import gradio as gr
import random
import os
# import json
import time
import shared
import modules.config
# import fooocus_version
import modules.html
import modules.async_worker as worker
import modules.constants as constants
import modules.flags as flags
import modules.gradio_hijack as grh
import modules.style_sorter as style_sorter
import modules.meta_parser
import args_manager
import copy
from modules.sdxl_styles import legal_style_names
from modules.private_logger import get_current_html_path
from modules.ui_gradio_extensions import reload_javascript
from modules.auth import auth_enabled, check_auth
# from modules.util import is_json
# def get_task(*args):
# args = list(args)
# args.pop(0)
# return worker.AsyncTask(args=args)
# def generate_clicked(task):
# import ldm_patched.modules.model_management as model_management
# with model_management.interrupt_processing_mutex:
# model_management.interrupt_processing = False
# # outputs=[progress_html, progress_window, progress_gallery, gallery]
# execution_start_time = time.perf_counter()
# finished = False
# yield gr.update(visible=True, value=modules.html.make_progress_html(1, 'Waiting for task to start ...')), \
# gr.update(visible=True, value=None), \
# gr.update(visible=False, value=None), \
# gr.update(visible=False)
# worker.async_tasks.append(task)
# while not finished:
# time.sleep(0.01)
# if len(task.yields) > 0:
# flag, product = task.yields.pop(0)
# if flag == 'preview':
# # help bad internet connection by skipping duplicated preview
# if len(task.yields) > 0: # if we have the next item
# if task.yields[0][0] == 'preview': # if the next item is also a preview
# # print('Skipped one preview for better internet connection.')
# continue
# percentage, title, image = product
# yield gr.update(visible=True, value=modules.html.make_progress_html(percentage, title)), \
# gr.update(visible=True, value=image) if image is not None else gr.update(), \
# gr.update(), \
# gr.update(visible=False)
# if flag == 'results':
# yield gr.update(visible=True), \
# gr.update(visible=True), \
# gr.update(visible=True, value=product), \
# gr.update(visible=False)
# if flag == 'finish':
# yield gr.update(visible=False), \
# gr.update(visible=False), \
# gr.update(visible=False), \
# gr.update(visible=True, value=product)
# finished = True
# # delete Fooocus temp images, only keep gradio temp images
# if args_manager.args.disable_image_log:
# for filepath in product:
# if isinstance(filepath, str) and os.path.exists(filepath):
# os.remove(filepath)
# execution_time = time.perf_counter() - execution_start_time
# print(f'Total time: {execution_time:.2f} seconds')
# return
reload_javascript()
title = 'AI Describe Image'
if isinstance(args_manager.args.preset, str):
title += ' ' + args_manager.args.preset
shared.gradio_root = gr.Blocks(
title=title,
css=modules.html.css).queue()
with shared.gradio_root:
# currentTask = gr.State(worker.AsyncTask(args=[]))
with gr.Row():
with gr.Column(scale=2):
# with gr.Row():
# progress_window = grh.Image(label='Preview', show_label=True, visible=False, height=768,
# elem_classes=['main_view'])
# progress_gallery = gr.Gallery(label='Finished Images', show_label=True, object_fit='contain',
# height=768, visible=False, elem_classes=['main_view', 'image_gallery'])
# progress_html = gr.HTML(value=modules.html.make_progress_html(32, 'Progress 32%'), visible=False,
# elem_id='progress-bar', elem_classes='progress-bar')
# gallery = gr.Gallery(label='Gallery', show_label=False, object_fit='contain', visible=True, height=768,
# elem_classes=['resizable_area', 'main_view', 'final_gallery', 'image_gallery'],
# elem_id='final_gallery')
with gr.Row(visible=True) as image_input_panel:
with gr.Tabs():
