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	# Copyright (c) 2024 Jaerin Lee

	# Permission is hereby granted, free of charge, to any person obtaining a copy
	# of this software and associated documentation files (the "Software"), to deal
	# in the Software without restriction, including without limitation the rights
	# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
	# copies of the Software, and to permit persons to whom the Software is
	# furnished to do so, subject to the following conditions:

	# The above copyright notice and this permission notice shall be included in all
	# copies or substantial portions of the Software.

	# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
	# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
	# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
	# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
	# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
	# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
	# SOFTWARE.

	import sys

	sys.path.append('../../src')

	import argparse
	import random
	import time
	import json
	import os
	import glob
	import pathlib
	from functools import partial
	from pprint import pprint

	import numpy as np
	from PIL import Image
	import torch

	import gradio as gr
	from huggingface_hub import snapshot_download
	import spaces

	from model import StableMultiDiffusionSDXLPipeline
	from util import seed_everything
	from prompt_util import preprocess_prompts, _quality_dict, _style_dict
	from share_btn import share_js


	### Utils




	def log_state(state):
	pprint(vars(opt))
	if isinstance(state, gr.State):
	state = state.value
	pprint(vars(state))


	def is_empty_image(im: Image.Image) -> bool:
	if im is None:
	return True
	im = np.array(im)
	has_alpha = (im.shape[2] == 4)
	if not has_alpha:
	return False
	elif im.sum() == 0:
	return True
	else:
	return False


	### Argument passing

	# parser = argparse.ArgumentParser(description='Semantic Palette demo powered by StreamMultiDiffusion with SDXL support.')
	# parser.add_argument('-H', '--height', type=int, default=1024)
	# parser.add_argument('-W', '--width', type=int, default=2560)
	# parser.add_argument('--model', type=str, default=None)
	# parser.add_argument('--bootstrap_steps', type=int, default=1)
	# parser.add_argument('--seed', type=int, default=-1)
	# parser.add_argument('--device', type=int, default=0)
	# parser.add_argument('--port', type=int, default=8000)
	# opt = parser.parse_args()
	opt = argparse.Namespace()
	opt.height = 1024
	opt.width = 2560
	opt.model = None
	opt.bootstrap_steps = 3
	opt.seed = -1
	# opt.device = 0
	# opt.port = 8000



	### Global variables and data structures

	device = 'cuda' if torch.cuda.is_available() else 'cpu'
	print(device)

	if opt.model is None:
	model_dict = {
	'Animagine XL 3.1': 'cagliostrolab/animagine-xl-3.1',
	}
	else:
	if opt.model.endswith('.safetensors'):
	opt.model = os.path.abspath(os.path.join('checkpoints', opt.model))
	model_dict = {os.path.splitext(os.path.basename(opt.model))[0]: opt.model}

	models = {
	k: StableMultiDiffusionSDXLPipeline(device, hf_key=v, has_i2t=False).cuda()
	for k, v in model_dict.items()
	}


	prompt_suggestions = [
	'1girl, souryuu asuka langley, neon genesis evangelion, solo, upper body, v, smile, looking at viewer',
	'1boy, solo, portrait, looking at viewer, white t-shirt, brown hair',
	'1girl, arima kana, oshi no ko, solo, upper body, from behind',
	]

	opt.max_palettes = 5
	opt.default_prompt_strength = 1.0
	opt.default_mask_strength = 1.0
	opt.default_mask_std = 0.0
	opt.default_negative_prompt = (
	'nsfw, worst quality, bad quality, normal quality, cropped, framed'
	)
	opt.verbose = True
	opt.colors = [
	'#000000',
	'#2692F3',
	'#F89E12',
	'#16C232',
	'#F92F6C',
	'#AC6AEB',
	# '#92C62C',
	# '#92C6EC',
	# '#FECAC0',
	]


