Upload ui.py

ui.py

@@ -0,0 +1,763 @@
+import random
+import gradio as gr
+
+from modules import sd_models
+from modules import sd_vae
+from modules import ui_components
+from modules import shared
+from modules import extras
+from modules import images
+
+from sd_bmab import constants
+from sd_bmab import util
+from sd_bmab import detectors
+from sd_bmab import parameters
+from sd_bmab.base import context
+from sd_bmab.base import filter
+from sd_bmab.base import installer
+from sd_bmab import pipeline
+from sd_bmab import masking
+from sd_bmab.util import debug_print
+
+
+bmab_version = 'v23.12.05.0'
+
+final_images = []
+last_process = None
+bmab_script = None
+gallery_select_index = 0
+
+
+def create_ui(bscript, is_img2img):
+	class ListOv(list):
+		def __iadd__(self, x):
+			self.append(x)
+			return self
+
+	elem = ListOv()
+	with gr.Group():
+		with gr.Row():
+			with gr.Column():
+				elem += gr.Checkbox(label=f'Enable BMAB', value=False)
+			with gr.Column():
+				btn_stop = ui_components.ToolButton('⏹️', visible=True, interactive=True, tooltip='stop generation', elem_id='bmab_stop_generation')
+		with gr.Accordion(f'BMAB Preprocessor', open=False):
+			with gr.Row():
+				with gr.Tab('Context', id='bmab_context', elem_id='bmab_context_tabs'):
+					with gr.Tab('Generic'):
+						with gr.Row():
+							with gr.Column():
+								with gr.Row():
+									checkpoints = [constants.checkpoint_default]
+									checkpoints.extend([str(x) for x in sd_models.checkpoints_list.keys()])
+									checkpoint_models = gr.Dropdown(label='CheckPoint', visible=True, value=checkpoints[0], choices=checkpoints)
+									elem += checkpoint_models
+									refresh_checkpoint_models = ui_components.ToolButton(value='🔄', visible=True, interactive=True)
+							with gr.Column():
+								with gr.Row():
+									vaes = [constants.vae_default]
+									vaes.extend([str(x) for x in sd_vae.vae_dict.keys()])
+									vaes_models = gr.Dropdown(label='SD VAE', visible=True, value=vaes[0], choices=vaes)
+									elem += vaes_models
+									refresh_vae_models = ui_components.ToolButton(value='🔄', visible=True, interactive=True)
+						with gr.Row():
+							gr.Markdown(constants.checkpoint_description)
+						with gr.Row():
+							elem += gr.Slider(minimum=0, maximum=1.5, value=1, step=0.001, label='txt2img noise multiplier for hires.fix (EXPERIMENTAL)', elem_id='bmab_txt2img_noise_multiplier')
+						with gr.Row():
+							elem += gr.Slider(minimum=0, maximum=1, value=0, step=0.01, label='txt2img extra noise multiplier for hires.fix (EXPERIMENTAL)', elem_id='bmab_txt2img_extra_noise_multiplier')
+						with gr.Row():
+							with gr.Column():
+								with gr.Row():
+									dd_hiresfix_filter1 = gr.Dropdown(label='Hires.fix filter before upscale', visible=True, value=filter.filters[0], choices=filter.filters)
+									elem += dd_hiresfix_filter1
+							with gr.Column():
+								with gr.Row():
+									dd_hiresfix_filter2 = gr.Dropdown(label='Hires.fix filter after upscale', visible=True, value=filter.filters[0], choices=filter.filters)
+									elem += dd_hiresfix_filter2
+					with gr.Tab('Kohya Hires.fix'):
+						with gr.Row():
+							with gr.Column():
+								elem += gr.Checkbox(label='Enable Kohya hires.fix', value=False)
+						with gr.Row():
+							gr.HTML(constants.kohya_hiresfix_description)
+						with gr.Row():
+							elem += gr.Slider(minimum=0, maximum=0.5, step=0.01, label="Stop at, first", value=0.15)
+							elem += gr.Slider(minimum=1, maximum=10, step=1, label="Depth, first", value=3)
+						with gr.Row():
+							elem += gr.Slider(minimum=0, maximum=0.5, step=0.01, label="Stop at, second", value=0.4)
+							elem += gr.Slider(minimum=1, maximum=10, step=1, label="Depth, second", value=4)
+						with gr.Row():
+							elem += gr.Dropdown(['bicubic', 'bilinear', 'nearest', 'nearest-exact'], label='Layer scaler', value='bicubic')
+							elem += gr.Slider(minimum=0.1, maximum=1.0, step=0.05, label="Downsampling scale", value=0.5)
+							elem += gr.Slider(minimum=1.0, maximum=4.0, step=0.1, label="Upsampling scale", value=2.0)
+						with gr.Row():
+							elem += gr.Checkbox(label="Smooth scaling", value=True)
+							elem += gr.Checkbox(label="Early upsampling", value=False)
+							elem += gr.Checkbox(label='Disable for additional passes', value=True)
+				with gr.Tab('Resample', id='bmab_resample', elem_id='bmab_resample_tabs'):
+					with gr.Row():
+						with gr.Column():
+							elem += gr.Checkbox(label='Enable self resample', value=False)
+						with gr.Column():
+							elem += gr.Checkbox(label='Save image before processing', value=False)
+					with gr.Row():
+						elem += gr.Checkbox(label='Enable resample before hires.fix', value=False)
+					with gr.Row():
+						with gr.Column():
+							with gr.Row():
+								checkpoints = [constants.checkpoint_default]
+								checkpoints.extend([str(x) for x in sd_models.checkpoints_list.keys()])
+								resample_models = gr.Dropdown(label='CheckPoint', visible=True, value=checkpoints[0], choices=checkpoints)
+								elem += resample_models
