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webui.py
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import gradio as gr
import random
import os
import sys
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 modules.pm as pm
import modules.instantid as instantid
import args_manager
import copy
import launch
from extras.inpaint_mask import SAMOptions
import PIL.Image as 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
def get_task(*args):
args = list(args)
args.pop(0)
return worker.AsyncTask(args=args)
def generate_clicked(task: worker.AsyncTask):
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]
if len(task.args) == 0:
return
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
def inpaint_mode_change(mode, inpaint_engine_version):
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 inpaint_engine_version == 'empty':
inpaint_engine_version = modules.config.default_inpaint_engine_version
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, 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, inpaint_engine_version, 1.0, 0.618
]
reload_javascript()
title = f'Fooocus {fooocus_version.version}'
if isinstance(args_manager.args.preset, str):
title += ' ' + args_manager.args.preset
shared.gradio_root = gr.Blocks(title=title).queue()
with shared.gradio_root:
currentTask = gr.State(worker.AsyncTask(args=[]))
inpaint_engine_state = gr.State('empty')
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():
with gr.Column(scale=17):
prompt = gr.Textbox(show_label=False, placeholder="Type prompt here or paste parameters.", elem_id='positive_prompt',
autofocus=True, lines=3)
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)
reset_button = gr.Button(label="Reconnect", value="Reconnect", elem_classes='type_row', elem_id='reset_button', visible=False)
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', elem_id='skip_button', 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')
enhance_checkbox = gr.Checkbox(label='Enhance', value=modules.config.default_enhance_checkbox, 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='Image', source='upload', type='numpy', show_label=False)
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 Documentation</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 Documentation</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='Image', source='upload', type='numpy', tool='sketch', height=500, brush_color="#FFFFFF", elem_id='inpaint_canvas', show_label=False)
inpaint_advanced_masking_checkbox = gr.Checkbox(label='Enable Advanced Masking Features', value=False)
inpaint_mode = gr.Dropdown(choices=modules.flags.inpaint_options, value=modules.config.default_inpaint_method, 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 Documentation</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', tool='sketch', height=500, brush_color="#FFFFFF", mask_opacity=1, elem_id='inpaint_mask_canvas')
invert_mask_checkbox = gr.Checkbox(label='Invert Mask When Generating', value=False)
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_dino_prompt_text = gr.Textbox(label='Detection prompt', value='', visible=False, info='Use singular whenever possible', placeholder='Describe what you want to detect.')
example_inpaint_mask_dino_prompt_text = gr.Dataset(
samples=modules.config.example_enhance_detection_prompts,
label='Detection Prompt Quick List',
components=[inpaint_mask_dino_prompt_text],
visible=modules.config.default_inpaint_mask_model == 'sam')
example_inpaint_mask_dino_prompt_text.click(lambda x: x[0],
inputs=example_inpaint_mask_dino_prompt_text,
outputs=inpaint_mask_dino_prompt_text,
show_progress=False, queue=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_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)
inpaint_mask_sam_max_detections = gr.Slider(label="Maximum number of detections", info="Set to 0 to detect all", minimum=0, maximum=10, value=modules.config.default_sam_max_detections, step=1, interactive=True)
generate_mask_button = gr.Button(value='Generate mask from image')
def generate_mask(image, mask_model, cloth_category, dino_prompt_text, sam_model, box_threshold, text_threshold, sam_max_detections, dino_erode_or_dilate, dino_debug):
from extras.inpaint_mask import generate_mask_from_image
extras = {}
sam_options = None
if mask_model == 'u2net_cloth_seg':
extras['cloth_category'] = cloth_category
elif mask_model == 'sam':
sam_options = SAMOptions(
dino_prompt=dino_prompt_text,