# with gr.TabItem(label='Upscale or Variation') as uov_tab:
# with gr.Row():
# with gr.Column():
# uov_input_image = grh.Image(label='Drag above image to here', source='upload', type='numpy')
# with gr.Column():
# uov_method = gr.Radio(label='Upscale or Variation:', choices=flags.uov_list, value=flags.disabled)
# gr.HTML('\U0001F4D4 Document')
# with gr.TabItem(label='Image Prompt') as ip_tab:
# with gr.Row():
# ip_images = []
# ip_types = []
# ip_stops = []
# ip_weights = []
# ip_ctrls = []
# ip_ad_cols = []
# for _ in range(flags.controlnet_image_count):
# with gr.Column():
# ip_image = grh.Image(label='Image', source='upload', type='numpy', show_label=False, height=300)
# ip_images.append(ip_image)
# ip_ctrls.append(ip_image)
# with gr.Column(visible=False) as ad_col:
# with gr.Row():
# default_end, default_weight = flags.default_parameters[flags.default_ip]
# ip_stop = gr.Slider(label='Stop At', minimum=0.0, maximum=1.0, step=0.001, value=default_end)
# ip_stops.append(ip_stop)
# ip_ctrls.append(ip_stop)
# ip_weight = gr.Slider(label='Weight', minimum=0.0, maximum=2.0, step=0.001, value=default_weight)
# ip_weights.append(ip_weight)
# ip_ctrls.append(ip_weight)
# ip_type = gr.Radio(label='Type', choices=flags.ip_list, value=flags.default_ip, container=False)
# ip_types.append(ip_type)
# ip_ctrls.append(ip_type)
# ip_type.change(lambda x: flags.default_parameters[x], inputs=[ip_type], outputs=[ip_stop, ip_weight], queue=False, show_progress=False)
# ip_ad_cols.append(ad_col)
# ip_advanced = gr.Checkbox(label='Advanced', value=False, container=False)
# gr.HTML('* \"Image Prompt\" is powered by Fooocus Image Mixture Engine (v1.0.1). \U0001F4D4 Document')
# def ip_advance_checked(x):
# return [gr.update(visible=x)] * len(ip_ad_cols) + \
# [flags.default_ip] * len(ip_types) + \
# [flags.default_parameters[flags.default_ip][0]] * len(ip_stops) + \
# [flags.default_parameters[flags.default_ip][1]] * len(ip_weights)
# ip_advanced.change(ip_advance_checked, inputs=ip_advanced,
# outputs=ip_ad_cols + ip_types + ip_stops + ip_weights,
# queue=False, show_progress=False)
# with gr.TabItem(label='Inpaint or Outpaint') as inpaint_tab:
# with gr.Row():
# inpaint_input_image = grh.Image(label='Drag inpaint or outpaint image to here', source='upload', type='numpy', tool='sketch', height=500, brush_color="#FFFFFF", elem_id='inpaint_canvas')
# inpaint_mask_image = grh.Image(label='Mask Upload', source='upload', type='numpy', height=500, visible=False)
# with gr.Row():
# inpaint_additional_prompt = gr.Textbox(placeholder="Describe what you want to inpaint.", elem_id='inpaint_additional_prompt', label='Inpaint Additional Prompt', visible=False)
# outpaint_selections = gr.CheckboxGroup(choices=['Left', 'Right', 'Top', 'Bottom'], value=[], label='Outpaint Direction')
# inpaint_mode = gr.Dropdown(choices=modules.flags.inpaint_options, value=modules.flags.inpaint_option_default, label='Method')
# example_inpaint_prompts = gr.Dataset(samples=modules.config.example_inpaint_prompts, label='Additional Prompt Quick List', components=[inpaint_additional_prompt], visible=False)
# gr.HTML('* Powered by Fooocus Inpaint Engine \U0001F4D4 Document')
# example_inpaint_prompts.click(lambda x: x[0], inputs=example_inpaint_prompts, outputs=inpaint_additional_prompt, show_progress=False, queue=False)
with gr.TabItem(label='Describe') as desc_tab:
with gr.Row():
with gr.Column():
desc_input_image = grh.Image(label='Drag any image to here', source='upload', type='numpy')
with gr.Column():
# with gr.Row(elem_classes='type_row'):
with gr.Row():
prompt = gr.Textbox(label="Output", show_label=True, elem_id='positive_prompt', container=True, autofocus=True, show_copy_button=True, interactive=True)
default_prompt = modules.config.default_prompt
if isinstance(default_prompt, str) and default_prompt != '':
shared.gradio_root.load(lambda: default_prompt, outputs=prompt)
