In control update part 2 (#30)

* custom artist lists

* override lists logic

and fix for artists

* support for TI and Lora

* Added support for custom lists

* overwrite type of image option

* some minor fixes

* Foods deserved their own list

* Added loads of new stuff in listwork

Also added an automatic duplicate remover

* Descriptions and lists

* Create custom_files.md

* Create override_subject_and_infinite_variations.md

* Update override_subject_and_infinite_variations.md

* Update README.md

* minor typos
This commit is contained in:
AIrjen
2023-05-19 08:09:46 +02:00
committed by GitHub
parent 9e224e3c7b
commit 858c78343c
32 changed files with 2562 additions and 208 deletions
+11 -2
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@@ -14,6 +14,15 @@ It is best used on all-purpose models, such as Stable Difussion 1.5 or those bas
A simple user guide for first time use and settings is available [here](https://github.com/AIrjen/OneButtonPrompt/blob/main/user_guides/my_first_generation.md).
# Features
- __Full prompt generation__ with the click of a button. ==> [guide to my first generation](https://github.com/AIrjen/OneButtonPrompt/blob/main/user_guides/my_first_generation.md)
- Supports __TXT2IMG, IMG2IMG, ControlNET, inpainting and latent couple__.
- __Workflow assist__, generate multiple prompts with One Button.
- Create __infinite variations__ of a __chosen subject__. ==> [guide to override subject](https://github.com/AIrjen/OneButtonPrompt/blob/main/user_guides/override_subject_and_infinite_variations.md)
- Fully __automated generation, classification and upscaling__. ==> [guide to one_button_run_and_upscale](https://github.com/AIrjen/OneButtonPrompt/blob/main/user_guides/one_button_run_and_upscale.md)
- Add __LoRA's__, customize lists and personal artists choices. ==> [guide to custom files](https://github.com/AIrjen/OneButtonPrompt/blob/main/user_guides/custom_files.md)
- __Compound__ multiple prompts together for unexpected results.
## How to use in automatic1111
In TXT2IMG or IMG2IMG, select the script "One Button Prompt".
@@ -166,8 +175,8 @@ All with a single press of __One Button__.
Some ideas I'd like to implement:
- The In Control update
- ~~Choose your own subject~~ Done
- Split up subjects more, and pick more detailed subjects, such as food, female, building, etc
- Support for LoRA and textual inversions
- ~~Split up subjects more, and pick more detailed subjects, such as food, female, building, etc~~ Done
- ~~Support for LoRA and textual inversions~~ Done
- Trigger word support
- ~~Bring upscale automation to front-end~~ Done
- ~~Better workflow management in workflow assist tab~~ Done
+86 -31
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@@ -10,7 +10,7 @@ from random_functions import *
# insanity level controls randomness of propmt 0-10
# forcesubject van be used to force a certain type of subject
# Set artistmode to none, to exclude artists
def build_dynamic_prompt(insanitylevel = 5, forcesubject = "all", artists = "all", imagetype = "all", onlyartists = False, antivalues = "", prefixprompt = "", suffixprompt ="",promptcompounderlevel ="1", seperator = "comma", givensubject="",smartsubject = True):
def build_dynamic_prompt(insanitylevel = 5, forcesubject = "all", artists = "all", imagetype = "all", onlyartists = False, antivalues = "", prefixprompt = "", suffixprompt ="",promptcompounderlevel ="1", seperator = "comma", givensubject="",smartsubject = True,giventypeofimage=""):
# first build up a complete anti list. Those values are removing during list building
@@ -66,6 +66,31 @@ def build_dynamic_prompt(insanitylevel = 5, forcesubject = "all", artists = "all
shotsizelist = csv_to_list("shotsizes",antilist)
timeperiodlist = csv_to_list("timeperiods",antilist)
vomitlist = csv_to_list("vomit",antilist)
foodlist = csv_to_list("foods", antilist)
# build artists list
# create artist list to use in the code, maybe based on category or personal lists
if(artists != "all" and artists != "none" and artists.startswith("personal_artists") == False and artists.startswith("personal artists") == False):
artistlist = artist_category_csv_to_list("artists_and_category",artists)
elif(artists.startswith("personal_artists") == True or artists.startswith("personal artists") == True):
artists = artists.replace(" ","_",-1) # add underscores back in
artistlist = csv_to_list(artists,antilist,"./userfiles/")
else:
artistlist = csv_to_list("artists",antilist)
# add any other custom lists
stylestiloralist = csv_to_list("styles_ti_lora",antilist,"./userfiles/")
generatestyle = bool(stylestiloralist) # True of not empty
custominputprefixlist = csv_to_list("custom_input_prefix",antilist,"./userfiles/")
generatecustominputprefix = bool(custominputprefixlist) # True of not empty
custominputmidlist = csv_to_list("custom_input_mid",antilist,"./userfiles/")
generatecustominputmid = bool(custominputmidlist) # True of not empty
custominputsuffixlist = csv_to_list("custom_input_suffix",antilist,"./userfiles/")
generatecustominputsuffix = bool(custominputsuffixlist) # True of not empty
generateoutfit = True
@@ -269,11 +294,11 @@ def build_dynamic_prompt(insanitylevel = 5, forcesubject = "all", artists = "all
completeprompt += ", "
# create artist list to use in the code, maybe based on category
if(artists != "all" and artists != "none"):
artistlist = artist_category_csv_to_list("artists_and_category",artists)
else:
artistlist = csv_to_list("artists")
# custom prefix list
if(uncommon_dist(insanitylevel) and generatecustominputprefix == True):
completeprompt += random.choice(custominputprefixlist) + ", "
if(uncommon_dist(insanitylevel)):
completeprompt += random.choice(custominputprefixlist) + ", "
@@ -308,7 +333,7 @@ def build_dynamic_prompt(insanitylevel = 5, forcesubject = "all", artists = "all
subjectchooser = subjectchooserlist[random.randint(0, 1)]
hybridlist = ["-animal-", "-object-", "-fictional-", "-nonfictional-", "-building-", "-vehicle-"]
hybridlist = ["-animal-", "-object-", "-fictional-", "-nonfictional-", "-building-", "-vehicle-","-food-"]
hybridhumanlist = ["-fictional-", "-nonfictional-"]
@@ -402,27 +427,33 @@ def build_dynamic_prompt(insanitylevel = 5, forcesubject = "all", artists = "all
# start image type
if(imagetype != "all" and imagetype != "all - force multiple" and imagetype != "only other types"):
completeprompt += " " + imagetype + ", "
elif(imagetype == "all - force multiple" or unique_dist(insanitylevel)):
amountofimagetypes = random.randint(2,3)
elif(imagetype == "only other types"):
othertype = 1
completeprompt += random.choice(othertypelist) + " of a "
if(imagetype == "all" and normal_dist(insanitylevel) and amountofimagetypes <= 1):
amountofimagetypes = 1
for i in range(amountofimagetypes):
# one in 6 images is a complex/other type
if(random.randint(0,5) < 5):
completeprompt += ", " + random.choice(imagetypelist) + " "
else:
if(giventypeofimage==""):
if(imagetype != "all" and imagetype != "all - force multiple" and imagetype != "only other types"):
completeprompt += " " + imagetype + ", "
elif(imagetype == "all - force multiple" or unique_dist(insanitylevel)):
amountofimagetypes = random.randint(2,3)
elif(imagetype == "only other types"):
othertype = 1
completeprompt += ", " + random.choice(othertypelist) + " "
if(othertype==1):
completeprompt += " of a "
completeprompt += random.choice(othertypelist)
if(imagetype == "all" and normal_dist(insanitylevel) and amountofimagetypes <= 1):
amountofimagetypes = 1
for i in range(amountofimagetypes):
# one in 6 images is a complex/other type
if(random.randint(0,5) < 5):
completeprompt += ", " + random.choice(imagetypelist) + " "
else:
othertype = 1
completeprompt += ", " + random.choice(othertypelist) + " "
if(othertype==1):
completeprompt += " of a "
else:
completeprompt += ", "
else:
othertype = 1
completeprompt += giventypeofimage + " of a "
# start shot size
@@ -453,7 +484,7 @@ def build_dynamic_prompt(insanitylevel = 5, forcesubject = "all", artists = "all
completeprompt += random.choice(culturelist) + " "
if(mainchooser == "object"):
objecttypelist = [objectlist, buildinglist, vehiclelist] # first select a random list, then randomly select from the corresponding list
objecttypelist = [objectlist, buildinglist, vehiclelist, foodlist] # first select a random list, then randomly select from the corresponding list
# if we have a given subject, we should skip making an actual subject
if(givensubject == ""):
@@ -641,7 +672,7 @@ def build_dynamic_prompt(insanitylevel = 5, forcesubject = "all", artists = "all
# riding an animal, holding an object or driving a vehicle, rare
if(subjectchooser in ["animal as human,","human","fictional", "non fictional", "humanoid"] and rare_dist(insanitylevel)):
humanspecial = 1
speciallist = [" riding a -animal- ", " holding a -object- ", " driving a -vehicle-", " visiting a -building-", " with a -animal-", " surrounded by -object-s"]
speciallist = [" riding a -animal- ", " holding a -object- ", " driving a -vehicle-", " visiting a -building-", " with a -animal-", " surrounded by -object-s", " eating -food-"]
completeprompt += random.choice(speciallist)
@@ -670,6 +701,13 @@ def build_dynamic_prompt(insanitylevel = 5, forcesubject = "all", artists = "all
skintypelist = ["-color-", "-material-"]
completeprompt += ", with " + random.choice(skintypelist) + " skin, "
# custom mid list
if(uncommon_dist(insanitylevel) and generatecustominputmid == True):
completeprompt += random.choice(custominputmidlist) + ", "
if(uncommon_dist(insanitylevel)):
completeprompt += random.choice(custominputmidlist) + ", "
# outfit builder
if(subjectchooser in ["animal as human","human","fictional", "non fictional", "humanoid"] and normal_dist(insanitylevel) and generateoutfit == True):
completeprompt += ", wearing "
@@ -719,7 +757,7 @@ def build_dynamic_prompt(insanitylevel = 5, forcesubject = "all", artists = "all
addontolocation = [locationlist,buildinglist]
completeprompt += random.choice(random.choice(addontolocation)) + ", "
if(subjectchooser not in ["landscape", "concept"] and humanspecial != 1 and insideshot == 0 and normal_dist(insanitylevel) and generatebackground == True):
if(subjectchooser not in ["landscape", "concept"] and humanspecial != 1 and insideshot == 0 and uncommon_dist(insanitylevel) and generatebackground == True):
backgroundtypelist = ["landscape", "buildingbackground", "insidebuilding"]
backgroundtype = random.choice(backgroundtypelist)
if(backgroundtype == "landscape"):
@@ -794,6 +832,20 @@ def build_dynamic_prompt(insanitylevel = 5, forcesubject = "all", artists = "all
if(uncommon_dist(insanitylevel)):
completeprompt += random.choice(qualitylist) + ", "
# custom style list
if(uncommon_dist(insanitylevel) and generatestyle == True):
completeprompt += random.choice(stylestiloralist) + ", "
if(uncommon_dist(insanitylevel)):
completeprompt += random.choice(stylestiloralist) + ", "
# custom suffix list
if(uncommon_dist(insanitylevel) and generatecustominputsuffix == True):
completeprompt += random.choice(custominputsuffixlist) + ", "
if(uncommon_dist(insanitylevel)):
completeprompt += random.choice(custominputsuffixlist) + ", "
if artistmode in ["enhancing"]:
completeprompt += "::" + str(random.randint(1,17)) + "]"
@@ -883,7 +935,7 @@ def build_dynamic_prompt(insanitylevel = 5, forcesubject = "all", artists = "all
#end of the while loop, now clean up the prompt
# lol, this needs a rewrite :D
while "-color-" in completeprompt or "-material-" in completeprompt or "-animal-" in completeprompt or "-object-" in completeprompt or "-fictional-" in completeprompt or "-nonfictional-" in completeprompt or "-conceptsuffix-" in completeprompt or "-building-" in completeprompt or "-vehicle-" in completeprompt or "-outfit-" in completeprompt or "-location-" in completeprompt or "-conceptprefix-" in completeprompt or "-descriptor-" in completeprompt:
while "-color-" in completeprompt or "-material-" in completeprompt or "-animal-" in completeprompt or "-object-" in completeprompt or "-fictional-" in completeprompt or "-nonfictional-" in completeprompt or "-conceptsuffix-" in completeprompt or "-building-" in completeprompt or "-vehicle-" in completeprompt or "-outfit-" in completeprompt or "-location-" in completeprompt or "-conceptprefix-" in completeprompt or "-descriptor-" in completeprompt or "-food-" in completeprompt:
while "-object-" in completeprompt:
completeprompt = completeprompt.replace('-object-', random.choice(objectlist),1)
@@ -922,6 +974,9 @@ def build_dynamic_prompt(insanitylevel = 5, forcesubject = "all", artists = "all
while "-descriptor-" in completeprompt:
completeprompt = completeprompt.replace('-descriptor-', random.choice(descriptorlist),1)
while "-food-" in completeprompt:
completeprompt = completeprompt.replace('-food-', random.choice(foodlist),1)
+40 -3
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@@ -18,21 +18,58 @@ def add_from_csv(completeprompt, csvfilename, addcomma, prefix, suffix):
return ", ".join([completeprompt,addtoprompt])
return " ".join([completeprompt,addtoprompt])
def csv_to_list(csvfilename, antilist=[], directory="./csvfiles/", lowerandstrip=0):
def csv_to_list(csvfilename, antilist=[], directory="./csvfiles/", lowerandstrip=0, delimiter=";"):
