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import base64
import html
import io
import json
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import math
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import mimetypes
import os
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import random
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import sys
import time
import traceback
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import platform
import subprocess as sp
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from functools import reduce
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import numpy as np
import torch
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from PIL import Image , PngImagePlugin
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import piexif
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import gradio as gr
import gradio . utils
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import gradio . routes
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from modules import sd_hijack
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from modules . paths import script_path
from modules . shared import opts , cmd_opts
import modules . shared as shared
from modules . sd_samplers import samplers , samplers_for_img2img
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from modules . sd_hijack import model_hijack
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import modules . ldsr_model
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import modules . scripts
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import modules . gfpgan_model
import modules . codeformer_model
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import modules . styles
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import modules . generation_parameters_copypaste
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from modules import prompt_parser
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from modules . images import save_image
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import modules . textual_inversion . ui
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import modules . hypernetwork . ui
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# this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the bowser will not show any UI
mimetypes . init ( )
mimetypes . add_type ( ' application/javascript ' , ' .js ' )
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if not cmd_opts . share and not cmd_opts . listen :
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# fix gradio phoning home
gradio . utils . version_check = lambda : None
gradio . utils . get_local_ip_address = lambda : ' 127.0.0.1 '
def gr_show ( visible = True ) :
return { " visible " : visible , " __type__ " : " update " }
sample_img2img = " assets/stable-samples/img2img/sketch-mountains-input.jpg "
sample_img2img = sample_img2img if os . path . exists ( sample_img2img ) else None
css_hide_progressbar = """
. wrap . m - 12 svg { display : none ! important ; }
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. wrap . m - 12 : : before { content : " Loading... " }
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. progress - bar { display : none ! important ; }
. meta - text { display : none ! important ; }
"""
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# Using constants for these since the variation selector isn't visible.
# Important that they exactly match script.js for tooltip to work.
random_symbol = ' \U0001f3b2 \ufe0f ' # 🎲️
reuse_symbol = ' \u267b \ufe0f ' # ♻️
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art_symbol = ' \U0001f3a8 ' # 🎨
paste_symbol = ' \u2199 \ufe0f ' # ↙
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folder_symbol = ' \U0001f4c2 ' # 📂
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def plaintext_to_html ( text ) :
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text = " <p> " + " <br> \n " . join ( [ f " { html . escape ( x ) } " for x in text . split ( ' \n ' ) ] ) + " </p> "
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return text
def image_from_url_text ( filedata ) :
if type ( filedata ) == list :
if len ( filedata ) == 0 :
return None
filedata = filedata [ 0 ]
if filedata . startswith ( " data:image/png;base64, " ) :
filedata = filedata [ len ( " data:image/png;base64, " ) : ]
filedata = base64 . decodebytes ( filedata . encode ( ' utf-8 ' ) )
image = Image . open ( io . BytesIO ( filedata ) )
return image
def send_gradio_gallery_to_image ( x ) :
if len ( x ) == 0 :
return None
return image_from_url_text ( x [ 0 ] )
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def save_files ( js_data , images , index ) :
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import csv
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filenames = [ ]
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#quick dictionary to class object conversion. Its neccesary due apply_filename_pattern requiring it
class MyObject :
def __init__ ( self , d = None ) :
if d is not None :
for key , value in d . items ( ) :
setattr ( self , key , value )
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data = json . loads ( js_data )
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p = MyObject ( data )
path = opts . outdir_save
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save_to_dirs = opts . use_save_to_dirs_for_ui
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extension : str = opts . samples_format
start_index = 0
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if index > - 1 and opts . save_selected_only and ( index > = data [ " index_of_first_image " ] ) : # ensures we are looking at a specific non-grid picture, and we have save_selected_only
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images = [ images [ index ] ]
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start_index = index
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with open ( os . path . join ( opts . outdir_save , " log.csv " ) , " a " , encoding = " utf8 " , newline = ' ' ) as file :
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at_start = file . tell ( ) == 0
writer = csv . writer ( file )
if at_start :
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writer . writerow ( [ " prompt " , " seed " , " width " , " height " , " sampler " , " cfgs " , " steps " , " filename " , " negative_prompt " ] )
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for image_index , filedata in enumerate ( images , start_index ) :
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if filedata . startswith ( " data:image/png;base64, " ) :
filedata = filedata [ len ( " data:image/png;base64, " ) : ]
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image = Image . open ( io . BytesIO ( base64 . decodebytes ( filedata . encode ( ' utf-8 ' ) ) ) )
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is_grid = image_index < p . index_of_first_image
i = 0 if is_grid else ( image_index - p . index_of_first_image )
fullfn = save_image ( image , path , " " , seed = p . all_seeds [ i ] , prompt = p . all_prompts [ i ] , extension = extension , info = p . infotexts [ image_index ] , grid = is_grid , p = p , save_to_dirs = save_to_dirs )
filename = os . path . relpath ( fullfn , path )
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filenames . append ( filename )
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writer . writerow ( [ data [ " prompt " ] , data [ " seed " ] , data [ " width " ] , data [ " height " ] , data [ " sampler " ] , data [ " cfg_scale " ] , data [ " steps " ] , filenames [ 0 ] , data [ " negative_prompt " ] ] )
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return ' ' , ' ' , plaintext_to_html ( f " Saved: { filenames [ 0 ] } " )
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def wrap_gradio_call ( func , extra_outputs = None ) :
def f ( * args , extra_outputs_array = extra_outputs , * * kwargs ) :
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run_memmon = opts . memmon_poll_rate > 0 and not shared . mem_mon . disabled
if run_memmon :
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shared . mem_mon . monitor ( )
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t = time . perf_counter ( )
try :
res = list ( func ( * args , * * kwargs ) )
except Exception as e :
print ( " Error completing request " , file = sys . stderr )
print ( " Arguments: " , args , kwargs , file = sys . stderr )
print ( traceback . format_exc ( ) , file = sys . stderr )
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shared . state . job = " "
shared . state . job_count = 0
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if extra_outputs_array is None :
extra_outputs_array = [ None , ' ' ]
res = extra_outputs_array + [ f " <div class= ' error ' > { plaintext_to_html ( type ( e ) . __name__ + ' : ' + str ( e ) ) } </div> " ]
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elapsed = time . perf_counter ( ) - t
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elapsed_m = int ( elapsed / / 60 )
elapsed_s = elapsed % 60
elapsed_text = f " { elapsed_s : .2f } s "
if ( elapsed_m > 0 ) :
elapsed_text = f " { elapsed_m } m " + elapsed_text
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if run_memmon :
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mem_stats = { k : - ( v / / - ( 1024 * 1024 ) ) for k , v in shared . mem_mon . stop ( ) . items ( ) }
active_peak = mem_stats [ ' active_peak ' ]
reserved_peak = mem_stats [ ' reserved_peak ' ]
sys_peak = mem_stats [ ' system_peak ' ]
sys_total = mem_stats [ ' total ' ]
sys_pct = round ( sys_peak / max ( sys_total , 1 ) * 100 , 2 )
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vram_html = f " <p class= ' vram ' >Torch active/reserved: { active_peak } / { reserved_peak } MiB, <wbr>Sys VRAM: { sys_peak } / { sys_total } MiB ( { sys_pct } %)</p> "
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else :
vram_html = ' '
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# last item is always HTML
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res [ - 1 ] + = f " <div class= ' performance ' ><p class= ' time ' >Time taken: <wbr> { elapsed_text } </p> { vram_html } </div> "
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shared . state . interrupted = False