+								refresh_resample_models = ui_components.ToolButton(value='🔄', visible=True, interactive=True)
+						with gr.Column():
+							with gr.Row():
+								vaes = [constants.vae_default]
+								vaes.extend([str(x) for x in sd_vae.vae_dict.keys()])
+								resample_vaes = gr.Dropdown(label='SD VAE', visible=True, value=vaes[0], choices=vaes)
+								elem += resample_vaes
+								refresh_resample_vaes = ui_components.ToolButton(value='🔄', visible=True, interactive=True)
+					with gr.Row():
+						with gr.Column(min_width=100):
+							methods = ['txt2img-1pass', 'txt2img-2pass', 'img2img-1pass']
+							elem += gr.Dropdown(label='Resample method', visible=True, value=methods[0], choices=methods)
+						with gr.Column():
+							dd_resample_filter = gr.Dropdown(label='Resample filter', visible=True, value=filter.filters[0], choices=filter.filters)
+							elem += dd_resample_filter
+					with gr.Row():
+						elem += gr.Textbox(placeholder='prompt. if empty, use main prompt', lines=3, visible=True, value='', label='Resample prompt')
+					with gr.Row():
+						elem += gr.Textbox(placeholder='negative prompt. if empty, use main negative prompt', lines=3, visible=True, value='', label='Resample negative prompt')
+					with gr.Row():
+						with gr.Column(min_width=100):
+							asamplers = [constants.sampler_default]
+							asamplers.extend([x.name for x in shared.list_samplers()])
+							elem += gr.Dropdown(label='Sampling method', visible=True, value=asamplers[0], choices=asamplers)
+						with gr.Column(min_width=100):
+							upscalers = [constants.fast_upscaler]
+							upscalers.extend([x.name for x in shared.sd_upscalers])
+							elem += gr.Dropdown(label='Upscaler', visible=True, value=upscalers[0], choices=upscalers)
+					with gr.Row():
+						with gr.Column(min_width=100):
+							elem += gr.Slider(minimum=1, maximum=150, value=20, step=1, label='Resample Sampling Steps', elem_id='bmab_resample_steps')
+							elem += gr.Slider(minimum=1, maximum=30, value=7, step=0.5, label='Resample CFG Scale', elem_id='bmab_resample_cfg_scale')
+							elem += gr.Slider(minimum=0, maximum=1, value=0.75, step=0.01, label='Resample Denoising Strength', elem_id='bmab_resample_denoising')
+							elem += gr.Slider(minimum=0.0, maximum=2, value=0.5, step=0.05, label='Resample strength', elem_id='bmab_resample_cn_strength')
+							elem += gr.Slider(minimum=0.0, maximum=1.0, value=0.1, step=0.01, label='Resample begin', elem_id='bmab_resample_cn_begin')
+							elem += gr.Slider(minimum=0.0, maximum=1.0, value=0.9, step=0.01, label='Resample end', elem_id='bmab_resample_cn_end')
+				with gr.Tab('Pretraining', id='bmab_pretraining', elem_id='bmab_pretraining_tabs'):
+					with gr.Row():
+						elem += gr.Checkbox(label='Enable pretraining detailer', value=False)
+					with gr.Row():
+						elem += gr.Checkbox(label='Enable pretraining before hires.fix', value=False)
+					with gr.Column(min_width=100):
+						with gr.Row():
+							models = ['Select Model']
+							models.extend(util.list_pretraining_models())
+							pretraining_models = gr.Dropdown(label='Pretraining Model', visible=True, value=models[0], choices=models, elem_id='bmab_pretraining_models')
+							elem += pretraining_models
+							refresh_pretraining_models = ui_components.ToolButton(value='🔄', visible=True, interactive=True)
+					with gr.Row():
+						elem += gr.Textbox(placeholder='prompt. if empty, use main prompt', lines=3, visible=True, value='', label='Pretraining prompt')
+					with gr.Row():
+						elem += gr.Textbox(placeholder='negative prompt. if empty, use main negative prompt', lines=3, visible=True, value='', label='Pretraining negative prompt')
+					with gr.Row():
+						with gr.Column(min_width=100):
+							asamplers = [constants.sampler_default]
+							asamplers.extend([x.name for x in shared.list_samplers()])
+							elem += gr.Dropdown(label='Sampling method', visible=True, value=asamplers[0], choices=asamplers)
+					with gr.Row():
+						with gr.Column(min_width=100):
+							elem += gr.Slider(minimum=1, maximum=150, value=20, step=1, label='Pretraining sampling steps', elem_id='bmab_pretraining_steps')
+							elem += gr.Slider(minimum=1, maximum=30, value=7, step=0.5, label='Pretraining CFG scale', elem_id='bmab_pretraining_cfg_scale')
+							elem += gr.Slider(minimum=0, maximum=1, value=0.75, step=0.01, label='Pretraining denoising Strength', elem_id='bmab_pretraining_denoising')
+							elem += gr.Slider(minimum=0, maximum=128, value=4, step=1, label='Pretraining dilation', elem_id='bmab_pretraining_dilation')
+							elem += gr.Slider(minimum=0.1, maximum=1, value=0.35, step=0.01, label='Pretraining box threshold', elem_id='bmab_pretraining_box_threshold')
+				with gr.Tab('Edge', elem_id='bmab_edge_tabs'):
+					with gr.Row():
+						elem += gr.Checkbox(label='Enable edge enhancement', value=False)
+					with gr.Row():
+						elem += gr.Slider(minimum=1, maximum=255, value=50, step=1, label='Edge low threshold')
+						elem += gr.Slider(minimum=1, maximum=255, value=200, step=1, label='Edge high threshold')
+					with gr.Row():
