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webui.py
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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
from modules.rembg import rembg_run
from modules.load_online import load_demos_names, load_tools_names, load_demos_url, load_tools_url
import args_manager
import copy
import launch
import cv2
import numpy as np
from PIL import Image
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
from extras.inpaint_mask import generate_mask_from_image
import traceback
def virtual_try_on(clothes_image, person_image, inpaint_mask):
try:
# Convert images to numpy arrays if they're not already
clothes_image = np.array(clothes_image)
person_image = np.array(person_image)
inpaint_mask = np.array(inpaint_mask)
# Prepare LoRA arguments
loras = []
for lora in modules.config.default_loras:
loras.append(lora[0])
loras.append(lora[1])
# Set up the arguments for the generation task
args = [
True, # generate_image_grid
"", # prompt (empty as per manual metadata)
modules.config.default_prompt_negative, # negative_prompt
False, # translate_prompts
modules.config.default_styles, # style_selections
modules.config.default_performance, # performance_selection
modules.config.default_aspect_ratio, # aspect_ratios_selection
1, # image_number
modules.config.default_output_format, # output_format
random.randint(constants.MIN_SEED, constants.MAX_SEED), # image_seed
modules.config.default_sample_sharpness, # sharpness
modules.config.default_cfg_scale, # guidance_scale
modules.config.default_base_model_name, # base_model_name
modules.config.default_refiner_model_name, # refiner_model_name
modules.config.default_refiner_switch, # refiner_switch
] + loras + [
True, # input_image_checkbox
"inpaint", # current_tab
flags.disabled, # uov_method
None, # uov_input_image
[], # outpaint_selections
{'image': person_image, 'mask': inpaint_mask}, # inpaint_input_image
"", # inpaint_additional_prompt
inpaint_mask, # inpaint_mask_image_upload
False, # disable_preview
False, # disable_intermediate_results
modules.config.default_black_out_nsfw, # black_out_nsfw
1.5, # adm_scaler_positive
0.8, # adm_scaler_negative
0.3, # adm_scaler_end
modules.config.default_cfg_tsnr, # adaptive_cfg
modules.config.default_sampler, # sampler_name
modules.config.default_scheduler, # scheduler_name
-1, # overwrite_step
-1, # overwrite_switch
-1, # overwrite_width
-1, # overwrite_height
-1, # overwrite_vary_strength
modules.config.default_overwrite_upscale, # overwrite_upscale_strength
False, # mixing_image_prompt_and_vary_upscale
True, # mixing_image_prompt_and_inpaint
False, # debugging_cn_preprocessor
False, # skipping_cn_preprocessor
100, # canny_low_threshold
200, # canny_high_threshold
flags.refiner_swap_method, # refiner_swap_method
0.5, # controlnet_softness
False, # freeu_enabled
1.0, # freeu_b1
1.0, # freeu_b2
1.0, # freeu_s1
1.0, # freeu_s2
False, # debugging_inpaint_preprocessor
False, # inpaint_disable_initial_latent
modules.config.default_inpaint_engine_version, # inpaint_engine
1.0, # inpaint_strength
0.618, # inpaint_respective_field
True, # inpaint_mask_upload_checkbox
False, # invert_mask_checkbox
0, # inpaint_erode_or_dilate
modules.config.default_save_metadata_to_images, # save_metadata_to_images
modules.config.default_metadata_scheme, # metadata_scheme
]
# Add Image Prompt for clothes image
args.extend([
clothes_image, # ip_image
0.6, # ip_stop
0.5, # ip_weight
flags.default_ip, # ip_type
])
task = worker.AsyncTask(args=args)
worker.async_tasks.append(task)
# Wait for the task to complete
while not task.processing:
time.sleep(0.1)
while task.processing:
time.sleep(0.1)
if isinstance(task.results, list) and len(task.results) > 0:
return task.results[0] # Return the first (and only) generated image
else:
return "Error: No results generated"
except Exception as e:
print("Error in virtual_try_on:", str(e))
traceback.print_exc()
return f"Error: {str(e)}"
def get_photopea_url_params():
return "#%7B%22resources%22:%5B%22data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAgAAAAIAAQMAAADOtka5AAAAAXNSR0IB2cksfwAAAAlwSFlzAAALEwAACxMBAJqcGAAAAANQTFRF////p8QbyAAAADZJREFUeJztwQEBAAAAgiD/r25IQAEAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAfBuCAAAB0niJ8AAAAABJRU5ErkJggg==%22%5D%7D"
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:
os.remove(filepath)
execution_time = time.perf_counter() - execution_start_time
global time_taken
time_taken = f"Total time: {execution_time:.2f} seconds"
print(time_taken)
return
reload_javascript()
title = f'DeFooocus {fooocus_version.version}'
if isinstance(args_manager.args.preset, str):
title += ' ' + args_manager.args.preset
shared.gradio_root = gr.Blocks(
title=title,
css=modules.html.css,