# with gr.Column(scale=3, min_width=0):
# generate_button = gr.Button(label="Generate", value="Generate", elem_classes='type_row', elem_id='generate_button', visible=True)
# load_parameter_button = gr.Button(label="Load Parameters", value="Load Parameters", elem_classes='type_row', elem_id='load_parameter_button', visible=False)
# skip_button = gr.Button(label="Skip", value="Skip", elem_classes='type_row_half', visible=False)
# stop_button = gr.Button(label="Stop", value="Stop", elem_classes='type_row_half', elem_id='stop_button', visible=False)
# def stop_clicked(currentTask):
# import ldm_patched.modules.model_management as model_management
# currentTask.last_stop = 'stop'
# if (currentTask.processing):
# model_management.interrupt_current_processing()
# return currentTask
# def skip_clicked(currentTask):
# import ldm_patched.modules.model_management as model_management
# currentTask.last_stop = 'skip'
# if (currentTask.processing):
# model_management.interrupt_current_processing()
# return currentTask
# stop_button.click(stop_clicked, inputs=currentTask, outputs=currentTask, queue=False, show_progress=False, _js='cancelGenerateForever')
# skip_button.click(skip_clicked, inputs=currentTask, outputs=currentTask, queue=False, show_progress=False)
# with gr.Row(elem_classes='advanced_check_row'):
# # input_image_checkbox = gr.Checkbox(label='Input Image', value=False, container=False, elem_classes='min_check')
# advanced_checkbox = gr.Checkbox(label='Advanced', value=modules.config.default_advanced_checkbox, container=False, elem_classes='min_check')
with gr.Row():
desc_method = gr.Radio(
label='Content Type',
choices=[flags.desc_type_photo, flags.desc_type_anime],
value=flags.desc_type_photo)
desc_btn = gr.Button(value='Describe this Image into Prompt')
# gr.HTML('\U0001F4D4 Document')
# with gr.TabItem(label='Metadata') as load_tab:
# with gr.Column():
# metadata_input_image = grh.Image(label='Drag any image generated by Fooocus here', source='upload', type='filepath')
# metadata_json = gr.JSON(label='Metadata')
# metadata_import_button = gr.Button(value='Apply Metadata')
# def trigger_metadata_preview(filepath):
# parameters, metadata_scheme = modules.meta_parser.read_info_from_image(filepath)
# results = {}
# if parameters is not None:
# results['parameters'] = parameters
# if isinstance(metadata_scheme, flags.MetadataScheme):
# results['metadata_scheme'] = metadata_scheme.value
# return results
# metadata_input_image.upload(trigger_metadata_preview, inputs=metadata_input_image,
# outputs=metadata_json, queue=False, show_progress=True)
switch_js = "(x) => {if(x){viewer_to_bottom(100);viewer_to_bottom(500);}else{viewer_to_top();} return x;}"
down_js = "() => {viewer_to_bottom();}"
# input_image_checkbox.change(lambda x: gr.update(visible=x), inputs=input_image_checkbox,
# outputs=image_input_panel, queue=False, show_progress=False, _js=switch_js)
# ip_advanced.change(lambda: None, queue=False, show_progress=False, _js=down_js)
# current_tab = gr.Textbox(value='desc', visible=False)
# # uov_tab.select(lambda: 'uov', outputs=current_tab, queue=False, _js=down_js, show_progress=False)
# # inpaint_tab.select(lambda: 'inpaint', outputs=current_tab, queue=False, _js=down_js, show_progress=False)
# # ip_tab.select(lambda: 'ip', outputs=current_tab, queue=False, _js=down_js, show_progress=False)
# desc_tab.select(lambda: 'desc', outputs=current_tab, queue=False, _js=down_js, show_progress=False)
# with gr.Column(scale=1, visible=modules.config.default_advanced_checkbox) as advanced_column:
# with gr.Tab(label='Setting'):
# performance_selection = gr.Radio(label='Performance',
# choices=modules.flags.performance_selections,
# value=modules.config.default_performance)
# aspect_ratios_selection = gr.Radio(label='Aspect Ratios', choices=modules.config.available_aspect_ratios,
# value=modules.config.default_aspect_ratio, info='width × height',
# elem_classes='aspect_ratios')
# image_number = gr.Slider(label='Image Number', minimum=1, maximum=modules.config.default_max_image_number, step=1, value=modules.config.default_image_number)
# output_format = gr.Radio(label='Output Format',
# choices=modules.flags.output_formats,
# value=modules.config.default_output_format)
# negative_prompt = gr.Textbox(label='Negative Prompt', show_label=True, placeholder="Type prompt here.",
# info='Describing what you do not want to see.', lines=2,