	### Event handlers

	def add_palette(state):
	old_actives = state.active_palettes
	state.active_palettes = min(state.active_palettes + 1, opt.max_palettes)

	if opt.verbose:
	log_state(state)

	if state.active_palettes != old_actives:
	return [state] + [
	gr.update() if state.active_palettes != opt.max_palettes else gr.update(visible=False)
	] + [
	gr.update() if i != state.active_palettes - 1 else gr.update(value=state.prompt_names[i + 1], visible=True)
	for i in range(opt.max_palettes)
	]
	else:
	return [state] + [gr.update() for i in range(opt.max_palettes + 1)]


	def select_palette(state, button, idx):
	if idx < 0 or idx > opt.max_palettes:
	idx = 0
	old_idx = state.current_palette
	if old_idx == idx:
	return [state] + [gr.update() for _ in range(opt.max_palettes + 7)]

	state.current_palette = idx

	if opt.verbose:
	log_state(state)

	updates = [state] + [
	gr.update() if i not in (idx, old_idx) else
	gr.update(variant='secondary') if i == old_idx else gr.update(variant='primary')
	for i in range(opt.max_palettes + 1)
	]
	label = 'Background' if idx == 0 else f'Palette {idx}'
	updates.extend([
	gr.update(value=button, interactive=(idx > 0)),
	gr.update(value=state.prompts[idx], label=f'Edit Prompt for {label}'),
	gr.update(value=state.neg_prompts[idx], label=f'Edit Negative Prompt for {label}'),
	(
	gr.update(value=state.mask_strengths[idx - 1], interactive=True) if idx > 0 else
	gr.update(value=opt.default_mask_strength, interactive=False)
	),
	(
	gr.update(value=state.prompt_strengths[idx - 1], interactive=True) if idx > 0 else
	gr.update(value=opt.default_prompt_strength, interactive=False)
	),
	(
	gr.update(value=state.mask_stds[idx - 1], interactive=True) if idx > 0 else
	gr.update(value=opt.default_mask_std, interactive=False)
	),
	])
	return updates


	def change_prompt_strength(state, strength):
	if state.current_palette == 0:
	return state

	state.prompt_strengths[state.current_palette - 1] = strength
	if opt.verbose:
	log_state(state)

	return state


	def change_std(state, std):
	if state.current_palette == 0:
	return state

	state.mask_stds[state.current_palette - 1] = std
	if opt.verbose:
	log_state(state)

	return state


	def change_mask_strength(state, strength):
	if state.current_palette == 0:
	return state

	state.mask_strengths[state.current_palette - 1] = strength
	if opt.verbose:
	log_state(state)

	return state


	def reset_seed(state, seed):
	state.seed = seed
	if opt.verbose:
	log_state(state)

	return state

	def rename_prompt(state, name):
	state.prompt_names[state.current_palette] = name
	if opt.verbose:
	log_state(state)

	return [state] + [
	gr.update() if i != state.current_palette else gr.update(value=name)
	for i in range(opt.max_palettes + 1)
	]


	def change_prompt(state, prompt):
	state.prompts[state.current_palette] = prompt
	if opt.verbose:
	log_state(state)

	return state


	def change_neg_prompt(state, neg_prompt):
	state.neg_prompts[state.current_palette] = neg_prompt
	if opt.verbose:
	log_state(state)

	return state


	def select_model(state, model_id):
	state.model_id = model_id
	if opt.verbose:
	log_state(state)

	return state


	def select_style(state, style_name):
	state.style_name = style_name
	if opt.verbose:
	log_state(state)

	return state


	def select_quality(state, quality_name):
	state.quality_name = quality_name
	if opt.verbose:
	log_state(state)

	return state


	def import_state(state, json_text):
	current_palette = state.current_palette
	# active_palettes = state.active_palettes
	state = argparse.Namespace(**json.loads(json_text))
	state.active_palettes = opt.max_palettes
	return [state] + [
	gr.update(value=v, visible=True) for v in state.prompt_names
	] + [
	state.model_id,
	state.style_name,
	state.quality_name,
	state.prompts[current_palette],
	state.prompt_names[current_palette],
	state.neg_prompts[current_palette],
	state.prompt_strengths[current_palette - 1],
	state.mask_strengths[current_palette - 1],
	state.mask_stds[current_palette - 1],
	state.seed,
	]