dino_box_threshold=box_threshold,
dino_text_threshold=text_threshold,
dino_erode_or_dilate=dino_erode_or_dilate,
dino_debug=dino_debug,
max_detections=sam_max_detections,
model_type=sam_model
)
mask, _, _, _ = generate_mask_from_image(image, mask_model, extras, sam_options)
return mask
inpaint_mask_model.change(lambda x: [gr.update(visible=x == 'u2net_cloth_seg')] +
[gr.update(visible=x == 'sam')] * 2 +
[gr.Dataset.update(visible=x == 'sam',
samples=modules.config.example_enhance_detection_prompts)],
inputs=inpaint_mask_model,
outputs=[inpaint_mask_cloth_category,
inpaint_mask_dino_prompt_text,
inpaint_mask_advanced_options,
example_inpaint_mask_dino_prompt_text],
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='Image', source='upload', type='numpy', show_label=False)
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')
desc_image_size = gr.Textbox(label='Image Size and Recommended Size', elem_id='desc_image_size', visible=False)
desc_image_size = gr.Textbox(label='Image Size and Recommended Size', elem_id='desc_image_size', visible=False)
gr.HTML('<a href="https://github.com/lllyasviel/Fooocus/discussions/1363" target="_blank">\U0001F4D4 Documentation</a>')
def trigger_show_image_properties(image):
value = modules.util.get_image_size_info(image, modules.flags.sdxl_aspect_ratios)
return gr.update(value=value, visible=True)
desc_input_image.upload(trigger_show_image_properties, inputs=desc_input_image,
outputs=desc_image_size, show_progress=False, queue=False)
with gr.TabItem(label='Enhance') as enhance_tab:
with gr.Row():
with gr.Column():
enhance_input_image = grh.Image(label='Use with Enhance, skips image generation', source='upload', type='numpy')
gr.HTML('<a href="https://github.com/lllyasviel/Fooocus/discussions/3281" target="_blank">\U0001F4D4 Documentation</a>')
with gr.TabItem(label="PhotoMaker") as photomaker_tab:
with gr.Row():
with gr.Column():
photomaker_enabled = gr.Checkbox(label="Enabled", value=False)
def handle_model(value):
if value is False:
pm.unload_model()
photomaker_enabled.change(fn=handle_model, inputs=[photomaker_enabled])
with gr.Row():
with gr.Column():
def swap_to_gallery(images):
pil_images = []
for image in images:
print(f"image path: {image.name}")
pil_images.append(Image.open(image.name))
return gr.update(value=pil_images, visible=True), gr.update(visible=True), gr.update(visible=False)
photomaker_images = gr.Files(label="Drag (Select) 1 or more photos of your face", file_types=["image"])
photomaker_gallery_images = gr.Gallery(label="Source Face Images", columns=5, rows=1, height=200)
photomaker_images.upload(fn=swap_to_gallery, inputs=photomaker_images, outputs=[photomaker_gallery_images, photomaker_images])
with gr.TabItem(label="InstantID") as instantid_tab:
with gr.Row():
with gr.Column():
instantid_enabled = gr.Checkbox(label="Enabled", value=False)
def handle_model(value):
if value is False:
instantid.unload_model()
instantid_enabled.change(fn=handle_model, inputs=[instantid_enabled])
with gr.Column():
instantid_identitynet_strength_ratio = gr.Slider(
label="IdentityNet strength (for fidelity)",
minimum=0,
maximum=1.5,
step=0.05,
value=0.80,
)
instantid_adapter_strength_ratio = gr.Slider(
label="Image adapter strength (for detail)",
minimum=0,
maximum=1.5,
step=0.05,
value=0.80,
)
with gr.Row():
with gr.Column():
instantid_source_image_path = grh.Image(label='Source Face Image', source='upload', type='filepath')
with gr.Column():
instantid_pose_image_path = grh.Image(label='Source Pose Image', source='upload', type='pil', tool='sketch', brush_color="#FFFFFF", elem_id='instantid_inpaint_canvas')
with gr.TabItem(label='Metadata') as metadata_tab:
with gr.Column():
metadata_input_image = grh.Image(label='For images created by Fooocus', source='upload', type='pil')
metadata_json = gr.JSON(label='Metadata')
metadata_import_button = gr.Button(value='Apply Metadata')
def trigger_metadata_preview(file):
parameters, metadata_scheme = modules.meta_parser.read_info_from_image(file)
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)
with gr.Row():
with gr.Accordion(label="Inswapper", open=False):
with gr.Row():
with gr.Column():
inswapper_enabled = gr.Checkbox(label="Enabled", value=False)
inswapper_source_image_indicies = gr.Text(label="Source Image Index", info="-1 will swap all faces, otherwise provide the 0-based index of the face (0, 1, etc)", value="0")
