Tab('About'):
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with gr.Tab('About'):
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gr.Markdown(f'kohya_ss GUI release {release}')
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with gr.Tab('README'):
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gr.Markdown(README)
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htmlStr = f'''
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htmlStr = f"""
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<html>
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<body>
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<div class="ver-class">{release}</div>
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</body>
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</html>
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'''
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"""
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gr.HTML(htmlStr)
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# Show the interface
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launch_kwargs = {}
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@ -35,8 +35,13 @@ V1_MODELS = [
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'runwayml/stable-diffusion-v1-5',
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]
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# define a list of substrings to search for SDXL base models
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SDXL_MODELS = [
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'stabilityai/stable-diffusion-SDXL-base',
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]
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# define a list of substrings to search for
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ALL_PRESET_MODELS = V2_BASE_MODELS + V_PARAMETERIZATION_MODELS + V1_MODELS
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ALL_PRESET_MODELS = V2_BASE_MODELS + V_PARAMETERIZATION_MODELS + V1_MODELS + SDXL_MODELS
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ENV_EXCLUSION = ['COLAB_GPU', 'RUNPOD_POD_ID']
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@ -479,7 +484,15 @@ def save_inference_file(output_dir, v2, v_parameterization, output_name):
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def set_pretrained_model_name_or_path_input(
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model_list, pretrained_model_name_or_path, v2, v_parameterization, sdxl
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):
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# check if $v2 and $v_parameterization are empty and if $pretrained_model_name_or_path contains any of the substrings in the v2 list
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# Check if the given model_list is in the list of SDXL models
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if str(model_list) in SDXL_MODELS:
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log.info('SDXL model detected. Setting parameters')
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v2 = True
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v_parameterization = True
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sdxl = True
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pretrained_model_name_or_path = str(model_list)
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# Check if the given model_list is in the list of V2 base models
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if str(model_list) in V2_BASE_MODELS:
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log.info('SD v2 model detected. Setting --v2 parameter')
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v2 = True
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@ -487,7 +500,7 @@ def set_pretrained_model_name_or_path_input(
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sdxl = False
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pretrained_model_name_or_path = str(model_list)
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# 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
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# Check if the given model_list is in the list of V parameterization models
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if str(model_list) in V_PARAMETERIZATION_MODELS:
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log.info(
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'SD v2 v_parameterization detected. Setting --v2 parameter and --v_parameterization'
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@ -497,13 +510,16 @@ def set_pretrained_model_name_or_path_input(
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sdxl = False
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pretrained_model_name_or_path = str(model_list)
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# Check if the given model_list is in the list of V1 models
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if str(model_list) in V1_MODELS:
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v2 = False
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v_parameterization = False
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sdxl = False
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pretrained_model_name_or_path = str(model_list)
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# Check if the model_list is set to 'custom'
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if model_list == 'custom':
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# Check if the pretrained_model_name_or_path is in any of the model lists
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if (
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str(pretrained_model_name_or_path) in V1_MODELS
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or str(pretrained_model_name_or_path) in V2_BASE_MODELS
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@ -513,25 +529,58 @@ def set_pretrained_model_name_or_path_input(
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v2 = False
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v_parameterization = False
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sdxl = False
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return model_list, pretrained_model_name_or_path, v2, sdxl
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# Return the updated variables
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return model_list, pretrained_model_name_or_path, v2, v_parameterization, sdxl
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def set_v2_checkbox(model_list, v2, v_parameterization):
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# check if $v2 and $v_parameterization are empty and if $pretrained_model_name_or_path contains any of the substrings in the v2 list
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def set_v2_checkbox(model_list, v2, v_parameterization, sdxl):
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if str(model_list) in SDXL_MODELS:
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if not v2:
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log.info(f'v2 can\'t be deselected because this {str(model_list)} is considered a v2 model...')
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v2 = True
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if not v_parameterization:
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log.info(f'v_parameterization can\'t be deselected because {str(model_list)} require v parameterization...')
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v_parameterization = True
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if not sdxl:
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log.info(f'sdxl can\'t be deselected because {str(model_list)} is an sdxl model...')
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sdxl = True
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if str(model_list) in V2_BASE_MODELS:
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v2 = True
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v_parameterization = False
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if not v2:
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log.info(f'v2 can\'t be deselected because this {str(model_list)} is a v2 model...')
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v2 = True
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if v_parameterization:
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log.info(f'v_parameterization can\'t be selected because {str(model_list)} does not support v parameterization...')