	### Main worker

	@spaces.GPU
	def generate(state, args, *kwargs):
	return models[state.model_id](args, *kwargs)



	def run(state, drawpad):
	seed_everything(state.seed if state.seed >=0 else np.random.randint(2147483647))
	print('Generate!')

	background = drawpad['background'].convert('RGBA')
	inpainting_mode = np.asarray(background).sum() != 0
	print('Inpainting mode: ', inpainting_mode)

	user_input = np.asarray(drawpad['layers'][0]) # (H, W, 4)
	foreground_mask = torch.tensor(user_input[..., -1])[None, None] # (1, 1, H, W)
	user_input = torch.tensor(user_input[..., :-1]) # (H, W, 3)

	palette = torch.tensor([
	tuple(int(s[i+1:i+3], 16) for i in (0, 2, 4))
	for s in opt.colors[1:]
	]) # (N, 3)
	masks = (palette[:, None, None, :] == user_input[None]).all(dim=-1)[:, None, ...] # (N, 1, H, W)
	has_masks = [i for i, m in enumerate(masks.sum(dim=(1, 2, 3)) == 0) if not m]
	print('Has mask: ', has_masks)
	masks = masks * foreground_mask
	masks = masks[has_masks]

	if inpainting_mode:
	prompts = [state.prompts[v + 1] for v in has_masks]
	negative_prompts = [state.neg_prompts[v + 1] for v in has_masks]
	mask_strengths = [state.mask_strengths[v] for v in has_masks]
	mask_stds = [state.mask_stds[v] for v in has_masks]
	prompt_strengths = [state.prompt_strengths[v] for v in has_masks]
	else:
	masks = torch.cat([torch.ones_like(foreground_mask), masks], dim=0)
	prompts = [state.prompts[0]] + [state.prompts[v + 1] for v in has_masks]
	negative_prompts = [state.neg_prompts[0]] + [state.neg_prompts[v + 1] for v in has_masks]
	mask_strengths = [1] + [state.mask_strengths[v] for v in has_masks]
	mask_stds = [0] + [state.mask_stds[v] for v in has_masks]
	prompt_strengths = [1] + [state.prompt_strengths[v] for v in has_masks]

	prompts, negative_prompts = preprocess_prompts(
	prompts, negative_prompts, style_name=state.style_name, quality_name=state.quality_name)

	image = generate(
	state,
	prompts,
	negative_prompts,
	masks=masks,
	mask_strengths=mask_strengths,
	mask_stds=mask_stds,
	prompt_strengths=prompt_strengths,
	background=background.convert('RGB'),
	background_prompt=state.prompts[0],
	background_negative_prompt=state.neg_prompts[0],
	height=opt.height,
	width=opt.width,
	bootstrap_steps=opt.bootstrap_steps,
	guidance_scale=0,
	)
	return image


	### Load examples


	root = pathlib.Path(__file__).parent
	print(root)
	example_root = os.path.join(root, 'examples')
	example_images = glob.glob(os.path.join(example_root, '*.png'))
	example_images = [Image.open(i) for i in example_images]

	with open(os.path.join(example_root, 'prompt_background_advanced.txt')) as f:
	prompts_background = [l.strip() for l in f.readlines() if l.strip() != '']

	with open(os.path.join(example_root, 'prompt_girl.txt')) as f:
	prompts_girl = [l.strip() for l in f.readlines() if l.strip() != '']

	with open(os.path.join(example_root, 'prompt_boy.txt')) as f:
	prompts_boy = [l.strip() for l in f.readlines() if l.strip() != '']

	with open(os.path.join(example_root, 'prompt_props.txt')) as f:
	prompts_props = [l.strip() for l in f.readlines() if l.strip() != '']
	prompts_props = {l.split(',')[0].strip(): ','.join(l.split(',')[1:]).strip() for l in prompts_props}

	prompt_background = lambda: random.choice(prompts_background)
	prompt_girl = lambda: random.choice(prompts_girl)
	prompt_boy = lambda: random.choice(prompts_boy)
	prompt_props = lambda: np.random.choice(list(prompts_props.keys()), size=(opt.max_palettes - 2), replace=False).tolist()