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shared . state . job_count = 0
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return tuple ( res )
return f
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def check_progress_call ( id_part ) :
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if shared . state . job_count == 0 :
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return " " , gr_show ( False ) , gr_show ( False ) , gr_show ( False )
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progress = 0
if shared . state . job_count > 0 :
progress + = shared . state . job_no / shared . state . job_count
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if shared . state . sampling_steps > 0 :
progress + = 1 / shared . state . job_count * shared . state . sampling_step / shared . state . sampling_steps
progress = min ( progress , 1 )
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progressbar = " "
if opts . show_progressbar :
progressbar = f """ <div class= ' progressDiv ' ><div class= ' progress ' style= " width: { progress * 100 } % " > { str ( int ( progress * 100 ) ) + " % " if progress > 0.01 else " " } </div></div> """
image = gr_show ( False )
preview_visibility = gr_show ( False )
if opts . show_progress_every_n_steps > 0 :
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if shared . parallel_processing_allowed :
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if shared . state . sampling_step - shared . state . current_image_sampling_step > = opts . show_progress_every_n_steps and shared . state . current_latent is not None :
shared . state . current_image = modules . sd_samplers . sample_to_image ( shared . state . current_latent )
shared . state . current_image_sampling_step = shared . state . sampling_step
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image = shared . state . current_image
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if image is None :
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image = gr . update ( value = None )
else :
preview_visibility = gr_show ( True )
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if shared . state . textinfo is not None :
textinfo_result = gr . HTML . update ( value = shared . state . textinfo , visible = True )
else :
textinfo_result = gr_show ( False )
return f " <span id= ' { id_part } _progress_span ' style= ' display: none ' > { time . time ( ) } </span><p> { progressbar } </p> " , preview_visibility , image , textinfo_result
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def check_progress_call_initial ( id_part ) :
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shared . state . job_count = - 1
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shared . state . current_latent = None
shared . state . current_image = None
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shared . state . textinfo = None
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return check_progress_call ( id_part )
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def roll_artist ( prompt ) :
allowed_cats = set ( [ x for x in shared . artist_db . categories ( ) if len ( opts . random_artist_categories ) == 0 or x in opts . random_artist_categories ] )
artist = random . choice ( [ x for x in shared . artist_db . artists if x . category in allowed_cats ] )
return prompt + " , " + artist . name if prompt != ' ' else artist . name
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def visit ( x , func , path = " " ) :
if hasattr ( x , ' children ' ) :
for c in x . children :
visit ( c , func , path )
elif x . label is not None :
func ( path + " / " + str ( x . label ) , x )
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def add_style ( name : str , prompt : str , negative_prompt : str ) :
if name is None :
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return [ gr_show ( ) , gr_show ( ) ]
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style = modules . styles . PromptStyle ( name , prompt , negative_prompt )
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shared . prompt_styles . styles [ style . name ] = style
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# Save all loaded prompt styles: this allows us to update the storage format in the future more easily, because we
# reserialize all styles every time we save them
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shared . prompt_styles . save_styles ( shared . styles_filename )
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return [ gr . Dropdown . update ( visible = True , choices = list ( shared . prompt_styles . styles ) ) for _ in range ( 4 ) ]
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def apply_styles ( prompt , prompt_neg , style1_name , style2_name ) :
prompt = shared . prompt_styles . apply_styles_to_prompt ( prompt , [ style1_name , style2_name ] )
prompt_neg = shared . prompt_styles . apply_negative_styles_to_prompt ( prompt_neg , [ style1_name , style2_name ] )
return [ gr . Textbox . update ( value = prompt ) , gr . Textbox . update ( value = prompt_neg ) , gr . Dropdown . update ( value = " None " ) , gr . Dropdown . update ( value = " None " ) ]
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def interrogate ( image ) :
prompt = shared . interrogator . interrogate ( image )
return gr_show ( True ) if prompt is None else prompt
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def create_seed_inputs ( ) :
with gr . Row ( ) :
with gr . Box ( ) :
with gr . Row ( elem_id = ' seed_row ' ) :
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seed = ( gr . Textbox if cmd_opts . use_textbox_seed else gr . Number ) ( label = ' Seed ' , value = - 1 )
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seed . style ( container = False )
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random_seed = gr . Button ( random_symbol , elem_id = ' random_seed ' )
reuse_seed = gr . Button ( reuse_symbol , elem_id = ' reuse_seed ' )
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with gr . Box ( elem_id = ' subseed_show_box ' ) :
seed_checkbox = gr . Checkbox ( label = ' Extra ' , elem_id = ' subseed_show ' , value = False )
# Components to show/hide based on the 'Extra' checkbox
seed_extras = [ ]
with gr . Row ( visible = False ) as seed_extra_row_1 :
seed_extras . append ( seed_extra_row_1 )
with gr . Box ( ) :
with gr . Row ( elem_id = ' subseed_row ' ) :
subseed = gr . Number ( label = ' Variation seed ' , value = - 1 )
subseed . style ( container = False )
random_subseed = gr . Button ( random_symbol , elem_id = ' random_subseed ' )
reuse_subseed = gr . Button ( reuse_symbol , elem_id = ' reuse_subseed ' )
subseed_strength = gr . Slider ( label = ' Variation strength ' , value = 0.0 , minimum = 0 , maximum = 1 , step = 0.01 )
with gr . Row ( visible = False ) as seed_extra_row_2 :
seed_extras . append ( seed_extra_row_2 )
seed_resize_from_w = gr . Slider ( minimum = 0 , maximum = 2048 , step = 64 , label = " Resize seed from width " , value = 0 )
seed_resize_from_h = gr . Slider ( minimum = 0 , maximum = 2048 , step = 64 , label = " Resize seed from height " , value = 0 )
random_seed . click ( fn = lambda : - 1 , show_progress = False , inputs = [ ] , outputs = [ seed ] )
random_subseed . click ( fn = lambda : - 1 , show_progress = False , inputs = [ ] , outputs = [ subseed ] )
def change_visibility ( show ) :
return { comp : gr_show ( show ) for comp in seed_extras }
seed_checkbox . change ( change_visibility , show_progress = False , inputs = [ seed_checkbox ] , outputs = seed_extras )
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return seed , reuse_seed , subseed , reuse_subseed , subseed_strength , seed_resize_from_h , seed_resize_from_w , seed_checkbox
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def connect_reuse_seed ( seed : gr . Number , reuse_seed : gr . Button , generation_info : gr . Textbox , dummy_component , is_subseed ) :
""" Connects a ' reuse (sub)seed ' button ' s click event so that it copies last used
( sub ) seed value from generation info the to the seed field . If copying subseed and subseed strength
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was 0 , i . e . no variation seed was used , it copies the normal seed value instead . """
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def copy_seed ( gen_info_string : str , index ) :
res = - 1
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try :
gen_info = json . loads ( gen_info_string )
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index - = gen_info . get ( ' index_of_first_image ' , 0 )
if is_subseed and gen_info . get ( ' subseed_strength ' , 0 ) > 0 :
all_subseeds = gen_info . get ( ' all_subseeds ' , [ - 1 ] )
res = all_subseeds [ index if 0 < = index < len ( all_subseeds ) else 0 ]
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else :
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all_seeds = gen_info . get ( ' all_seeds ' , [ - 1 ] )
res = all_seeds [ index if 0 < = index < len ( all_seeds ) else 0 ]
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except json . decoder . JSONDecodeError as e :
if gen_info_string != ' ' :
print ( " Error parsing JSON generation info: " , file = sys . stderr )
print ( gen_info_string , file = sys . stderr )
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return [ res , gr_show ( False ) ]
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reuse_seed . click (
fn = copy_seed ,
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_js = " (x, y) => [x, selected_gallery_index()] " ,
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show_progress = False ,
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inputs = [ generation_info , dummy_component ] ,
outputs = [ seed , dummy_component ]
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)
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def update_token_counter ( text , steps ) :
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try :
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_ , prompt_flat_list , _ = prompt_parser . get_multicond_prompt_list ( [ text ] )
prompt_schedules = prompt_parser . get_learned_conditioning_prompt_schedules ( prompt_flat_list , steps )
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except Exception :
# a parsing error can happen here during typing, and we don't want to bother the user with
# messages related to it in console
prompt_schedules = [ [ [ steps , text ] ] ]