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v_parameterization = False
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if sdxl:
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log.info(f'sdxl can\'t be selected because {str(model_list)} is not an sdxl model...')
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sdxl = False
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# 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
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if str(model_list) in V_PARAMETERIZATION_MODELS:
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v2 = True
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v_parameterization = True
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if not v2:
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log.info(f'v2 can\'t be deselected because this {str(model_list)} supports v parameterization...')
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v2 = True
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if not v_parameterization:
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log.info(f'v_parameterization can\'t be deselected because {str(model_list)} supports v parameterization...')
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v_parameterization = True
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if sdxl:
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log.info(f'sdxl can\'t be selected because {str(model_list)} is not an sdxl model...')
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sdxl = False
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if str(model_list) in V1_MODELS:
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v2 = False
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v_parameterization = False
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if v2:
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log.info(f'v2 can\'t be selected because this {str(model_list)} is a v1 model...')
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v2 = False
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if v_parameterization:
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log.info(f'v_parameterization can\'t be selected because {str(model_list)} is a v1 model...')
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v_parameterization = False
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if sdxl:
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log.info(f'sdxl can\'t be selected because {str(model_list)} is not an sdxl model...')
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sdxl = False
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return v2, v_parameterization
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return v2, v_parameterization, sdxl
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def set_model_list(
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@ -665,14 +714,20 @@ def gradio_source_model(
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)
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v2.change(
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set_v2_checkbox,
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inputs=[model_list, v2, v_parameterization],
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outputs=[v2, v_parameterization],
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inputs=[model_list, v2, v_parameterization, sdxl],
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outputs=[v2, v_parameterization, sdxl],
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show_progress=False,
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)
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v_parameterization.change(
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set_v2_checkbox,
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inputs=[model_list, v2, v_parameterization],
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outputs=[v2, v_parameterization],
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inputs=[model_list, v2, v_parameterization, sdxl],
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outputs=[v2, v_parameterization, sdxl],
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show_progress=False,
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)
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sdxl.change(
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set_v2_checkbox,
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inputs=[model_list, v2, v_parameterization, sdxl],
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outputs=[v2, v_parameterization, sdxl],
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show_progress=False,
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)
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model_list.change(
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52
lora_gui.py
52
lora_gui.py
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@ -69,7 +69,8 @@ def save_configuration(
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file_path,
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pretrained_model_name_or_path,
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v2,
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v_parameterization, sdxl,
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v_parameterization,
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sdxl,
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logging_dir,
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train_data_dir,
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reg_data_dir,
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@ -215,7 +216,8 @@ def open_configuration(
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file_path,
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pretrained_model_name_or_path,
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v2,
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v_parameterization, sdxl,
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v_parameterization,
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sdxl,
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logging_dir,
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train_data_dir,
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reg_data_dir,
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@ -313,14 +315,14 @@ def open_configuration(
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network_dropout,
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rank_dropout,
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module_dropout,
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training_preset
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training_preset,
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):
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# Get list of function parameters and values
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parameters = list(locals().items())
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ask_for_file = True if ask_for_file.get('label') == 'True' else False
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apply_preset = True if apply_preset.get('label') == 'True' else False
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# Check if we are "applying" a preset or a config
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if apply_preset:
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log.info(f'Applying preset {training_preset}...')