inswapper_target_image_indicies = gr.Text(label = "Target Image Index", info="-1 will swap all faces, otherwise provide the 0-based index of the face (0, 1, etc)", value="0")
with gr.Column():
inswapper_source_image = grh.Image(label='Source Face Image', source='upload', type='numpy')
with gr.Row(visible=modules.config.default_enhance_checkbox) as enhance_input_panel:
with gr.Tabs():
with gr.TabItem(label='Upscale or Variation'):
with gr.Row():
with gr.Column():
enhance_uov_method = gr.Radio(label='Upscale or Variation:', choices=flags.uov_list,
value=modules.config.default_enhance_uov_method)
enhance_uov_processing_order = gr.Radio(label='Order of Processing',
info='Use before to enhance small details and after to enhance large areas.',
choices=flags.enhancement_uov_processing_order,
value=modules.config.default_enhance_uov_processing_order)
enhance_uov_prompt_type = gr.Radio(label='Prompt',
info='Choose which prompt to use for Upscale or Variation.',
choices=flags.enhancement_uov_prompt_types,
value=modules.config.default_enhance_uov_prompt_type,
visible=modules.config.default_enhance_uov_processing_order == flags.enhancement_uov_after)
enhance_uov_processing_order.change(lambda x: gr.update(visible=x == flags.enhancement_uov_after),
inputs=enhance_uov_processing_order,
outputs=enhance_uov_prompt_type,
queue=False, show_progress=False)
gr.HTML('<a href="https://github.com/lllyasviel/Fooocus/discussions/3281" target="_blank">\U0001F4D4 Documentation</a>')
enhance_ctrls = []
enhance_inpaint_mode_ctrls = []
enhance_inpaint_engine_ctrls = []
enhance_inpaint_update_ctrls = []
for index in range(modules.config.default_enhance_tabs):
with gr.TabItem(label=f'#{index + 1}') as enhance_tab_item:
enhance_enabled = gr.Checkbox(label='Enable', value=False, elem_classes='min_check',
container=False)
enhance_mask_dino_prompt_text = gr.Textbox(label='Detection prompt',
info='Use singular whenever possible',
placeholder='Describe what you want to detect.',
interactive=True,
visible=modules.config.default_enhance_inpaint_mask_model == 'sam')
example_enhance_mask_dino_prompt_text = gr.Dataset(
samples=modules.config.example_enhance_detection_prompts,
label='Detection Prompt Quick List',
components=[enhance_mask_dino_prompt_text],
visible=modules.config.default_enhance_inpaint_mask_model == 'sam')
example_enhance_mask_dino_prompt_text.click(lambda x: x[0],
inputs=example_enhance_mask_dino_prompt_text,
outputs=enhance_mask_dino_prompt_text,
show_progress=False, queue=False)
enhance_prompt = gr.Textbox(label="Enhancement positive prompt",
placeholder="Uses original prompt instead if empty.",
elem_id='enhance_prompt')
enhance_negative_prompt = gr.Textbox(label="Enhancement negative prompt",
placeholder="Uses original negative prompt instead if empty.",
elem_id='enhance_negative_prompt')
with gr.Accordion("Detection", open=False):
enhance_mask_model = gr.Dropdown(label='Mask generation model',
choices=flags.inpaint_mask_models,
value=modules.config.default_enhance_inpaint_mask_model)
enhance_mask_cloth_category = gr.Dropdown(label='Cloth category',
choices=flags.inpaint_mask_cloth_category,
value=modules.config.default_inpaint_mask_cloth_category,
visible=modules.config.default_enhance_inpaint_mask_model == 'u2net_cloth_seg',
interactive=True)
with gr.Accordion("SAM Options",
visible=modules.config.default_enhance_inpaint_mask_model == 'sam',
open=False) as sam_options:
enhance_mask_sam_model = gr.Dropdown(label='SAM model',
choices=flags.inpaint_mask_sam_model,
value=modules.config.default_inpaint_mask_sam_model,
interactive=True)
enhance_mask_box_threshold = gr.Slider(label="Box Threshold", minimum=0.0,
maximum=1.0, value=0.3, step=0.05,
interactive=True)
enhance_mask_text_threshold = gr.Slider(label="Text Threshold", minimum=0.0,
maximum=1.0, value=0.25, step=0.05,
interactive=True)
enhance_mask_sam_max_detections = gr.Slider(label="Maximum number of detections",
info="Set to 0 to detect all",
minimum=0, maximum=10,
value=modules.config.default_sam_max_detections,
step=1, interactive=True)
with gr.Accordion("Inpaint", visible=True, open=False):
enhance_inpaint_mode = gr.Dropdown(choices=modules.flags.inpaint_options,
value=modules.config.default_inpaint_method,
label='Method', interactive=True)
enhance_inpaint_disable_initial_latent = gr.Checkbox(
label='Disable initial latent in inpaint', value=False)
enhance_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. If set, use performance Quality or Speed (no performance LoRAs) for best results.')