# elem_id='negative_prompt',
# value=modules.config.default_prompt_negative)
# seed_random = gr.Checkbox(label='Random', value=True)
# image_seed = gr.Textbox(label='Seed', value=0, max_lines=1, visible=False) # workaround for https://github.com/gradio-app/gradio/issues/5354
# def random_checked(r):
# return gr.update(visible=not r)
# def refresh_seed(r, seed_string):
# if r:
# return random.randint(constants.MIN_SEED, constants.MAX_SEED)
# else:
# try:
# seed_value = int(seed_string)
# if constants.MIN_SEED <= seed_value <= constants.MAX_SEED:
# return seed_value
# except ValueError:
# pass
# return random.randint(constants.MIN_SEED, constants.MAX_SEED)
# seed_random.change(random_checked, inputs=[seed_random], outputs=[image_seed],
# queue=False, show_progress=False)
# def update_history_link():
# if args_manager.args.disable_image_log:
# return gr.update(value='')
# return gr.update(value=f'\U0001F4DA History Log')
# history_link = gr.HTML()
# shared.gradio_root.load(update_history_link, outputs=history_link, queue=False, show_progress=False)
# with gr.Tab(label='Style'):
# style_sorter.try_load_sorted_styles(
# style_names=legal_style_names,
# default_selected=modules.config.default_styles)
# style_search_bar = gr.Textbox(show_label=False, container=False,
# placeholder="\U0001F50E Type here to search styles ...",
# value="",
# label='Search Styles')
# style_selections = gr.CheckboxGroup(show_label=False, container=False,
# choices=copy.deepcopy(style_sorter.all_styles),
# value=copy.deepcopy(modules.config.default_styles),
# label='Selected Styles',
# elem_classes=['style_selections'])
# gradio_receiver_style_selections = gr.Textbox(elem_id='gradio_receiver_style_selections', visible=False)
# shared.gradio_root.load(lambda: gr.update(choices=copy.deepcopy(style_sorter.all_styles)),
# outputs=style_selections)
# style_search_bar.change(style_sorter.search_styles,
# inputs=[style_selections, style_search_bar],
# outputs=style_selections,
# queue=False,
# show_progress=False).then(
# lambda: None, _js='()=>{refresh_style_localization();}')
# gradio_receiver_style_selections.input(style_sorter.sort_styles,
# inputs=style_selections,
# outputs=style_selections,
# queue=False,
# show_progress=False).then(
# lambda: None, _js='()=>{refresh_style_localization();}')
# with gr.Tab(label='Model'):
# with gr.Group():
# with gr.Row():
# base_model = gr.Dropdown(label='Base Model (SDXL only)', choices=modules.config.model_filenames, value=modules.config.default_base_model_name, show_label=True)
# refiner_model = gr.Dropdown(label='Refiner (SDXL or SD 1.5)', choices=['None'] + modules.config.model_filenames, value=modules.config.default_refiner_model_name, show_label=True)
# refiner_switch = gr.Slider(label='Refiner Switch At', minimum=0.1, maximum=1.0, step=0.0001,
# info='Use 0.4 for SD1.5 realistic models; '
# 'or 0.667 for SD1.5 anime models; '
# 'or 0.8 for XL-refiners; '
# 'or any value for switching two SDXL models.',
# value=modules.config.default_refiner_switch,
# visible=modules.config.default_refiner_model_name != 'None')
# refiner_model.change(lambda x: gr.update(visible=x != 'None'),
# inputs=refiner_model, outputs=refiner_switch, show_progress=False, queue=False)
# with gr.Group():
# lora_ctrls = []
# for i, (n, v) in enumerate(modules.config.default_loras):
# with gr.Row():
# lora_enabled = gr.Checkbox(label='Enable', value=True,
# elem_classes=['lora_enable', 'min_check'], scale=1)
# lora_model = gr.Dropdown(label=f'LoRA {i + 1}',
# choices=['None'] + modules.config.lora_filenames, value=n,
# elem_classes='lora_model', scale=5)
# lora_weight = gr.Slider(label='Weight', minimum=modules.config.default_loras_min_weight,
# maximum=modules.config.default_loras_max_weight, step=0.01, value=v,
# elem_classes='lora_weight', scale=5)
# lora_ctrls += [lora_enabled, lora_model, lora_weight]
# with gr.Row():
# model_refresh = gr.Button(label='Refresh', value='\U0001f504 Refresh All Files', variant='secondary', elem_classes='refresh_button')
