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.Column(scale=17):
prompt = gr.Textbox(label="Output", show_label=True, placeholder="", elem_id='positive_prompt',
container=True, autofocus=True, elem_classes='type_row', lines=1024, show_copy_button=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')
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, style_selections], 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]
)