enhance_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)')
enhance_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)')
enhance_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 processed before any mask invert)')
enhance_mask_invert = gr.Checkbox(label='Invert Mask', value=False)
gr.HTML('<a href="https://github.com/lllyasviel/Fooocus/discussions/3281" target="_blank">\U0001F4D4 Documentation</a>')
enhance_ctrls += [
enhance_enabled,
enhance_mask_dino_prompt_text,
enhance_prompt,
enhance_negative_prompt,
enhance_mask_model,
enhance_mask_cloth_category,
enhance_mask_sam_model,
enhance_mask_text_threshold,
enhance_mask_box_threshold,
enhance_mask_sam_max_detections,
enhance_inpaint_disable_initial_latent,
enhance_inpaint_engine,
enhance_inpaint_strength,
enhance_inpaint_respective_field,
enhance_inpaint_erode_or_dilate,
enhance_mask_invert
]
enhance_inpaint_mode_ctrls += [enhance_inpaint_mode]
enhance_inpaint_engine_ctrls += [enhance_inpaint_engine]
enhance_inpaint_update_ctrls += [[
enhance_inpaint_mode, enhance_inpaint_disable_initial_latent, enhance_inpaint_engine,
enhance_inpaint_strength, enhance_inpaint_respective_field
]]
enhance_inpaint_mode.change(inpaint_mode_change, inputs=[enhance_inpaint_mode, inpaint_engine_state], outputs=[
inpaint_additional_prompt, outpaint_selections, example_inpaint_prompts,
enhance_inpaint_disable_initial_latent, enhance_inpaint_engine,
enhance_inpaint_strength, enhance_inpaint_respective_field
], show_progress=False, queue=False)
enhance_mask_model.change(
lambda x: [gr.update(visible=x == 'u2net_cloth_seg')] +
[gr.update(visible=x == 'sam')] * 2 +
[gr.Dataset.update(visible=x == 'sam',
samples=modules.config.example_enhance_detection_prompts)],
inputs=enhance_mask_model,
outputs=[enhance_mask_cloth_category, enhance_mask_dino_prompt_text, sam_options,
example_enhance_mask_dino_prompt_text],
queue=False, show_progress=False)
with gr.Row(visible=modules.config.default_enhance_checkbox) as enhance_input_panel:
with gr.Tabs():
with gr.TabItem(label='Upscale or Variation'):
with gr.Row():
with gr.Column():
enhance_uov_method = gr.Radio(label='Upscale or Variation:', choices=flags.uov_list,
value=modules.config.default_enhance_uov_method)
enhance_uov_processing_order = gr.Radio(label='Order of Processing',
info='Use before to enhance small details and after to enhance large areas.',
choices=flags.enhancement_uov_processing_order,
value=modules.config.default_enhance_uov_processing_order)
enhance_uov_prompt_type = gr.Radio(label='Prompt',
info='Choose which prompt to use for Upscale or Variation.',
choices=flags.enhancement_uov_prompt_types,
value=modules.config.default_enhance_uov_prompt_type,
visible=modules.config.default_enhance_uov_processing_order == flags.enhancement_uov_after)
enhance_uov_processing_order.change(lambda x: gr.update(visible=x == flags.enhancement_uov_after),
inputs=enhance_uov_processing_order,
outputs=enhance_uov_prompt_type,
queue=False, show_progress=False)
gr.HTML('<a href="https://github.com/lllyasviel/Fooocus/discussions/3281" target="_blank">\U0001F4D4 Documentation</a>')