userfilesdirectory = "./userfiles/"
userfileaddonname = csvfilename + "_addon.csv"
userfilereplacename = csvfilename + "_replace.csv"
csvlist = []
script_dir = os.path.dirname(os.path.abspath(__file__))
full_path = os.path.join(script_dir, directory )
userfilesfolder = os.path.join(script_dir, userfilesdirectory )
# check if there is a replace file
if(directory=="./csvfiles/"):
for filename in os.listdir(userfilesfolder):
if(filename == userfilereplacename):
# Just override the parameters, and let it run normally
full_path = os.path.join(script_dir, userfilesdirectory )
csvfilename = csvfilename + "_replace"
# return empty list if we can't find the file. Build for antilist.csv
if(os.path.isfile(full_path + csvfilename + ".csv")):
with open(full_path + csvfilename + ".csv", "r", newline="",encoding="utf8") as file:
reader = csv.reader(file, delimiter=",")
reader = csv.reader(file, delimiter=delimiter)
for row in reader:
value = row[0]
if(value.lower().strip() not in antilist):
if(lowerandstrip == 1):
csvlist.append(row[0].lower().strip())
csvlist.append(row[0])
return csvlist
# do the add ons!
if(directory=="./csvfiles/"):
if(os.path.isfile(userfilesfolder + csvfilename + "_addon" + ".csv")):
with open(userfilesfolder + csvfilename + "_addon" + ".csv", "r", newline="",encoding="utf8") as file:
reader = csv.reader(file, delimiter=",")
for row in reader:
value = row[0]
if(value.lower().strip() not in antilist):
if(lowerandstrip == 1):
csvlist.append(row[0].lower().strip())
csvlist.append(row[0])
# remove duplicates, but check only for lowercase stuff
deduplicated_list = []
lowercase_elements = set()
for element in csvlist:
lowercase_element = element.lower()
if lowercase_element not in lowercase_elements:
lowercase_elements.add(lowercase_element)
deduplicated_list.append(element)
return deduplicated_list
def artist_category_csv_to_list(csvfilename,category):
csvlist = []
+9 -2
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@@ -63,7 +63,7 @@ Day of the Dead face paint
-animal- face paint
tribal face paint
clown face paint
Halloween face pain
Halloween face paint
Fantasy face paint
Sports team face paint
Superhero face paint
@@ -110,4 +110,11 @@ Belts
Cape
Cloak
-material- trimmings
Large head
Large head
Geass
Sharingan
crazy eyes
empty eyes
dashed eyes
heart-shaped eyes
amphibian eyes
1 Glasses
63 -animal- face paint
64 tribal face paint
65 clown face paint
66 Halloween face pain Halloween face paint
67 Fantasy face paint
68 Sports team face paint
69 Superhero face paint
110 Cape
111 Cloak
112 -material- trimmings
113 Large head
114 Geass
115 Sharingan
116 crazy eyes
117 empty eyes
118 dashed eyes
119 heart-shaped eyes
120 amphibian eyes
+91 -1
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@@ -176,4 +176,94 @@ still life
stonepunk
voidpunk
voxel art
weirdcore
weirdcore
Auroracore
Barbiecore
Bardcore
Bastardcore
Bimbocore
Bombacore
Breakcore
Britpop
Brocore
Bronzepunk
Cartelcore
Cartooncore
Christcore
Cleancore
Clockpunk
Cutecore
Corporate Punk
Crowcore
Crustpunk
Cryptidcore
Darkcore
Decopunk
Decora
De Stijl
Dieselpunk
Dragoncore
Dreamcore
Duckcore
Dullcore
Dual Kawaii
Europunk
Fashwave
Feralcore
Gamercore
Gadgetpunk
Goblincore
Glitchcore
Gloomcore
Glowwave
Grindhouse
Hackercore
Hermitpunk
Hispanicore
Hyperpop
Icepunk
Junglecore
Kingcore
Knightcore
Lightcore
Libertywave
Magewave
Lunarpunk
Miniaturecore
Mushroomcore
Mythpunk
Nanopunk
Naturecore
Nihilcore
Nintencore
Pixiecore
Post-Apocalyptic
Post-Impressionism
Post-Punk
Post-rock
Prehistoricore
Princecore
Princesscore
Printcore
Queencore
Royalcore
Rusticcore
Rollerwave
Sleepycore
Slimepunk
Terrorwave
Teslapunk
Thriftcore
Tinkercore
Tinycore
Trendercore
Trillwave
Vaporwave
Villagecore
Villaincore
Warmcore
Weirdcore
Witchcore
Wizardcore
Yankeecore
Yume Kawaii
1 50s Art
176 stonepunk
177 voidpunk
178 voxel art
179 weirdcore
180 Auroracore
181 Barbiecore
182 Bardcore
183 Bastardcore
184 Bimbocore
185 Bombacore
186 Breakcore
187 Britpop
188 Brocore
189 Bronzepunk
190 Cartelcore
191 Cartooncore
192 Christcore
193 Cleancore
194 Clockpunk
195 Cutecore
196 Corporate Punk
197 Crowcore
198 Crustpunk
199 Cryptidcore
200 Darkcore
201 Decopunk
202 Decora
203 De Stijl
204 Dieselpunk
205 Dragoncore
206 Dreamcore
207 Duckcore
208 Dullcore
209 Dual Kawaii
210 Europunk
211 Fashwave
212 Feralcore
213 Gamercore
214 Gadgetpunk
215 Goblincore
216 Glitchcore
217 Gloomcore
218 Glowwave
219 Grindhouse
220 Hackercore
221 Hermitpunk
222 Hispanicore
223 Hyperpop
224 Icepunk
225 Junglecore
226 Kingcore
227 Knightcore
228 Lightcore
229 Libertywave
230 Magewave
231 Lunarpunk
232 Miniaturecore
233 Mushroomcore
234 Mythpunk
235 Nanopunk
236 Naturecore
237 Nihilcore
238 Nintencore
239 Pixiecore
240 Post-Apocalyptic
241 Post-Impressionism
242 Post-Punk
243 Post-rock
244 Prehistoricore
245 Princecore
246 Princesscore
247 Printcore
248 Queencore
249 Royalcore
250 Rusticcore
251 Rollerwave
252 Sleepycore
253 Slimepunk
254 Terrorwave
255 Teslapunk
256 Thriftcore
257 Tinkercore
258 Tinycore
259 Trendercore
260 Trillwave
261 Vaporwave
262 Villagecore
263 Villaincore
264 Warmcore
265 Weirdcore
266 Witchcore
267 Wizardcore
268 Yankeecore
269 Yume Kawaii
+7 -1
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@@ -188,4 +188,10 @@ St. Basil's Cathedral
Canterbury Cathedral
St. Patrick's Cathedral
Duomo di Milano
St. Stephen's Cathedral
St. Stephen's Cathedral
bakery
food stand
ice cream stand
cafeteria
tavern
supermarket
1 Archticture
188 Canterbury Cathedral
189 St. Patrick's Cathedral
190 Duomo di Milano
191 St. Stephen's Cathedral
192 bakery
193 food stand
194 ice cream stand
195 cafeteria
196 tavern
197 supermarket
+2 -1
View File
@@ -21,4 +21,5 @@ Kodak gold 200
Nikon d850
Sony A9 II
Fuji superia 400
dslr
dslr
Ilford HP5
1 Fujifilm XT3
21 Nikon d850
22 Sony A9 II
23 Fuji superia 400
24 dslr
25 Ilford HP5
+2 -1
View File
@@ -101,4 +101,5 @@ Spirit
Agent
Moon
Gift
Brain
Brain
Isles
1 Ocean
101 Agent
102 Moon
103 Gift
104 Brain
105 Isles
+3 -1
View File
@@ -79,4 +79,6 @@ Study
Metal
Paradise
Sea
The night
The night
Shattering
Mirrors
1 Infinity
79 Metal
80 Paradise
81 Sea
82 The night
83 Shattering
84 Mirrors
+313 -37
View File
@@ -35,7 +35,6 @@ Enticing
Tempting
Captivating
Fascinating
Alluring
Irresistible
Sensual
Sexy
@@ -63,12 +62,10 @@ Stimulating
Lively
Vibrant
Dynamic
Radiant
Sparkling
Glittering
Shimmering
Glistening
Dazzling
Glowing
Smiling
Illuminating
@@ -149,7 +146,7 @@ Stale
Tired
Exhausted
Weary
Worn-out
Worn-Out
Fatigued
Uninspired
Unmotivated
@@ -209,7 +206,7 @@ Hairy
Fluffy
Old
Young
Middle aged
Middle Aged
Mecha
Giant
Tiny
@@ -218,12 +215,11 @@ Liquid
Enchanted
Mystical
Otherworldly
Fairy-tale
Fairy-Tale
Supernatural
Fantastical
Mystifying
Mythical
Spellbinding
Magical
Bioluminescent
Elder
@@ -234,19 +230,18 @@ Flaming
Feminine
Masculine
Cartoony
2000's
1990's
1980's
1970's
1960's
1950's
1940's
1940's
1920's
1900's
1800's
1700's
1600's
2000'S
1990'S
1980'S
1970'S
1960'S
1950'S
1940'S
1920'S
1900'S
1800'S
1700'S
1600'S
Luminescent
Floating
Shiny
@@ -281,7 +276,6 @@ Maximalist
Glad
Folded
Glam
Chic
Zen
Rustic
Rich
@@ -314,19 +308,301 @@ Arcane
Ruthless
Eerie
16-Bit
1950s suburbia
alhambresque
alien
Amiga 500 style
Atari 2600 style
apocalyptic
avant-garde
basic
biochemical
biomorphic
biophilic
biopunk
deserted
desolate
feline
Morbid
1950S Suburbia
Alhambresque
Alien
Amiga 500 Style
Atari 2600 Style
Apocalyptic
Avant-Garde
Basic
Biochemical
Biomorphic
Biophilic
Biopunk
Deserted
Desolate
Feline
Morbid
Frilled
Dried
Frosted
Oversized
Lo-Fi
Shabby Chic
Tiki
Wanderlust
Fur-Trimmed
Warrior
Abandoned
Abhorrent
Abrasive
Absorbing
Abstract
Acclaimed
Accomplished
Adroit
Aesthetic
Aesthetically Pleasing
Angular
Appealing
Artistic
Aspiring
Astonishing
Atmospheric
Authentic
Average
Award-Winning
Awe-Inspiring
Balanced
Baroque
Biomechanical
Blue
Bold
Boorish
Boundless
Brilliant
Candid
Capricious
Ceramic
Characteristic
Classic
Clockwork
Cloudy
Cluttered
Cold
Collectable
Colorful
Colossal
Complementary
Complex
Conceptual
Conscious
Contemplative
Contemporary
Controversial
Conversational
Cool
Creative
Cromulent
Cyberpunk
Daring
Deafening
Decorative
Deeply Thoughtful
Delicious
Delightful
Detailed
Deteriorating
Dieselpunk
Dilapidated
Dimensional
Dire
Disciplined
Disruptive
Distinctive
Distinguished
Divine
Draconian
Dreamlike
Dusty
Dystopian
Eclectic
Efficacious
Elastic
Elevated
Elevating
Elite
Elusive
Emergent
Emerging
Emotional
Emotionally Charged
Energetic
Engrossing
Enigmatic
Epic
Epochal
Evil
Evocative
Exceptional
Explosive
Expressive
Extraordinary
Extreme
Fallacious
Feigned
Festive
Fiery
Figural
Figurative
Flowery
Foggy
Frantic
Freelance
Fresh
Fruit-Flavored
Functional
Funny
Gentle
Ghastly
Ghostly
Gigantic
Glib
Good
Granular
Groovy
Grouchy
Grumpy
Harsh
Hesitant
Holistic
Honest
Honorable
Human
Hyper-Creative
Hyperdetailed
Icy
Illuminated
Imaginative
Impassioned
Impeccable
Impressionist
Impressive
Incredibly Detailed
Infused
Inspirational
Inspired
Instinctive
Intellectual
Intensive
Interesting
Intuitive
Inventive
Jealous
Kindhearted
Labyrinthine
Layered
Level
Lifelike
Literal
Luminous
Lyrical
Macho
Maniacal
Masterpiece
Matte
Mature
Meandering
Meek
Mind-Bending
Modular
Moody
Mosaic-Like
Moving
Mysterious
Narrative
Neat
Nonchalant
Noxious
Nutty
Oppressive
Organic
Original
Overwrought
Painterly
Paradoxical
Parched
Passionate
Pastel
Personal
Petrified
Phenomenal
Pictorial
Potent
Primordial
Profound
Provoking
Psychadelic
Psychotic
Puny
Pure
Rainbow
Refined
Remarkable
Repressive
Resonant
Resourceful
Retrofuturistic
Revealing
Rickety
Romantic
Rounded
Ruined
Rusty
Saturated
Sculptural
Secret
Semi-Abstract
Serious
Shadowy
Shaky
Sharp
Signature
Simple
Skilled
Skillful
Spiritual
Sprawling
Squealing
Steampunk
Stirring
Stone
Striped
Studied
Sturdy
Sublime
Substantive
Superficial
Supersized
Supple
Sweltering
Swirling
Symbolic
Tactile
Talented
Tangible
Tasteful
Terrifying
Textile
Thought-Provoking
Thundering
Timeless
Touching
Tough
Traditional
Tremendous
Twisted
Unadvised
Unappetizing
Unconventional
Underwater
Unexpected
Unforgettable
Unique
Universal
Unnatural
Unpredictable
Untidy
Unusual
Uptight
Vengeful
Visionary
Visual
Visually Stimulating
Voyeuristic
Warlike
Weird
Wispy
Witty
Zany
1 Handsome
35 Tempting
36 Captivating
37 Fascinating
Alluring
38 Irresistible
39 Sensual
40 Sexy
62 Lively
63 Vibrant
64 Dynamic
Radiant
65 Sparkling
66 Glittering
67 Shimmering
68 Glistening
Dazzling
69 Glowing
70 Smiling
71 Illuminating
146 Tired
147 Exhausted
148 Weary
149 Worn-out Worn-Out
150 Fatigued
151 Uninspired
152 Unmotivated
206 Fluffy
207 Old
208 Young
209 Middle aged Middle Aged
210 Mecha
211 Giant
212 Tiny
215 Enchanted
216 Mystical
217 Otherworldly
218 Fairy-tale Fairy-Tale
219 Supernatural
220 Fantastical
221 Mystifying
222 Mythical
Spellbinding
223 Magical
224 Bioluminescent
225 Elder
230 Feminine
231 Masculine
232 Cartoony
233 2000's 2000'S
234 1990's 1990'S
235 1980's 1980'S
236 1970's 1970'S
237 1960's 1960'S
238 1950's 1950'S
239 1940's 1940'S
240 1940's 1920'S
241 1920's 1900'S
242 1900's 1800'S
243 1800's 1700'S
244 1700's 1600'S
1600's
245 Luminescent
246 Floating
247 Shiny
276 Glad
277 Folded
278 Glam
Chic
279 Zen
280 Rustic
281 Rich
308 Ruthless
309 Eerie
310 16-Bit
311 1950s suburbia 1950S Suburbia
312 alhambresque Alhambresque
313 alien Alien
314 Amiga 500 style Amiga 500 Style
315 Atari 2600 style Atari 2600 Style
316 apocalyptic Apocalyptic
317 avant-garde Avant-Garde
318 basic Basic
319 biochemical Biochemical
320 biomorphic Biomorphic
321 biophilic Biophilic
322 biopunk Biopunk
323 deserted Deserted
324 desolate Desolate
325 feline Feline
326 Morbid
327 Frilled
328 Dried
329 Frosted
330 Oversized
331 Lo-Fi
332 Shabby Chic
333 Tiki
334 Wanderlust
335 Fur-Trimmed
336 Warrior
337 Abandoned
338 Abhorrent
339 Abrasive
340 Absorbing
341 Abstract
342 Acclaimed
343 Accomplished
344 Adroit
345 Aesthetic
346 Aesthetically Pleasing
347 Angular
348 Appealing
349 Artistic
350 Aspiring
351 Astonishing
352 Atmospheric
353 Authentic
354 Average
355 Award-Winning
356 Awe-Inspiring
357 Balanced
358 Baroque
359 Biomechanical
360 Blue
361 Bold
362 Boorish
363 Boundless
364 Brilliant
365 Candid
366 Capricious
367 Ceramic
368 Characteristic
369 Classic
370 Clockwork
371 Cloudy
372 Cluttered
373 Cold
374 Collectable
375 Colorful
376 Colossal
377 Complementary
378 Complex
379 Conceptual
380 Conscious
381 Contemplative
382 Contemporary
383 Controversial
384 Conversational
385 Cool
386 Creative
387 Cromulent
388 Cyberpunk
389 Daring
390 Deafening
391 Decorative
392 Deeply Thoughtful
393 Delicious
394 Delightful
395 Detailed
396 Deteriorating
397 Dieselpunk
398 Dilapidated
399 Dimensional
400 Dire
401 Disciplined
402 Disruptive
403 Distinctive
404 Distinguished
405 Divine
406 Draconian
407 Dreamlike
408 Dusty
409 Dystopian
410 Eclectic
411 Efficacious
412 Elastic
413 Elevated
414 Elevating
415 Elite
416 Elusive
417 Emergent
418 Emerging
419 Emotional
420 Emotionally Charged
421 Energetic
422 Engrossing
423 Enigmatic
424 Epic
425 Epochal
426 Evil
427 Evocative
428 Exceptional
429 Explosive
430 Expressive
431 Extraordinary
432 Extreme
433 Fallacious
434 Feigned
435 Festive
436 Fiery
437 Figural
