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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')))
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