enhance_ctrls = []
enhance_inpaint_mode_ctrls = []
enhance_inpaint_engine_ctrls = []
enhance_inpaint_update_ctrls = []
for index in range(modules.config.default_enhance_tabs):
with gr.TabItem(label=f'#{index + 1}') as enhance_tab_item:
enhance_enabled = gr.Checkbox(label='Enable', value=False, elem_classes='min_check',
container=False)
enhance_mask_dino_prompt_text = gr.Textbox(label='Detection prompt',
info='Use singular whenever possible',
placeholder='Describe what you want to detect.',
interactive=True,
visible=modules.config.default_enhance_inpaint_mask_model == 'sam')
example_enhance_mask_dino_prompt_text = gr.Dataset(
samples=modules.config.example_enhance_detection_prompts,
label='Detection Prompt Quick List',
components=[enhance_mask_dino_prompt_text],
visible=modules.config.default_enhance_inpaint_mask_model == 'sam')
example_enhance_mask_dino_prompt_text.click(lambda x: x[0],
inputs=example_enhance_mask_dino_prompt_text,
outputs=enhance_mask_dino_prompt_text,
show_progress=False, queue=False)
enhance_prompt = gr.Textbox(label="Enhancement positive prompt",
placeholder="Uses original prompt instead if empty.",
elem_id='enhance_prompt')
enhance_negative_prompt = gr.Textbox(label="Enhancement negative prompt",
placeholder="Uses original negative prompt instead if empty.",
elem_id='enhance_negative_prompt')
with gr.Accordion("Detection", open=False):
enhance_mask_model = gr.Dropdown(label='Mask generation model',
choices=flags.inpaint_mask_models,
value=modules.config.default_enhance_inpaint_mask_model)
enhance_mask_cloth_category = gr.Dropdown(label='Cloth category',
choices=flags.inpaint_mask_cloth_category,
value=modules.config.default_inpaint_mask_cloth_category,
visible=modules.config.default_enhance_inpaint_mask_model == 'u2net_cloth_seg',
interactive=True)
with gr.Accordion("SAM Options",
visible=modules.config.default_enhance_inpaint_mask_model == 'sam',
open=False) as sam_options:
enhance_mask_sam_model = gr.Dropdown(label='SAM model',
choices=flags.inpaint_mask_sam_model,
value=modules.config.default_inpaint_mask_sam_model,
interactive=True)
enhance_mask_box_threshold = gr.Slider(label="Box Threshold", minimum=0.0,
maximum=1.0, value=0.3, step=0.05,
interactive=True)
enhance_mask_text_threshold = gr.Slider(label="Text Threshold", minimum=0.0,
maximum=1.0, value=0.25, step=0.05,
interactive=True)
enhance_mask_sam_max_detections = gr.Slider(label="Maximum number of detections",
info="Set to 0 to detect all",
minimum=0, maximum=10,
value=modules.config.default_sam_max_detections,
step=1, interactive=True)
with gr.Accordion("Inpaint", visible=True, open=False):
enhance_inpaint_mode = gr.Dropdown(choices=modules.flags.inpaint_options,
value=modules.config.default_inpaint_method,
label='Method', interactive=True)
enhance_inpaint_disable_initial_latent = gr.Checkbox(
label='Disable initial latent in inpaint', value=False)
enhance_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. If set, use performance Quality or Speed (no performance LoRAs) for best results.')