438 Figurative
439 Flowery
440 Foggy
441 Frantic
442 Freelance
443 Fresh
444 Fruit-Flavored
445 Functional
446 Funny
447 Gentle
448 Ghastly
449 Ghostly
450 Gigantic
451 Glib
452 Good
453 Granular
454 Groovy
455 Grouchy
456 Grumpy
457 Harsh
458 Hesitant
459 Holistic
460 Honest
461 Honorable
462 Human
463 Hyper-Creative
464 Hyperdetailed
465 Icy
466 Illuminated
467 Imaginative
468 Impassioned
469 Impeccable
470 Impressionist
471 Impressive
472 Incredibly Detailed
473 Infused
474 Inspirational
475 Inspired
476 Instinctive
477 Intellectual
478 Intensive
479 Interesting
480 Intuitive
481 Inventive
482 Jealous
483 Kindhearted
484 Labyrinthine
485 Layered
486 Level
487 Lifelike
488 Literal
489 Luminous
490 Lyrical
491 Macho
492 Maniacal
493 Masterpiece
494 Matte
495 Mature
496 Meandering
497 Meek
498 Mind-Bending
499 Modular
500 Moody
501 Mosaic-Like
502 Moving
503 Mysterious
504 Narrative
505 Neat
506 Nonchalant
507 Noxious
508 Nutty
509 Oppressive
510 Organic
511 Original
512 Overwrought
513 Painterly
514 Paradoxical
515 Parched
516 Passionate
517 Pastel
518 Personal
519 Petrified
520 Phenomenal
521 Pictorial
522 Potent
523 Primordial
524 Profound
525 Provoking
526 Psychadelic
527 Psychotic
528 Puny
529 Pure
530 Rainbow
531 Refined
532 Remarkable
533 Repressive
534 Resonant
535 Resourceful
536 Retrofuturistic
537 Revealing
538 Rickety
539 Romantic
540 Rounded
541 Ruined
542 Rusty
543 Saturated
544 Sculptural
545 Secret
546 Semi-Abstract
547 Serious
548 Shadowy
549 Shaky
550 Sharp
551 Signature
552 Simple
553 Skilled
554 Skillful
555 Spiritual
556 Sprawling
557 Squealing
558 Steampunk
559 Stirring
560 Stone
561 Striped
562 Studied
563 Sturdy
564 Sublime
565 Substantive
566 Superficial
567 Supersized
568 Supple
569 Sweltering
570 Swirling
571 Symbolic
572 Tactile
573 Talented
574 Tangible
575 Tasteful
576 Terrifying
577 Textile
578 Thought-Provoking
579 Thundering
580 Timeless
581 Touching
582 Tough
583 Traditional
584 Tremendous
585 Twisted
586 Unadvised
587 Unappetizing
588 Unconventional
589 Underwater
590 Unexpected
591 Unforgettable
592 Unique
593 Universal
594 Unnatural
595 Unpredictable
596 Untidy
597 Unusual
598 Uptight
599 Vengeful
600 Visionary
601 Visual
602 Visually Stimulating
603 Voyeuristic
604 Warlike
605 Weird
606 Wispy
607 Witty
608 Zany
+863
View File
@@ -0,0 +1,863 @@
Apple
Banana
Orange
Pineapple
Mango
Papaya
Watermelon
Kiwi
Grapefruit
Pear
Peach
Cherry
Blueberry
Strawberry
Raspberry
Blackberry
Cranberry
Lemon
Lime
Grape
Cantaloupe
Honeydew
Pomegranate
Fig
Plum
Apricot
Nectarine
Passionfruit
Guava
Starfruit
Dragonfruit
Avocado
Coconut
Lychee
Persimmon
Tangerine
Clementine
Grapefruit
Mango
Jackfruit
Papaya
Blackcurrant
Redcurrant
Gooseberry
Elderberry
Boysenberry
Kiwano
Melon
Rhubarb
Tomato
Pizza
Pasta
Sandwiches
Burgers
Tacos
Wraps
Stir-fry
Fried Rice
Soup
Salad
Roast chicken
Sushi
Fish and chips
Curry
BBQ
Grilled meat
Omelette
Quiche
Fried chicken
Meatloaf
Lasagne
Casserole
Stew
Meatballs
Cup of Coffee
Cup of Tea
Croissant
Danish pastry
Pain au chocolat
Brioche
Puff pastry
Éclair
Baklava
Strudel
Beignet
Scone
Bread
Charcuterie
Tortilla
abalone
açaí berries
açaí juice
acorn squash
aged japanese kurozu
albacore tuna
alcohol
ale
alfalfa sprouts
algae
almond
anchovies
anchovy
angus beef
appetizer
apple juice
apricot
arborio rice
arctic char
artichoke
artichokes
arugula
asian greens
asparagus
aspic
ate
autumn crocus
avocado
avocados
bacon
bagel
bake
balsamic vinegar
bamboo shoots
banana
banana split
barbecue
barbecued meat
barley
basil
batter
bean curd
beancurd
beans
beef
beer
beets
bell pepper
berry
biscuit
blackbeans
blackberries
blackberry
bland
blood orange
blueberry
boil
boiled egg
bok choy
bowl
boysenberries
boysenberry
bran
brazil nuts
breadfruit
breakfast
lunch
brunch
broccoli
broccoli sprouts
broil
brownie
brussels sprouts
buckwheat
buns
burrito
butter
butternut squash
cabbage
cake
calorie
candy
cantaloupe
capers
caramel
carbohydrate
carrot
cashew
cassava
casserole
cater
cauliflower
caviar
cayenne pepper
celeriac
celery
celery seed
cereal
chamomile
chard
cheddar
cheese
cheesecake
cherries
chew
chick peas
chicken
chickpeas
chili
chilli peppers
chips
chives
chocolate
chopsticks
chow
chutney
cilantro
cinnamon
citron
citrus
clam
clams
cloves
cobbler
coconut
cod
coffee
coleslaw
collard greens
cook
cookie
corn
cornflakes
cornmeal
cottage cheese
crab
crackers
cranberries
cranberry
cream
cream cheese
crepe
crisp
crunch
crust
cucumber
cuisine
cumin
cupboard
cupcake
curcumin
curds
curry
custard
daidzein
daikon
dairy
dal
dandelion greens
danish pastry
dates
dessert
dill
dish
dough
doughnut
dragonfruit
dressing
drink
dry
dry beans
durian
eat
edam cheese
edamame
egg
eggplant
elderberry
endive
entree
escargot
fastfood
fava beans
fennel
fermented bean paste
fermented milk
fig
fillet
fire
fish
flan
flax
flour
food
fork
french fries
fried potatoes
fruit
garbanzo beans
garden cress
garlic
gastronomy
gelatin
genistein
ghee
ginger
gingerale
gingerbread
glasses
gouda cheese
grain
granola
grape
grapefruit
grapes
grated
gravy
greenbean
guacamole
guava
gyro
halibut
ham
hamburger
hash
hazelnut
herbs
herring
holy basil
honey
horseradish
hot dog
hot peppers
hot sauce
hotdog
hummus
ice
ice cream
ice cream cone
iceberg lettuce
iced tea
icing
indian mustard
jackfruit
jalapeno
jam
jelly
jellybeans
jicama
jimmies
jordan almonds
juice
kale
kebab
kefir
kelp
ketchup
kettle
kidney beans
king mackerel
kitchen
kiwi
kiwifruit
knife
kohlrabi
kumquat
ladle
lake trout
lamb
lard
lasagna
lavender
leeks
legumes
lentils
lettuce
licorice
lima beans
limes
lingonberries
liquor
liver
loaf
lobster
loganberries
lollipop
long pepper
loquat
low-fat yogurt
lox
lunch
lunchmeat
lychee
macadamia nuts
macaroni
macaroon
mackerel
main course
maitake mushrooms
maize
mandarin orange
mango
mangoes
mangosteen
maple syrup
margarine
marionberrries
marionberry
marmalade
marshmallow
mashed
maté
mayonnaise
meat
meatballs
meatloaf
melon
melons
melted cheese
menu
meringue
mexican oregano
milk
milkshake
millet
mincemeat
minerals
mints
molasses
mozzarella
muffin
mug
munch
mung beans
mushrooms
muskmelon
mussels
mustard
mutton
napkin
navy beans
nectar
nectarine
nibble
noodles
nosh
nourish
nourishment
nut
nutella
nutmeg
oatmeal
oats
octopus
ohyo
okra
oleo
olive
olive oil
omelet
omnivore
onion
onions
orange
oranges
order
oregano
oven
oyster
oysters
pan
pancake
papaya
paprika
parsley
parsnips
passion fruit
pate
patty
pattypan squash
pea
peach
peanut
peanutbutter
peanuts
peapod
peas
pecan
pecans
pepper
peppermint
pepperoni
persimmon
persipan
pesto sauce
pickles
picnic
pie
pilaf
pineapple
pinto beans
pistachio nuts
pita bread
pitcher
plantago
plantains
plum
poached
pomegranate
pomegranate juice
pomegranates
pomelo
popcorn
popovers
popsicle
pork
pork chops
portobello mushrooms
pot
potato
potatoes
preserves
pretzel
protein
prune
pudding
pumpernickel
pumpkin
pumpkin seeds
punch
quiche
quinoa
radicchio
radish
raisins
rapini
raspberries
raspberry
ravioli
reishi mushrooms
relish
rhubarb
ribs
rice
rice bran
rice wine vinegar
risotto
roast
roast beef
roasted almonds
roasted pork
roe
roll
rolled oats
rolling pin
romaine
romaine lettuce
rosemary
rutabagas
rye
saffron
sage
sage tea
salad
salami
salmon
salsa
salt
sandwich
sardines
sauce
sauerkraut
sausage
sausages
savory
scallions
scallops
scrambled
seaweed
seeds
shallots
shellfish
sherbet
shiitake mushrooms
shish kebab
shrimp
slaw
slice
smoked
smoked mackerel
snails
snickers chocolate bar
snow peas
soba noodles
soda
sole
sorbet
sorghum
sorrel
sour cream
soy infant formula
soy milk
soy protein bars
soy protein isolate
soybean curd
soybeans
soysauce
spaghetti
spareribs
spatula
spearmint
spices
spinach
split peas
spoon
spork
sprinkles
sprouts
spuds
squash
squid
steak
stew
stir-fry
stomach
stove
straw
strawberries
strawberry
string bean
string beans
stringy
strudel
succotash
sugar beets
sugar snap peas
summer squash
sundae
supper
sweet peas
sweet potato
sweet potatoes
swiss chard
syrup
tabasco sauce
tahini
take-out
tallow
tamale
tangerine
tangerines
tapioca
taro
tarragon
tart
tartary buckwheat
tea
teriyaki
thyme
toast
toaster
toffee
tofu
tomatillo
tomato paste
torte
tuber
tuna
turkey
turkey bacon
turmeric
turnip
turnip greens
turnips
ugli fruit
vanilla
veal
vegetable
venison
vinegar
vitamins
wafer
waffle
wakame
walnut
walnuts
wasabi
water
water chestnut
watercress
watermelon
well-done meat
wheat
wheat bran
wheat germ
wheat grass
whey
whipped cream
wild ginger
wild rice
wine
winter squash
wok
yam
yams
yeast
yerba maté
yogurt
yolk
zucchini
chikuwa
chips
bugles
doritos
potato chips
pringles
popcorn
pretzel
umaibou
anmitsu
mitsumame
batter
bread
anpan
cinnamon roll
gumball
hot cross bun
melon bread
noppo bread
pastry box
cake
baumkuchen
birthday cake
black forest cake
charlotte cake
cheesecake
rare cheesecake
brownie
chocolate cake
chocolate framboise
christmas cake
cupcake
fondant au chocolat
layer cake
madeleine
mille-feuille
mont blanc
opera cake
pound cake
red velvet cake
cake slice
stollen
strawberry shortcake
swiss roll
strawberry swiss roll
tiramisu
wedding cake
yule log
candy
candy cane
chitose ame
cigarette candy
creme egg
jelly bean
kitkat
konpeitou
lollipop
chupa chups
shaped lollipop
swirl lollipop
m&m's
marble chocolate
nerunerunerune
pinata
tootsweets
candy apple
caramel
chewing gum
chocolate
apollo chocolate
chocolate bar
chocolate cake
chocolate fountain
heart-shaped chocolate
chocolate marquise
chocolate syrup
kinoko no yama
takenoko no sato
white chocolate
churro
cookie
checkerboard cookie
chocolate chip cookie
country ma'am
gingerbread cookie
gingerbread house
gingerbread man
pocky
sandwich cookie
oreo
thumbprint cookie
thumbprint cookie
toppo
wafer
wafer stick
cotton candy
cream
bavarois
icing
crepe
dorayaki
dough
doughnut
chocolate doughnut
french cruller
sakura french
old-fashioned doughnut
gelatin
ice cream
banana split
coolish
ice cream cone
waffle cone
ice cream float
ice cream sandwich
parfait
strawberry parfait
single scoop
double scoop
triple scoop
quadruple scoop
too many scoops
soft serve
sprinkles
sundae
imagawayaki
marshmallow
mooncake
muffin
pastry
chocolate cornet
cream puff
macaron
pie
apple pie
popsicle
popsicle stick
tupet
watermelon bar
pudding
custard
pudding a la mode
shaved ice
slushie
tanghulu
tart
blueberry tart
fruit tart
strawberry tart
wagashi
baozi
momiji manjuu
mochi
dango
ichigo daifuku
kashiwa mochi
namagashi
sakura mochi
songpyeon
suama
taiyaki
uirou
warabimochi
youkan
1 Apple
2 Banana
3 Orange
4 Pineapple
5 Mango
6 Papaya
7 Watermelon
8 Kiwi
9 Grapefruit
10 Pear
11 Peach
12 Cherry
13 Blueberry
14 Strawberry
15 Raspberry
16 Blackberry
17 Cranberry
18 Lemon
19 Lime
20 Grape
21 Cantaloupe
22 Honeydew
23 Pomegranate
24 Fig
25 Plum
26 Apricot
27 Nectarine
28 Passionfruit
29 Guava
30 Starfruit
31 Dragonfruit
32 Avocado
33 Coconut
34 Lychee
35 Persimmon
36 Tangerine
37 Clementine
38 Grapefruit
39 Mango
40 Jackfruit
41 Papaya
42 Blackcurrant
43 Redcurrant
44 Gooseberry
45 Elderberry
46 Boysenberry
47 Kiwano
48 Melon
49 Rhubarb
50 Tomato
51 Pizza
52 Pasta
53 Sandwiches
54 Burgers
55 Tacos
56 Wraps
57 Stir-fry
58 Fried Rice
59 Soup
60 Salad
61 Roast chicken
62 Sushi
63 Fish and chips
64 Curry
65 BBQ
66 Grilled meat
67 Omelette
68 Quiche
69 Fried chicken
70 Meatloaf
71 Lasagne
72 Casserole
73 Stew
74 Meatballs
75 Cup of Coffee
76 Cup of Tea
77 Croissant
78 Danish pastry
79 Pain au chocolat
80 Brioche
81 Puff pastry
82 Éclair
83 Baklava
84 Strudel
85 Beignet
86 Scone
87 Bread
88 Charcuterie
89 Tortilla
90 abalone
91 açaí berries
92 açaí juice
93 acorn squash
94 aged japanese kurozu
95 albacore tuna
96 alcohol
97 ale
98 alfalfa sprouts
99 algae
100 almond
101 anchovies
102 anchovy
103 angus beef
104 appetizer
105 apple juice
106 apricot
107 arborio rice
108 arctic char
109 artichoke
110 artichokes
111 arugula
112 asian greens
113 asparagus
114 aspic
115 ate
116 autumn crocus
117 avocado
118 avocados
119 bacon
120 bagel
121 bake
122 balsamic vinegar
123 bamboo shoots
124 banana
125 banana split
126 barbecue
127 barbecued meat
128 barley
129 basil
130 batter
131 bean curd
132 beancurd
133 beans
134 beef
135 beer
136 beets
137 bell pepper
138 berry
139 biscuit
140 blackbeans
141 blackberries
142 blackberry
143 bland
144 blood orange
145 blueberry
146 boil
147 boiled egg
148 bok choy
149 bowl
150 boysenberries
151 boysenberry
152 bran
153 brazil nuts
154 breadfruit
155 breakfast
156 lunch
157 brunch
158 broccoli
159 broccoli sprouts
160 broil
161 brownie
162 brussels sprouts
163 buckwheat
164 buns
165 burrito
166 butter
167 butternut squash
168 cabbage
169 cake
170 calorie
171 candy
172 cantaloupe
173 capers
174 caramel
175 carbohydrate
176 carrot
177 cashew
178 cassava
179 casserole
180 cater
181 cauliflower
182 caviar
183 cayenne pepper
184 celeriac
185 celery
186 celery seed
187 cereal
188 chamomile
189 chard
190 cheddar
191 cheese
192 cheesecake
193 cherries
194 chew
195 chick peas
196 chicken
197 chickpeas
198 chili
199 chilli peppers
200 chips
201 chives
202 chocolate
203 chopsticks
204 chow
205 chutney
206 cilantro
207 cinnamon
208 citron
209 citrus
210 clam
211 clams
212 cloves
213 cobbler
214 coconut
215 cod
216 coffee
217 coleslaw
218 collard greens
219 cook
220 cookie
221 corn
222 cornflakes
223 cornmeal
224 cottage cheese