	### Main application

	css = f"""
	#run-button {{
	font-size: 30pt;
	background-image: linear-gradient(to right, #4338ca 0%, #26a0da 51%, #4338ca 100%);
	margin: 0;
	padding: 15px 45px;
	text-align: center;
	text-transform: uppercase;
	transition: 0.5s;
	background-size: 200% auto;
	color: white;
	box-shadow: 0 0 20px #eee;
	border-radius: 10px;
	display: block;
	background-position: right center;
	}}

	#run-button:hover {{
	background-position: left center;
	color: #fff;
	text-decoration: none;
	}}

	#semantic-palette {{
	border-style: solid;
	border-width: 0.2em;
	border-color: #eee;
	}}

	#semantic-palette:hover {{
	box-shadow: 0 0 20px #eee;
	}}

	#output-screen {{
	width: 100%;
	aspect-ratio: {opt.width} / {opt.height};
	}}

	.layer-wrap {{
	display: none;
	}}

	#share-btn {{
	color: #ffffff;font-weight: 600;
	background-color: #000000;
	font-family: 'IBM Plex Sans', sans-serif;
	border-radius: 9999px !important;
	}}

	#share-btn:hover {{
	color: #ffffff;font-weight: 600;
	background-color: #000000;
	font-family: 'IBM Plex Sans', sans-serif;
	border-radius: 9999px !important;
	box-shadow: 0 0 20px #eee;
	}}
	"""

	for i in range(opt.max_palettes + 1):
	css = css + f"""
	.secondary#semantic-palette-{i} {{
	background-image: linear-gradient(to right, #374151 0%, #374151 71%, {opt.colors[i]} 100%);
	color: white;
	}}

	.primary#semantic-palette-{i} {{
	background-image: linear-gradient(to right, #4338ca 0%, #4338ca 71%, {opt.colors[i]} 100%);
	color: white;
	}}
	"""


	with gr.Blocks(theme=gr.themes.Soft(), css=css) as demo:

	iface = argparse.Namespace()

	def _define_state():
	state = argparse.Namespace()

	# Cursor.
	state.current_palette = 0 # 0: Background; 1,2,3,...: Layers
	state.model_id = list(model_dict.keys())[0]
	state.style_name = '(None)'
	state.quality_name = 'Standard v3.1'

	# State variables (one-hot).
	state.active_palettes = 1

	# Front-end initialized to the default values.
	prompt_props_ = prompt_props()
	state.prompt_names = [
	'🌄 Background',
	'👧 Girl',
	'👦 Boy',
	] + prompt_props_ + ['🎨 New Palette' for _ in range(opt.max_palettes - 5)]
	state.prompts = [
	prompt_background(),
	prompt_girl(),
	prompt_boy(),
	] + [prompts_props[k] for k in prompt_props_] + ['' for _ in range(opt.max_palettes - 5)]
	state.neg_prompts = [
	opt.default_negative_prompt
	+ (', humans, humans, humans' if i == 0 else '')
	for i in range(opt.max_palettes + 1)
	]
	state.prompt_strengths = [opt.default_prompt_strength for _ in range(opt.max_palettes)]
	state.mask_strengths = [opt.default_mask_strength for _ in range(opt.max_palettes)]
	state.mask_stds = [opt.default_mask_std for _ in range(opt.max_palettes)]
	state.seed = opt.seed
	return state

	state = gr.State(value=_define_state)


	### Demo user interface

	gr.HTML(
	"""
	<div style="display: flex; justify-content: center; align-items: center; text-align: center;">
	<div>
	<h1>🔥🧠 Semantic Palette <b>X</b> Animagine XL 3.1 🎨🔥</h1>
	<h5 style="margin: 0;">powered by</h5>
	<h3 style="margin-bottom: 0;"><a href="https://github.com/ironjr/StreamMultiDiffusion">StreamMultiDiffusion: Real-Time Interactive Generation with Region-Based Semantic Control</a>   <em>and</em></h3>
	<h3 style="margin-top: 0;"><a href="https://huggingface.co/cagliostrolab/animagine-xl-3.1">Animagine XL 3.1</a> by <a href="https://cagliostrolab.net/">Cagliostro Research Lab</a></h3>
	<h5 style="margin: 0;">If you ❤️ our project, please visit our Github and give us a 🌟!</h5>
	</br>
	<div style="display: flex; justify-content: center; align-items: center; text-align: center;">
	<a href='https://arxiv.org/abs/2403.09055'>
	<img src="https://img.shields.io/badge/arXiv-2403.09055-red">
	</a>