theme="ehristoforu/Indigo_Theme").queue()
with shared.gradio_root:
currentTask = gr.State(worker.AsyncTask(args=[]))
with gr.Row():
with gr.Column(scale=2):
with gr.Tab("Generation"):
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',
value=["assets/favicon.png"],
preview=True)
# Update the Gradio interface
with gr.Tab("Virtual Try-On"):
with gr.Row():
clothes_input = gr.Image(label="Clothes Image", source='upload', type='numpy')
person_input = gr.Image(label="Person Image", source='upload', type='numpy', tool='sketch', elem_id='inpaint_canvas')
try_on_button = gr.Button("Try On")
try_on_output = gr.Image(label="Try-On Result")
error_output = gr.Textbox(label="Error", visible=False)
def process_virtual_try_on(clothes_image, person_image):
# Extract the image and mask from the person_input
inpaint_image = person_image['image']
inpaint_mask = person_image['mask']
result = virtual_try_on(clothes_image, inpaint_image, inpaint_mask)
if isinstance(result, str): # Error occurred
return gr.update(value=None, visible=False), gr.update(value=result, visible=True)
else: # Successfully generated image
return gr.update(value=result, visible=True), gr.update(value="", visible=False)
try_on_button.click(
process_virtual_try_on,
inputs=[clothes_input, person_input],
outputs=[try_on_output, error_output]
)
with gr.Tab("rembg"):
with gr.Column(scale=1):
rembg_input = grh.Image(label='Drag above image to here', source='upload', type='filepath', scale=20)
rembg_button = gr.Button(value="Remove Background", interactive=True, scale=1)
with gr.Column(scale=3):
rembg_output = grh.Image(label='rembg Output', interactive=False, height=380)
gr.Markdown("Powered by [🪄 rembg 2.0.53](https://github.com/danielgatis/rembg/releases/tag/v2.0.53)")
rembg_button.click(rembg_run, inputs=rembg_input, outputs=rembg_output, show_progress="full")
with gr.Tab("Online"):
with gr.Tab("Demos"):
for name in load_demos_names():
url = load_demos_url(name)
with gr.Tab(name):
gr.HTML(f"<iframe src='{url}' width='100%' height='1080px' style='border-radius: 8px;'></iframe>")
with gr.Tab("Tools"):
for name in load_tools_names():
url = load_tools_url(name)
with gr.Tab(name):
gr.HTML(f"<iframe src='{url}' width='100%' height='1080px' style='border-radius: 8px;'></iframe>")
with gr.Row(elem_classes='type_row'):
with gr.Column(scale=17):
prompt = gr.Textbox(show_label=False, placeholder="Type prompt here or paste parameters.", elem_id='positive_prompt',
container=False, autofocus=True, elem_classes='type_row', lines=1024)
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(visible=False) 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('<a href="https://github.com/lllyasviel/Fooocus/discussions/390" target="_blank">\U0001F4D4 Document</a>')
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). <a href="https://github.com/lllyasviel/Fooocus/discussions/557" target="_blank">\U0001F4D4 Document</a>')
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():
with gr.Column():
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_mode = gr.Dropdown(choices=modules.flags.inpaint_options, value=modules.flags.inpaint_option_default, label='Method')
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')
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 <a href="https://github.com/lllyasviel/Fooocus/discussions/414" target="_blank">\U0001F4D4 Document</a>')
example_inpaint_prompts.click(lambda x: x[0], inputs=example_inpaint_prompts, outputs=inpaint_additional_prompt, show_progress=False, queue=False)
with gr.Column(visible=False) as inpaint_mask_generation_col:
inpaint_mask_image = grh.Image(label='Mask Upload', source='upload', type='numpy',
height=500)
inpaint_mask_model = gr.Dropdown(label='Mask generation model',
choices=flags.inpaint_mask_models,
value=modules.config.default_inpaint_mask_model)
inpaint_mask_cloth_category = gr.Dropdown(label='Cloth category',
choices=flags.inpaint_mask_cloth_category,
value=modules.config.default_inpaint_mask_cloth_category,
visible=False)
inpaint_mask_sam_prompt_text = gr.Textbox(label='Segmentation prompt', value='', visible=False)
with gr.Accordion("Advanced options", visible=False, open=False) as inpaint_mask_advanced_options:
inpaint_mask_sam_model = gr.Dropdown(label='SAM model', choices=flags.inpaint_mask_sam_model, value=modules.config.default_inpaint_mask_sam_model)
inpaint_mask_sam_quant = gr.Checkbox(label='Quantization', value=False)
inpaint_mask_box_threshold = gr.Slider(label="Box Threshold", minimum=0.0, maximum=1.0, value=0.3, step=0.05)
inpaint_mask_text_threshold = gr.Slider(label="Text Threshold", minimum=0.0, maximum=1.0, value=0.25, step=0.05)
generate_mask_button = gr.Button(value='Generate mask from image')