225 crab
226 crackers
227 cranberries
228 cranberry
229 cream
230 cream cheese
231 crepe
232 crisp
233 crunch
234 crust
235 cucumber
236 cuisine
237 cumin
238 cupboard
239 cupcake
240 curcumin
241 curds
242 curry
243 custard
244 daidzein
245 daikon
246 dairy
247 dal
248 dandelion greens
249 danish pastry
250 dates
251 dessert
252 dill
253 dish
254 dough
255 doughnut
256 dragonfruit
257 dressing
258 drink
259 dry
260 dry beans
261 durian
262 eat
263 edam cheese
264 edamame
265 egg
266 eggplant
267 elderberry
268 endive
269 entree
270 escargot
271 fastfood
272 fava beans
273 fennel
274 fermented bean paste
275 fermented milk
276 fig
277 fillet
278 fire
279 fish
280 flan
281 flax
282 flour
283 food
284 fork
285 french fries
286 fried potatoes
287 fruit
288 garbanzo beans
289 garden cress
290 garlic
291 gastronomy
292 gelatin
293 genistein
294 ghee
295 ginger
296 gingerale
297 gingerbread
298 glasses
299 gouda cheese
300 grain
301 granola
302 grape
303 grapefruit
304 grapes
305 grated
306 gravy
307 greenbean
308 guacamole
309 guava
310 gyro
311 halibut
312 ham
313 hamburger
314 hash
315 hazelnut
316 herbs
317 herring
318 holy basil
319 honey
320 horseradish
321 hot dog
322 hot peppers
323 hot sauce
324 hotdog
325 hummus
326 ice
327 ice cream
328 ice cream cone
329 iceberg lettuce
330 iced tea
331 icing
332 indian mustard
333 jackfruit
334 jalapeno
335 jam
336 jelly
337 jellybeans
338 jicama
339 jimmies
340 jordan almonds
341 juice
342 kale
343 kebab
344 kefir
345 kelp
346 ketchup
347 kettle
348 kidney beans
349 king mackerel
350 kitchen
351 kiwi
352 kiwifruit
353 knife
354 kohlrabi
355 kumquat
356 ladle
357 lake trout
358 lamb
359 lard
360 lasagna
361 lavender
362 leeks
363 legumes
364 lentils
365 lettuce
366 licorice
367 lima beans
368 limes
369 lingonberries
370 liquor
371 liver
372 loaf
373 lobster
374 loganberries
375 lollipop
376 long pepper
377 loquat
378 low-fat yogurt
379 lox
380 lunch
381 lunchmeat
382 lychee
383 macadamia nuts
384 macaroni
385 macaroon
386 mackerel
387 main course
388 maitake mushrooms
389 maize
390 mandarin orange
391 mango
392 mangoes
393 mangosteen
394 maple syrup
395 margarine
396 marionberrries
397 marionberry
398 marmalade
399 marshmallow
400 mashed
401 maté
402 mayonnaise
403 meat
404 meatballs
405 meatloaf
406 melon
407 melons
408 melted cheese
409 menu
410 meringue
411 mexican oregano
412 milk
413 milkshake
414 millet
415 mincemeat
416 minerals
417 mints
418 molasses
419 mozzarella
420 muffin
421 mug
422 munch
423 mung beans
424 mushrooms
425 muskmelon
426 mussels
427 mustard
428 mutton
429 napkin
430 navy beans
431 nectar
432 nectarine
433 nibble
434 noodles
435 nosh
436 nourish
437 nourishment
438 nut
439 nutella
440 nutmeg
441 oatmeal
442 oats
443 octopus
444 ohyo
445 okra
446 oleo
447 olive
448 olive oil
449 omelet
450 omnivore
451 onion
452 onions
453 orange
454 oranges
455 order
456 oregano
457 oven
458 oyster
459 oysters
460 pan
461 pancake
462 papaya
463 paprika
464 parsley
465 parsnips
466 passion fruit
467 pate
468 patty
469 pattypan squash
470 pea
471 peach
472 peanut
473 peanutbutter
474 peanuts
475 peapod
476 peas
477 pecan
478 pecans
479 pepper
480 peppermint
481 pepperoni
482 persimmon
483 persipan
484 pesto sauce
485 pickles
486 picnic
487 pie
488 pilaf
489 pineapple
490 pinto beans
491 pistachio nuts
492 pita bread
493 pitcher
494 plantago
495 plantains
496 plum
497 poached
498 pomegranate
499 pomegranate juice
500 pomegranates
501 pomelo
502 popcorn
503 popovers
504 popsicle
505 pork
506 pork chops
507 portobello mushrooms
508 pot
509 potato
510 potatoes
511 preserves
512 pretzel
513 protein
514 prune
515 pudding
516 pumpernickel
517 pumpkin
518 pumpkin seeds
519 punch
520 quiche
521 quinoa
522 radicchio
523 radish
524 raisins
525 rapini
526 raspberries
527 raspberry
528 ravioli
529 reishi mushrooms
530 relish
531 rhubarb
532 ribs
533 rice
534 rice bran
535 rice wine vinegar
536 risotto
537 roast
538 roast beef
539 roasted almonds
540 roasted pork
541 roe
542 roll
543 rolled oats
544 rolling pin
545 romaine
546 romaine lettuce
547 rosemary
548 rutabagas
549 rye
550 saffron
551 sage
552 sage tea
553 salad
554 salami
555 salmon
556 salsa
557 salt
558 sandwich
559 sardines
560 sauce
561 sauerkraut
562 sausage
563 sausages
564 savory
565 scallions
566 scallops
567 scrambled
568 seaweed
569 seeds
570 shallots
571 shellfish
572 sherbet
573 shiitake mushrooms
574 shish kebab
575 shrimp
576 slaw
577 slice
578 smoked
579 smoked mackerel
580 snails
581 snickers chocolate bar
582 snow peas
583 soba noodles
584 soda
585 sole
586 sorbet
587 sorghum
588 sorrel
589 sour cream
590 soy infant formula
591 soy milk
592 soy protein bars
593 soy protein isolate
594 soybean curd
595 soybeans
596 soysauce
597 spaghetti
598 spareribs
599 spatula
600 spearmint
601 spices
602 spinach
603 split peas
604 spoon
605 spork
606 sprinkles
607 sprouts
608 spuds
609 squash
610 squid
611 steak
612 stew
613 stir-fry
614 stomach
615 stove
616 straw
617 strawberries
618 strawberry
619 string bean
620 string beans
621 stringy
622 strudel
623 succotash
624 sugar beets
625 sugar snap peas
626 summer squash
627 sundae
628 supper
629 sweet peas
630 sweet potato
631 sweet potatoes
632 swiss chard
633 syrup
634 tabasco sauce
635 tahini
636 take-out
637 tallow
638 tamale
639 tangerine
640 tangerines
641 tapioca
642 taro
643 tarragon
644 tart
645 tartary buckwheat
646 tea
647 teriyaki
648 thyme
649 toast
650 toaster
651 toffee
652 tofu
653 tomatillo
654 tomato paste
655 torte
656 tuber
657 tuna
658 turkey
659 turkey bacon
660 turmeric
661 turnip
662 turnip greens
663 turnips
664 ugli fruit
665 vanilla
666 veal
667 vegetable
668 venison
669 vinegar
670 vitamins
671 wafer
672 waffle
673 wakame
674 walnut
675 walnuts
676 wasabi
677 water
678 water chestnut
679 watercress
680 watermelon
681 well-done meat
682 wheat
683 wheat bran
684 wheat germ
685 wheat grass
686 whey
687 whipped cream
688 wild ginger
689 wild rice
690 wine
691 winter squash
692 wok
693 yam
694 yams
695 yeast
696 yerba maté
697 yogurt
698 yolk
699 zucchini
700 chikuwa
701 chips
702 bugles
703 doritos
704 potato chips
705 pringles
706 popcorn
707 pretzel
708 umaibou
709 anmitsu
710 mitsumame
711 batter
712 bread
713 anpan
714 cinnamon roll
715 gumball
716 hot cross bun
717 melon bread
718 noppo bread
719 pastry box
720 cake
721 baumkuchen
722 birthday cake
723 black forest cake
724 charlotte cake
725 cheesecake
726 rare cheesecake
727 brownie
728 chocolate cake
729 chocolate framboise
730 christmas cake
731 cupcake
732 fondant au chocolat
733 layer cake
734 madeleine
735 mille-feuille
736 mont blanc
737 opera cake
738 pound cake
739 red velvet cake
740 cake slice
741 stollen
742 strawberry shortcake
743 swiss roll
744 strawberry swiss roll
745 tiramisu
746 wedding cake
747 yule log
748 candy
749 candy cane
750 chitose ame
751 cigarette candy
752 creme egg
753 jelly bean
754 kitkat
755 konpeitou
756 lollipop
757 chupa chups
758 shaped lollipop
759 swirl lollipop
760 m&m's
761 marble chocolate
762 nerunerunerune
763 pinata
764 tootsweets
765 candy apple
766 caramel
767 chewing gum
768 chocolate
769 apollo chocolate
770 chocolate bar
771 chocolate cake
772 chocolate fountain
773 heart-shaped chocolate
774 chocolate marquise
775 chocolate syrup
776 kinoko no yama
777 takenoko no sato
778 white chocolate
779 churro
780 cookie
781 checkerboard cookie
782 chocolate chip cookie
783 country ma'am
784 gingerbread cookie
785 gingerbread house
786 gingerbread man
787 pocky
788 sandwich cookie
789 oreo
790 thumbprint cookie
791 thumbprint cookie
792 toppo
793 wafer
794 wafer stick
795 cotton candy
796 cream
797 bavarois
798 icing
799 crepe
800 dorayaki
801 dough
802 doughnut
803 chocolate doughnut
804 french cruller
805 sakura french
806 old-fashioned doughnut
807 gelatin
808 ice cream
809 banana split
810 coolish
811 ice cream cone
812 waffle cone
813 ice cream float
814 ice cream sandwich
815 parfait
816 strawberry parfait
817 single scoop
818 double scoop
819 triple scoop
820 quadruple scoop
821 too many scoops
822 soft serve
823 sprinkles
824 sundae
825 imagawayaki
826 marshmallow
827 mooncake
828 muffin
829 pastry
830 chocolate cornet
831 cream puff
832 macaron
833 pie
834 apple pie
835 popsicle
836 popsicle stick
837 tupet
838 watermelon bar
839 pudding
840 custard
841 pudding a la mode
842 shaved ice
843 slushie
844 tanghulu
845 tart
846 blueberry tart
847 fruit tart
848 strawberry tart
849 wagashi
850 baozi
851 momiji manjuu
852 mochi
853 dango
854 ichigo daifuku
855 kashiwa mochi
856 namagashi
857 sakura mochi
858 songpyeon
859 suama
860 taiyaki
861 uirou
862 warabimochi
863 youkan
+3 -1
View File
@@ -10,4 +10,6 @@ Venus of Willendorf
Bust of Nefertiti
Christ Pantocrator
The Creation of Adam
The Scream
The Scream
Star Wars
Lord of the Rings
1 Starry Night
10 Bust of Nefertiti
11 Christ Pantocrator
12 The Creation of Adam
13 The Scream
14 Star Wars
15 Lord of the Rings
+3 -1
View File
@@ -35,4 +35,6 @@ Gel lighting
Moonlight
Sunlight
Accent lighting
Rembrandt lighting
Rembrandt lighting
Candle light
beautifully lit
1 natural lighting
35 Moonlight
36 Sunlight
37 Accent lighting
38 Rembrandt lighting
39 Candle light
40 beautifully lit
+4 -1
View File
@@ -30,4 +30,7 @@ Light
Relaxed
Tranquil
Regret
Suffering
Suffering
Flustered
Confused
Smug
1 Amusing
30 Relaxed
31 Tranquil
32 Regret
33 Suffering
34 Flustered
35 Confused
36 Smug
+766 -12
View File
@@ -34,7 +34,6 @@ Chris Hemsworth
Ryan Reynolds
Chris Evans
Robert Downey Jr.
Samuel L. Jackson
Nicolas Cage
Keanu Reeves
Pierce Brosnan
@@ -49,7 +48,6 @@ Hugh Grant
Jude Law
Matthew McConaughey
Liam Neeson
Brad Pitt
Joaquin Phoenix
Eddie Redmayne
Adam Sandler
@@ -76,7 +74,6 @@ Jeremy Renner
Chris Pine
Armie Hammer
Ashton Kutcher
Shia LaBeouf
Timothy Olyphant
Terrence Howard
Michael B. Jordan
@@ -141,7 +138,6 @@ Maggie Gyllenhaal
Rachel Weisz
Naomi Watts
Liv Tyler
Uma Thurman
Michelle Rodriguez
Emma Thompson
Kirsten Dunst
@@ -295,7 +291,6 @@ Dua Lipa
Selena Gomez
Billie Eilish
Janet Jackson
Adele
Demi Lovato
Sia
Kelly Clarkson
@@ -382,8 +377,6 @@ Neil Armstrong
Mao Zedong
Che Guevara
Pablo Picasso
Elvis Presley
John Lennon
Ronald Reagan
Margaret Thatcher
Nelson Mandela
@@ -394,15 +387,776 @@ Osama bin Laden
Steve Jobs
Bill Gates
Barack Obama
Vladimir Putin
Angela Merkel
Pope Francis
Queen Elizabeth II
Kim Jong-un
Elon Musk
Jeff Bezos
Mark Zuckerberg
Oprah Winfrey
Serena Williams
Usain Bolt
Michael Jordan
Michael Jordan
a.j. cook
abbie cornish
adrianne palicki
agam darshi
aimee garcia
aishwariya rai
alanna ubach
alexa penavega
alexandra daddario
alexis bledel
ali cobrin
ali larter
alice braga
alice eve
alice greczyn
alicia silverstone
alicia vikander
alicia witt
alison lohman
alison pill
aly michalka
alyson hannigan
alyssa milano
amanda bynes
amanda peet
amanda seyfried
amber heard
amrita acharia
amy smart
ana de la reguera
andie macdowell
andrea navedo
andrea savage
angela trimbur
anita ekberg
ann-margret
anna chlumsky
anna faris
anna kendrick
antoinette kalaj
april bowlby
april hernandez castillo
ashley greene
ashley judd
ashley olsen
ashley scott
astrid bergès-frisbey
aubrey plaza
ava gardner
berenice marlohe
beverly d'angelo
bianca kajlich
blake lively
bo derek
bree turner
briana evigan
bridget fonda
bridgette wilson-sampras
brigitte bardot
brittany daniel
brittany murphy
brittany snow
brooke burns
brooke d'orsay
brooke shields
brooklyn decker
cameron diaz
cameron richardson
camilla belle
candace cameron bure
carey mulligan
carla gallo
carla gugino
carly craig
carmen electra
casey wilson
chandra west
chloë grace moretz
chloë sevigny
christian serratos
christina applegate
christina ricci
christine lakin
christine taylor
chyler leigh
claire danes
claire forlani
cobie smulders
cody horn
connie nielsen
courteney cox
courtney thorne-smith
dakota fanning
dana davis
dania ramirez
danica mckellar
daniela bobadilla
danielle fishel
danielle harris
danneel ackles
daryl hannah
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debra messing
demi moore
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diane kruger
diane lane
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drea de matteo
drew barrymore
elena anaya
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eliza coupe
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ellen wong
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elliot page
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esmé bianco
estella warren
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freida pinto
gabrielle anwar
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gal gadot
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gemma arterton
gemma ward
gene tierney
genesis rodriguez
genevieve padalecki
gina gershon
ginnifer goodwin
goldie hawn
gugu mbatha-raw
gwyneth paltrow
hayden panettiere
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heather locklear
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helena christensen
helena mattsson
hilary duff
isla fisher
izabella miko
izabella scorupco
jada pinkett smith
jaime king
jaime murray
jaime pressly
jamie chung
jamie-lynn sigler
janina gavankar
january jones
jayne mansfield
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jenna elfman