+						elem += gr.Slider(minimum=0, maximum=1, value=0.5, step=0.05, label='Edge strength')
+						gr.Markdown('')
+				with gr.Tab('Resize', elem_id='bmab_preprocess_resize_tab'):
+					with gr.Row():
+						elem += gr.Checkbox(label='Enable resize (intermediate)', value=False)
+					with gr.Row():
+						elem += gr.Checkbox(label='Resized by person', value=True)
+					with gr.Row():
+						gr.HTML(constants.resize_description)
+					with gr.Row():
+						with gr.Column():
+							methods = ['stretching', 'inpaint', 'inpaint+lama', 'inpaint_only', 'inpaint_only+lama']
+							elem += gr.Dropdown(label='Method', visible=True, value=methods[0], choices=methods)
+						with gr.Column():
+							align = [x for x in util.alignment.keys()]
+							elem += gr.Dropdown(label='Alignment', visible=True, value=align[4], choices=align)
+					with gr.Row():
+						with gr.Column():
+							dd_resize_filter = gr.Dropdown(label='Resize filter', visible=True, value=filter.filters[0], choices=filter.filters)
+							elem += dd_resize_filter
+						with gr.Column():
+							gr.Markdown('')
+					with gr.Row():
+						elem += gr.Slider(minimum=0.10, maximum=0.95, value=0.85, step=0.01, label='Resize by person intermediate')
+					with gr.Row():
+						elem += gr.Slider(minimum=0, maximum=1, value=0.75, step=0.01, label='Denoising Strength for inpaint and inpaint+lama', elem_id='bmab_resize_intermediate_denoising')
+				with gr.Tab('Refiner', id='bmab_refiner', elem_id='bmab_refiner_tabs'):
+					with gr.Row():
+						elem += gr.Checkbox(label='Enable refiner', value=False)
+					with gr.Row():
+						with gr.Column():
+							with gr.Row():
+								checkpoints = [constants.checkpoint_default]
+								checkpoints.extend([str(x) for x in sd_models.checkpoints_list.keys()])
+								refiner_models = gr.Dropdown(label='CheckPoint', visible=True, value=checkpoints[0], choices=checkpoints)
+								elem += refiner_models
+								refresh_refiner_models = ui_components.ToolButton(value='🔄', visible=True, interactive=True)
+						with gr.Column():
+							gr.Markdown('')
+					with gr.Row():
+						elem += gr.Checkbox(label='Use this checkpoint for detailing(Face, Person, Hand)', value=True)
+					with gr.Row():
+						elem += gr.Textbox(placeholder='prompt. if empty, use main prompt', lines=3, visible=True, value='', label='Prompt')
+					with gr.Row():
+						elem += gr.Textbox(placeholder='negative prompt. if empty, use main negative prompt', lines=3, visible=True, value='', label='Negative Prompt')
+					with gr.Row():
+						with gr.Column(min_width=100):
+							asamplers = [constants.sampler_default]
+							asamplers.extend([x.name for x in shared.list_samplers()])
+							elem += gr.Dropdown(label='Sampling method', visible=True, value=asamplers[0], choices=asamplers)
+						with gr.Column(min_width=100):
+							upscalers = [constants.fast_upscaler]
+							upscalers.extend([x.name for x in shared.sd_upscalers])
+							elem += gr.Dropdown(label='Upscaler', visible=True, value=upscalers[0], choices=upscalers)
+					with gr.Row():
+						with gr.Column(min_width=100):
+							elem += gr.Slider(minimum=1, maximum=150, value=20, step=1, label='Refiner Sampling Steps', elem_id='bmab_refiner_steps')
+							elem += gr.Slider(minimum=1, maximum=30, value=7, step=0.5, label='Refiner CFG Scale', elem_id='bmab_refiner_cfg_scale')
+							elem += gr.Slider(minimum=0, maximum=1, value=0.75, step=0.01, label='Refiner Denoising Strength', elem_id='bmab_refiner_denoising')
+					with gr.Row():
+						with gr.Column(min_width=100):
+							elem += gr.Slider(minimum=0, maximum=4, value=1, step=0.1, label='Refiner Scale', elem_id='bmab_refiner_scale')
+							elem += gr.Slider(minimum=0, maximum=2048, value=0, step=1, label='Refiner Width', elem_id='bmab_refiner_width')
+							elem += gr.Slider(minimum=0, maximum=2048, value=0, step=1, label='Refiner Height', elem_id='bmab_refiner_height')
+		with gr.Accordion(f'BMAB', open=False):
+			with gr.Row():
+				with gr.Tabs(elem_id='bmab_tabs'):
+					with gr.Tab('Basic', elem_id='bmab_basic_tabs'):
+						with gr.Row():
+							with gr.Column():
+								elem += gr.Slider(minimum=0, maximum=2, value=1, step=0.05, label='Contrast')
+								elem += gr.Slider(minimum=0, maximum=2, value=1, step=0.05, label='Brightness')
+								elem += gr.Slider(minimum=-5, maximum=5, value=1, step=0.1, label='Sharpeness')
+								elem += gr.Slider(minimum=0, maximum=2, value=1, step=0.01, label='Color')
+							with gr.Column():
+								elem += gr.Slider(minimum=-2000, maximum=+2000, value=0, step=1, label='Color temperature')
+								elem += gr.Slider(minimum=0, maximum=1, value=0, step=0.05, label='Noise alpha')
+								elem += gr.Slider(minimum=0, maximum=1, value=0, step=0.05, label='Noise alpha at final stage')
+					with gr.Tab('Imaging', elem_id='bmab_imaging_tabs'):
+						with gr.Row():
+							elem += gr.Image(source='upload', type='pil')
+						with gr.Row():
+							elem += gr.Checkbox(label='Blend enabled', value=False)
+						with gr.Row():
+							with gr.Column():
+								elem += gr.Slider(minimum=0, maximum=1, value=1, step=0.05, label='Blend alpha')
+							with gr.Column():
