| 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'))) | |