jenna fischer
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jennifer carpenter
jennifer connelly
jennifer esposito
jennifer garner
jennifer hudson
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jennifer morrison
jennifer taylor
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jeri ryan
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jessica biel
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jessica szohr
joanne whalley
jodi lyn o'keefe
jordan ladd
jordana brewster
jud tylor
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julie benz
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julie christie
juliette lewis
k.d. aubert
kaitlin doubleday
kaley cuoco
kat dennings
kate beckinsale
kate bosworth
kate hudson
kate mara
katherine heigl
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kelly hu
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keri russell
kerry washington
kim basinger
krista allen
kristen bell
kristen stewart
kristen wiig
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kristina klebe
kristy swanson
lacey chabert
lake bell
lalaine
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lark voorhies
laura wiggins
lauren ambrose
lauren bacall
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lea michele
leah remini
leighton meester
lena headey
leslie bibb
leslie mann
li gong
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linda cardellini
lindsay lohan
lisa bonet
lizzy caplan
lucy lawless
lucy liu
lynda carter
lyndsy fonseca
madeleine stowe
madison riley
mae whitman
maggie q
mahaley patel
majandra delfino
malin akerman
maria bello
maria grazia cucinotta
maria thayer
marin hinkle
marisa coughlan
marisa tomei
marley shelton
marsha thomason
mary elizabeth winstead
mary mccormack
mary-kate olsen
mary-louise parker
meg ryan
megan fox
megyn price
melanie lynskey
melissa george
melissa sagemiller
melora hardin
mena suvari
mia wasikowska
michelle borth
michelle burke
michelle monaghan
michelle trachtenberg
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milla jovovich
ming-na wen
mini anden
minka kelly
molly ringwald
molly sims
moon bloodgood
natalia tena
natalie dormer
natasha henstridge
nathalie kelley
neve campbell
nicky whelan
nicole ari parker
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nikki reed
nina dobrev
noureen dewulf
odette annable
olga kurylenko
olivia thirlby
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paget brewster
pamela anderson
parker posey
patricia arquette
paula garcés
paula patton
paz vega
penelope cruz
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phoebe cates
piper perabo
qi shu
rachael leigh cook
rachel bilson
rachel nichols
rachel specter
rachel true
raquel welch
rashida jones
rebecca hall
rebecca romijn
regina hall
renée zellweger
rhona mitra
rita hayworth
robin tunney
rochelle aytes
rooney mara
rosario dawson
rose byrne
rose mcgowan
roselyn sanchez
rumer willis
saffron burrows
samantha mathis
sara paxton
sarah chalke
sarah hyland
sarah lancaster
sarah michelle gellar
sarah roemer
sarah shahi
sarah silverman
sasha alexander
scout taylor-compton
selma blair
seychelle gabriel
shailene woodley
shannon elizabeth
shannyn sossamon
shanola hampton
sharon stone
shawnee smith
sheri moon zombie
shu qi
sienna miller
sofia vergara
sofía vergara
sophia loren
sophie winkleman
stacey dash
stella maeve
stephanie fantauzzi
stephanie szostak
susan ward
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tara reid
teresa palmer
teri polo
thandiwe newton
thora birch
tia carrere
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traci bingham
tracie thoms
ursula andress
vanessa angel
vanessa ferlito
vanessa hudgens
vanessa lengies
vanessa williams
vera farmiga
veronica lake
vinessa shaw
vivica a. fox
winona ryder
yvonne de carlo
yvonne strahovski
ziyi zhang
zooey deschanel
zoë bell
zoë kravitz
zulay henao
arnold schwarzenegger
bruce willis
chris pratt
daniel craig
david duchovny
dustin hoffman
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gerald butler
grant gustin
ian somerhalder
jack nicholson
jackie chan
jason momoa
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john cusack
johnny deep
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kevin hart
kit harington
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michael j. fox
michael sheen
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nathan lane
paul walker
ray romano
russel crowe
scott speedman
sebastian stan
shah rukh khan
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timothée chalamet
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tom hiddleston
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tyler hoechlin
vince vaughn
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Adèle Exarchopoulos
Aimee Lou Wood
AJ Lee
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Alejandra Guilmant
Alex Morgan
Alexa Bliss
Alexis Ren
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Allison Stokke
Allison Williams
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Alycia Debnam-Carey
Amanda Cerny
Ana de Armas
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Angela Sarafyan
Anitta
Anna Akana
Anna Kournikova
Anna Paquin
AnnaSophia Robb
Anya Chalotra
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Ashley Benson
Ashley Graham
Ashley Park
Ashley Tisdale
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Awkwafina
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Bar Refaeli
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Becky G
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Bonnie Wright
Brenda Song
Brianna Hildebrand
Brie Bella
Brighton Sharbino
Caity Lotz
Camila Cabello
Camila Mendes
Camilla Luddington
Candice Swanepoel
Cardi B
Carice van Houten
Carly Rae Jepsen
Caroline Vreeland
Caylee Cowan
Charisma Carpenter
Charli D’Amelio
Charli XCX
Charlotte Flair
Charlotte McKinney
Chelsea Handler
Chloe Bennet
Chloe Kim
Chrissy Teigen
Christen Harper
Christina Hendricks
CJ Perry
Cristin Milioti
Daisy Edgar-Jones
Daisy Ridley
Dakota Johnson
Danielle Panabaker
Debby Ryan
Demi Rose
Diana Silvers
Diane Guerrero
Diora Baird
Disha Patani
Dixie D’Amelio
Doja Cat
Dominique McElligott
Dove Cameron
Eiza González
Eleanor Tomlinson
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Elizabeth Debicki
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Elizabeth Turner
Ellie Goulding
Elsa Hosk
Elsa Pataky
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Hailey Bieber
Halsey
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HoYeon Jung
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Iris Apatow
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Jessica Paré
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JoJo
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Katherine McNamara
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Kelly Rohrbach
Kesha
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Leelee Sobieski
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Lena Meyer-Landrut
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Lili Simmons
Lily Chee
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Lily Sheen
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Lindsey Vonn
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Lucy Pinder
Lupita Nyong’o
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Mackenzie Foy
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Maria Menounos
Maria Sharapova
Maryse
María Pedraza
Maude Apatow
Maya Hawke
McKayla Maroney
Meagan Good
Megan Thee Stallion
Meghan Markle
Melissa Benoist
Melissa Fumero
Melissa Rauch
Michelle Jenneke
Miesha Tate
Milana Vayntrub
Millie Bobby Brown
Miranda Cosgrove
Miranda Kerr
Morena Baccarin
Naomi Scott
Natalia Dyer
Natalie Alyn Lind
Natasha Lyonne
Nathalie Emmanuel
Nicki Minaj
Nicole Scherzinger
Nikki Bella
Nikki Glaser
Nina Agdal
Normani
Olivia Culpo
Olivia Munn
Olivia Rodrigo
Olivia Taylor Dudley
Padma Lakshmi
Paige
Paige Spiranac
Paige VanZant
Palina Rojinski
Paris Hilton
Paulina Gaitán
Peyton List
Phoebe Tonkin
Pokimane
Pom Klementieff
Priyanka Chopra
Rachel Brosnahan
Rachel Cook
Rachel Zegler
Rebecca Black
Rebecca Ferguson
Rhea Seehorn
Rita Ora
Ronda Rousey
Ronja Forcher
Rosamund Pike
Rose Leslie
Rose McIver
Rosie Huntington-Whiteley
Ruby O. Fee
Ruby Rose
Sabrina Carpenter
Sadie Sink
Samara Weaving
Sammi Hanratty
Sara Sampaio
Sara Underwood
Sarah McDaniel
Sarah Paulson
Sasha Banks
Saweetie
Shantel VanSanten
Simone Biles
Sophia Lillis
Sophia Thomalla
Sophie Turner
Stacy Keibler
Stephanie Beatriz
Stephanie McMahon
Susan Sarandon
Sydney Sweeney
Taylor Hill
Taylor Schilling
Tessa Thompson
Thomasin McKenzie
Tina Fey
Torrie Wilson
Tove Lo
Ursula Corberó
Vanessa Kirby
Vica Kerekes
Victoria De Angelis
Victoria Justice
Whitney Cummings
Willa Fitzgerald
Willa Holland
Yanet Garcia
Zazie Beetz
Zendaya
Zoey Deutch
1 Tom Hanks
34 Ryan Reynolds
35 Chris Evans
36 Robert Downey Jr.
Samuel L. Jackson
37 Nicolas Cage
38 Keanu Reeves
39 Pierce Brosnan
48 Jude Law
49 Matthew McConaughey
50 Liam Neeson
Brad Pitt
51 Joaquin Phoenix
52 Eddie Redmayne
53 Adam Sandler
74 Chris Pine
75 Armie Hammer
76 Ashton Kutcher
Shia LaBeouf
77 Timothy Olyphant
78 Terrence Howard
79 Michael B. Jordan
138 Rachel Weisz
139 Naomi Watts
140 Liv Tyler
Uma Thurman
141 Michelle Rodriguez
142 Emma Thompson
143 Kirsten Dunst
291 Selena Gomez
292 Billie Eilish
293 Janet Jackson
Adele
294 Demi Lovato
295 Sia
296 Kelly Clarkson
377 Mao Zedong
378 Che Guevara
379 Pablo Picasso
Elvis Presley
John Lennon
380 Ronald Reagan
381 Margaret Thatcher
382 Nelson Mandela
387 Steve Jobs
388 Bill Gates
389 Barack Obama
Vladimir Putin
Angela Merkel
Pope Francis
390 Queen Elizabeth II
Kim Jong-un
391 Elon Musk
392 Jeff Bezos
393 Mark Zuckerberg
394 Oprah Winfrey
395 Serena Williams
396 Usain Bolt
397 Michael Jordan
398 a.j. cook
399 abbie cornish
400 adrianne palicki
401 agam darshi
402 aimee garcia
403 aishwariya rai
404 alanna ubach
405 alexa penavega
406 alexandra daddario
407 alexis bledel
408 ali cobrin
409 ali larter
410 alice braga
411 alice eve
412 alice greczyn
413 alicia silverstone
414 alicia vikander
415 alicia witt
416 alison lohman
417 alison pill
418 aly michalka
419 alyson hannigan
420 alyssa milano
421 amanda bynes
422 amanda peet
423 amanda seyfried
424 amber heard
425 amrita acharia
426 amy smart
427 ana de la reguera
428 andie macdowell
429 andrea navedo
430 andrea savage
431 angela trimbur
432 anita ekberg
433 ann-margret
434 anna chlumsky
435 anna faris
436 anna kendrick
437 antoinette kalaj
438 april bowlby
439 april hernandez castillo
440 ashley greene
441 ashley judd
442 ashley olsen
443 ashley scott
444 astrid bergès-frisbey
445 aubrey plaza
446 ava gardner
447 berenice marlohe
448 beverly d'angelo
449 bianca kajlich
450 blake lively
451 bo derek
452 bree turner
453 briana evigan
454 bridget fonda
455 bridgette wilson-sampras
456 brigitte bardot
457 brittany daniel
458 brittany murphy
459 brittany snow
460 brooke burns
461 brooke d'orsay
462 brooke shields
463 brooklyn decker
464 cameron diaz
465 cameron richardson
466 camilla belle
467 candace cameron bure
468 carey mulligan
469 carla gallo
470 carla gugino
471 carly craig
472 carmen electra
473 casey wilson
474 chandra west
475 chloë grace moretz
476 chloë sevigny
477 christian serratos
478 christina applegate
479 christina ricci
480 christine lakin
481 christine taylor
482 chyler leigh
483 claire danes
484 claire forlani
485 cobie smulders
486 cody horn
487 connie nielsen
488 courteney cox
489 courtney thorne-smith
490 dakota fanning
491 dana davis
492 dania ramirez
493 danica mckellar
494 daniela bobadilla
495 danielle fishel
496 danielle harris
497 danneel ackles
498 daryl hannah
499 deborah ann woll
500 debra messing
501 demi moore
502 denise richards
503 diane kruger
504 diane lane
505 dianna agron
506 drea de matteo
507 drew barrymore
508 elena anaya
509 elisha cuthbert
510 eliza coupe
511 eliza dushku
512 elizabeth banks
513 elizabeth berkley
514 elizabeth hurley
515 elizabeth olsen
516 ellen wong
517 ellie kemper
518 elliot page
519 emily blunt
520 emily browning
521 emily deschanel
522 emma greenwell
523 emma roberts
524 emmanuelle chriqui
525 emmanuelle vaugier
526 emmy rossum
527 esmé bianco
528 estella warren
529 eva longoria
530 eva mendes
531 evan rachel wood
532 frankie shaw
533 freida pinto
534 gabrielle anwar
535 gabrielle union
536 gal gadot
537 garcelle beauvais
538 gemma arterton
539 gemma ward
540 gene tierney
541 genesis rodriguez
542 genevieve padalecki
543 gina gershon
544 ginnifer goodwin
545 goldie hawn
546 gugu mbatha-raw
547 gwyneth paltrow
548 hayden panettiere
549 heather graham
550 heather locklear
551 helena boham carter
552 helena christensen
553 helena mattsson
554 hilary duff
555 isla fisher
556 izabella miko
557 izabella scorupco
558 jada pinkett smith
559 jaime king
560 jaime murray
561 jaime pressly
562 jamie chung
563 jamie-lynn sigler
564 janina gavankar
565 january jones
566 jayne mansfield
567 jeanne tripplehorn
568 jenna coleman
569 jenna elfman
570 jenna fischer
571 jennie garth
572 jennifer carpenter
573 jennifer connelly
574 jennifer esposito
575 jennifer garner