+								gr.Markdown('')
+						with gr.Row():
+							elem += gr.Checkbox(label='Enable detect', value=False)
+						with gr.Row():
+							elem += gr.Textbox(placeholder='1girl', visible=True, value='', label='Prompt')
+					with gr.Tab('Person', elem_id='bmab_person_tabs'):
+						with gr.Row():
+							elem += gr.Checkbox(label='Enable person detailing for landscape', value=False)
+						with gr.Row():
+							elem += gr.Checkbox(label='Enable best quality (EXPERIMENTAL, Use more GPU)', value=False)
+							elem += gr.Checkbox(label='Force upscale ratio 1:1 without area limit', value=False)
+						with gr.Row():
+							elem += gr.Checkbox(label='Block over-scaled image', value=True)
+							elem += gr.Checkbox(label='Auto Upscale if Block over-scaled image enabled', value=True)
+						with gr.Row():
+							with gr.Column(min_width=100):
+								elem += gr.Slider(minimum=0.1, maximum=8, value=4, step=0.01, label='Upscale Ratio')
+								elem += gr.Slider(minimum=0, maximum=20, value=3, step=1, label='Dilation mask')
+								elem += gr.Slider(minimum=0.01, maximum=1, value=0.1, step=0.01, label='Large person area limit')
+								elem += gr.Slider(minimum=0, maximum=20, value=1, step=1, label='Limit')
+								elem += gr.Slider(minimum=0, maximum=2, value=1, step=0.01, visible=shared.opts.data.get('bmab_test_function', False), label='Background color (HIDDEN)')
+								elem += gr.Slider(minimum=0, maximum=30, value=0, step=1, visible=shared.opts.data.get('bmab_test_function', False), label='Background blur (HIDDEN)')
+							with gr.Column(min_width=100):
+								elem += gr.Slider(minimum=0, maximum=1, value=0.4, step=0.01, label='Denoising Strength')
+								elem += gr.Slider(minimum=1, maximum=30, value=7, step=0.5, label='CFG Scale')
+								gr.Markdown('')
+					with gr.Tab('Face', elem_id='bmab_face_tabs'):
+						with gr.Row():
+							elem += gr.Checkbox(label='Enable face detailing', value=False)
+						with gr.Row():
+							elem += gr.Checkbox(label='Enable face detailing before hires.fix', value=False)
+						with gr.Row():
+							elem += gr.Checkbox(label='Disable extra networks in prompt (LORA, Hypernetwork, ...)', value=False)
+						with gr.Row():
+							with gr.Column(min_width=100):
+								elem += gr.Dropdown(label='Face detailing sort by', choices=['Score', 'Size', 'Left', 'Right', 'Center'], type='value', value='Score')
+							with gr.Column(min_width=100):
+								elem += gr.Slider(minimum=0, maximum=20, value=1, step=1, label='Limit')
+						with gr.Tab('Face1', elem_id='bmab_face1_tabs'):
+							with gr.Row():
+								elem += gr.Textbox(placeholder='prompt. if empty, use main prompt', lines=3, visible=True, value='', label='Prompt')
+							with gr.Row():
+								elem += gr.Textbox(placeholder='negative prompt. if empty, use main negative prompt', lines=3, visible=True, value='', label='Negative Prompt')
+						with gr.Tab('Face2', elem_id='bmab_face2_tabs'):
+							with gr.Row():
+								elem += gr.Textbox(placeholder='prompt. if empty, use main prompt', lines=3, visible=True, value='', label='Prompt')
+							with gr.Row():
+								elem += gr.Textbox(placeholder='negative prompt. if empty, use main negative prompt', lines=3, visible=True, value='', label='Negative Prompt')
+						with gr.Tab('Face3', elem_id='bmab_face3_tabs'):
+							with gr.Row():
+								elem += gr.Textbox(placeholder='prompt. if empty, use main prompt', lines=3, visible=True, value='', label='Prompt')
+							with gr.Row():
+								elem += gr.Textbox(placeholder='negative prompt. if empty, use main negative prompt', lines=3, visible=True, value='', label='Negative Prompt')
+						with gr.Tab('Face4', elem_id='bmab_face4_tabs'):
+							with gr.Row():
+								elem += gr.Textbox(placeholder='prompt. if empty, use main prompt', lines=3, visible=True, value='', label='Prompt')
+							with gr.Row():
+								elem += gr.Textbox(placeholder='negative prompt. if empty, use main negative prompt', lines=3, visible=True, value='', label='Negative Prompt')
+						with gr.Tab('Face5', elem_id='bmab_face5_tabs'):
+							with gr.Row():
+								elem += gr.Textbox(placeholder='prompt. if empty, use main prompt', lines=3, visible=True, value='', label='Prompt')
+							with gr.Row():
+								elem += gr.Textbox(placeholder='negative prompt. if empty, use main negative prompt', lines=3, visible=True, value='', label='Negative Prompt')
+						with gr.Row():
+							with gr.Tab('Parameters', elem_id='bmab_parameter_tabs'):
+								with gr.Row():
+									elem += gr.Checkbox(label='Overide Parameters', value=False)
+								with gr.Row():
+									with gr.Column(min_width=100):
+										elem += gr.Slider(minimum=64, maximum=2048, value=512, step=8, label='Width')
+										elem += gr.Slider(minimum=64, maximum=2048, value=512, step=8, label='Height')
+									with gr.Column(min_width=100):
+										elem += gr.Slider(minimum=1, maximum=30, value=7, step=0.5, label='CFG Scale')
+										elem += gr.Slider(minimum=1, maximum=150, value=20, step=1, label='Steps')
+										elem += gr.Slider(minimum=0, maximum=64, value=4, step=1, label='Mask Blur')