def generate_mask(image, mask_model, cloth_category, sam_prompt_text, sam_model, sam_quant, box_threshold, text_threshold):
from extras.inpaint_mask import generate_mask_from_image
extras = {}
if mask_model == 'u2net_cloth_seg':
extras['cloth_category'] = cloth_category
elif mask_model == 'sam':
extras['sam_prompt_text'] = sam_prompt_text
extras['sam_model'] = sam_model
extras['sam_quant'] = sam_quant
extras['box_threshold'] = box_threshold
extras['text_threshold'] = text_threshold
return generate_mask_from_image(image, mask_model, extras)
generate_mask_button.click(fn=generate_mask,
inputs=[
inpaint_input_image, inpaint_mask_model,
inpaint_mask_cloth_category,
inpaint_mask_sam_prompt_text,
inpaint_mask_sam_model,
inpaint_mask_sam_quant,
inpaint_mask_box_threshold,
inpaint_mask_text_threshold
],
outputs=inpaint_mask_image, show_progress=True, queue=True)
inpaint_mask_model.change(lambda x: [gr.update(visible=x == 'u2net_cloth_seg'), gr.update(visible=x == 'sam'), gr.update(visible=x == 'sam')],
inputs=inpaint_mask_model,
outputs=[inpaint_mask_cloth_category, inpaint_mask_sam_prompt_text, inpaint_mask_advanced_options],
queue=False, show_progress=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():
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('<a href="https://github.com/lllyasviel/Fooocus/discussions/1363" target="_blank">\U0001F4D4 Document</a>')
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='uov', 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='Settings'):
with gr.Row():
performance_selection = gr.Radio(label='Performance',
choices=modules.flags.performance_selections,
value=modules.config.default_performance,
elem_classes='performance_selections')
if not args_manager.args.disable_preset_selection:
preset_selection = gr.Dropdown(label='Preset',
choices=modules.config.available_presets,
value=args_manager.args.preset if args_manager.args.preset else "initial",
interactive=True)
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')
with gr.Column():
sampling_apply = gr.Checkbox(label="Sampling", value=False)
with gr.Row(visible=False) as sampling:
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)
sampling_apply.change(
fn=lambda x: gr.update(visible=x),
inputs=sampling_apply,
outputs=sampling,
queue=False,
api_name=False,
)
image_number = gr.Slider(label='Image Number', minimum=1, maximum=modules.config.default_max_image_number, step=1, value=modules.config.default_image_number)
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)
translate_prompts = gr.Checkbox(label='Translate Prompts',
info='Uses the internet to translate prompts to English.',
value=False)
seed_random = gr.Checkbox(label='Randomize seed', 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'<a href="file={get_current_html_path()}" target="_blank">\U0001F4DA History Log</a>')
history_link = gr.HTML()
shared.gradio_root.load(update_history_link, outputs=history_link, queue=False, show_progress=False)
with gr.Tab(label='Styles'):
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='Models'):
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_model = gr.Dropdown(label=f'LoRA {i + 1}',
choices=['None'] + modules.config.lora_filenames, value=n)
lora_weight = gr.Slider(label='Weight', minimum=-2, maximum=2, step=0.01, value=v,
elem_classes='lora_weight')
lora_ctrls += [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('<a href="https://github.com/lllyasviel/Fooocus/discussions/117" target="_blank">\U0001F4D4 Document</a>')
output_format = gr.Radio(label='Output Format',
choices=modules.flags.output_formats,
value=modules.config.default_output_format)
play_notification = gr.Checkbox(label='Play notification after rendering', value=False)
notification_file = 'notification.mp3'
if os.path.exists(notification_file):
notification = gr.State(value=notification_file)
notification_input = gr.Audio(label='Notification', interactive=True, elem_id='audio_notification', visible=False, show_edit_button=False)
def play_notification_checked(r, notification):
return gr.update(visible=r, value=notification if r else None)
def notification_input_changed(notification_input, notification):
if notification_input:
notification = notification_input
return notification
play_notification.change(fn=play_notification_checked, inputs=[play_notification, notification], outputs=[notification_input], queue=False)
notification_input.change(fn=notification_input_changed, inputs=[notification_input, notification], outputs=[notification], queue=False)
dev_mode = gr.Checkbox(label='Advanced mode', value=True, container=False)
with gr.Column(visible=True) 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=modules.config.default_overwrite_upscale,
info='Set as negative number to disable. For developer debugging.')