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@ -328,10 +330,12 @@ def open_configuration(
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else:
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# If not applying a preset, set the `training_preset` field to an empty string
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# Find the index of the `training_preset` parameter using the `index()` method
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training_preset_index = parameters.index(("training_preset", training_preset))
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training_preset_index = parameters.index(
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('training_preset', training_preset)
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)
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# Update the value of `training_preset` by directly assigning an empty string value
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parameters[training_preset_index] = ("training_preset", "")
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parameters[training_preset_index] = ('training_preset', '')
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original_file_path = file_path
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@ -376,7 +380,8 @@ def train_model(
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print_only,
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pretrained_model_name_or_path,
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v2,
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v_parameterization, sdxl,
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v_parameterization,
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sdxl,
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logging_dir,
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train_data_dir,
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reg_data_dir,
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@ -496,7 +501,7 @@ def train_model(
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msg='Image folder does not exist', headless=headless_bool
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)
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return
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if not verify_image_folder_pattern(train_data_dir):
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return
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@ -507,7 +512,7 @@ def train_model(
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headless=headless_bool,
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)
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return
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if not verify_image_folder_pattern(reg_data_dir):
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return
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@ -1095,15 +1100,18 @@ def lora_tab(
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outputs=[logging_dir],
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)
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with gr.Tab('Training parameters'):
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def list_presets(path):
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json_files = []
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for file in os.listdir(path):
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if file.endswith(".json"):
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if file.endswith('.json'):
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json_files.append(os.path.splitext(file)[0])
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return json_files
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training_preset = gr.Dropdown(
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label='Presets',
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choices=list_presets('./presets/lora'), elem_id="myDropdown"
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choices=list_presets('./presets/lora'),
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elem_id='myDropdown',
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)
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with gr.Row():
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LoRA_type = gr.Dropdown(
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@ -1695,21 +1703,31 @@ def lora_tab(
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button_open_config.click(
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open_configuration,
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inputs=[dummy_db_true, dummy_db_false, config_file_name] + settings_list + [training_preset],
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outputs=[config_file_name] + settings_list + [training_preset, LoCon_row],
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inputs=[dummy_db_true, dummy_db_false, config_file_name]
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+ settings_list
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+ [training_preset],
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outputs=[config_file_name]
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+ settings_list
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+ [training_preset, LoCon_row],
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show_progress=False,
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)
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button_load_config.click(
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open_configuration,
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inputs=[dummy_db_false, dummy_db_false, config_file_name] + settings_list + [training_preset],
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outputs=[config_file_name] + settings_list + [training_preset, LoCon_row],
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inputs=[dummy_db_false, dummy_db_false, config_file_name]
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+ settings_list
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+ [training_preset],
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outputs=[config_file_name]
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+ settings_list
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+ [training_preset, LoCon_row],
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show_progress=False,
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)
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training_preset.input(
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open_configuration,
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inputs=[dummy_db_false, dummy_db_true, config_file_name] + settings_list + [training_preset],
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inputs=[dummy_db_false, dummy_db_true, config_file_name]
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+ settings_list
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+ [training_preset],
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outputs=[gr.Textbox()] + settings_list + [training_preset, LoCon_row],
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show_progress=False,
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)
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|
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|
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@ -59,7 +59,8 @@ def save_configuration(
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file_path,
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pretrained_model_name_or_path,
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v2,
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v_parameterization, sdxl,
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||||
v_parameterization,
|
||||
sdxl,
|
||||
logging_dir,
|
||||
train_data_dir,
|
||||
reg_data_dir,
|
||||
|
|
@ -181,7 +182,8 @@ def open_configuration(
|
|||
file_path,
|
||||
pretrained_model_name_or_path,
|
||||
v2,
|
||||
v_parameterization, sdxl,
|
||||
v_parameterization,
|
||||
sdxl,
|
||||
logging_dir,
|
||||
train_data_dir,
|
||||
reg_data_dir,
|
||||
|
|
@ -291,7 +293,8 @@ def train_model(
|
|||
print_only,
|
||||
pretrained_model_name_or_path,
|
||||
v2,
|
||||
v_parameterization, sdxl,
|
||||
v_parameterization,
|
||||
sdxl,
|
||||
logging_dir,
|
||||
train_data_dir,
|
||||
reg_data_dir,
|
||||
|
|
@ -389,7 +392,7 @@ def train_model(
|
|||
msg='Image folder does not exist', headless=headless_bool
|
||||
)
|
||||
return
|
||||
|
||||
|
||||
if not verify_image_folder_pattern(train_data_dir):
|
||||
return
|
||||
|
||||
|
|
@ -400,7 +403,7 @@ def train_model(
|
|||
headless=headless_bool,
|
||||
)
|
||||
return
|
||||
|
||||
|
||||
if not verify_image_folder_pattern(reg_data_dir):
|
||||
return
|
||||
|
||||
|
|
@ -425,9 +428,12 @@ def train_model(
|
|||
output_name, output_dir, save_model_as, headless_bool
|
||||
):
|
||||
return
|
||||
|
||||
|
||||
if sdxl:
|
||||
output_message(msg='TI training is not compatible with an SDXL model.', headless=headless_bool)
|
||||
output_message(
|
||||
msg='TI training is not compatible with an SDXL model.',
|
||||
headless=headless_bool,
|
||||
)
|
||||
return
|
||||
|
||||
# if float(noise_offset) > 0 and (
|
||||
|
|
|
|||
Loading…
Reference in New Issue