+						with gr.Row():
+							with gr.Column(min_width=100):
+								asamplers = [constants.sampler_default]
+								asamplers.extend([x.name for x in shared.list_samplers()])
+								elem += gr.Dropdown(label='Sampler', visible=True, value=asamplers[0], choices=asamplers)
+								inpaint_area = gr.Radio(label='Inpaint area', choices=['Whole picture', 'Only masked'], type='value', value='Only masked')
+								elem += inpaint_area
+								elem += gr.Slider(label='Only masked padding, pixels', minimum=0, maximum=256, step=4, value=32)
+								choices = detectors.list_face_detectors()
+								elem += gr.Dropdown(label='Detection Model', choices=choices, type='value', value=choices[0])
+							with gr.Column():
+								elem += gr.Slider(minimum=0, maximum=1, value=0.4, step=0.01, label='Face Denoising Strength', elem_id='bmab_face_denoising_strength')
+								elem += gr.Slider(minimum=0, maximum=64, value=4, step=1, label='Face Dilation', elem_id='bmab_face_dilation')
+								elem += gr.Slider(minimum=0.1, maximum=1, value=0.35, step=0.01, label='Face Box threshold')
+								elem += gr.Checkbox(label='Skip face detailing by area', value=False)
+								elem += gr.Slider(minimum=0.0, maximum=3.0, value=0.26, step=0.01, label='Face area (MegaPixel)')
+					with gr.Tab('Hand', elem_id='bmab_hand_tabs'):
+						with gr.Row():
+							elem += gr.Checkbox(label='Enable hand detailing (EXPERIMENTAL)', value=False)
+							elem += gr.Checkbox(label='Block over-scaled image', value=True)
+						with gr.Row():
+							elem += gr.Checkbox(label='Enable best quality (EXPERIMENTAL, Use more GPU)', value=False)
+						with gr.Row():
+							elem += gr.Dropdown(label='Method', visible=True, interactive=True, value='subframe', choices=['subframe', 'each hand', 'inpaint each hand', 'at once'])
+						with gr.Row():
+							elem += gr.Textbox(placeholder='prompt. if empty, use main prompt', lines=3, visible=True, value='', label='Prompt')
+						with gr.Row():
+							elem += gr.Textbox(placeholder='negative prompt. if empty, use main negative prompt', lines=3, visible=True, value='', label='Negative Prompt')
+						with gr.Row():
+							with gr.Column():
+								elem += gr.Slider(minimum=0, maximum=1, value=0.4, step=0.01, label='Denoising Strength')
+								elem += gr.Slider(minimum=1, maximum=30, value=7, step=0.5, label='CFG Scale')
+								elem += gr.Checkbox(label='Auto Upscale if Block over-scaled image enabled', value=True)
+							with gr.Column():
+								elem += gr.Slider(minimum=1, maximum=4, value=2, step=0.01, label='Upscale Ratio')
+								elem += gr.Slider(minimum=0, maximum=1, value=0.3, step=0.01, label='Box Threshold')
+								elem += gr.Slider(minimum=0, maximum=0.3, value=0.1, step=0.01, label='Box Dilation')
+						with gr.Row():
+							inpaint_area = gr.Radio(label='Inpaint area', choices=['Whole picture', 'Only masked'], type='value', value='Whole picture')
+							elem += inpaint_area
+						with gr.Row():
+							with gr.Column():
+								elem += gr.Slider(label='Only masked padding, pixels', minimum=0, maximum=256, step=4, value=32)
+							with gr.Column():
+								gr.Markdown('')
+						with gr.Row():
+							elem += gr.Textbox(placeholder='Additional parameter for advanced user', visible=True, value='', label='Additional Parameter')
+					with gr.Tab('ControlNet', elem_id='bmab_controlnet_tabs'):
+						with gr.Row():
+							elem += gr.Checkbox(label='Enable ControlNet access', value=False)
+						with gr.Row():
+							elem += gr.Checkbox(label='Process with BMAB refiner', value=False)
+						with gr.Row():
+							with gr.Tab('Noise', elem_id='bmab_cn_noise_tabs'):
+								with gr.Row():
+									elem += gr.Checkbox(label='Enable noise', value=False)
+								with gr.Row():
+									with gr.Column():
+										elem += gr.Slider(minimum=0.0, maximum=2, value=0.4, step=0.05, elem_id='bmab_cn_noise', label='Noise strength')
+										elem += gr.Slider(minimum=0.0, maximum=1.0, value=0.1, step=0.01, elem_id='bmab_cn_noise_begin', label='Noise begin')
+										elem += gr.Slider(minimum=0.0, maximum=1.0, value=0.9, step=0.01, elem_id='bmab_cn_noise_end', label='Noise end')
+									with gr.Column():
+										gr.Markdown('')
+		with gr.Accordion(f'BMAB Postprocessor', open=False):
+			with gr.Row():
+				with gr.Tab('Resize by person', elem_id='bmab_postprocess_resize_tab'):
+					with gr.Row():
+						elem += gr.Checkbox(label='Enable resize by person', value=False)
+						mode = ['Inpaint', 'ControlNet inpaint+lama']
+						elem += gr.Dropdown(label='Mode', visible=True, value=mode[0], choices=mode)
+					with gr.Row():
+						with gr.Column():
+							elem += gr.Slider(minimum=0.15, maximum=0.95, value=0.15, step=0.01, label='Resize by person')
+						with gr.Column():
+							elem += gr.Slider(minimum=0, maximum=1, value=0.6, step=0.01, label='Denoising Strength for Inpaint, ControlNet')
+					with gr.Row():
+						with gr.Column():
+							gr.Markdown('')
+						with gr.Column():
+							elem += gr.Slider(minimum=4, maximum=128, value=30, step=1, label='Mask Dilation')