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flat_prompts = reduce ( lambda list1 , list2 : list1 + list2 , prompt_schedules )
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prompts = [ prompt_text for step , prompt_text in flat_prompts ]
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tokens , token_count , max_length = max ( [ model_hijack . tokenize ( prompt ) for prompt in prompts ] , key = lambda args : args [ 1 ] )
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style_class = ' class= " red " ' if ( token_count > max_length ) else " "
return f " <span { style_class } > { token_count } / { max_length } </span> "
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def create_toprow ( is_img2img ) :
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id_part = " img2img " if is_img2img else " txt2img "
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with gr . Row ( elem_id = " toprow " ) :
with gr . Column ( scale = 4 ) :
with gr . Row ( ) :
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with gr . Column ( scale = 80 ) :
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with gr . Row ( ) :
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prompt = gr . Textbox ( label = " Prompt " , elem_id = f " { id_part } _prompt " , show_label = False , placeholder = " Prompt " , lines = 2 )
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with gr . Column ( scale = 1 , elem_id = " roll_col " ) :
roll = gr . Button ( value = art_symbol , elem_id = " roll " , visible = len ( shared . artist_db . artists ) > 0 )
paste = gr . Button ( value = paste_symbol , elem_id = " paste " )
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token_counter = gr . HTML ( value = " <span></span> " , elem_id = f " { id_part } _token_counter " )
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token_button = gr . Button ( visible = False , elem_id = f " { id_part } _token_button " )
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with gr . Column ( scale = 10 , elem_id = " style_pos_col " ) :
prompt_style = gr . Dropdown ( label = " Style 1 " , elem_id = f " { id_part } _style_index " , choices = [ k for k , v in shared . prompt_styles . styles . items ( ) ] , value = next ( iter ( shared . prompt_styles . styles . keys ( ) ) ) , visible = len ( shared . prompt_styles . styles ) > 1 )
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with gr . Row ( ) :
with gr . Column ( scale = 8 ) :
negative_prompt = gr . Textbox ( label = " Negative prompt " , elem_id = " negative_prompt " , show_label = False , placeholder = " Negative prompt " , lines = 2 )
with gr . Column ( scale = 1 , elem_id = " style_neg_col " ) :
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prompt_style2 = gr . Dropdown ( label = " Style 2 " , elem_id = f " { id_part } _style2_index " , choices = [ k for k , v in shared . prompt_styles . styles . items ( ) ] , value = next ( iter ( shared . prompt_styles . styles . keys ( ) ) ) , visible = len ( shared . prompt_styles . styles ) > 1 )
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with gr . Column ( scale = 1 ) :
with gr . Row ( ) :
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interrupt = gr . Button ( ' Interrupt ' , elem_id = f " { id_part } _interrupt " )
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submit = gr . Button ( ' Generate ' , elem_id = f " { id_part } _generate " , variant = ' primary ' )
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interrupt . click (
fn = lambda : shared . state . interrupt ( ) ,
inputs = [ ] ,
outputs = [ ] ,
)
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with gr . Row ( ) :
if is_img2img :
interrogate = gr . Button ( ' Interrogate ' , elem_id = " interrogate " )
else :
interrogate = None
prompt_style_apply = gr . Button ( ' Apply style ' , elem_id = " style_apply " )
save_style = gr . Button ( ' Create style ' , elem_id = " style_create " )
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return prompt , roll , prompt_style , negative_prompt , prompt_style2 , submit , interrogate , prompt_style_apply , save_style , paste , token_counter , token_button
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def setup_progressbar ( progressbar , preview , id_part , textinfo = None ) :
if textinfo is None :
textinfo = gr . HTML ( visible = False )
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check_progress = gr . Button ( ' Check progress ' , elem_id = f " { id_part } _check_progress " , visible = False )
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check_progress . click (
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fn = lambda : check_progress_call ( id_part ) ,
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show_progress = False ,
inputs = [ ] ,
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outputs = [ progressbar , preview , preview , textinfo ] ,
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)
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check_progress_initial = gr . Button ( ' Check progress (first) ' , elem_id = f " { id_part } _check_progress_initial " , visible = False )
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check_progress_initial . click (
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fn = lambda : check_progress_call_initial ( id_part ) ,
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show_progress = False ,
inputs = [ ] ,
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outputs = [ progressbar , preview , preview , textinfo ] ,
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)
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def create_ui ( wrap_gradio_gpu_call ) :
import modules . img2img
import modules . txt2img
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with gr . Blocks ( analytics_enabled = False ) as txt2img_interface :
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txt2img_prompt , roll , txt2img_prompt_style , txt2img_negative_prompt , txt2img_prompt_style2 , submit , _ , txt2img_prompt_style_apply , txt2img_save_style , paste , token_counter , token_button = create_toprow ( is_img2img = False )
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dummy_component = gr . Label ( visible = False )
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with gr . Row ( elem_id = ' txt2img_progress_row ' ) :
with gr . Column ( scale = 1 ) :
pass
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with gr . Column ( scale = 1 ) :
progressbar = gr . HTML ( elem_id = " txt2img_progressbar " )
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txt2img_preview = gr . Image ( elem_id = ' txt2img_preview ' , visible = False )
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setup_progressbar ( progressbar , txt2img_preview , ' txt2img ' )
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with gr . Row ( ) . style ( equal_height = False ) :
with gr . Column ( variant = ' panel ' ) :
steps = gr . Slider ( minimum = 1 , maximum = 150 , step = 1 , label = " Sampling Steps " , value = 20 )
sampler_index = gr . Radio ( label = ' Sampling method ' , elem_id = " txt2img_sampling " , choices = [ x . name for x in samplers ] , value = samplers [ 0 ] . name , type = " index " )
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with gr . Group ( ) :
width = gr . Slider ( minimum = 64 , maximum = 2048 , step = 64 , label = " Width " , value = 512 )
height = gr . Slider ( minimum = 64 , maximum = 2048 , step = 64 , label = " Height " , value = 512 )
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with gr . Row ( ) :
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restore_faces = gr . Checkbox ( label = ' Restore faces ' , value = False , visible = len ( shared . face_restorers ) > 1 )
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tiling = gr . Checkbox ( label = ' Tiling ' , value = False )
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enable_hr = gr . Checkbox ( label = ' Highres. fix ' , value = False )
with gr . Row ( visible = False ) as hr_options :
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scale_latent = gr . Checkbox ( label = ' Scale latent ' , value = False )
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denoising_strength = gr . Slider ( minimum = 0.0 , maximum = 1.0 , step = 0.01 , label = ' Denoising strength ' , value = 0.7 )
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with gr . Row ( ) :
batch_count = gr . Slider ( minimum = 1 , maximum = cmd_opts . max_batch_count , step = 1 , label = ' Batch count ' , value = 1 )
batch_size = gr . Slider ( minimum = 1 , maximum = 8 , step = 1 , label = ' Batch size ' , value = 1 )
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cfg_scale = gr . Slider ( minimum = 1.0 , maximum = 30.0 , step = 0.5 , label = ' CFG Scale ' , value = 7.0 )
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seed , reuse_seed , subseed , reuse_subseed , subseed_strength , seed_resize_from_h , seed_resize_from_w , seed_checkbox = create_seed_inputs ( )
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with gr . Group ( ) :
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custom_inputs = modules . scripts . scripts_txt2img . setup_ui ( is_img2img = False )
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with gr . Column ( variant = ' panel ' ) :
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with gr . Group ( ) :
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txt2img_preview = gr . Image ( elem_id = ' txt2img_preview ' , visible = False )
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txt2img_gallery = gr . Gallery ( label = ' Output ' , show_label = False , elem_id = ' txt2img_gallery ' ) . style ( grid = 4 )
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with gr . Group ( ) :
with gr . Row ( ) :
save = gr . Button ( ' Save ' )
send_to_img2img = gr . Button ( ' Send to img2img ' )
send_to_inpaint = gr . Button ( ' Send to inpaint ' )
send_to_extras = gr . Button ( ' Send to extras ' )
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button_id = " hidden_element " if shared . cmd_opts . hide_ui_dir_config else ' open_folder '
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open_txt2img_folder = gr . Button ( folder_symbol , elem_id = button_id )
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with gr . Group ( ) :
html_info = gr . HTML ( )