	<a href='https://jaerinlee.com/research/StreamMultiDiffusion'>
	<img src='https://img.shields.io/badge/Project-Page-green' alt='Project Page'>
	</a>

	<a href='https://github.com/ironjr/StreamMultiDiffusion'>
	<img src='https://img.shields.io/github/stars/ironjr/StreamMultiDiffusion?label=Github&color=blue'>
	</a>

	<a href='https://twitter.com/_ironjr_'>
	<img src='https://img.shields.io/twitter/url?label=_ironjr_&url=https%3A%2F%2Ftwitter.com%2F_ironjr_'>
	</a>

	<a href='https://github.com/ironjr/StreamMultiDiffusion/blob/main/LICENSE'>
	<img src='https://img.shields.io/badge/license-MIT-lightgrey'>
	</a>

	<a href='https://huggingface.co/papers/2403.09055'>
	<img src='https://img.shields.io/badge/%F0%9F%A4%97%20Paper-StreamMultiDiffusion-yellow'>
	</a>

	<a href='https://huggingface.co/cagliostrolab/animagine-xl-3.1'>
	<img src='https://img.shields.io/badge/%F0%9F%A4%97%20Model-AnimagineXL3.1-yellow'>
	</a>

	<a href='https://huggingface.co/spaces/ironjr/SemanticPalette'>
	<img src='https://img.shields.io/badge/%F0%9F%A4%97%20Demo-v1.5-yellow'>
	</a>

	<a href='https://huggingface.co/spaces/ironjr/StreamMultiDiffusion'>
	<img src='https://img.shields.io/badge/%F0%9F%A4%97%20Demo-StreamMultiDiffusion-yellow'>
	</a>
	</div>
	</div>
	</div>
	<div>
	</br>
	</div>
	"""
	)

	with gr.Row():

	iface.image_slot = gr.Image(
	interactive=False,
	show_label=False,
	show_download_button=True,
	type='pil',
	label='Generated Result',
	elem_id='output-screen',
	value=lambda: random.choice(example_images),
	)

	with gr.Row():

	with gr.Column(scale=1):

	with gr.Group(elem_id='semantic-palette'):

	gr.HTML(
	"""
	<div style="justify-content: center; align-items: center;">
	<br/>
	<h3 style="margin: 0; text-align: center;"><b>🧠 Semantic Palette 🎨</b></h3>
	<br/>
	</div>
	"""
	)

	iface.btn_semantics = [gr.Button(
	value=state.value.prompt_names[0],
	variant='primary',
	elem_id='semantic-palette-0',
	)]
	for i in range(opt.max_palettes):
	iface.btn_semantics.append(gr.Button(
	value=state.value.prompt_names[i + 1],
	variant='secondary',
	visible=(i < state.value.active_palettes),
	elem_id=f'semantic-palette-{i + 1}'
	))

	iface.btn_add_palette = gr.Button(
	value='Create New Semantic Brush',
	variant='primary',
	)

	with gr.Accordion(label='Import/Export Semantic Palette', open=False):
	iface.tbox_state_import = gr.Textbox(label='Put Palette JSON Here To Import')
	iface.json_state_export = gr.JSON(label='Exported Palette')
	iface.btn_export_state = gr.Button("Export Palette ➡️ JSON", variant='primary')
	iface.btn_import_state = gr.Button("Import JSON ➡️ Palette", variant='secondary')