+				with gr.Tab('Upscale', elem_id='bmab_postprocess_upscale_tab'):
+					with gr.Row():
+						with gr.Column(min_width=100):
+							elem += gr.Checkbox(label='Enable upscale at final stage', value=False)
+							elem += gr.Checkbox(label='Detailing after upscale', value=True)
+						with gr.Column(min_width=100):
+							gr.Markdown('')
+					with gr.Row():
+						with gr.Column(min_width=100):
+							upscalers = [x.name for x in shared.sd_upscalers]
+							elem += gr.Dropdown(label='Upscaler', visible=True, value=upscalers[0], choices=upscalers)
+							elem += gr.Slider(minimum=1, maximum=4, value=1.5, step=0.1, label='Upscale ratio')
+				with gr.Tab('Filter', id='bmab_final_filter', elem_id='bmab_final_filter_tab'):
+					with gr.Row():
+						dd_final_filter = gr.Dropdown(label='Final filter', visible=True, value=filter.filters[0], choices=filter.filters)
+						elem += dd_final_filter
+		with gr.Accordion(f'BMAB Config, Preset, Installer', open=False):
+			with gr.Row():
+				configs = parameters.Parameters().list_config()
+				config = '' if not configs else configs[0]
+				with gr.Tab('Configuration', elem_id='bmab_configuration_tabs'):
+					with gr.Row():
+						with gr.Column(scale=2):
+							with gr.Row():
+								config_dd = gr.Dropdown(label='Configuration', visible=True, interactive=True, allow_custom_value=True, value=config, choices=configs)
+								elem += config_dd
+								load_btn = ui_components.ToolButton('⬇️', visible=True, interactive=True, tooltip='load configuration', elem_id='bmab_load_configuration')
+								save_btn = ui_components.ToolButton('⬆️', visible=True, interactive=True, tooltip='save configuration', elem_id='bmab_save_configuration')
+								reset_btn = ui_components.ToolButton('🔃', visible=True, interactive=True, tooltip='reset to default', elem_id='bmab_reset_configuration')
+						with gr.Column(scale=1):
+							gr.Markdown('')
+					with gr.Row():
+						with gr.Column(scale=1):
+							btn_reload_filter = gr.Button('reload filter', visible=True, interactive=True, elem_id='bmab_reload_filter')
+						with gr.Column(scale=1):
+							gr.Markdown('')
+						with gr.Column(scale=1):
+							gr.Markdown('')
+						with gr.Column(scale=1):
+							gr.Markdown('')
+				with gr.Tab('Preset', elem_id='bmab_configuration_tabs'):
+					with gr.Row():
+						with gr.Column(min_width=100):
+							gr.Markdown('Preset Loader : preset override UI configuration.')
+					with gr.Row():
+						presets = parameters.Parameters().list_preset()
+						with gr.Column(min_width=100):
+							with gr.Row():
+								preset_dd = gr.Dropdown(label='Preset', visible=True, interactive=True, allow_custom_value=True, value=presets[0], choices=presets)
+								elem += preset_dd
+								refresh_btn = ui_components.ToolButton('🔄', visible=True, interactive=True, tooltip='refresh preset', elem_id='bmab_preset_refresh')
+				with gr.Tab('Toy', elem_id='bmab_toy_tabs'):
+					with gr.Row():
+						merge_result = gr.Markdown('Result here')
+					with gr.Row():
+						random_checkpoint = gr.Button('Merge Random Checkpoint', visible=True, interactive=True, elem_id='bmab_merge_random_checkpoint')
+				with gr.Tab('Installer', elem_id='bmab_install_tabs'):
+					with gr.Row():
+						pkgs = ['GroundingDINO']
+						dd_pkg = gr.Dropdown(label='Package', visible=True, value=pkgs[0], choices=pkgs)
+						btn_install = ui_components.ToolButton('🔄', visible=True, interactive=True, tooltip='Install package', elem_id='bmab_btn_install')
+					with gr.Row():
+						markdown_install = gr.Markdown('')
+		with gr.Accordion(f'BMAB Testroom', open=False, visible=shared.opts.data.get('bmab_for_developer', False)):
+			with gr.Row():
+				gallery = gr.Gallery(label='Images', value=[], elem_id='bmab_testroom_gallery')
+				result_image = gr.Image(elem_id='bmab_result_image')
+			with gr.Row():
+				btn_fetch_images = ui_components.ToolButton('🔄', visible=True, interactive=True, tooltip='fetch images', elem_id='bmab_fetch_images')
+				btn_process_pipeline = ui_components.ToolButton('▶️', visible=True, interactive=True, tooltip='fetch images', elem_id='bmab_fetch_images')
+
+		gr.Markdown(f'<div style="text-align: right; vertical-align: bottom"><span style="color: green">{bmab_version}</span></div>')
+
+		def load_config(*args):
+			name = args[0]
+			ret = parameters.Parameters().load_config(name)
+			return ret
+
+		def save_config(*args):
+			name = parameters.Parameters().get_save_config_name(args)
+			parameters.Parameters().save_config(args)
+			return {
+				config_dd: {
+					'choices': parameters.Parameters().list_config(),
+					'value': name,
+					'__type__': 'update'
+				}
+			}
+
+		def reset_config(*args):
+			return parameters.Parameters().get_default()
+
+		def refresh_preset(*args):
+			return {
+				preset_dd: {
+					'choices': parameters.Parameters().list_preset(),
+					'value': 'None',
+					'__type__': 'update'
+				}
+			}
+
+		def hit_refiner_model(value, *args):
+			checkpoints = [constants.checkpoint_default]
+			checkpoints.extend([str(x) for x in sd_models.checkpoints_list.keys()])
+			if value not in checkpoints:
+				value = checkpoints[0]
+			return {
+				refiner_models: {
+					'choices': checkpoints,
+					'value': value,
+					'__type__': 'update'
+				}
+			}
+
+		def hit_pretraining_model(value, *args):
+			models = ['Select Model']
+			models.extend(util.list_pretraining_models())
+			if value not in models:
+				value = models[0]
+			return {
+				pretraining_models: {
+					'choices': models,
+					'value': value,
+					'__type__': 'update'
+				}
+			}
+
+		def hit_resample_model(value, *args):
+			checkpoints = [constants.checkpoint_default]
+			checkpoints.extend([str(x) for x in sd_models.checkpoints_list.keys()])
+			if value not in checkpoints:
+				value = checkpoints[0]
+			return {
+				resample_models: {
+					'choices': checkpoints,
+					'value': value,
+					'__type__': 'update'
+				}
+			}
+
+		def hit_resample_vae(value, *args):
+			vaes = [constants.vae_default]
+			vaes.extend([str(x) for x in sd_vae.vae_dict.keys()])
+			if value not in vaes:
+				value = vaes[0]
+			return {
+				resample_vaes: {
+					'choices': vaes,
+					'value': value,
+					'__type__': 'update'
+				}
+			}
+
+		def hit_checkpoint_model(value, *args):
+			checkpoints = [constants.checkpoint_default]
+			checkpoints.extend([str(x) for x in sd_models.checkpoints_list.keys()])
+			if value not in checkpoints:
+				value = checkpoints[0]
+			return {
+				checkpoint_models: {
+					'choices': checkpoints,
+					'value': value,
+					'__type__': 'update'
+				}
+			}
+
+		def hit_vae_models(value, *args):
+			vaes = [constants.vae_default]
+			vaes.extend([str(x) for x in sd_vae.vae_dict.keys()])
+			if value not in vaes:
+				value = vaes[0]
+			return {
+				vaes_models: {
+					'choices': vaes,
+					'value': value,
+					'__type__': 'update'
+				}
+			}
+
+		def merge_random_checkpoint(*args):
+			def find_random(k, f):
+				for v in k:
+					if v.startswith(f):
+						return v
+
+			result = ''
+			checkpoints = [str(x) for x in sd_models.checkpoints_list.keys()]
+			target = random.choices(checkpoints, k=3)
+			multiplier = random.randrange(10, 90, 1) / 100
+			index = random.randrange(0x10000000, 0xFFFFFFFF, 1)
+			output = f'bmab_random_{format(index, "08X")}'
+			extras.run_modelmerger(None, target[0], target[1], target[2], 'Weighted sum', multiplier, False, output, 'safetensors', 0, None, '', True, True, True, '{}')
+			result += f'{output}.safetensors generated<br>'
+			for x in range(1, random.randrange(0, 5, 1)):
+				checkpoints = [str(x) for x in sd_models.checkpoints_list.keys()]
+				br = find_random(checkpoints, f'{output}.safetensors')
+				if br is None:
+					return
+				index = random.randrange(0x10000000, 0xFFFFFFFF, 1)
+				output = f'bmab_random_{format(index, "08X")}'
+				target = random.choices(checkpoints, k=2)
+				multiplier = random.randrange(10, 90, 1) / 100
+				extras.run_modelmerger(None, br, target[0], target[1], 'Weighted sum', multiplier, False, output, 'safetensors', 0, None, '', True, True, True, '{}')
+				result += f'{output}.safetensors generated<br>'
+			debug_print('done')
+			return {
+				merge_result: {
+					'value': result,
+					'__type__': 'update'
+				}
+			}
+
+		def fetch_images(*args):
+			global gallery_select_index
+			gallery_select_index = 0
+			return {
+				gallery: {
+					'value': final_images,
+					'__type__': 'update'
+				}
+			}
+
+		def process_pipeline(*args):
+			config, a = parameters.parse_args(args)
+			preview = final_images[gallery_select_index]
+			p = last_process
+			ctx = context.Context.newContext(bmab_script, p, a, gallery_select_index)
+			preview = pipeline.process(ctx, preview)
+			images.save_image(
+				preview, p.outpath_samples, '',
+				p.all_seeds[gallery_select_index], p.all_prompts[gallery_select_index],
+				shared.opts.samples_format, p=p, suffix="-testroom")
+			return {
+				result_image: {
+					'value': preview,
+					'__type__': 'update'
+				}
+			}
+
+		def reload_filter(f1, f2, f3, f4, f5, *args):
+			filter.reload_filters()
+			return {
+				dd_hiresfix_filter1: {
+					'choices': filter.filters,
+					'value': f1,
+					'__type__': 'update'
+				},
+				dd_hiresfix_filter2: {
+					'choices': filter.filters,
+					'value': f2,
+					'__type__': 'update'
+				},
+				dd_resample_filter: {
+					'choices': filter.filters,
+					'value': f3,
+					'__type__': 'update'
+				},
+				dd_resize_filter: {
+					'choices': filter.filters,
+					'value': f4,
+					'__type__': 'update'
+				},
+				dd_final_filter: {
+					'choices': filter.filters,
+					'value': f5,
+					'__type__': 'update'
+				}
+			}
+
+		def image_selected(data: gr.SelectData, *args):
+			debug_print(data.index)
+			global gallery_select_index
+			gallery_select_index = data.index
+
+		def hit_install(*args):
+			pkg_name = args[0]
+			if pkg_name == 'GroundingDINO':
+				installer.install_groudingdino()
+				msg = f'{pkg_name} installed'
+			else:
+				msg = 'Nothing installed.'
+			return {
+				markdown_install: {
+					'value': msg,
+					'__type__': 'update'
+				}
+			}
+
+		def stop_process(*args):
+			bscript.stop_generation = True
+			gr.Info('Waiting for processing done.')