generation_info = gr . Textbox ( visible = False )
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connect_reuse_seed ( seed , reuse_seed , generation_info , dummy_component , is_subseed = False )
connect_reuse_seed ( subseed , reuse_subseed , generation_info , dummy_component , is_subseed = True )
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txt2img_args = dict (
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fn = wrap_gradio_gpu_call ( modules . txt2img . txt2img ) ,
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_js = " submit " ,
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inputs = [
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txt2img_prompt ,
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txt2img_negative_prompt ,
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txt2img_prompt_style ,
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txt2img_prompt_style2 ,
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steps ,
sampler_index ,
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restore_faces ,
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tiling ,
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batch_count ,
batch_size ,
cfg_scale ,
seed ,
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subseed , subseed_strength , seed_resize_from_h , seed_resize_from_w , seed_checkbox ,
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height ,
width ,
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enable_hr ,
scale_latent ,
denoising_strength ,
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] + custom_inputs ,
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outputs = [
txt2img_gallery ,
generation_info ,
html_info
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] ,
show_progress = False ,
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)
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txt2img_prompt . submit ( * * txt2img_args )
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submit . click ( * * txt2img_args )
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enable_hr . change (
fn = lambda x : gr_show ( x ) ,
inputs = [ enable_hr ] ,
outputs = [ hr_options ] ,
)
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save . click (
fn = wrap_gradio_call ( save_files ) ,
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_js = " (x, y, z) => [x, y, selected_gallery_index()] " ,
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inputs = [
generation_info ,
txt2img_gallery ,
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html_info ,
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] ,
outputs = [
html_info ,
html_info ,
html_info ,
]
)
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roll . click (
fn = roll_artist ,
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_js = " update_txt2img_tokens " ,
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inputs = [
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txt2img_prompt ,
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] ,
outputs = [
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txt2img_prompt ,
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]
)
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txt2img_paste_fields = [
( txt2img_prompt , " Prompt " ) ,
( txt2img_negative_prompt , " Negative prompt " ) ,
( steps , " Steps " ) ,
( sampler_index , " Sampler " ) ,
( restore_faces , " Face restoration " ) ,
( cfg_scale , " CFG scale " ) ,
( seed , " Seed " ) ,
( width , " Size-1 " ) ,
( height , " Size-2 " ) ,
( batch_size , " Batch size " ) ,
( subseed , " Variation seed " ) ,
( subseed_strength , " Variation seed strength " ) ,
( seed_resize_from_w , " Seed resize from-1 " ) ,
( seed_resize_from_h , " Seed resize from-2 " ) ,
( denoising_strength , " Denoising strength " ) ,
( enable_hr , lambda d : " Denoising strength " in d ) ,
( hr_options , lambda d : gr . Row . update ( visible = " Denoising strength " in d ) ) ,
]
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modules . generation_parameters_copypaste . connect_paste ( paste , txt2img_paste_fields , txt2img_prompt )
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token_button . click ( fn = update_token_counter , inputs = [ txt2img_prompt , steps ] , outputs = [ token_counter ] )
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with gr . Blocks ( analytics_enabled = False ) as img2img_interface :
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img2img_prompt , roll , img2img_prompt_style , img2img_negative_prompt , img2img_prompt_style2 , submit , img2img_interrogate , img2img_prompt_style_apply , img2img_save_style , paste , token_counter , token_button = create_toprow ( is_img2img = True )
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with gr . Row ( elem_id = ' img2img_progress_row ' ) :
with gr . Column ( scale = 1 ) :
pass
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with gr . Column ( scale = 1 ) :
progressbar = gr . HTML ( elem_id = " img2img_progressbar " )
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img2img_preview = gr . Image ( elem_id = ' img2img_preview ' , visible = False )
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setup_progressbar ( progressbar , img2img_preview , ' img2img ' )
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with gr . Row ( ) . style ( equal_height = False ) :
with gr . Column ( variant = ' panel ' ) :
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with gr . Tabs ( elem_id = " mode_img2img " ) as tabs_img2img_mode :
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with gr . TabItem ( ' img2img ' , id = ' img2img ' ) :
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init_img = gr . Image ( label = " Image for img2img " , elem_id = " img2img_image " , show_label = False , source = " upload " , interactive = True , type = " pil " , tool = cmd_opts . gradio_img2img_tool )
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with gr . TabItem ( ' Inpaint ' , id = ' inpaint ' ) :
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init_img_with_mask = gr . Image ( label = " Image for inpainting with mask " , show_label = False , elem_id = " img2maskimg " , source = " upload " , interactive = True , type = " pil " , tool = " sketch " , image_mode = " RGBA " )
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init_img_inpaint = gr . Image ( label = " Image for img2img " , show_label = False , source = " upload " , interactive = True , type = " pil " , visible = False , elem_id = " img_inpaint_base " )
init_mask_inpaint = gr . Image ( label = " Mask " , source = " upload " , interactive = True , type = " pil " , visible = False , elem_id = " img_inpaint_mask " )
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mask_blur = gr . Slider ( label = ' Mask blur ' , minimum = 0 , maximum = 64 , step = 1 , value = 4 )
with gr . Row ( ) :
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mask_mode = gr . Radio ( label = " Mask mode " , show_label = False , choices = [ " Draw mask " , " Upload mask " ] , type = " index " , value = " Draw mask " , elem_id = " mask_mode " )
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inpainting_mask_invert = gr . Radio ( label = ' Masking mode ' , show_label = False , choices = [ ' Inpaint masked ' , ' Inpaint not masked ' ] , value = ' Inpaint masked ' , type = " index " )
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inpainting_fill = gr . Radio ( label = ' Masked content ' , choices = [ ' fill ' , ' original ' , ' latent noise ' , ' latent nothing ' ] , value = ' original ' , type = " index " )
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with gr . Row ( ) :
inpaint_full_res = gr . Checkbox ( label = ' Inpaint at full resolution ' , value = False )
inpaint_full_res_padding = gr . Slider ( label = ' Inpaint at full resolution padding, pixels ' , minimum = 0 , maximum = 256 , step = 4 , value = 32 )
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with gr . TabItem ( ' Batch img2img ' , id = ' batch ' ) :
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hidden = ' <br>Disabled when launched with --hide-ui-dir-config. ' if shared . cmd_opts . hide_ui_dir_config else ' '
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gr . HTML ( f " <p class= \" text-gray-500 \" >Process images in a directory on the same machine where the server is running.<br>Use an empty output directory to save pictures normally instead of writing to the output directory. { hidden } </p> " )
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img2img_batch_input_dir = gr . Textbox ( label = " Input directory " , * * shared . hide_dirs )
img2img_batch_output_dir = gr . Textbox ( label = " Output directory " , * * shared . hide_dirs )
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with gr . Row ( ) :
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resize_mode = gr . Radio ( label = " Resize mode " , elem_id = " resize_mode " , show_label = False , choices = [ " Just resize " , " Crop and resize " , " Resize and fill " ] , type = " index " , value = " Just resize " )
steps = gr . Slider ( minimum = 1 , maximum = 150 , step = 1 , label = " Sampling Steps " , value = 20 )
sampler_index = gr . Radio ( label = ' Sampling method ' , choices = [ x . name for x in samplers_for_img2img ] , value = samplers_for_img2img [ 0 ] . name , type = " index " )
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with gr . Group ( ) :
width = gr . Slider ( minimum = 64 , maximum = 2048 , step = 64 , label = " Width " , value = 512 )
height = gr . Slider ( minimum = 64 , maximum = 2048 , step = 64 , label = " Height " , value = 512 )
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with gr . Row ( ) :
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restore_faces = gr . Checkbox ( label = ' Restore faces ' , value = False , visible = len ( shared . face_restorers ) > 1 )
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tiling = gr . Checkbox ( label = ' Tiling ' , value = False )
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with gr . Row ( ) :
batch_count = gr . Slider ( minimum = 1 , maximum = cmd_opts . max_batch_count , step = 1 , label = ' Batch count ' , value = 1 )
batch_size = gr . Slider ( minimum = 1 , maximum = 8 , step = 1 , label = ' Batch size ' , value = 1 )
with gr . Group ( ) :
