Add support for LyCORIS LoCON extraction
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bmaltais
parent
baf009d2b1
commit
25d84ecffe
@ -6,6 +6,7 @@ from finetune_gui import finetune_tab
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from textual_inversion_gui import ti_tab
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from library.utilities import utilities_tab
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from library.extract_lora_gui import gradio_extract_lora_tab
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from library.extract_lycoris_locon_gui import gradio_extract_lycoris_locon_tab
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from library.merge_lora_gui import gradio_merge_lora_tab
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from library.resize_lora_gui import gradio_resize_lora_tab
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from lora_gui import lora_tab
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@ -44,6 +45,7 @@ def UI(**kwargs):
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enable_copy_info_button=True,
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)
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gradio_extract_lora_tab()
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gradio_extract_lycoris_locon_tab()
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gradio_merge_lora_tab()
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gradio_resize_lora_tab()
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273
library/extract_lycoris_locon_gui.py
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273
library/extract_lycoris_locon_gui.py
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@ -0,0 +1,273 @@
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import gradio as gr
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from easygui import msgbox
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import subprocess
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import os
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from .common_gui import (
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get_saveasfilename_path,
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get_any_file_path,
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get_file_path,
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)
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folder_symbol = '\U0001f4c2' # 📂
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refresh_symbol = '\U0001f504' # 🔄
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save_style_symbol = '\U0001f4be' # 💾
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document_symbol = '\U0001F4C4' # 📄
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PYTHON = 'python3' if os.name == 'posix' else './venv/Scripts/python.exe'
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def extract_lycoris_locon(
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db_model, base_model, output_name, device,
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is_v2, mode, linear_dim, conv_dim,
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linear_threshold, conv_threshold,
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linear_ratio, conv_ratio,
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linear_quantile, conv_quantile,
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use_sparse_bias, sparsity, disable_cp
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):
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# Check for caption_text_input
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if db_model == '':
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msgbox('Invalid finetuned model file')
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return
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if base_model == '':
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msgbox('Invalid base model file')
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return
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# Check if source model exist
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if not os.path.isfile(db_model):
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msgbox('The provided finetuned model is not a file')
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return
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if not os.path.isfile(base_model):
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msgbox('The provided base model is not a file')
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return
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run_cmd = (
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f'{PYTHON} "{os.path.join("tools","lycoris_locon_extract.py")}"'
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)
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if is_v2:
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run_cmd += f' --is_v2'
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run_cmd += f' --device {device}'
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run_cmd += f' --mode {mode}'
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run_cmd += f' --safetensors'
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run_cmd += f' --linear_dim {linear_dim}'
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run_cmd += f' --conv_dim {conv_dim}'
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run_cmd += f' --linear_threshold {linear_threshold}'
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run_cmd += f' --conv_threshold {conv_threshold}'
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run_cmd += f' --linear_ratio {linear_ratio}'
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run_cmd += f' --conv_ratio {conv_ratio}'
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run_cmd += f' --linear_quantile {linear_quantile}'
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run_cmd += f' --conv_quantile {conv_quantile}'
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if use_sparse_bias:
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run_cmd += f' --use_sparse_bias'
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run_cmd += f' --sparsity {sparsity}'
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if disable_cp:
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run_cmd += f' --disable_cp'
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run_cmd += f' "{base_model}"'
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run_cmd += f' "{db_model}"'
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run_cmd += f' "{output_name}"'
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print(run_cmd)
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# Run the command
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if os.name == 'posix':
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os.system(run_cmd)
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else:
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subprocess.run(run_cmd)
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###
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# Gradio UI
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###
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# def update_mode(mode):
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# # 'fixed', 'threshold','ratio','quantile'
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# if mode == 'fixed':
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# return gr.Row.update(visible=True), gr.Row.update(visible=False), gr.Row.update(visible=False), gr.Row.update(visible=False)
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# if mode == 'threshold':
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# return gr.Row.update(visible=False), gr.Row.update(visible=True), gr.Row.update(visible=False), gr.Row.update(visible=False)
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# if mode == 'ratio':
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# return gr.Row.update(visible=False), gr.Row.update(visible=False), gr.Row.update(visible=True), gr.Row.update(visible=False)
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# if mode == 'threshold':
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# return gr.Row.update(visible=False), gr.Row.update(visible=False), gr.Row.update(visible=False), gr.Row.update(visible=True)
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def update_mode(mode):
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# Create a list of possible mode values
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modes = ['fixed', 'threshold', 'ratio', 'quantile']
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# Initialize an empty list to store visibility updates
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updates = []
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# Iterate through the possible modes
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for m in modes:
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# Add a visibility update for each mode, setting it to True if the input mode matches the current mode in the loop
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updates.append(gr.Row.update(visible=(mode == m)))
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# Return the visibility updates as a tuple
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return tuple(updates)
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def gradio_extract_lycoris_locon_tab():
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with gr.Tab('Extract LyCORIS LoCON'):
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gr.Markdown(
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'This utility can extract a LyCORIS LoCon network from a finetuned model.'