# with gr.Tab(label='Advanced'):
# guidance_scale = gr.Slider(label='Guidance Scale', minimum=1.0, maximum=30.0, step=0.01,
# value=modules.config.default_cfg_scale,
# info='Higher value means style is cleaner, vivider, and more artistic.')
# sharpness = gr.Slider(label='Image Sharpness', minimum=0.0, maximum=30.0, step=0.001,
# value=modules.config.default_sample_sharpness,
# info='Higher value means image and texture are sharper.')
# gr.HTML('\U0001F4D4 Document')
# dev_mode = gr.Checkbox(label='Developer Debug Mode', value=False, container=False)
# with gr.Column(visible=False) as dev_tools:
# with gr.Tab(label='Debug Tools'):
# adm_scaler_positive = gr.Slider(label='Positive ADM Guidance Scaler', minimum=0.1, maximum=3.0,
# step=0.001, value=1.5, info='The scaler multiplied to positive ADM (use 1.0 to disable). ')
# adm_scaler_negative = gr.Slider(label='Negative ADM Guidance Scaler', minimum=0.1, maximum=3.0,
# step=0.001, value=0.8, info='The scaler multiplied to negative ADM (use 1.0 to disable). ')
# adm_scaler_end = gr.Slider(label='ADM Guidance End At Step', minimum=0.0, maximum=1.0,
# step=0.001, value=0.3,
# info='When to end the guidance from positive/negative ADM. ')
# refiner_swap_method = gr.Dropdown(label='Refiner swap method', value=flags.refiner_swap_method,
# choices=['joint', 'separate', 'vae'])
# adaptive_cfg = gr.Slider(label='CFG Mimicking from TSNR', minimum=1.0, maximum=30.0, step=0.01,
# value=modules.config.default_cfg_tsnr,
# info='Enabling Fooocus\'s implementation of CFG mimicking for TSNR '
# '(effective when real CFG > mimicked CFG).')
# sampler_name = gr.Dropdown(label='Sampler', choices=flags.sampler_list,
# value=modules.config.default_sampler)
# scheduler_name = gr.Dropdown(label='Scheduler', choices=flags.scheduler_list,
# value=modules.config.default_scheduler)
# generate_image_grid = gr.Checkbox(label='Generate Image Grid for Each Batch',
# info='(Experimental) This may cause performance problems on some computers and certain internet conditions.',
# value=False)
# overwrite_step = gr.Slider(label='Forced Overwrite of Sampling Step',
# minimum=-1, maximum=200, step=1,
# value=modules.config.default_overwrite_step,
# info='Set as -1 to disable. For developer debugging.')
# overwrite_switch = gr.Slider(label='Forced Overwrite of Refiner Switch Step',
# minimum=-1, maximum=200, step=1,
# value=modules.config.default_overwrite_switch,
# info='Set as -1 to disable. For developer debugging.')
# overwrite_width = gr.Slider(label='Forced Overwrite of Generating Width',
# minimum=-1, maximum=2048, step=1, value=-1,
# info='Set as -1 to disable. For developer debugging. '
# 'Results will be worse for non-standard numbers that SDXL is not trained on.')
# overwrite_height = gr.Slider(label='Forced Overwrite of Generating Height',
# minimum=-1, maximum=2048, step=1, value=-1,
# info='Set as -1 to disable. For developer debugging. '
# 'Results will be worse for non-standard numbers that SDXL is not trained on.')
# overwrite_vary_strength = gr.Slider(label='Forced Overwrite of Denoising Strength of "Vary"',
# minimum=-1, maximum=1.0, step=0.001, value=-1,
# info='Set as negative number to disable. For developer debugging.')
# overwrite_upscale_strength = gr.Slider(label='Forced Overwrite of Denoising Strength of "Upscale"',
# minimum=-1, maximum=1.0, step=0.001, value=-1,
# info='Set as negative number to disable. For developer debugging.')
# disable_preview = gr.Checkbox(label='Disable Preview', value=False,
# info='Disable preview during generation.')
# disable_intermediate_results = gr.Checkbox(label='Disable Intermediate Results',
# value=modules.config.default_performance == 'Extreme Speed',
# interactive=modules.config.default_performance != 'Extreme Speed',
# info='Disable intermediate results during generation, only show final gallery.')
# disable_seed_increment = gr.Checkbox(label='Disable seed increment',
# info='Disable automatic seed increment when image number is > 1.',
# value=False)
# # if not args_manager.args.disable_metadata:
# # save_metadata_to_images = gr.Checkbox(label='Save Metadata to Images', value=modules.config.default_save_metadata_to_images,
# # info='Adds parameters to generated images allowing manual regeneration.')
# # metadata_scheme = gr.Radio(label='Metadata Scheme', choices=flags.metadata_scheme, value=modules.config.default_metadata_scheme,
# # info='Image Prompt parameters are not included. Use png and a1111 for compatibility with Civitai.',
# # visible=modules.config.default_save_metadata_to_images)
# # save_metadata_to_images.change(lambda x: gr.update(visible=x), inputs=[save_metadata_to_images], outputs=[metadata_scheme],