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cfg_scale = gr . Slider ( minimum = 1.0 , maximum = 30.0 , step = 0.5 , label = ' CFG Scale ' , value = 7.0 )
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denoising_strength = gr . Slider ( minimum = 0.0 , maximum = 1.0 , step = 0.01 , label = ' Denoising strength ' , value = 0.75 )
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seed , reuse_seed , subseed , reuse_subseed , subseed_strength , seed_resize_from_h , seed_resize_from_w , seed_checkbox = create_seed_inputs ( )
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with gr . Group ( ) :
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custom_inputs = modules . scripts . scripts_img2img . setup_ui ( is_img2img = True )
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with gr . Column ( variant = ' panel ' ) :
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with gr . Group ( ) :
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img2img_preview = gr . Image ( elem_id = ' img2img_preview ' , visible = False )
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img2img_gallery = gr . Gallery ( label = ' Output ' , show_label = False , elem_id = ' img2img_gallery ' ) . style ( grid = 4 )
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with gr . Group ( ) :
with gr . Row ( ) :
save = gr . Button ( ' Save ' )
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img2img_send_to_img2img = gr . Button ( ' Send to img2img ' )
img2img_send_to_inpaint = gr . Button ( ' Send to inpaint ' )
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img2img_send_to_extras = gr . Button ( ' Send to extras ' )
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button_id = " hidden_element " if shared . cmd_opts . hide_ui_dir_config else ' open_folder '
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open_img2img_folder = gr . Button ( folder_symbol , elem_id = button_id )
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with gr . Group ( ) :
html_info = gr . HTML ( )
generation_info = gr . Textbox ( visible = False )
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connect_reuse_seed ( seed , reuse_seed , generation_info , dummy_component , is_subseed = False )
connect_reuse_seed ( subseed , reuse_subseed , generation_info , dummy_component , is_subseed = True )
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mask_mode . change (
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lambda mode , img : {
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init_img_with_mask : gr_show ( mode == 0 ) ,
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init_img_inpaint : gr_show ( mode == 1 ) ,
init_mask_inpaint : gr_show ( mode == 1 ) ,
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} ,
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inputs = [ mask_mode , init_img_with_mask ] ,
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outputs = [
init_img_with_mask ,
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init_img_inpaint ,
init_mask_inpaint ,
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] ,
)
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img2img_args = dict (
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fn = wrap_gradio_gpu_call ( modules . img2img . img2img ) ,
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_js = " submit_img2img " ,
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inputs = [
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dummy_component ,
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img2img_prompt ,
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img2img_negative_prompt ,
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img2img_prompt_style ,
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img2img_prompt_style2 ,
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init_img ,
init_img_with_mask ,
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init_img_inpaint ,
init_mask_inpaint ,
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mask_mode ,
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steps ,
sampler_index ,
mask_blur ,
inpainting_fill ,
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restore_faces ,
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tiling ,
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batch_count ,
batch_size ,
cfg_scale ,
denoising_strength ,
seed ,
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subseed , subseed_strength , seed_resize_from_h , seed_resize_from_w , seed_checkbox ,
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height ,
width ,
resize_mode ,
inpaint_full_res ,
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inpaint_full_res_padding ,
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inpainting_mask_invert ,
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img2img_batch_input_dir ,
img2img_batch_output_dir ,
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] + custom_inputs ,
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outputs = [
img2img_gallery ,
generation_info ,
html_info
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] ,
show_progress = False ,
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)
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img2img_prompt . submit ( * * img2img_args )
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submit . click ( * * img2img_args )
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img2img_interrogate . click (
fn = interrogate ,
inputs = [ init_img ] ,
outputs = [ img2img_prompt ] ,
)
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save . click (
fn = wrap_gradio_call ( save_files ) ,
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_js = " (x, y, z) => [x, y, selected_gallery_index()] " ,
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inputs = [
generation_info ,
img2img_gallery ,
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html_info
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] ,
outputs = [
html_info ,
html_info ,
html_info ,
]
)
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roll . click (
fn = roll_artist ,
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_js = " update_img2img_tokens " ,
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inputs = [
img2img_prompt ,
] ,
outputs = [
img2img_prompt ,
]
)
prompts = [ ( txt2img_prompt , txt2img_negative_prompt ) , ( img2img_prompt , img2img_negative_prompt ) ]
style_dropdowns = [ ( txt2img_prompt_style , txt2img_prompt_style2 ) , ( img2img_prompt_style , img2img_prompt_style2 ) ]
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style_js_funcs = [ " update_txt2img_tokens " , " update_img2img_tokens " ]
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for button , ( prompt , negative_prompt ) in zip ( [ txt2img_save_style , img2img_save_style ] , prompts ) :
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button . click (
fn = add_style ,
_js = " ask_for_style_name " ,
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# Have to pass empty dummy component here, because the JavaScript and Python function have to accept
# the same number of parameters, but we only know the style-name after the JavaScript prompt
inputs = [ dummy_component , prompt , negative_prompt ] ,
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outputs = [ txt2img_prompt_style , img2img_prompt_style , txt2img_prompt_style2 , img2img_prompt_style2 ] ,
)
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for button , ( prompt , negative_prompt ) , ( style1 , style2 ) , js_func in zip ( [ txt2img_prompt_style_apply , img2img_prompt_style_apply ] , prompts , style_dropdowns , style_js_funcs ) :
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button . click (
fn = apply_styles ,
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_js = js_func ,
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inputs = [ prompt , negative_prompt , style1 , style2 ] ,
outputs = [ prompt , negative_prompt , style1 , style2 ] ,
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)
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img2img_paste_fields = [
( img2img_prompt , " Prompt " ) ,
( img2img_negative_prompt , " Negative prompt " ) ,
( steps , " Steps " ) ,
( sampler_index , " Sampler " ) ,
( restore_faces , " Face restoration " ) ,
( cfg_scale , " CFG scale " ) ,
( seed , " Seed " ) ,
( width , " Size-1 " ) ,
( height , " Size-2 " ) ,
( batch_size , " Batch size " ) ,
( subseed , " Variation seed " ) ,
( subseed_strength , " Variation seed strength " ) ,
( seed_resize_from_w , " Seed resize from-1 " ) ,
( seed_resize_from_h , " Seed resize from-2 " ) ,
( denoising_strength , " Denoising strength " ) ,
]
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modules . generation_parameters_copypaste . connect_paste ( paste , img2img_paste_fields , img2img_prompt )
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token_button . click ( fn = update_token_counter , inputs = [ img2img_prompt , steps ] , outputs = [ token_counter ] )
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with gr . Blocks ( analytics_enabled = False ) as extras_interface :
with gr . Row ( ) . style ( equal_height = False ) :
with gr . Column ( variant = ' panel ' ) :
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with gr . Tabs ( elem_id = " mode_extras " ) :
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with gr . TabItem ( ' Single Image ' ) :
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extras_image = gr . Image ( label = " Source " , source = " upload " , interactive = True , type = " pil " )
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with gr . TabItem ( ' Batch Process ' ) :
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image_batch = gr . File ( label = " Batch Process " , file_count = " multiple " , interactive = True , type = " file " )
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upscaling_resize = gr . Slider ( minimum = 1.0 , maximum = 4.0 , step = 0.05 , label = " Resize " , value = 2 )
with gr . Group ( ) :
extras_upscaler_1 = gr . Radio ( label = ' Upscaler 1 ' , choices = [ x . name for x in shared . sd_upscalers ] , value = shared . sd_upscalers [ 0 ] . name , type = " index " )
with gr . Group ( ) :
extras_upscaler_2 = gr . Radio ( label = ' Upscaler 2 ' , choices = [ x . name for x in shared . sd_upscalers ] , value = shared . sd_upscalers [ 0 ] . name , type = " index " )