disable_preview = gr.Checkbox(label='Disable Preview', value=modules.config.default_black_out_nsfw,
interactive=not modules.config.default_black_out_nsfw,
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.')
black_out_nsfw = gr.Checkbox(label='Black Out NSFW', value=modules.config.default_black_out_nsfw,
interactive=not modules.config.default_black_out_nsfw,
info='Use black image if NSFW is detected.')
black_out_nsfw.change(lambda x: gr.update(value=x, interactive=not x),
inputs=black_out_nsfw, outputs=disable_preview, queue=False, show_progress=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 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)] * 2,
inputs=inpaint_mask_upload_checkbox,
outputs=[inpaint_mask_image, inpaint_mask_generation_col],
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()
modules.config.update_presets()
results = []
results += [gr.update(choices=modules.config.model_filenames),
gr.update(choices=['None'] + modules.config.model_filenames)]
if not args_manager.args.disable_preset_selection:
results += [gr.update(choices=modules.config.available_presets)]
for i in range(flags.lora_count):
results += [gr.update(choices=['None'] + modules.config.lora_filenames), gr.update()]
return results
model_refresh_output = [base_model, refiner_model]
if not args_manager.args.disable_preset_selection:
model_refresh_output += [preset_selection]
model_refresh.click(model_refresh_clicked, [], model_refresh_output + lora_ctrls,
queue=False, show_progress=False)
state_is_generating = gr.State(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
if not args_manager.args.disable_preset_selection:
def preset_selection_change(preset, is_generating):
preset_content = modules.config.try_get_preset_content(preset) if preset != 'initial' else {}
preset_prepared = modules.meta_parser.parse_meta_from_preset(preset_content)
default_model = preset_prepared.get('base_model')
previous_default_models = preset_prepared.get('previous_default_models', [])
checkpoint_downloads = preset_prepared.get('checkpoint_downloads', {})
embeddings_downloads = preset_prepared.get('embeddings_downloads', {})
lora_downloads = preset_prepared.get('lora_downloads', {})
preset_prepared['base_model'], preset_prepared['lora_downloads'] = launch.download_models(
default_model, previous_default_models, checkpoint_downloads, embeddings_downloads, lora_downloads)
if 'prompt' in preset_prepared and preset_prepared.get('prompt') == '':
del preset_prepared['prompt']
return modules.meta_parser.load_parameter_button_click(json.dumps(preset_prepared), is_generating)
preset_selection.change(preset_selection_change, inputs=[preset_selection, state_is_generating], outputs=load_data_outputs, queue=False, show_progress=True) \
.then(fn=style_sorter.sort_styles, inputs=style_selections, outputs=style_selections, queue=False, show_progress=False) \
.then(lambda: None, _js='()=>{refresh_style_localization();}')
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, translate_prompts, 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, black_out_nsfw]
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
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_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')
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 trigger_uov_describe(mode, img, prompt):
# keep prompt if not empty
if prompt == '':
return trigger_describe(mode, img)
return gr.update(), gr.update()
uov_input_image.upload(trigger_uov_describe, inputs=[desc_method, uov_input_image, prompt],
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,