576 jennifer hudson
577 jennifer love hewitt
578 jennifer morrison
579 jennifer taylor
580 jenny mccarthy-wahlberg
581 jeri ryan
582 jessica alba
583 jessica biel
584 jessica lange
585 jessica szohr
586 joanne whalley
587 jodi lyn o'keefe
588 jordan ladd
589 jordana brewster
590 jud tylor
591 julianna guill
592 julie benz
593 julie bowen
594 julie christie
595 juliette lewis
596 k.d. aubert
597 kaitlin doubleday
598 kaley cuoco
599 kat dennings
600 kate beckinsale
601 kate bosworth
602 kate hudson
603 kate mara
604 katherine heigl
605 katherine lanasa
606 kathryn hahn
607 katie cassidy
608 katie holmes
609 katrina bowden
610 katy mixon
611 kelly carlson
612 kelly hu
613 kelly stables
614 keri russell
615 kerry washington
616 kim basinger
617 krista allen
618 kristen bell
619 kristen stewart
620 kristen wiig
621 kristin chenoweth
622 kristin davis
623 kristin kreuk
624 kristina klebe
625 kristy swanson
626 lacey chabert
627 lake bell
628 lalaine
629 lara flynn boyle
630 lark voorhies
631 laura wiggins
632 lauren ambrose
633 lauren bacall
634 lauren holly
635 lauren london
636 lea michele
637 leah remini
638 leighton meester
639 lena headey
640 leslie bibb
641 leslie mann
642 li gong
643 lily collins
644 linda cardellini
645 lindsay lohan
646 lisa bonet
647 lizzy caplan
648 lucy lawless
649 lucy liu
650 lynda carter
651 lyndsy fonseca
652 madeleine stowe
653 madison riley
654 mae whitman
655 maggie q
656 mahaley patel
657 majandra delfino
658 malin akerman
659 maria bello
660 maria grazia cucinotta
661 maria thayer
662 marin hinkle
663 marisa coughlan
664 marisa tomei
665 marley shelton
666 marsha thomason
667 mary elizabeth winstead
668 mary mccormack
669 mary-kate olsen
670 mary-louise parker
671 meg ryan
672 megan fox
673 megyn price
674 melanie lynskey
675 melissa george
676 melissa sagemiller
677 melora hardin
678 mena suvari
679 mia wasikowska
680 michelle borth
681 michelle burke
682 michelle monaghan
683 michelle trachtenberg
684 michelle williams
685 milla jovovich
686 ming-na wen
687 mini anden
688 minka kelly
689 molly ringwald
690 molly sims
691 moon bloodgood
692 natalia tena
693 natalie dormer
694 natasha henstridge
695 nathalie kelley
696 neve campbell
697 nicky whelan
698 nicole ari parker
699 nicole eggert
700 nikki reed
701 nina dobrev
702 noureen dewulf
703 odette annable
704 olga kurylenko
705 olivia thirlby
706 olivia wilde
707 paget brewster
708 pamela anderson
709 parker posey
710 patricia arquette
711 paula garcés
712 paula patton
713 paz vega
714 penelope cruz
715 penélope cruz
716 phoebe cates
717 piper perabo
718 qi shu
719 rachael leigh cook
720 rachel bilson
721 rachel nichols
722 rachel specter
723 rachel true
724 raquel welch
725 rashida jones
726 rebecca hall
727 rebecca romijn
728 regina hall
729 renée zellweger
730 rhona mitra
731 rita hayworth
732 robin tunney
733 rochelle aytes
734 rooney mara
735 rosario dawson
736 rose byrne
737 rose mcgowan
738 roselyn sanchez
739 rumer willis
740 saffron burrows
741 samantha mathis
742 sara paxton
743 sarah chalke
744 sarah hyland
745 sarah lancaster
746 sarah michelle gellar
747 sarah roemer
748 sarah shahi
749 sarah silverman
750 sasha alexander
751 scout taylor-compton
752 selma blair
753 seychelle gabriel
754 shailene woodley
755 shannon elizabeth
756 shannyn sossamon
757 shanola hampton
758 sharon stone
759 shawnee smith
760 sheri moon zombie
761 shu qi
762 sienna miller
763 sofia vergara
764 sofía vergara
765 sophia loren
766 sophie winkleman
767 stacey dash
768 stella maeve
769 stephanie fantauzzi
770 stephanie szostak
771 susan ward
772 sydney tamiia poitier
773 tara reid
774 teresa palmer
775 teri polo
776 thandiwe newton
777 thora birch
778 tia carrere
779 tiffani thiessen
780 traci bingham
781 tracie thoms
782 ursula andress
783 vanessa angel
784 vanessa ferlito
785 vanessa hudgens
786 vanessa lengies
787 vanessa williams
788 vera farmiga
789 veronica lake
790 vinessa shaw
791 vivica a. fox
792 winona ryder
793 yvonne de carlo
794 yvonne strahovski
795 ziyi zhang
796 zooey deschanel
797 zoë bell
798 zoë kravitz
799 zulay henao
800 arnold schwarzenegger
801 bruce willis
802 chris pratt
803 daniel craig
804 david duchovny
805 dustin hoffman
806 dwayne johnson
807 emile hirsch
808 erik per sullivan
809 gerald butler
810 grant gustin
811 ian somerhalder
812 jack nicholson
813 jackie chan
814 jason momoa
815 jimmy fallon
816 john boyega
817 john cusack
818 johnny deep
819 josh brolin
820 kevin hart
821 kit harington
822 michael douglas
823 michael j. fox
824 michael sheen
825 michael weatherly
826 mike myers
827 nathan fillion
828 nathan lane
829 paul walker
830 ray romano
831 russel crowe
832 scott speedman
833 sebastian stan
834 shah rukh khan
835 stephen amell
836 taylor lautner
837 timothée chalamet
838 toby kebbell
839 tom hiddleston
840 tommy lee jones
841 tony goldwyn
842 tyler hoechlin
843 vince vaughn
844 Addison Rae
845 Adèle Exarchopoulos
846 Aimee Lou Wood
847 AJ Lee
848 Alanna Masterson
849 Alejandra Guilmant
850 Alex Morgan
851 Alexa Bliss
852 Alexis Ren
853 Alica Schmidt
854 Alison Brie
855 Allison Stokke
856 Allison Williams
857 Aly Raisman
858 Alycia Debnam-Carey
859 Amanda Cerny
860 Ana de Armas
861 Angela Kinsey
862 Angela Sarafyan
863 Anitta
864 Anna Akana
865 Anna Kournikova
866 Anna Paquin
867 AnnaSophia Robb
868 Anya Chalotra
869 Ariel Winter
870 Ashley Benson
871 Ashley Graham
872 Ashley Park
873 Ashley Tisdale
874 Auli’i Cravalho
875 Avril Lavigne
876 Awkwafina
877 Bailee Madison
878 Bar Refaeli
879 Barbara Palvin
880 Bebe Rexha
881 Becky G
882 Becky Lynch
883 Bella Thorne
884 Beth Behrs
885 Betty Gilpin
886 Bonnie Wright
887 Brenda Song
888 Brianna Hildebrand
889 Brie Bella
890 Brighton Sharbino
891 Caity Lotz
892 Camila Cabello
893 Camila Mendes
894 Camilla Luddington
895 Candice Swanepoel
896 Cardi B
897 Carice van Houten
898 Carly Rae Jepsen
899 Caroline Vreeland
900 Caylee Cowan
901 Charisma Carpenter
902 Charli D’Amelio
903 Charli XCX
904 Charlotte Flair
905 Charlotte McKinney
906 Chelsea Handler
907 Chloe Bennet
908 Chloe Kim
909 Chrissy Teigen
910 Christen Harper
911 Christina Hendricks
912 CJ Perry
913 Cristin Milioti
914 Daisy Edgar-Jones
915 Daisy Ridley
916 Dakota Johnson
917 Danielle Panabaker
918 Debby Ryan
919 Demi Rose
920 Diana Silvers
921 Diane Guerrero
922 Diora Baird
923 Disha Patani
924 Dixie D’Amelio
925 Doja Cat
926 Dominique McElligott
927 Dove Cameron
928 Eiza González
929 Eleanor Tomlinson
930 Elisabeth Shue
931 Elizabeth Debicki
932 Elizabeth Gillies
933 Elizabeth Turner
934 Ellie Goulding
935 Elsa Hosk
936 Elsa Pataky
937 Elsie Hewitt
938 Emily Bett Rickards
939 Emily Kinney
940 Emily Osment
941 Emily Ratajkowski
942 Emily VanCamp
943 Emma Kenney
944 Emma Mackey
945 Emma Raducanu
946 Erin Moriarty
947 Eugenie Bouchard
948 Eva Amurri
949 Evangeline Lilly
950 Felicity Jones
951 Florence Pugh
952 Freya Allan
953 Giada De Laurentiis
954 Gillian Anderson
955 Gillian Jacobs
956 Gina Carano
957 Hailee Steinfeld
958 Hailey Bieber
959 Halsey
960 Hannah Simone
961 Hayley Atwell
962 Helene Fischer
963 Holly Willoughby
964 HoYeon Jung
965 Hunter King
966 Hunter Schafer
967 Iggy Azalea
968 Ilana Glazer
969 Iliza Shlesinger
970 Imogen Poots
971 Ireland Baldwin
972 Iris Apatow
973 Isabela Merced
974 Isidora Goreshter
975 Jackie Cruz
976 Jaimie Alexander
977 Jamie Lee Curtis
978 Jane Levy
979 Jenna Dewan
980 Jennette McCurdy
981 Jennifer Holland
982 Jenny Slate
983 Jessica Parker Kennedy
984 Jessica Paré
985 Jessica Simpson
986 Jewel Staite
987 Jodie Comer
988 Joey King
989 JoJo
990 Jordyn Huitema
991 Jordyn Jones
992 Josephine Skriver
993 Judy Greer
994 Julia Fox
995 Julia Garner
996 Julianne Hough
997 Juno Temple
998 Jutta Leerdam
999 Kaia Gerber
1000 Kaitlyn Dever
1001 Karen Fukuhara
1002 Karen Gillan
1003 Kate Upton
1004 Katelyn Nacon
1005 Katelyn Ohashi
1006 Katherine Langford
1007 Katherine McNamara
1008 Katherine Waterston
1009 Katheryn Winnick
1010 Kaya Scodelario
1011 Kelly Brook
1012 Kelly Rohrbach
1013 Kesha
1014 Khloé Kardashian
1015 Kiernan Shipka
1016 Kim Kardashian
1017 Kimberley Garner
1018 Kimiko Glenn
1019 Kira Kosarin
1020 Kourtney Kardashian
1021 Krysten Ritter
1022 Kylie Jenner
1023 Kylie Minogue
1024 Lais Ribeiro
1025 Lana Del Rey
1026 Larsen Thompson
1027 Laura Harrier
1028 Laura Prepon
1029 Lauren Cohan
1030 Lea Seydoux
1031 Leelee Sobieski
1032 Leila Lowfire
1033 Lena Gercke
1034 Lena Meyer-Landrut
1035 Lili Reinhart
1036 Lili Simmons
1037 Lily Chee
1038 Lily James
1039 Lily Sheen
1040 Lily-Rose Depp
1041 Lindsey Pelas
1042 Lindsey Vonn
1043 Lisa Kudrow
1044 Lucy Hale
1045 Lucy Pinder
1046 Lupita Nyong’o
1047 Mackenzie Davis
1048 Mackenzie Foy
1049 Mackenzie Ziegler
1050 Maddie Ziegler
1051 Madelaine Petsch
1052 Madison Beer
1053 Madison Pettis
1054 Madisyn Shipman
1055 Maggie Grace
1056 Maia Mitchell
1057 Maisie Williams
1058 Malin Åkerman
1059 Mandy Moore
1060 Margaret Qualley
1061 Maria Bakalova
1062 Maria Menounos
1063 Maria Sharapova
1064 Maryse
1065 María Pedraza
1066 Maude Apatow
1067 Maya Hawke
1068 McKayla Maroney
1069 Meagan Good
1070 Megan Thee Stallion
1071 Meghan Markle
1072 Melissa Benoist
1073 Melissa Fumero
1074 Melissa Rauch
1075 Michelle Jenneke
1076 Miesha Tate
1077 Milana Vayntrub
1078 Millie Bobby Brown
1079 Miranda Cosgrove
1080 Miranda Kerr
1081 Morena Baccarin
1082 Naomi Scott
1083 Natalia Dyer
1084 Natalie Alyn Lind
1085 Natasha Lyonne
1086 Nathalie Emmanuel
1087 Nicki Minaj
1088 Nicole Scherzinger
1089 Nikki Bella
1090 Nikki Glaser
1091 Nina Agdal
1092 Normani
1093 Olivia Culpo
1094 Olivia Munn
1095 Olivia Rodrigo
1096 Olivia Taylor Dudley
1097 Padma Lakshmi
1098 Paige
1099 Paige Spiranac
1100 Paige VanZant
1101 Palina Rojinski
1102 Paris Hilton
1103 Paulina Gaitán
1104 Peyton List
1105 Phoebe Tonkin
1106 Pokimane
1107 Pom Klementieff
1108 Priyanka Chopra
1109 Rachel Brosnahan
1110 Rachel Cook
1111 Rachel Zegler
1112 Rebecca Black
1113 Rebecca Ferguson
1114 Rhea Seehorn
1115 Rita Ora
1116 Ronda Rousey
1117 Ronja Forcher
1118 Rosamund Pike
1119 Rose Leslie
1120 Rose McIver
1121 Rosie Huntington-Whiteley
1122 Ruby O. Fee
1123 Ruby Rose
1124 Sabrina Carpenter
1125 Sadie Sink
1126 Samara Weaving
1127 Sammi Hanratty
1128 Sara Sampaio
1129 Sara Underwood
1130 Sarah McDaniel
1131 Sarah Paulson
1132 Sasha Banks
1133 Saweetie
1134 Shantel VanSanten
1135 Simone Biles
1136 Sophia Lillis
1137 Sophia Thomalla
1138 Sophie Turner
1139 Stacy Keibler
1140 Stephanie Beatriz
1141 Stephanie McMahon
1142 Susan Sarandon
1143 Sydney Sweeney
1144 Taylor Hill
1145 Taylor Schilling
1146 Tessa Thompson
1147 Thomasin McKenzie
1148 Tina Fey
1149 Torrie Wilson
1150 Tove Lo
1151 Ursula Corberó
1152 Vanessa Kirby
1153 Vica Kerekes
1154 Victoria De Angelis
1155 Victoria Justice
1156 Whitney Cummings
1157 Willa Fitzgerald
1158 Willa Holland
1159 Yanet Garcia
1160 Zazie Beetz
1161 Zendaya
1162 Zoey Deutch
+1 -90
View File
@@ -157,56 +157,6 @@ Bar cart
Wine rack
Sideboard
Throne
Apple
Banana
Orange
Pineapple
Mango
Papaya
Watermelon
Kiwi
Grapefruit
Pear
Peach
Cherry
Blueberry
Strawberry
Raspberry
Blackberry
Cranberry
Lemon
Lime
Grape
Cantaloupe
Honeydew
Pomegranate
Fig
Plum
Apricot
Nectarine
Passionfruit
Guava
Starfruit
Dragonfruit
Avocado
Coconut
Lychee
Persimmon
Tangerine
Clementine
Grapefruit
Mango
Jackfruit
Papaya
Blackcurrant
Redcurrant
Gooseberry
Elderberry
Boysenberry
Kiwano
Melon
Rhubarb
Tomato
Sword
Katana
Mace
@@ -330,30 +280,6 @@ Chainsaw
Fractal
Pentagram
-conceptsuffix-potion
Pizza
Pasta
Sandwiches
Burgers
Tacos
Wraps
Stir-fry
Fried Rice
Soup
Salad
Roast chicken
Sushi
Fish and chips
Curry
BBQ
Grilled meat
Omelette
Quiche
Fried chicken
Meatloaf
Lasagne
Casserole
Stew
Meatballs
Roast beef
Oak tree
Maple tree
@@ -376,8 +302,6 @@ Hydrangeas
Irises
Marigolds
-object- in a bottle
Cup of Coffee
Cup of Tea
Faberge egg
Quilt
Circlet
@@ -391,17 +315,6 @@ Supernova
Comet
Planet
Galaxy
Croissant
Danish pastry
Pain au chocolat
Brioche
Puff pastry
Éclair
Baklava
Strudel
Beignet
Scone
Bread
Skull
Snowflake
Seashell
@@ -436,6 +349,4 @@ Paraboloid
Hyperboloid
Mobius strip
Klein bottle
Sphericon
Charcuterie
Tortilla
Sphericon
1 Pen
157 Wine rack
158 Sideboard
159 Throne
Apple
Banana
Orange
Pineapple
Mango
Papaya
Watermelon
Kiwi
Grapefruit
Pear
Peach
Cherry
Blueberry
Strawberry
Raspberry
Blackberry
Cranberry
Lemon
Lime
Grape
Cantaloupe
Honeydew
Pomegranate
Fig
Plum
Apricot
Nectarine
Passionfruit
Guava
Starfruit
Dragonfruit
Avocado
Coconut
Lychee
Persimmon
Tangerine
Clementine
Grapefruit
Mango
Jackfruit
Papaya
Blackcurrant
Redcurrant
Gooseberry
Elderberry
Boysenberry
Kiwano
Melon
Rhubarb
Tomato
160 Sword
161 Katana
162 Mace
280 Fractal
281 Pentagram
282 -conceptsuffix-potion
Pizza
Pasta
Sandwiches
Burgers
Tacos
Wraps
Stir-fry
Fried Rice
Soup
Salad
Roast chicken
Sushi
Fish and chips
Curry
BBQ
Grilled meat
Omelette
Quiche
Fried chicken
Meatloaf
Lasagne
Casserole
Stew
Meatballs
283 Roast beef
284 Oak tree
285 Maple tree
302 Irises
303 Marigolds
304 -object- in a bottle
Cup of Coffee
Cup of Tea
305 Faberge egg
306 Quilt
307 Circlet
315 Comet
316 Planet
317 Galaxy
Croissant
Danish pastry
Pain au chocolat
Brioche
Puff pastry
Éclair
Baklava
Strudel
Beignet
Scone
Bread
318 Skull
319 Snowflake
320 Seashell
349 Hyperboloid