extras_upscaler_2_visibility = gr . Slider ( minimum = 0.0 , maximum = 1.0 , step = 0.001 , label = " Upscaler 2 visibility " , value = 1 )
with gr . Group ( ) :
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gfpgan_visibility = gr . Slider ( minimum = 0.0 , maximum = 1.0 , step = 0.001 , label = " GFPGAN visibility " , value = 0 , interactive = modules . gfpgan_model . have_gfpgan )
with gr . Group ( ) :
codeformer_visibility = gr . Slider ( minimum = 0.0 , maximum = 1.0 , step = 0.001 , label = " CodeFormer visibility " , value = 0 , interactive = modules . codeformer_model . have_codeformer )
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codeformer_weight = gr . Slider ( minimum = 0.0 , maximum = 1.0 , step = 0.001 , label = " CodeFormer weight (0 = maximum effect, 1 = minimum effect) " , value = 0 , interactive = modules . codeformer_model . have_codeformer )
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submit = gr . Button ( ' Generate ' , elem_id = " extras_generate " , variant = ' primary ' )
with gr . Column ( variant = ' panel ' ) :
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result_images = gr . Gallery ( label = " Result " , show_label = False )
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html_info_x = gr . HTML ( )
html_info = gr . HTML ( )
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extras_send_to_img2img = gr . Button ( ' Send to img2img ' )
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extras_send_to_inpaint = gr . Button ( ' Send to inpaint ' )
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button_id = " hidden_element " if shared . cmd_opts . hide_ui_dir_config else ' '
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open_extras_folder = gr . Button ( ' Open output directory ' , elem_id = button_id )
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submit . click (
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fn = wrap_gradio_gpu_call ( modules . extras . run_extras ) ,
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_js = " get_extras_tab_index " ,
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inputs = [
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dummy_component ,
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extras_image ,
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image_batch ,
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gfpgan_visibility ,
codeformer_visibility ,
codeformer_weight ,
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upscaling_resize ,
extras_upscaler_1 ,
extras_upscaler_2 ,
extras_upscaler_2_visibility ,
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] ,
outputs = [
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result_images ,
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html_info_x ,
html_info ,
]
)
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extras_send_to_img2img . click (
fn = lambda x : image_from_url_text ( x ) ,
_js = " extract_image_from_gallery_img2img " ,
inputs = [ result_images ] ,
outputs = [ init_img ] ,
)
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extras_send_to_inpaint . click (
fn = lambda x : image_from_url_text ( x ) ,
_js = " extract_image_from_gallery_img2img " ,
inputs = [ result_images ] ,
outputs = [ init_img_with_mask ] ,
)
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with gr . Blocks ( analytics_enabled = False ) as pnginfo_interface :
with gr . Row ( ) . style ( equal_height = False ) :
with gr . Column ( variant = ' panel ' ) :
image = gr . Image ( elem_id = " pnginfo_image " , label = " Source " , source = " upload " , interactive = True , type = " pil " )
with gr . Column ( variant = ' panel ' ) :
html = gr . HTML ( )
generation_info = gr . Textbox ( visible = False )
html2 = gr . HTML ( )
with gr . Row ( ) :
pnginfo_send_to_txt2img = gr . Button ( ' Send to txt2img ' )
pnginfo_send_to_img2img = gr . Button ( ' Send to img2img ' )
image . change (
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fn = wrap_gradio_call ( modules . extras . run_pnginfo ) ,
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inputs = [ image ] ,
outputs = [ html , generation_info , html2 ] ,
)
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with gr . Blocks ( ) as modelmerger_interface :
with gr . Row ( ) . style ( equal_height = False ) :
with gr . Column ( variant = ' panel ' ) :
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gr . HTML ( value = " <p>A merger of the two checkpoints will be generated in your <b>checkpoint</b> directory.</p> " )
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with gr . Row ( ) :
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primary_model_name = gr . Dropdown ( modules . sd_models . checkpoint_tiles ( ) , elem_id = " modelmerger_primary_model_name " , label = " Primary Model Name " )
secondary_model_name = gr . Dropdown ( modules . sd_models . checkpoint_tiles ( ) , elem_id = " modelmerger_secondary_model_name " , label = " Secondary Model Name " )
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custom_name = gr . Textbox ( label = " Custom Name (Optional) " )
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interp_amount = gr . Slider ( minimum = 0.0 , maximum = 1.0 , step = 0.05 , label = ' Interpolation Amount ' , value = 0.3 )
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interp_method = gr . Radio ( choices = [ " Weighted Sum " , " Sigmoid " , " Inverse Sigmoid " ] , value = " Weighted Sum " , label = " Interpolation Method " )
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save_as_half = gr . Checkbox ( value = False , label = " Safe as float16 " )
modelmerger_merge = gr . Button ( elem_id = " modelmerger_merge " , label = " Merge " , variant = ' primary ' )
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with gr . Column ( variant = ' panel ' ) :
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submit_result = gr . Textbox ( elem_id = " modelmerger_result " , show_label = False )
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sd_hijack . model_hijack . embedding_db . load_textual_inversion_embeddings ( )
with gr . Blocks ( ) as textual_inversion_interface :
with gr . Row ( ) . style ( equal_height = False ) :
with gr . Column ( ) :
with gr . Group ( ) :
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gr . HTML ( value = " <p style= ' margin-bottom: 0.7em ' >See <b><a href= \" https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Textual-Inversion \" >wiki</a></b> for detailed explanation.</p> " )
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gr . HTML ( value = " <p style= ' margin-bottom: 0.7em ' >Create a new embedding</p> " )
new_embedding_name = gr . Textbox ( label = " Name " )
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initialization_text = gr . Textbox ( label = " Initialization text " , value = " * " )
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nvpt = gr . Slider ( label = " Number of vectors per token " , minimum = 1 , maximum = 75 , step = 1 , value = 1 )
with gr . Row ( ) :
with gr . Column ( scale = 3 ) :
gr . HTML ( value = " " )
with gr . Column ( ) :
create_embedding = gr . Button ( value = " Create " , variant = ' primary ' )
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with gr . Group ( ) :
gr . HTML ( value = " <p style= ' margin-bottom: 0.7em ' >Create a new hypernetwork</p> " )
new_hypernetwork_name = gr . Textbox ( label = " Name " )
with gr . Row ( ) :
with gr . Column ( scale = 3 ) :
gr . HTML ( value = " " )
with gr . Column ( ) :
create_hypernetwork = gr . Button ( value = " Create " , variant = ' primary ' )
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with gr . Group ( ) :
gr . HTML ( value = " <p style= ' margin-bottom: 0.7em ' >Preprocess images</p> " )
process_src = gr . Textbox ( label = ' Source directory ' )
process_dst = gr . Textbox ( label = ' Destination directory ' )
with gr . Row ( ) :
process_flip = gr . Checkbox ( label = ' Flip ' )
process_split = gr . Checkbox ( label = ' Split into two ' )
process_caption = gr . Checkbox ( label = ' Add caption ' )
with gr . Row ( ) :
with gr . Column ( scale = 3 ) :
gr . HTML ( value = " " )
with gr . Column ( ) :
run_preprocess = gr . Button ( value = " Preprocess " , variant = ' primary ' )
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with gr . Group ( ) :
gr . HTML ( value = " <p style= ' margin-bottom: 0.7em ' >Train an embedding; must specify a directory with a set of 512x512 images</p> " )
train_embedding_name = gr . Dropdown ( label = ' Embedding ' , choices = sorted ( sd_hijack . model_hijack . embedding_db . word_embeddings . keys ( ) ) )
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train_hypernetwork_name = gr . Dropdown ( label = ' Hypernetwork ' , choices = [ x for x in shared . hypernetworks . keys ( ) ] )
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learn_rate = gr . Number ( label = ' Learning rate ' , value = 5.0e-03 )
dataset_directory = gr . Textbox ( label = ' Dataset directory ' , placeholder = " Path to directory with input images " )
log_directory = gr . Textbox ( label = ' Log directory ' , placeholder = " Path to directory where to write outputs " , value = " textual_inversion " )
template_file = gr . Textbox ( label = ' Prompt template file ' , value = os . path . join ( script_path , " textual_inversion_templates " , " style_filewords.txt " ) )
steps = gr . Number ( label = ' Max steps ' , value = 100000 , precision = 0 )
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create_image_every = gr . Number ( label = ' Save an image to log directory every N steps, 0 to disable ' , value = 500 , precision = 0 )
save_embedding_every = gr . Number ( label = ' Save a copy of embedding to log directory every N steps, 0 to disable ' , value = 500 , precision = 0 )
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preview_image_prompt = gr . Textbox ( label = ' Preview prompt ' , value = " " )
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with gr . Row ( ) :
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interrupt_training = gr . Button ( value = " Interrupt " )
train_hypernetwork = gr . Button ( value = " Train Hypernetwork " , variant = ' primary ' )
train_embedding = gr . Button ( value = " Train Embedding " , variant = ' primary ' )
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with gr . Column ( ) :