	gr.HTML(
	"""
	<div>
	</br>
	</div>
	<div style="justify-content: center; align-items: center;">
	<h3 style="margin: 0; text-align: center;"><b>❓Usage❓</b></h3>
	</br>
	<div style="justify-content: center; align-items: left; text-align: left;">
	<p>1-1. Type in the background prompt. Background is not required if you paint the whole drawpad.</p>
	<p>1-2. (Optional: <em><b>Inpainting mode</b></em>) Uploading a background image will make the app into inpainting mode. Removing the image returns to the creation mode. In the inpainting mode, increasing the <em>Mask Blur STD</em> > 8 for every colored palette is recommended for smooth boundaries.</p>
	<p>2. Select a semantic brush by clicking onto one in the <b>Semantic Palette</b> above. Edit prompt for the semantic brush.</p>
	<p>2-1. If you are willing to draw more diverse images, try <b>Create New Semantic Brush</b>.</p>
	<p>3. Start drawing in the <b>Semantic Drawpad</b> tab. The brush color is directly linked to the semantic brushes.</p>
	<p>4. Click [<b>GENERATE!</b>] button to create your (large-scale) artwork!</p>
	</div>
	</div>
	"""
	)

	gr.HTML(
	"""
	<div style="display: flex; justify-content: center; align-items: center; text-align: center;">
	<h5 style="margin: 0;"><b>... or run in your own 🤗 space!</b></h5>
	</div>
	"""
	)

	gr.DuplicateButton()

	with gr.Column(scale=4):

	with gr.Row():

	with gr.Column(scale=3):

	iface.ctrl_semantic = gr.ImageEditor(
	image_mode='RGBA',
	sources=['upload', 'clipboard', 'webcam'],
	transforms=['crop'],
	crop_size=(opt.width, opt.height),
	brush=gr.Brush(
	colors=opt.colors[1:],
	color_mode="fixed",
	),
	type='pil',
	label='Semantic Drawpad',
	elem_id='drawpad',
	)

	with gr.Column(scale=1):

	iface.btn_generate = gr.Button(
	value='Generate!',
	variant='primary',
	# scale=1,
	elem_id='run-button'
	)
	iface.btn_share = gr.Button('🤗 Share with Community', elem_id='share-btn')

	iface.model_select = gr.Radio(
	list(model_dict.keys()),
	label='Stable Diffusion Checkpoint',
	info='Choose your favorite style.',
	value=state.value.model_id,
	)

	with gr.Accordion(label='Prompt Engineering', open=True):
	iface.quality_select = gr.Dropdown(
	label='Quality Presets',
	interactive=True,
	choices=list(_quality_dict.keys()),
	value='Standard v3.1',
	)
	iface.style_select = gr.Radio(
	label='Style Preset',
	container=True,
	interactive=True,
	choices=list(_style_dict.keys()),
	value='(None)',
	)

	with gr.Group(elem_id='control-panel'):

	with gr.Row():
	iface.tbox_prompt = gr.Textbox(
	label='Edit Prompt for Background',
	info='What do you want to draw?',
	value=state.value.prompts[0],
	placeholder=lambda: random.choice(prompt_suggestions),
	scale=2,
	)

	iface.tbox_name = gr.Textbox(
	label='Edit Brush Name',
	info='Just for your convenience.',
	value=state.value.prompt_names[0],
	placeholder='🌄 Background',
	scale=1,
	)

	with gr.Row():
	iface.tbox_neg_prompt = gr.Textbox(
	label='Edit Negative Prompt for Background',
	info='Add unwanted objects for this semantic brush.',
	value=opt.default_negative_prompt,
	scale=2,
	)

	iface.slider_strength = gr.Slider(
	label='Prompt Strength',
	info='Blends fg & bg in the prompt level, >0.8 Preferred.',
	minimum=0.5,
	maximum=1.0,
	value=opt.default_prompt_strength,
	scale=1,
	)

	with gr.Row():
	iface.slider_alpha = gr.Slider(
	label='Mask Alpha',
	info='Factor multiplied to the mask before quantization. Extremely sensitive, >0.98 Preferred.',
	minimum=0.5,
	maximum=1.0,
	value=opt.default_mask_strength,
	)

	iface.slider_std = gr.Slider(
	label='Mask Blur STD',
	info='Blends fg & bg in the latent level, 0 for generation, 8-32 for inpainting.',
	minimum=0.0001,
	maximum=100.0,
	value=opt.default_mask_std,
	)

	iface.slider_seed = gr.Slider(
	label='Seed',
	info='The global seed.',
	minimum=-1,
	maximum=2147483647,
	step=1,
	value=opt.seed,
	)