350 Mobius strip
351 Klein bottle
352 Sphericon
Charcuterie
Tortilla
+2 -1
View File
@@ -104,4 +104,5 @@ Postal delivery truck
Armored vehicle
Snow plow
Tractor trailer
Utility van
Utility van
Tuk-tuk
1 Train
104 Armored vehicle
105 Snow plow
106 Tractor trailer
107 Utility van
108 Tuk-tuk
+2 -2
View File
@@ -12,7 +12,7 @@ from call_extras import *
from model_lists import *
def generateimages(amount = 1, size = "all",model = "currently selected model",samplingsteps = "40",cfg= "7",hiresfix = True,hiressteps ="0",denoisestrength="0.6",samplingmethod="DPM++ SDE Karras", upscaler="R-ESRGAN 4x+", hiresscale="2",apiurl="http://127.0.0.1:7860",qualitygate=False,quality="7.6",runs="5",insanitylevel="5",subject="all", artist="all", imagetype="all",silentmode=False, workprompt="", antistring="",prefixprompt="", suffixprompt="", negativeprompt="",promptcompounderlevel = "1", seperator="comma", img2imgbatch = "1", img2imgsamplingsteps = "20", img2imgcfg = "7", img2imgsamplingmethod = "DPM++ SDE Karras", img2imgupscaler = "R-ESRGAN 4x+", img2imgmodel = "currently selected model", img2imgactivate = False, img2imgscale = "2", img2imgpadding = "64",img2imgdenoisestrength="0.3",ultimatesdupscale=False,usdutilewidth = "512", usdutileheight = "0", usdumaskblur = "8", usduredraw ="Linear", usduSeamsfix = "None", usdusdenoise = "0.35", usduswidth = "64", usduspadding ="32", usdusmaskblur = "8",controlnetenabled=False, controlnetmodel="",img2imgdenoisestrengthmod="-0.05",enableextraupscale = False,controlnetblockymode = False,extrasupscaler1 = "all",extrasupscaler2 ="all",extrasupscaler2visiblity="0.5",extrasupscaler2gfpgan="0",extrasupscaler2codeformer="0.15",extrasupscaler2codeformerweight="0.1",extrasresize="2",onlyupscale="false",givensubject="",smartsubject=True):
def generateimages(amount = 1, size = "all",model = "currently selected model",samplingsteps = "40",cfg= "7",hiresfix = True,hiressteps ="0",denoisestrength="0.6",samplingmethod="DPM++ SDE Karras", upscaler="R-ESRGAN 4x+", hiresscale="2",apiurl="http://127.0.0.1:7860",qualitygate=False,quality="7.6",runs="5",insanitylevel="5",subject="all", artist="all", imagetype="all",silentmode=False, workprompt="", antistring="",prefixprompt="", suffixprompt="", negativeprompt="",promptcompounderlevel = "1", seperator="comma", img2imgbatch = "1", img2imgsamplingsteps = "20", img2imgcfg = "7", img2imgsamplingmethod = "DPM++ SDE Karras", img2imgupscaler = "R-ESRGAN 4x+", img2imgmodel = "currently selected model", img2imgactivate = False, img2imgscale = "2", img2imgpadding = "64",img2imgdenoisestrength="0.3",ultimatesdupscale=False,usdutilewidth = "512", usdutileheight = "0", usdumaskblur = "8", usduredraw ="Linear", usduSeamsfix = "None", usdusdenoise = "0.35", usduswidth = "64", usduspadding ="32", usdusmaskblur = "8",controlnetenabled=False, controlnetmodel="",img2imgdenoisestrengthmod="-0.05",enableextraupscale = False,controlnetblockymode = False,extrasupscaler1 = "all",extrasupscaler2 ="all",extrasupscaler2visiblity="0.5",extrasupscaler2gfpgan="0",extrasupscaler2codeformer="0.15",extrasupscaler2codeformerweight="0.1",extrasresize="2",onlyupscale="false",givensubject="",smartsubject=True,giventypeofimage=""):
loops = int(amount) # amount of images to generate
steps = 0
upscalefilelist=[]
@@ -67,7 +67,7 @@ def generateimages(amount = 1, size = "all",model = "currently selected model",s
print(workprompt)
else:
randomprompt = build_dynamic_prompt(insanitylevel,subject,artist,imagetype, False,antistring,prefixprompt,suffixprompt,promptcompounderlevel, seperator,givensubject,smartsubject)
randomprompt = build_dynamic_prompt(insanitylevel,subject,artist,imagetype, False,antistring,prefixprompt,suffixprompt,promptcompounderlevel, seperator,givensubject,smartsubject,giventypeofimage)
# make the filename, from from a to the first comma
start_index = randomprompt.find("of a ") + len("of a ")
+1 -1
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@@ -6,7 +6,7 @@ def normal_dist(insanitylevel):
return (random.randint(1, 10)<insanitylevel or insanitylevel >= 10)
def uncommon_dist(insanitylevel):
return (random.randint(1, 15)<insanitylevel or insanitylevel >= 10)
return (random.randint(1, 18)<insanitylevel or insanitylevel >= 10)
def rare_dist(insanitylevel):
roll = (random.randint(1, 30)<insanitylevel or insanitylevel >= 10)
+21 -8
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@@ -53,7 +53,18 @@ redraw_modes = ["Linear","Chess","None"]
folder_symbol = '\U0001f4c2' # 📂
sys.path.append(os.path.abspath(".."))
# Load up stuff for personal artists list, if any
# find all artist files starting with personal_artits in userfiles
script_dir = os.path.dirname(os.path.abspath(__file__)) # Script directory
userfilesfolder = os.path.join(script_dir, "../userfiles/" )
for filename in os.listdir(userfilesfolder):
if(filename.endswith(".csv") and filename.startswith("personal_artists") and filename != "personal_artists_sample.csv"):
name = os.path.splitext(filename)[0]
name = name.replace("_"," ",-1).lower()
# directly insert into the artists list
artists.insert(2, name)
class Script(scripts.Script):
@@ -65,12 +76,12 @@ class Script(scripts.Script):
def ui(self, is_img2img):
def gen_prompt(insanitylevel, subject, artist, imagetype, antistring, prefixprompt, suffixprompt, promptcompounderlevel, seperator,givensubject,smartsubject):
def gen_prompt(insanitylevel, subject, artist, imagetype, antistring, prefixprompt, suffixprompt, promptcompounderlevel, seperator,givensubject,smartsubject,giventypeofimage):
promptlist = []
for i in range(5):
promptlist.append(build_dynamic_prompt(insanitylevel,subject,artist, imagetype, False, antistring,prefixprompt,suffixprompt,promptcompounderlevel,seperator,givensubject,smartsubject))
promptlist.append(build_dynamic_prompt(insanitylevel,subject,artist, imagetype, False, antistring,prefixprompt,suffixprompt,promptcompounderlevel,seperator,givensubject,smartsubject, giventypeofimage))
return promptlist
@@ -96,7 +107,7 @@ class Script(scripts.Script):
with gr.Tab("Main"):
with gr.Row():
insanitylevel = gr.Slider(1, 10, value=7, step=1, label="Higher levels increases complexity and randomness of generated prompt")
insanitylevel = gr.Slider(1, 10, value=5, step=1, label="Higher levels increases complexity and randomness of generated prompt")
with gr.Row():
with gr.Column(scale=1, variant="compact"):
subject = gr.Dropdown(
@@ -117,6 +128,8 @@ class Script(scripts.Script):
with gr.Row():
givensubject = gr.Textbox(label="Overwrite subject: ", value="")
smartsubject = gr.Checkbox(label="Smart subject", value = True)
with gr.Row():
giventypeofimage = gr.Textbox(label="Overwrite type of image: ", value="")
with gr.Row():
gr.Markdown("""
<font size="2">
@@ -497,7 +510,7 @@ class Script(scripts.Script):
extrasupscaler2codeformerweight = gr.Slider(0, 1, value="0.1", step=0.05, label="CodeFormer weight", visible = False)
genprom.click(gen_prompt, inputs=[insanitylevel,subject, artist, imagetype, antistring,prefixprompt, suffixprompt,promptcompounderlevel, seperator, givensubject,smartsubject], outputs=[prompt1, prompt2, prompt3,prompt4,prompt5])
genprom.click(gen_prompt, inputs=[insanitylevel,subject, artist, imagetype, antistring,prefixprompt, suffixprompt,promptcompounderlevel, seperator, givensubject,smartsubject,giventypeofimage], outputs=[prompt1, prompt2, prompt3,prompt4,prompt5])
prompt1toworkflow.click(prompttoworkflowprompt, inputs=prompt1, outputs=workprompt)
prompt2toworkflow.click(prompttoworkflowprompt, inputs=prompt2, outputs=workprompt)
@@ -505,7 +518,7 @@ class Script(scripts.Script):
prompt4toworkflow.click(prompttoworkflowprompt, inputs=prompt4, outputs=workprompt)
prompt5toworkflow.click(prompttoworkflowprompt, inputs=prompt5, outputs=workprompt)
startmain.click(generateimages, inputs=[amountofimages,size,model,samplingsteps,cfg,hiresfix,hiressteps,denoisestrength,samplingmethod, upscaler,hiresscale, apiurl, qualitygate, quality, runs,insanitylevel,subject, artist, imagetype, silentmode, workprompt, antistring, prefixprompt, suffixprompt,negativeprompt,promptcompounderlevel, seperator, img2imgbatch, img2imgsamplingsteps, img2imgcfg, img2imgsamplingmethod, img2imgupscaler, img2imgmodel,img2imgactivate, img2imgscale, img2imgpadding,img2imgdenoisestrength,ultimatesdupscale,usdutilewidth, usdutileheight, usdumaskblur, usduredraw, usduSeamsfix, usdusdenoise, usduswidth, usduspadding, usdusmaskblur, controlnetenabled, controlnetmodel,img2imgdenoisestrengthmod,enableextraupscale,controlnetblockymode,extrasupscaler1,extrasupscaler2,extrasupscaler2visiblity,extrasupscaler2gfpgan,extrasupscaler2codeformer,extrasupscaler2codeformerweight,extrasresize,onlyupscale,givensubject,smartsubject])
startmain.click(generateimages, inputs=[amountofimages,size,model,samplingsteps,cfg,hiresfix,hiressteps,denoisestrength,samplingmethod, upscaler,hiresscale, apiurl, qualitygate, quality, runs,insanitylevel,subject, artist, imagetype, silentmode, workprompt, antistring, prefixprompt, suffixprompt,negativeprompt,promptcompounderlevel, seperator, img2imgbatch, img2imgsamplingsteps, img2imgcfg, img2imgsamplingmethod, img2imgupscaler, img2imgmodel,img2imgactivate, img2imgscale, img2imgpadding,img2imgdenoisestrength,ultimatesdupscale,usdutilewidth, usdutileheight, usdumaskblur, usduredraw, usduSeamsfix, usdusdenoise, usduswidth, usduspadding, usdusmaskblur, controlnetenabled, controlnetmodel,img2imgdenoisestrengthmod,enableextraupscale,controlnetblockymode,extrasupscaler1,extrasupscaler2,extrasupscaler2visiblity,extrasupscaler2gfpgan,extrasupscaler2codeformer,extrasupscaler2codeformerweight,extrasresize,onlyupscale,givensubject,smartsubject,giventypeofimage])
automatedoutputsfolderbutton.click(openfolder)
@@ -611,12 +624,12 @@ class Script(scripts.Script):
return [insanitylevel,subject, artist, imagetype, prefixprompt,suffixprompt,negativeprompt, promptcompounderlevel, ANDtoggle, silentmode, workprompt, antistring, seperator, givensubject, smartsubject]
return [insanitylevel,subject, artist, imagetype, prefixprompt,suffixprompt,negativeprompt, promptcompounderlevel, ANDtoggle, silentmode, workprompt, antistring, seperator, givensubject, smartsubject, giventypeofimage]
def run(self, p, insanitylevel, subject, artist, imagetype, prefixprompt,suffixprompt,negativeprompt, promptcompounderlevel, ANDtoggle, silentmode, workprompt, antistring,seperator, givensubject, smartsubject):
def run(self, p, insanitylevel, subject, artist, imagetype, prefixprompt,suffixprompt,negativeprompt, promptcompounderlevel, ANDtoggle, silentmode, workprompt, antistring,seperator, givensubject, smartsubject, giventypeofimage):
images = []
infotexts = []
@@ -680,7 +693,7 @@ class Script(scripts.Script):
preppedprompt += " \n " + seperator + " "
#Here is where we build a "normal" prompt
preppedprompt += build_dynamic_prompt(insanitylevel,subject,artist, imagetype, False, antistring, prefixprompt, suffixprompt,promptcompounderlevel, seperator,givensubject,smartsubject)
preppedprompt += build_dynamic_prompt(insanitylevel,subject,artist, imagetype, False, antistring, prefixprompt, suffixprompt,promptcompounderlevel, seperator,givensubject,smartsubject,giventypeofimage)
# set everything ready
p.prompt = preppedprompt
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@@ -0,0 +1,80 @@
# Custom files
In the /userfiles/ directory it is possible to include personal additions and overwrites. This is the place to include LoRA's and textual inversions. You can also maintain your own artist lists.
These are the things that are possible:
- Adding style LoRA's and textual inversions
- Maintaining own personal favorite artists lists
- Adding on to, or replacing One Button Prompt main lists
- Adding custom lists
- filling an antilist, that removes values during prompt generation
In the /userfiles/ directory, there are already samples placed to help. These are ignored.
Files created here will not be overwritten during upgrading of One Button Prompt.
## Adding style LoRA's and textual inversions
Filename: __styles_ti_lora.csv__
Fill this one with style textual inversions and LoRA's. They will then randomly be used in prompt generation and placed at the end of the prompt.
You need to have the appropiate TI or LoRA installed for this to work.
Example values:
> style-sylvamagic
>
> <lora:LowRA:0.6>
>
> <lora:add_detail:1>
## Maintaining own personal favorite artists lists
Filename: __personal_artists_xyz.csv__ (replace xyz with the name of your choice)
Add any number of files formed as personal_artists_xyz.csv.
These will show up in the artists selection in One Button Prompt.
For example, you can create a personal_artists_portrait.csv and a personal_artists_favorites.csv. Both will then show up as options.
Example values:
> Alena Aenami
>
> Victo Ngai
>
> Victor Nizovtsev
This is how it will then show up in the WebUI:
![image](https://github.com/AIrjen/OneButtonPrompt/assets/130234949/ce100d74-8a0a-4927-8b90-fab9cb8f2e48)
## Adding on to, or replacing One Button Prompt main lists
Filename: __listname_addon.csv__ (replace listname with the name of the corresponding csv file in /csvfiles/ directory)