# # queue=False, show_progress=False)
# # with gr.Tab(label='Control'):
# # debugging_cn_preprocessor = gr.Checkbox(label='Debug Preprocessors', value=False,
# # info='See the results from preprocessors.')
# # skipping_cn_preprocessor = gr.Checkbox(label='Skip Preprocessors', value=False,
# # info='Do not preprocess images. (Inputs are already canny/depth/cropped-face/etc.)')
# # mixing_image_prompt_and_vary_upscale = gr.Checkbox(label='Mixing Image Prompt and Vary/Upscale',
# # value=False)
# # mixing_image_prompt_and_inpaint = gr.Checkbox(label='Mixing Image Prompt and Inpaint',
# # value=False)
# # controlnet_softness = gr.Slider(label='Softness of ControlNet', minimum=0.0, maximum=1.0,
# # step=0.001, value=0.25,
# # info='Similar to the Control Mode in A1111 (use 0.0 to disable). ')
# # with gr.Tab(label='Canny'):
# # canny_low_threshold = gr.Slider(label='Canny Low Threshold', minimum=1, maximum=255,
# # step=1, value=64)
# # canny_high_threshold = gr.Slider(label='Canny High Threshold', minimum=1, maximum=255,
# # step=1, value=128)
# # with gr.Tab(label='Inpaint'):
# # debugging_inpaint_preprocessor = gr.Checkbox(label='Debug Inpaint Preprocessing', value=False)
# # inpaint_disable_initial_latent = gr.Checkbox(label='Disable initial latent in inpaint', value=False)
# # inpaint_engine = gr.Dropdown(label='Inpaint Engine',
# # value=modules.config.default_inpaint_engine_version,
# # choices=flags.inpaint_engine_versions,
# # info='Version of Fooocus inpaint model')
# # inpaint_strength = gr.Slider(label='Inpaint Denoising Strength',
# # minimum=0.0, maximum=1.0, step=0.001, value=1.0,
# # info='Same as the denoising strength in A1111 inpaint. '
# # 'Only used in inpaint, not used in outpaint. '
# # '(Outpaint always use 1.0)')
# # inpaint_respective_field = gr.Slider(label='Inpaint Respective Field',
# # minimum=0.0, maximum=1.0, step=0.001, value=0.618,
# # info='The area to inpaint. '
# # 'Value 0 is same as "Only Masked" in A1111. '
# # 'Value 1 is same as "Whole Image" in A1111. '
# # 'Only used in inpaint, not used in outpaint. '
# # '(Outpaint always use 1.0)')
# # inpaint_erode_or_dilate = gr.Slider(label='Mask Erode or Dilate',
# # minimum=-64, maximum=64, step=1, value=0,
# # info='Positive value will make white area in the mask larger, '
# # 'negative value will make white area smaller.'
# # '(default is 0, always process before any mask invert)')
# # inpaint_mask_upload_checkbox = gr.Checkbox(label='Enable Mask Upload', value=False)
# # invert_mask_checkbox = gr.Checkbox(label='Invert Mask', value=False)
# # inpaint_ctrls = [debugging_inpaint_preprocessor, inpaint_disable_initial_latent, inpaint_engine,
# # inpaint_strength, inpaint_respective_field,
# # inpaint_mask_upload_checkbox, invert_mask_checkbox, inpaint_erode_or_dilate]
# # inpaint_mask_upload_checkbox.change(lambda x: gr.update(visible=x),
# # inputs=inpaint_mask_upload_checkbox,
# # outputs=inpaint_mask_image, queue=False, show_progress=False)
# with gr.Tab(label='FreeU'):
# freeu_enabled = gr.Checkbox(label='Enabled', value=False)
# freeu_b1 = gr.Slider(label='B1', minimum=0, maximum=2, step=0.01, value=1.01)
# freeu_b2 = gr.Slider(label='B2', minimum=0, maximum=2, step=0.01, value=1.02)
# freeu_s1 = gr.Slider(label='S1', minimum=0, maximum=4, step=0.01, value=0.99)
# freeu_s2 = gr.Slider(label='S2', minimum=0, maximum=4, step=0.01, value=0.95)
# freeu_ctrls = [freeu_enabled, freeu_b1, freeu_b2, freeu_s1, freeu_s2]
# def dev_mode_checked(r):
# return gr.update(visible=r)
# dev_mode.change(dev_mode_checked, inputs=[dev_mode], outputs=[dev_tools],
# queue=False, show_progress=False)
# def model_refresh_clicked():
# modules.config.update_all_model_names()
# results = [gr.update(choices=modules.config.model_filenames)]
# results += [gr.update(choices=['None'] + modules.config.model_filenames)]
# for i in range(modules.config.default_max_lora_number):
# results += [gr.update(interactive=True), gr.update(choices=['None'] + modules.config.lora_filenames), gr.update()]
# return results
# model_refresh.click(model_refresh_clicked, [], [base_model, refiner_model] + lora_ctrls,
# queue=False, show_progress=False)
# performance_selection.change(lambda x: [gr.update(interactive=x != 'Extreme Speed')] * 11 +
# [gr.update(visible=x != 'Extreme Speed')] * 1 +
# [gr.update(interactive=x != 'Extreme Speed', value=x == 'Extreme Speed', )] * 1,
# inputs=performance_selection,
# outputs=[
# guidance_scale, sharpness, adm_scaler_end, adm_scaler_positive,
# adm_scaler_negative, refiner_switch, refiner_model, sampler_name,
# scheduler_name, adaptive_cfg, refiner_swap_method, negative_prompt, disable_intermediate_results
# ], queue=False, show_progress=False)
# output_format.input(lambda x: gr.update(output_format=x), inputs=output_format)
# advanced_checkbox.change(lambda x: gr.update(visible=x), advanced_checkbox, advanced_column,
# queue=False, show_progress=False) \
# .then(fn=lambda: None, _js='refresh_grid_delayed', queue=False, show_progress=False)
# def inpaint_mode_change(mode):
# assert mode in modules.flags.inpaint_options
# # inpaint_additional_prompt, outpaint_selections, example_inpaint_prompts,
# # inpaint_disable_initial_latent, inpaint_engine,
# # inpaint_strength, inpaint_respective_field
# if mode == modules.flags.inpaint_option_detail:
# return [
# gr.update(visible=True), gr.update(visible=False, value=[]),
# gr.Dataset.update(visible=True, samples=modules.config.example_inpaint_prompts),
# False, 'None', 0.5, 0.0
# ]
# if mode == modules.flags.inpaint_option_modify:
# return [
# gr.update(visible=True), gr.update(visible=False, value=[]),
# gr.Dataset.update(visible=False, samples=modules.config.example_inpaint_prompts),
# True, modules.config.default_inpaint_engine_version, 1.0, 0.0
# ]
# return [
# gr.update(visible=False, value=''), gr.update(visible=True),
# gr.Dataset.update(visible=False, samples=modules.config.example_inpaint_prompts),
# False, modules.config.default_inpaint_engine_version, 1.0, 0.618
# ]
# inpaint_mode.input(inpaint_mode_change, inputs=inpaint_mode, outputs=[
# inpaint_additional_prompt, outpaint_selections, example_inpaint_prompts,
# inpaint_disable_initial_latent, inpaint_engine,
# inpaint_strength, inpaint_respective_field
# ], show_progress=False, queue=False)
# ctrls = [currentTask, generate_image_grid]
# ctrls += [
# prompt, negative_prompt, style_selections,
# performance_selection, aspect_ratios_selection, image_number, output_format, image_seed, sharpness, guidance_scale
# ]
# ctrls += [base_model, refiner_model, refiner_switch] + lora_ctrls
# # ctrls += [input_image_checkbox, current_tab]
# # ctrls += [uov_method, uov_input_image]
# # ctrls += [outpaint_selections, inpaint_input_image, inpaint_additional_prompt, inpaint_mask_image]
# ctrls += [disable_preview, disable_intermediate_results, disable_seed_increment]
# ctrls += [adm_scaler_positive, adm_scaler_negative, adm_scaler_end, adaptive_cfg]
# ctrls += [sampler_name, scheduler_name]
# ctrls += [overwrite_step, overwrite_switch, overwrite_width, overwrite_height, overwrite_vary_strength]
# ctrls += [overwrite_upscale_strength, mixing_image_prompt_and_vary_upscale, mixing_image_prompt_and_inpaint]
# ctrls += [debugging_cn_preprocessor, skipping_cn_preprocessor, canny_low_threshold, canny_high_threshold]
# ctrls += [refiner_swap_method, controlnet_softness]
# ctrls += freeu_ctrls
# ctrls += inpaint_ctrls
# if not args_manager.args.disable_metadata:
# ctrls += [save_metadata_to_images, metadata_scheme]
# ctrls += ip_ctrls
# state_is_generating = gr.State(False)
# def parse_meta(raw_prompt_txt, is_generating):
# loaded_json = None
# if is_json(raw_prompt_txt):
# loaded_json = json.loads(raw_prompt_txt)
# if loaded_json is None:
# if is_generating:
# return gr.update(), gr.update(), gr.update()
# else:
# return gr.update(), gr.update(visible=True), gr.update(visible=False)
# return