	### Attach event handlers

	for idx, btn in enumerate(iface.btn_semantics):
	btn.click(
	fn=partial(select_palette, idx=idx),
	inputs=[state, btn],
	outputs=[state] + iface.btn_semantics + [
	iface.tbox_name,
	iface.tbox_prompt,
	iface.tbox_neg_prompt,
	iface.slider_alpha,
	iface.slider_strength,
	iface.slider_std,
	],
	api_name=f'select_palette_{idx}',
	)

	iface.btn_add_palette.click(
	fn=add_palette,
	inputs=state,
	outputs=[state, iface.btn_add_palette] + iface.btn_semantics[1:],
	api_name='create_new',
	)

	iface.btn_generate.click(
	fn=run,
	inputs=[state, iface.ctrl_semantic],
	outputs=iface.image_slot,
	api_name='run',
	)

	iface.slider_alpha.input(
	fn=change_mask_strength,
	inputs=[state, iface.slider_alpha],
	outputs=state,
	api_name='change_alpha',
	)
	iface.slider_std.input(
	fn=change_std,
	inputs=[state, iface.slider_std],
	outputs=state,
	api_name='change_std',
	)
	iface.slider_strength.input(
	fn=change_prompt_strength,
	inputs=[state, iface.slider_strength],
	outputs=state,
	api_name='change_strength',
	)
	iface.slider_seed.input(
	fn=reset_seed,
	inputs=[state, iface.slider_seed],
	outputs=state,
	api_name='reset_seed',
	)

	iface.tbox_name.input(
	fn=rename_prompt,
	inputs=[state, iface.tbox_name],
	outputs=[state] + iface.btn_semantics,
	api_name='prompt_rename',
	)
	iface.tbox_prompt.input(
	fn=change_prompt,
	inputs=[state, iface.tbox_prompt],
	outputs=state,
	api_name='prompt_edit',
	)
	iface.tbox_neg_prompt.input(
	fn=change_neg_prompt,
	inputs=[state, iface.tbox_neg_prompt],
	outputs=state,
	api_name='neg_prompt_edit',
	)

	iface.model_select.change(
	fn=select_model,
	inputs=[state, iface.model_select],
	outputs=state,
	api_name='model_select',
	)
	iface.style_select.change(
	fn=select_style,
	inputs=[state, iface.style_select],
	outputs=state,
	api_name='style_select',
	)
	iface.quality_select.change(
	fn=select_quality,
	inputs=[state, iface.quality_select],
	outputs=state,
	api_name='quality_select',
	)

	iface.btn_share.click(None, [], [], js=share_js)

	iface.btn_export_state.click(lambda x: vars(x), state, iface.json_state_export)
	iface.btn_import_state.click(import_state, [state, iface.tbox_state_import], [
	state,
	*iface.btn_semantics,
	iface.model_select,
	iface.style_select,
	iface.quality_select,
	iface.tbox_prompt,
	iface.tbox_name,
	iface.tbox_neg_prompt,
	iface.slider_strength,
	iface.slider_alpha,
	iface.slider_std,
	iface.slider_seed,
	])

	gr.HTML(
	"""
	<div class="footer">
	<p>We thank <a href="https://cagliostrolab.net/">Cagliostro Research Lab</a> for their permission to use <a href="https://huggingface.co/cagliostrolab/animagine-xl-3.1">Animagine XL 3.1</a> model under academic purpose.
	Note that the MIT license only applies to StreamMultiDiffusion and Semantic Palette demo app, but not Animagine XL 3.1 model, which is distributed under <a href="https://freedevproject.org/faipl-1.0-sd/">Fair AI Public License 1.0-SD</a>.
	</p>
	</div>
	"""
	)

	if __name__ == '__main__':
	demo.queue(max_size=20).launch()