Filename: __listname_replace.csv__ (replace listname with the name of the corresponding csv file in /csvfiles/ directory)
Addon files will be automatically added to the existing csv files in the ./csvfiles/ directory. So you can add your own personal stuff. They need to be named the same, such as vehicles_addon.csv
Replace files will automatically replace the existing csv files in the ./csvfiles/ directory during prompt generation. This way, you can run of your own custom lists if you need. They need to be named the same, such as vehicles_replace.csv
## Adding custom lists
Filename: __custom_input_prefix.csv__
Filename: __custom_input_mid.csv__
Filename: __custom_input_suffix.csv__
Add anything in these files, and they will show up in the front of the prompt, in the middle, or at the end. This adds some additional flexiblity, if you want more freedom or maybe some certain keywords to show up. They will be randomly used in the prompt generation.
These are added for flexibility.
## filling an antilist, that removes values during prompt generation
Filename: __antilist.csv__
Any value in this csv file will be removed from any list/csv file used during prompt generation.
For example, if you don't want "film grain" to show up, add this to the antilist.csv
Example values:
> film grain
>
> purple
>
> cat
@@ -0,0 +1,191 @@
# Override subject and how to create infinite variations of a set subject
Overriding a subject is one of the most __powerful__ ways of experiencing One Button Prompt.
This will allow you to explore infinite variants of a subject of your own choice.
This also creates the option of adding a subject that requires a LoRA.
There are 2 settings that can be found in the Main tab of One Button Prompt:
### Overwrite Subject
Any text you place in "Subject overwrite" will be treated as the subject to generate.
It is very helpful to first set the "Subject Type" to the matching subject, as this will guide the prompt generation. For example, set it to humanoid if you are trying to generate people. It will then use the logic used for generating people.
You can also for example, set something LoRA specific in the subject field, and place the actual LoRA in the prompt suffix field.
### Smart Subject
Smart Subject is standardly activated, and this will try to interpret your prompt, and turn off any related lists.
For example, if you set the subject to: "Obese man wearing a kimono", it will see that "obese" is an existing body type, so it won't generate another.
"wearing" is a keyword that turns off outfit generation. Other keywords such as "hair" and "pose" are also in.
Basically, leave this on. Especially if you are trying to do something more specific.
## Example: Tree of Life
![deliberate tree of life](https://github.com/AIrjen/OneButtonPrompt/assets/130234949/6a49fb69-06f0-4866-92aa-44c44a96ad5e)
In this example, I used the Deliberate model.
In the settings, I set the following properties:
Subject Types: __Landscape__
Artists: __landscape__
type of image: __digital art__
Overwrite subject:__Tree of life__
What these settings to, is to focus the generation on landscapes, while also only using landscape artists. It will also create only "digital art" of this, instead of random results.
With the overwrite subject: "Tree of life", it will now only create epic trees for us. Nice.
<img src="https://github.com/AIrjen/OneButtonPrompt/assets/130234949/d7430e7b-2d60-4ac5-b7c2-ad85ec74931f.png" alt="Tree of life settings" width="60%" height="60%">
## Example: Daenerys Targaryen
![deliberate examples](https://github.com/AIrjen/OneButtonPrompt/assets/130234949/0c595158-73c0-4001-8af2-1fe00009b90a)
In this example, I used the Deliberate model.
In the settings, I set the following properties:
Subject Types: __humanoid__
Artists: __all__
type of image: __all__
Overwrite subject: __Daenerys Targaryen__
These settings give One Button Prompt a lot of freedom, because we set Artists and Type of image both to "All". As a result, we get a wide range of diversity. It also creates less good results because of this, but that is to be expected. It also creates some really imaginitive stuff, especially running on a versatile model such as Deliberate.
<img src="https://github.com/AIrjen/OneButtonPrompt/assets/130234949/c69ecc7f-af8d-4451-be05-60c21a17783a.png" alt="Daenerys Targaryen settings" width="60%" height="60%">
## Example: Daenerys Targaryen with LoRA's and a TI
![dreamshaper and lora examples](https://github.com/AIrjen/OneButtonPrompt/assets/130234949/de914d6c-fe81-41a7-b574-86aad5f488b3)
In this example, I used the Dreamshaper model, which is less flexbile than deliberate. I am also using 2 LoRA's and a Textual Inversion, to show the possiblities.
These are added in the prompt suffix field.
In the settings, I set the following properties:
Subject Types: __humanoid__
Artists: __popular__
type of image: __all__
Overwrite subject: __Daenerys Targaryen__
Prompt suffix: __style-sylvamagic, <lora:add_detail:0.8>,<lora:LowRa:0.6>__
There is a lot less freedom here, also because of the model used. But the variety can still be ssen. You can also see the effects of the LoRA's on the results.
<img src="https://github.com/AIrjen/OneButtonPrompt/assets/130234949/a5c7669c-952d-441e-8e58-644ff8a594b2.png" alt="Daenerys Targaryen settings" width="60%" height="60%">
## Using a LoRA as a subject: Gul Dukat
__ATTENTION BAJORAN WORKERS: You can use a LoRA as a subject as well.__
![LoRA character example](https://github.com/AIrjen/OneButtonPrompt/assets/130234949/476f84a7-e71e-4ecb-a790-54629ec0e386)
Sometimes you need to add the triggerword as the subject, and the LoRA in the prompt suffix field. Sometimes you need to add both in the overwrite subject field.
In this example, I'm using the main character of star trek's DS9 series, Gul Dukat. Thanks to the amazing [Terok Nor Lora](https://civitai.com/models/58470/terok-nor-lora)!
In this example, I used the Dreamshaper model. In the settings, I set the following properties:
Subject Types: __humanoid__
Artists: __all__
type of image: __all__
Overwrite subject: __gul dukat wearing cardassian uniform__
Prompt prefix: __sdn__
Prompt suffix: __<lora:diffusiondesign_SDN_LoRA_1.12:0.6>, star trek__
With this method, the initial trigger word is in the prompt prefix field, sdn. The subject is "gul dukat wearing cardassian uniform", since smart subject is activated, it will not generate another outfit for our buddy during prompt generation.
Lastly, in the prompt suffix is the actual LoRA and another trigger word, star trek.
<img src="https://github.com/AIrjen/OneButtonPrompt/assets/130234949/0ccf8cf3-c2c9-4cd7-b183-eb6b7a640a5a.png" alt="Gul Dukat" width="60%" height="60%">
### Smart Subject in detail
Smart subject will try to interpret your override subject, and turn off any related lists.
Example, if the word "Obese" in the override subject, it will find this exact word in the "body_types" list. It will disable body type generation. And so it works for all individual words in the override subject, as well as all combined words between comma's.
Additionaly the following keywords have additional hardcoded keyword triggers:
"wearing","outfit" or "dressed" will turn off outfit generation
"bodytype" or "body type" will turn off body type generation
"hair" will turn of hairstyle generation
"location" or "background" will turn off background generation
"lighting" will turn off lighting generation
"mood" will turn off mood generation
"pose" or "posing" will turn of pose generation
"quality" will turn off all quality generation
"shot" will turn off all framing/shot size generation
This means, you can form override subject prompts like this:
"full body shot of a obese donald trump" --> turns off shot sizing and body type generation
"fantastical skull, candle lighting" --> turns off description (via fantastical) and lighting generation
"lifeguard, background is beach" --> turns off background generation
### Wildcards
In the backend of the code, some of the things run on wildcards. These can be used in the prompt prefix, prompt suffix and subject override fields. They get values from their specific related lists.
Currently, One Button Prompt supports the following wildcards.
- -color-
- -material-
- -animal-
- -object-
- -fictional-
- -nonfictional-
- -building-
- -vehicle-
- -outfit-
- -location-
- -conceptprefix-
- -conceptsuffix-
- -descriptor-
- -food-
## Bringing it all together, generating awesome cyborgs with One Button
![awesome cyborgs](https://github.com/AIrjen/OneButtonPrompt/assets/130234949/9e252a45-2600-44a3-a789-de7a66211e1b)
In this example, I used the (Lyriel)https://civitai.com/models/22922/lyriel model. In the settings, I set the following properties:
Subject Types: __humanoid__
Artists: __sci-fi__
type of image: __portrait__
Overwrite subject: __-descriptor- cyberpunk cyborg, -material- details__
In this case, I am using the wildcards to my advantage in the override subject. Since I use the word "cyberpunk" the standard description generation is turned off. In this case, I add a random descriptor in my subject override, to get some randomness back.
Next to that, I add -material- details. This way, it will generate some random materials to the results, creating interesting stuff.
With these settings, all results are good and varied.
<img src="https://github.com/AIrjen/OneButtonPrompt/assets/130234949/9dad7d31-cb1e-4375-9d64-600942e8e884.png" alt="Beep Boop!" width="60%" height="60%">
# Thank you
Hope to have inspired you, and get generating!
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In this directory, manually add a file called antilist.csv
Any value in this csv file will be removed from any list/csv file used during prompt generation.
For example, if you don't want "film grain" to show up, add this to the antilist.csv
Each word should be on his own row (so don't comma seperate it)
It should look like this:
film grain
purple
cat
Happy prompting!
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@@ -0,0 +1,33 @@
In this directory, manually add Files that are used during the generation process.
The sample files here are ignored, but can be used as a copy or base to start with.
Each word should be on his own row (so don't comma seperate it)
Adding files here, will not be overwritten during updating this extension.
There are 5 types of files that can be manually added here:
- personal_artists_xyz.csv
Add any number of files formed as personal_artists_xyz.csv. These will show up in the artists selection.
For example, you can create a personal_artists_portrait.csv and a personal_artists_favorites.csv. Both will then show up as options.
- styles_ti_lora.csv
Fill this one with style textual inversions and LoRA's. They will then randomly be used in prompt generation. See sample file.
- listname_addon.csv
Addon files will be automatically added to the existing csv files in the ./csvfiles/ directory. So you can add your own personal stuff. They need to be named the same, such as vehicles_addon.csv
- listname_replace.csv
Replace files will automatically replace the existing csv files in the ./csvfiles/ directory during prompt generation. This way, you can run of your own custom lists if you need. They need to be named the same, such as vehicles_replace.csv
- custom_input_prefix.csv, custom_input_mid.csv and custom_input_suffix.csv
Add anything in these files, and they will show up in the front of the prompt, in the middle, or at the end. This adds some additional flexiblity, if you want certain specific results. They will be randomly used in the prompt generation.
- antilist.csv
Any value in this csv file will be removed from any list/csv file used during prompt generation.
For example, if you don't want "film grain" to show up, add this to the antilist.csv
It should look like this:
film grain
purple
cat
Happy prompting!
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@@ -0,0 +1,3 @@
film grain
purple
cat
1 film grain
2 purple
3 cat
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@@ -0,0 +1,8 @@
Alena Aenami
Victo Ngai
Victor Nizovtsev
RHADS
Yoji Shinkawa
Hikari Shimoda
Brandon Woelfel
Eddie Del Rio
1 Alena Aenami
2 Victo Ngai
3 Victor Nizovtsev
4 RHADS
5 Yoji Shinkawa
6 Hikari Shimoda
7 Brandon Woelfel
8 Eddie Del Rio
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@@ -0,0 +1,12 @@
style-sylvamagic
Style-NebMagic
RFKTR_rootrex
<lora:add_detail:1>
<lora:COOLKIDS_MERGE_V2.5:1>
<lora:diffusiondesign_SDN_LoRA_1.12:1>
<lora:epiNoiseoffset_v2:1>
fairytaleai <lora:FairyTaleV1s_SD1.5:1>
<lora:lit:1>
<lora:LowRA:0.6>
pixelart <lora:Pixhell_15:1>
ghibli style <lora:studioGhibliStyle_offset:1>
1 style-sylvamagic
2 Style-NebMagic
3 RFKTR_rootrex
4 <lora:add_detail:1>
5 <lora:COOLKIDS_MERGE_V2.5:1>
6 <lora:diffusiondesign_SDN_LoRA_1.12:1>
7 <lora:epiNoiseoffset_v2:1>
8 fairytaleai <lora:FairyTaleV1s_SD1.5:1>
9 <lora:lit:1>
10 <lora:LowRA:0.6>
11 pixelart <lora:Pixhell_15:1>
12 ghibli style <lora:studioGhibliStyle_offset:1>
+1
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@@ -0,0 +1 @@
fire truck
1 fire truck
+4
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@@ -0,0 +1,4 @@
Sports car
Ferrari
Lamborghini
Porsche
1 Sports car
2 Ferrari
3 Lamborghini
4 Porsche