json.dumps(loaded_json), gr.update(visible=False), gr.update(visible=True)
# prompt.input(parse_meta, inputs=[prompt, state_is_generating], outputs=[prompt, generate_button, load_parameter_button], queue=False, show_progress=False)
# load_data_outputs = [advanced_checkbox, image_number, prompt, negative_prompt, style_selections,
# performance_selection, overwrite_step, overwrite_switch, aspect_ratios_selection,
# overwrite_width, overwrite_height, guidance_scale, sharpness, adm_scaler_positive,
# adm_scaler_negative, adm_scaler_end, refiner_swap_method, adaptive_cfg, base_model,
# refiner_model, refiner_switch, sampler_name, scheduler_name, seed_random, image_seed,
# generate_button, load_parameter_button] + freeu_ctrls + lora_ctrls
# load_parameter_button.click(modules.meta_parser.load_parameter_button_click, inputs=[prompt, state_is_generating], outputs=load_data_outputs, queue=False, show_progress=False)
# # def trigger_metadata_import(filepath, state_is_generating):
# # parameters, metadata_scheme = modules.meta_parser.read_info_from_image(filepath)
# # if parameters is None:
# # print('Could not find metadata in the image!')
# # parsed_parameters = {}
# # else:
# # metadata_parser = modules.meta_parser.get_metadata_parser(metadata_scheme)
# # parsed_parameters = metadata_parser.parse_json(parameters)
# # return modules.meta_parser.load_parameter_button_click(parsed_parameters, state_is_generating)
# # metadata_import_button.click(trigger_metadata_import, inputs=[metadata_input_image, state_is_generating], outputs=load_data_outputs, queue=False, show_progress=True) \
# # .then(style_sorter.sort_styles, inputs=style_selections, outputs=style_selections, queue=False, show_progress=False)
# generate_button.click(lambda: (gr.update(visible=True, interactive=True), gr.update(visible=True, interactive=True), gr.update(visible=False, interactive=False), [], True),
# outputs=[stop_button, skip_button, generate_button, gallery, state_is_generating]) \
# .then(fn=refresh_seed, inputs=[seed_random, image_seed], outputs=image_seed) \
# .then(fn=get_task, inputs=ctrls, outputs=currentTask) \
# .then(fn=generate_clicked, inputs=currentTask, outputs=[progress_html, progress_window, progress_gallery, gallery]) \
# .then(lambda: (gr.update(visible=True, interactive=True), gr.update(visible=False, interactive=False), gr.update(visible=False, interactive=False), False),
# outputs=[generate_button, stop_button, skip_button, state_is_generating]) \
# .then(fn=update_history_link, outputs=history_link) \
# .then(fn=lambda: None, _js='playNotification').then(fn=lambda: None, _js='refresh_grid_delayed')
for notification_file in ['notification.ogg', 'notification.mp3']:
if os.path.exists(notification_file):
gr.Audio(interactive=False, value=notification_file, elem_id='audio_notification', visible=False)
break
def trigger_describe(mode, img):
if mode == flags.desc_type_photo:
from extras.interrogate import default_interrogator as default_interrogator_photo
return default_interrogator_photo(img), ["Fooocus V2", "Fooocus Enhance", "Fooocus Sharp"]
if mode == flags.desc_type_anime:
from extras.wd14tagger import default_interrogator as default_interrogator_anime
return default_interrogator_anime(img), ["Fooocus V2", "Fooocus Masterpiece"]
return mode, ["Fooocus V2"]
desc_btn.click(trigger_describe, inputs=[desc_method, desc_input_image],
outputs=prompt, show_progress=True, queue=True)
def dump_default_english_config():
from modules.localization import dump_english_config
dump_english_config(grh.all_components)
# dump_default_english_config()
shared.gradio_root.launch(
inbrowser=args_manager.args.in_browser,
server_name=args_manager.args.listen,
server_port=args_manager.args.port,
share=args_manager.args.share,
auth=check_auth if (args_manager.args.share or args_manager.args.listen) and auth_enabled else None,
allowed_paths=[modules.config.path_outputs],
blocked_paths=[constants.AUTH_FILENAME]
)