progressbar = gr . HTML ( elem_id = " ti_progressbar " )
ti_output = gr . Text ( elem_id = " ti_output " , value = " " , show_label = False )
ti_gallery = gr . Gallery ( label = ' Output ' , show_label = False , elem_id = ' ti_gallery ' ) . style ( grid = 4 )
ti_preview = gr . Image ( elem_id = ' ti_preview ' , visible = False )
ti_progress = gr . HTML ( elem_id = " ti_progress " , value = " " )
ti_outcome = gr . HTML ( elem_id = " ti_error " , value = " " )
setup_progressbar ( progressbar , ti_preview , ' ti ' , textinfo = ti_progress )
create_embedding . click (
fn = modules . textual_inversion . ui . create_embedding ,
inputs = [
new_embedding_name ,
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initialization_text ,
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nvpt ,
] ,
outputs = [
train_embedding_name ,
ti_output ,
ti_outcome ,
]
)
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create_hypernetwork . click (
fn = modules . hypernetwork . ui . create_hypernetwork ,
inputs = [
new_hypernetwork_name ,
] ,
outputs = [
train_hypernetwork_name ,
ti_output ,
ti_outcome ,
]
)
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run_preprocess . click (
fn = wrap_gradio_gpu_call ( modules . textual_inversion . ui . preprocess , extra_outputs = [ gr . update ( ) ] ) ,
_js = " start_training_textual_inversion " ,
inputs = [
process_src ,
process_dst ,
process_flip ,
process_split ,
process_caption ,
] ,
outputs = [
ti_output ,
ti_outcome ,
] ,
)
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train_embedding . click (
fn = wrap_gradio_gpu_call ( modules . textual_inversion . ui . train_embedding , extra_outputs = [ gr . update ( ) ] ) ,
_js = " start_training_textual_inversion " ,
inputs = [
train_embedding_name ,
learn_rate ,
dataset_directory ,
log_directory ,
steps ,
create_image_every ,
save_embedding_every ,
template_file ,
] ,
outputs = [
ti_output ,
ti_outcome ,
]
)
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train_hypernetwork . click (
fn = wrap_gradio_gpu_call ( modules . hypernetwork . ui . train_hypernetwork , extra_outputs = [ gr . update ( ) ] ) ,
_js = " start_training_textual_inversion " ,
inputs = [
train_hypernetwork_name ,
learn_rate ,
dataset_directory ,
log_directory ,
steps ,
create_image_every ,
save_embedding_every ,
template_file ,
preview_image_prompt ,
] ,
outputs = [
ti_output ,
ti_outcome ,
]
)
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interrupt_training . click (
fn = lambda : shared . state . interrupt ( ) ,
inputs = [ ] ,
outputs = [ ] ,
)
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def create_setting_component ( key ) :
def fun ( ) :
return opts . data [ key ] if key in opts . data else opts . data_labels [ key ] . default
info = opts . data_labels [ key ]
t = type ( info . default )
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args = info . component_args ( ) if callable ( info . component_args ) else info . component_args
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if info . component is not None :
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comp = info . component
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elif t == str :
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comp = gr . Textbox
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elif t == int :
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comp = gr . Number
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elif t == bool :
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comp = gr . Checkbox
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else :
raise Exception ( f ' bad options item type: { str ( t ) } for key { key } ' )
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return comp ( label = info . label , value = fun , * * ( args or { } ) )
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components = [ ]
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component_dict = { }
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def open_folder ( f ) :
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if not shared . cmd_opts . hide_ui_dir_config :
path = os . path . normpath ( f )
if platform . system ( ) == " Windows " :
os . startfile ( path )
elif platform . system ( ) == " Darwin " :
sp . Popen ( [ " open " , path ] )
else :
sp . Popen ( [ " xdg-open " , path ] )
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def run_settings ( * args ) :
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changed = 0
for key , value , comp in zip ( opts . data_labels . keys ( ) , args , components ) :
if not opts . same_type ( value , opts . data_labels [ key ] . default ) :
return f " Bad value for setting { key } : { value } ; expecting { type ( opts . data_labels [ key ] . default ) . __name__ } "
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for key , value , comp in zip ( opts . data_labels . keys ( ) , args , components ) :
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comp_args = opts . data_labels [ key ] . component_args
if comp_args and isinstance ( comp_args , dict ) and comp_args . get ( ' visible ' ) is False :
continue
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oldval = opts . data . get ( key , None )
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opts . data [ key ] = value
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if oldval != value :
if opts . data_labels [ key ] . onchange is not None :
opts . data_labels [ key ] . onchange ( )
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changed + = 1
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opts . save ( shared . config_filename )
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return f ' { changed } settings changed. ' , opts . dumpjson ( )
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with gr . Blocks ( analytics_enabled = False ) as settings_interface :
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settings_submit = gr . Button ( value = " Apply settings " , variant = ' primary ' )
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result = gr . HTML ( )
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settings_cols = 3
items_per_col = int ( len ( opts . data_labels ) * 0.9 / settings_cols )
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cols_displayed = 0
items_displayed = 0
previous_section = None
column = None
with gr . Row ( elem_id = " settings " ) . style ( equal_height = False ) :
for i , ( k , item ) in enumerate ( opts . data_labels . items ( ) ) :
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if previous_section != item . section :
if cols_displayed < settings_cols and ( items_displayed > = items_per_col or previous_section is None ) :
if column is not None :
column . __exit__ ( )
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column = gr . Column ( variant = ' panel ' )
column . __enter__ ( )
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items_displayed = 0
cols_displayed + = 1
previous_section = item . section
gr . HTML ( elem_id = " settings_header_text_ {} " . format ( item . section [ 0 ] ) , value = ' <h1 class= " gr-button-lg " > {} </h1> ' . format ( item . section [ 1 ] ) )
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component = create_setting_component ( k )
component_dict [ k ] = component
components . append ( component )
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items_displayed + = 1
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request_notifications = gr . Button ( value = ' Request browser notifications ' , elem_id = " request_notifications " )
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request_notifications . click (
fn = lambda : None ,
inputs = [ ] ,
outputs = [ ] ,
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_js = ' function() {} '
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)
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with gr . Row ( ) :
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reload_script_bodies = gr . Button ( value = ' Reload custom script bodies (No ui updates, No restart) ' , variant = ' secondary ' )
restart_gradio = gr . Button ( value = ' Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only) ' , variant = ' primary ' )
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def reload_scripts ( ) :
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modules . scripts . reload_script_body_only ( )
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reload_script_bodies . click (
fn = reload_scripts ,
inputs = [ ] ,
outputs = [ ] ,
_js = ' function() {} '
)
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def request_restart ( ) :
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shared . state . interrupt ( )
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settings_interface . gradio_ref . do_restart = True
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restart_gradio . click (
fn = request_restart ,
inputs = [ ] ,
outputs = [ ] ,
_js = ' function() { restart_reload()} '
)
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if column is not None :
column . __exit__ ( )
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interfaces = [
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( txt2img_interface , " txt2img " , " txt2img " ) ,
( img2img_interface , " img2img " , " img2img " ) ,
( extras_interface , " Extras " , " extras " ) ,
( pnginfo_interface , " PNG Info " , " pnginfo " ) ,
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( modelmerger_interface , " Checkpoint Merger " , " modelmerger " ) ,
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( textual_inversion_interface , " Textual inversion " , " ti " ) ,
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( settings_interface , " Settings " , " settings " ) ,
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]
with open ( os . path . join ( script_path , " style.css " ) , " r " , encoding = " utf8 " ) as file :
css = file . read ( )
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if os . path . exists ( os . path . join ( script_path , " user.css " ) ) :
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with open ( os . path . join ( script_path , " user.css " ) , " r " , encoding = " utf8 " ) as file :
usercss = file . read ( )
css + = usercss
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if not cmd_opts . no_progressbar_hiding :