+
+		load_btn.click(load_config, inputs=[config_dd], outputs=elem)
+		save_btn.click(save_config, inputs=elem, outputs=[config_dd])
+		reset_btn.click(reset_config, outputs=elem)
+		refresh_btn.click(refresh_preset, outputs=elem)
+		refresh_refiner_models.click(hit_refiner_model, inputs=[refiner_models], outputs=[refiner_models])
+		refresh_pretraining_models.click(hit_pretraining_model, inputs=[pretraining_models], outputs=[pretraining_models])
+		refresh_resample_models.click(hit_resample_model, inputs=[resample_models], outputs=[resample_models])
+		refresh_resample_vaes.click(hit_resample_vae, inputs=[resample_vaes], outputs=[resample_vaes])
+		refresh_checkpoint_models.click(hit_checkpoint_model, inputs=[checkpoint_models], outputs=[checkpoint_models])
+		refresh_vae_models.click(hit_vae_models, inputs=[vaes_models], outputs=[vaes_models])
+		random_checkpoint.click(merge_random_checkpoint, outputs=[merge_result])
+		btn_fetch_images.click(fetch_images, outputs=[gallery])
+		btn_reload_filter.click(reload_filter, inputs=[dd_hiresfix_filter1, dd_hiresfix_filter2, dd_resample_filter, dd_resize_filter, dd_final_filter], outputs=[dd_hiresfix_filter1, dd_hiresfix_filter2, dd_resample_filter, dd_resize_filter, dd_final_filter])
+
+		btn_process_pipeline.click(process_pipeline, inputs=elem, outputs=[result_image])
+		gallery.select(image_selected, inputs=[gallery])
+
+		btn_install.click(hit_install, inputs=[dd_pkg], outputs=[markdown_install])
+		btn_stop.click(stop_process)
+
+	return elem
+
+
+def on_ui_settings():
+	shared.opts.add_option('bmab_debug_print', shared.OptionInfo(False, 'Print debug message.', section=('bmab', 'BMAB')))
+	shared.opts.add_option('bmab_debug_logging', shared.OptionInfo(False, 'Enable developer logging.', section=('bmab', 'BMAB')))
+	shared.opts.add_option('bmab_show_extends', shared.OptionInfo(False, 'Show before processing image. (DO NOT ENABLE IN CLOUD)', section=('bmab', 'BMAB')))
+	shared.opts.add_option('bmab_test_function', shared.OptionInfo(False, 'Show Test Function', section=('bmab', 'BMAB')))
+	shared.opts.add_option('bmab_keep_original_setting', shared.OptionInfo(False, 'Keep original setting', section=('bmab', 'BMAB')))
+	shared.opts.add_option('bmab_save_image_before_process', shared.OptionInfo(False, 'Save image that before processing', section=('bmab', 'BMAB')))
+	shared.opts.add_option('bmab_save_image_after_process', shared.OptionInfo(False, 'Save image that after processing (some bugs)', section=('bmab', 'BMAB')))
+	shared.opts.add_option('bmab_for_developer', shared.OptionInfo(False, 'Show developer hidden function.', section=('bmab', 'BMAB')))
+	shared.opts.add_option('bmab_use_dino_predict', shared.OptionInfo(False, 'Use GroudingDINO for detecting hand. GroudingDINO should be installed manually.', section=('bmab', 'BMAB')))
+	shared.opts.add_option('bmab_max_detailing_element', shared.OptionInfo(
+		default=0, label='Max Detailing Element', component=gr.Slider, component_args={'minimum': 0, 'maximum': 10, 'step': 1}, section=('bmab', 'BMAB')))
+	shared.opts.add_option('bmab_detail_full', shared.OptionInfo(True, 'Allways use FULL, VAE type for encode when detail anything. (v1.6.0)', section=('bmab', 'BMAB')))
+	shared.opts.add_option('bmab_optimize_vram', shared.OptionInfo(default='None', label='Checkpoint for Person, Face, Hand', component=gr.Radio, component_args={'choices': ['None', 'low vram', 'med vram']}, section=('bmab', 'BMAB')))
+	mask_names = masking.list_mask_names()
+	shared.opts.add_option('bmab_mask_model', shared.OptionInfo(default=mask_names[0], label='Masking model', component=gr.Radio, component_args={'choices': mask_names}, section=('bmab', 'BMAB')))
+	shared.opts.add_option('bmab_use_specific_model', shared.OptionInfo(False, 'Use specific model', section=('bmab', 'BMAB')))
+	shared.opts.add_option('bmab_model', shared.OptionInfo(default='', label='Checkpoint for Person, Face, Hand', component=gr.Textbox, component_args='', section=('bmab', 'BMAB')))
+	shared.opts.add_option('bmab_cn_openpose', shared.OptionInfo(default='control_v11p_sd15_openpose_fp16 [73c2b67d]', label='ControlNet openpose model', component=gr.Textbox, component_args='', section=('bmab', 'BMAB')))
+	shared.opts.add_option('bmab_cn_lineart', shared.OptionInfo(default='control_v11p_sd15_lineart [43d4be0d]', label='ControlNet lineart model', component=gr.Textbox, component_args='', section=('bmab', 'BMAB')))
+	shared.opts.add_option('bmab_cn_inpaint', shared.OptionInfo(default='control_v11p_sd15_inpaint_fp16 [be8bc0ed]', label='ControlNet inpaint model', component=gr.Textbox, component_args='', section=('bmab', 'BMAB')))
+	shared.opts.add_option('bmab_cn_tile_resample', shared.OptionInfo(default='control_v11f1e_sd15_tile_fp16 [3b860298]', label='ControlNet tile model', component=gr.Textbox, component_args='', section=('bmab', 'BMAB')))
+