enhance_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)')
enhance_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)')
enhance_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 processed before any mask invert)')
enhance_mask_invert = gr.Checkbox(label='Invert Mask', value=False)
gr.HTML('<a href="https://github.com/lllyasviel/Fooocus/discussions/3281" target="_blank">\U0001F4D4 Documentation</a>')
enhance_ctrls += [
enhance_enabled,
enhance_mask_dino_prompt_text,
enhance_prompt,
enhance_negative_prompt,
enhance_mask_model,
enhance_mask_cloth_category,
enhance_mask_sam_model,
enhance_mask_text_threshold,
enhance_mask_box_threshold,
enhance_mask_sam_max_detections,
enhance_inpaint_disable_initial_latent,
enhance_inpaint_engine,
enhance_inpaint_strength,
enhance_inpaint_respective_field,
enhance_inpaint_erode_or_dilate,
enhance_mask_invert
]
enhance_inpaint_mode_ctrls += [enhance_inpaint_mode]
enhance_inpaint_engine_ctrls += [enhance_inpaint_engine]
enhance_inpaint_update_ctrls += [[
enhance_inpaint_mode, enhance_inpaint_disable_initial_latent, enhance_inpaint_engine,
enhance_inpaint_strength, enhance_inpaint_respective_field
]]
enhance_inpaint_mode.change(inpaint_mode_change, inputs=[enhance_inpaint_mode, inpaint_engine_state], outputs=[
inpaint_additional_prompt, outpaint_selections, example_inpaint_prompts,
enhance_inpaint_disable_initial_latent, enhance_inpaint_engine,
enhance_inpaint_strength, enhance_inpaint_respective_field
], show_progress=False, queue=False)
enhance_mask_model.change(
lambda x: [gr.update(visible=x == 'u2net_cloth_seg')] +
[gr.update(visible=x == 'sam')] * 2 +
[gr.Dataset.update(visible=x == 'sam',
samples=modules.config.example_enhance_detection_prompts)],
inputs=enhance_mask_model,
outputs=[enhance_mask_cloth_category, enhance_mask_dino_prompt_text, sam_options,
example_enhance_mask_dino_prompt_text],
queue=False, show_progress=False)
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)
enhance_tab.select(lambda: 'enhance', outputs=current_tab, queue=False, _js=down_js, show_progress=False)
photomaker_tab.select(lambda: 'photomaker', outputs=current_tab, queue=False, _js=down_js, show_progress=False)
instantid_tab.select(lambda: 'instantid', outputs=current_tab, queue=False, _js=down_js, show_progress=False)
metadata_tab.select(lambda: 'metadata', outputs=current_tab, queue=False, _js=down_js, show_progress=False)
enhance_checkbox.change(lambda x: gr.update(visible=x), inputs=enhance_checkbox,
outputs=enhance_input_panel, queue=False, show_progress=False, _js=switch_js)
with gr.Column(scale=1, visible=modules.config.default_advanced_checkbox) as advanced_column:
with gr.Tab(label='Settings'):
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)
performance_selection = gr.Radio(label='Performance',
choices=flags.Performance.values(),
value=modules.config.default_performance,
elem_classes=['performance_selection'])
with gr.Accordion(label='Aspect Ratios', open=False, elem_id='aspect_ratios_accordion') as aspect_ratios_accordion:
aspect_ratios_selection = gr.Radio(label='Aspect Ratios', show_label=False,
choices=modules.config.available_aspect_ratios_labels,
value=modules.config.default_aspect_ratio,
info='width × height',
elem_classes='aspect_ratios')
aspect_ratios_selection.change(lambda x: None, inputs=aspect_ratios_selection, queue=False, show_progress=False, _js='(x)=>{refresh_aspect_ratios_label(x);}')
shared.gradio_root.load(lambda x: None, inputs=aspect_ratios_selection, queue=False, show_progress=False, _js='(x)=>{refresh_aspect_ratios_label(x);}')
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=flags.OutputFormat.list(),
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'<a href="file={get_current_html_path(output_format)}" 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', elem_classes=['style_selections_tab']):
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, (enabled, filename, weight) in enumerate(modules.config.default_loras):
with gr.Row():
lora_enabled = gr.Checkbox(label='Enable', value=enabled,
elem_classes=['lora_enable', 'min_check'], scale=1)
lora_model = gr.Dropdown(label=f'LoRA {i + 1}',
choices=['None'] + modules.config.lora_filenames, value=filename,
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=weight,
elem_classes='lora_weight', scale=5)
lora_ctrls += [lora_enabled, lora_model, lora_weight]
with gr.Row():
refresh_files = 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 Documentation</a>')
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).')
clip_skip = gr.Slider(label='CLIP Skip', minimum=1, maximum=flags.clip_skip_max, step=1,
value=modules.config.default_clip_skip,
info='Bypass CLIP layers to avoid overfitting (use 1 to not skip any layers, 2 is recommended).')
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)
vae_name = gr.Dropdown(label='VAE', choices=[modules.flags.default_vae] + modules.config.vae_filenames,
value=modules.config.default_vae, show_label=True)
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=flags.Performance.has_restricted_features(modules.config.default_performance),
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)
read_wildcards_in_order = gr.Checkbox(label="Read wildcards in order", value=False)
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 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). ')