css + = css_hide_progressbar
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with gr . Blocks ( css = css , analytics_enabled = False , title = " Stable Diffusion " ) as demo :
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settings_interface . gradio_ref = demo
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with gr . Tabs ( ) as tabs :
for interface , label , ifid in interfaces :
with gr . TabItem ( label , id = ifid ) :
interface . render ( )
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if os . path . exists ( os . path . join ( script_path , " notification.mp3 " ) ) :
audio_notification = gr . Audio ( interactive = False , value = os . path . join ( script_path , " notification.mp3 " ) , elem_id = " audio_notification " , visible = False )
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text_settings = gr . Textbox ( elem_id = " settings_json " , value = lambda : opts . dumpjson ( ) , visible = False )
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settings_submit . click (
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fn = run_settings ,
inputs = components ,
outputs = [ result , text_settings ] ,
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)
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def modelmerger ( * args ) :
try :
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results = modules . extras . run_modelmerger ( * args )
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except Exception as e :
print ( " Error loading/saving model file: " , file = sys . stderr )
print ( traceback . format_exc ( ) , file = sys . stderr )
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modules . sd_models . list_models ( ) # to remove the potentially missing models from the list
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return [ " Error loading/saving model file. It doesn ' t exist or the name contains illegal characters " ] + [ gr . Dropdown . update ( choices = modules . sd_models . checkpoint_tiles ( ) ) for _ in range ( 3 ) ]
return results
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modelmerger_merge . click (
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fn = modelmerger ,
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inputs = [
primary_model_name ,
secondary_model_name ,
interp_method ,
interp_amount ,
save_as_half ,
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custom_name ,
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] ,
outputs = [
submit_result ,
primary_model_name ,
secondary_model_name ,
component_dict [ ' sd_model_checkpoint ' ] ,
]
)
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paste_field_names = [ ' Prompt ' , ' Negative prompt ' , ' Steps ' , ' Face restoration ' , ' Seed ' , ' Size-1 ' , ' Size-2 ' ]
txt2img_fields = [ field for field , name in txt2img_paste_fields if name in paste_field_names ]
img2img_fields = [ field for field , name in img2img_paste_fields if name in paste_field_names ]
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send_to_img2img . click (
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fn = lambda img , * args : ( image_from_url_text ( img ) , * args ) ,
_js = " (gallery, ...args) => [extract_image_from_gallery_img2img(gallery), ...args] " ,
inputs = [ txt2img_gallery ] + txt2img_fields ,
outputs = [ init_img ] + img2img_fields ,
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)
send_to_inpaint . click (
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fn = lambda x , * args : ( image_from_url_text ( x ) , * args ) ,
_js = " (gallery, ...args) => [extract_image_from_gallery_inpaint(gallery), ...args] " ,
inputs = [ txt2img_gallery ] + txt2img_fields ,
outputs = [ init_img_with_mask ] + img2img_fields ,
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)
img2img_send_to_img2img . click (
fn = lambda x : image_from_url_text ( x ) ,
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_js = " extract_image_from_gallery_img2img " ,
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inputs = [ img2img_gallery ] ,
outputs = [ init_img ] ,
)
img2img_send_to_inpaint . click (
fn = lambda x : image_from_url_text ( x ) ,
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_js = " extract_image_from_gallery_inpaint " ,
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inputs = [ img2img_gallery ] ,
outputs = [ init_img_with_mask ] ,
)
send_to_extras . click (
fn = lambda x : image_from_url_text ( x ) ,
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_js = " extract_image_from_gallery_extras " ,
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inputs = [ txt2img_gallery ] ,
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outputs = [ extras_image ] ,
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)
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open_txt2img_folder . click (
fn = lambda : open_folder ( opts . outdir_samples or opts . outdir_txt2img_samples ) ,
inputs = [ ] ,
outputs = [ ] ,
)
open_img2img_folder . click (
fn = lambda : open_folder ( opts . outdir_samples or opts . outdir_img2img_samples ) ,
inputs = [ ] ,
outputs = [ ] ,
)
open_extras_folder . click (
fn = lambda : open_folder ( opts . outdir_samples or opts . outdir_extras_samples ) ,
inputs = [ ] ,
outputs = [ ] ,
)
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img2img_send_to_extras . click (
fn = lambda x : image_from_url_text ( x ) ,
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_js = " extract_image_from_gallery_extras " ,
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inputs = [ img2img_gallery ] ,
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outputs = [ extras_image ] ,
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)
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modules . generation_parameters_copypaste . connect_paste ( pnginfo_send_to_txt2img , txt2img_paste_fields , generation_info , ' switch_to_txt2img ' )
modules . generation_parameters_copypaste . connect_paste ( pnginfo_send_to_img2img , img2img_paste_fields , generation_info , ' switch_to_img2img_img2img ' )
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ui_config_file = cmd_opts . ui_config_file
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ui_settings = { }
settings_count = len ( ui_settings )
error_loading = False
try :
if os . path . exists ( ui_config_file ) :
with open ( ui_config_file , " r " , encoding = " utf8 " ) as file :
ui_settings = json . load ( file )
except Exception :
error_loading = True
print ( " Error loading settings: " , file = sys . stderr )
print ( traceback . format_exc ( ) , file = sys . stderr )
def loadsave ( path , x ) :
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def apply_field ( obj , field , condition = None ) :
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key = path + " / " + field
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if getattr ( obj , ' custom_script_source ' , None ) is not None :
key = ' customscript/ ' + obj . custom_script_source + ' / ' + key
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if getattr ( obj , ' do_not_save_to_config ' , False ) :
return
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saved_value = ui_settings . get ( key , None )
if saved_value is None :
ui_settings [ key ] = getattr ( obj , field )
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elif condition is None or condition ( saved_value ) :
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setattr ( obj , field , saved_value )
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if type ( x ) in [ gr . Slider , gr . Radio , gr . Checkbox , gr . Textbox , gr . Number ] and x . visible :
apply_field ( x , ' visible ' )
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if type ( x ) == gr . Slider :
apply_field ( x , ' value ' )
apply_field ( x , ' minimum ' )
apply_field ( x , ' maximum ' )
apply_field ( x , ' step ' )
if type ( x ) == gr . Radio :
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apply_field ( x , ' value ' , lambda val : val in x . choices )
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if type ( x ) == gr . Checkbox :
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apply_field ( x , ' value ' )
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if type ( x ) == gr . Textbox :
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apply_field ( x , ' value ' )
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if type ( x ) == gr . Number :
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apply_field ( x , ' value ' )
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visit ( txt2img_interface , loadsave , " txt2img " )
visit ( img2img_interface , loadsave , " img2img " )
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visit ( extras_interface , loadsave , " extras " )
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if not error_loading and ( not os . path . exists ( ui_config_file ) or settings_count != len ( ui_settings ) ) :
with open ( ui_config_file , " w " , encoding = " utf8 " ) as file :
json . dump ( ui_settings , file , indent = 4 )
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return demo
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with open ( os . path . join ( script_path , " script.js " ) , " r " , encoding = " utf8 " ) as jsfile :
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javascript = f ' <script> { jsfile . read ( ) } </script> '
jsdir = os . path . join ( script_path , " javascript " )
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for filename in sorted ( os . listdir ( jsdir ) ) :
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with open ( os . path . join ( jsdir , filename ) , " r " , encoding = " utf8 " ) as jsfile :
javascript + = f " \n <script> { jsfile . read ( ) } </script> "
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if ' gradio_routes_templates_response ' not in globals ( ) :
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def template_response ( * args , * * kwargs ) :
res = gradio_routes_templates_response ( * args , * * kwargs )
res . body = res . body . replace ( b ' </head> ' , f ' { javascript } </head> ' . encode ( " utf8 " ) )
res . init_headers ( )
return res
gradio_routes_templates_response = gradio . routes . templates . TemplateResponse
gradio . routes . templates . TemplateResponse = template_response