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)
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lora_ext = gr.Textbox(value='*.safetensors', visible=False) # lora_ext = gr.Textbox(value='*.safetensors *.pt', visible=False)
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lora_ext_name = gr.Textbox(value='LoRA model types', visible=False)
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model_ext = gr.Textbox(value='*.safetensors *.ckpt', visible=False)
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model_ext_name = gr.Textbox(value='Model types', visible=False)
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with gr.Row():
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db_model = gr.Textbox(
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label='Finetuned model',
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placeholder='Path to the finetuned model to extract',
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interactive=True,
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)
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button_db_model_file = gr.Button(
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folder_symbol, elem_id='open_folder_small'
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)
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button_db_model_file.click(
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get_file_path,
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inputs=[db_model, model_ext, model_ext_name],
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outputs=db_model,
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show_progress=False,
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)
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base_model = gr.Textbox(
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label='Stable Diffusion base model',
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placeholder='Stable Diffusion original model: ckpt or safetensors file',
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interactive=True,
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)
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button_base_model_file = gr.Button(
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folder_symbol, elem_id='open_folder_small'
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)
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button_base_model_file.click(
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get_file_path,
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inputs=[base_model, model_ext, model_ext_name],
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outputs=base_model,
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show_progress=False,
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)
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with gr.Row():
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output_name = gr.Textbox(
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label='Save to',
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placeholder='path where to save the extracted LoRA model...',
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interactive=True,
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)
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button_output_name = gr.Button(
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folder_symbol, elem_id='open_folder_small'
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)
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button_output_name.click(
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get_saveasfilename_path,
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inputs=[output_name, lora_ext, lora_ext_name],
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outputs=output_name,
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show_progress=False,
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)
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device = gr.Dropdown(
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label='Device',
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choices=['cpu', 'cuda',],
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value='cuda',
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interactive=True,
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)
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is_v2 = gr.Checkbox(label='is v2', value=False, interactive=True)
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mode = gr.Dropdown(
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label='Mode',
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choices=['fixed', 'threshold','ratio','quantile'],
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value='fixed',
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interactive=True,
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)
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with gr.Row(visible=True) as fixed:
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linear_dim = gr.Slider(
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minimum=1,
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maximum=1024,
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label='Network Dimension',
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value=1,
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step=1,
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interactive=True,
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)
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conv_dim = gr.Slider(
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minimum=1,
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maximum=1024,
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label='Conv Dimension',
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value=1,
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step=1,
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interactive=True,
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)
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with gr.Row(visible=False) as threshold:
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linear_threshold = gr.Slider(
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minimum=0,
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maximum=1,
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label='Linear threshold',
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value=0,
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step=0.01,
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interactive=True,
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)
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conv_threshold = gr.Slider(
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minimum=0,
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maximum=1,
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label='Conv threshold',
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value=0,
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step=0.01,
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interactive=True,
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)
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with gr.Row(visible=False) as ratio:
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linear_ratio = gr.Slider(
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minimum=0,
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maximum=1,
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label='Linear ratio',
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value=0,
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step=0.01,
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interactive=True,
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)
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conv_ratio = gr.Slider(
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minimum=0,
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maximum=1,
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label='Conv ratio',
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value=0,
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step=0.01,
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interactive=True,
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)
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with gr.Row(visible=False) as quantile:
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linear_quantile = gr.Slider(
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minimum=0,
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maximum=1,
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label='Linear quantile',
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value=0.75,
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step=0.01,
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interactive=True,
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)
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conv_quantile = gr.Slider(
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minimum=0,
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maximum=1,
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label='Conv quantile',
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value=0.75,
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step=0.01,
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interactive=True,
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)
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with gr.Row():
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use_sparse_bias = gr.Checkbox(label='Use sparse biais', value=False, interactive=True)
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sparsity = gr.Slider(
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minimum=0,
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maximum=1,
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label='Sparsity',
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value=0.98,
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step=0.01,
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interactive=True,
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)
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disable_cp = gr.Checkbox(label='Disable CP decomposition', value=False, interactive=True)
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mode.change(
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update_mode,
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inputs=[mode],
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outputs=[
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fixed, threshold, ratio, quantile,
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]
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)
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extract_button = gr.Button('Extract LyCORIS LoCon')
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extract_button.click(
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extract_lycoris_locon,
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inputs=[db_model, base_model, output_name, device,
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is_v2, mode, linear_dim, conv_dim,
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linear_threshold, conv_threshold,
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linear_ratio, conv_ratio,
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linear_quantile, conv_quantile,
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use_sparse_bias, sparsity, disable_cp],
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show_progress=False,
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)
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