Repository: bobvinch/comfy-server
Branch: master
Commit: 137cfdd59a16
Files: 89
Total size: 2.1 MB
Directory structure:
gitextract_m15fhcc9/
├── .eslintrc.js
├── .gitignore
├── .prettierrc
├── .run/
│ └── Comfy-server.run.xml
├── Dockerfile
├── README.md
├── docker-compose.yml.example
├── init.sql
├── nest-cli.json
├── package.json
├── pm2.config.js
├── src/
│ ├── app.controller.ts
│ ├── app.module.ts
│ ├── app.service.ts
│ ├── auth/
│ │ ├── auth.controller.ts
│ │ ├── auth.guard.ts
│ │ ├── auth.module.ts
│ │ ├── auth.service.ts
│ │ └── constants.ts
│ ├── cache/
│ │ ├── cache.controller.ts
│ │ ├── cache.module.ts
│ │ └── cache.service.ts
│ ├── draw/
│ │ ├── DrawCosumer.ts
│ │ ├── data/
│ │ │ ├── AImodel_api.json
│ │ │ ├── DrawConfig.ts
│ │ │ ├── aistudio_api.json
│ │ │ ├── api_matting.json
│ │ │ ├── api_removebg.json
│ │ │ ├── api_upscale.json
│ │ │ ├── api_upscale_detailfix.json
│ │ │ ├── api_wechat_img2imgi.json
│ │ │ ├── impaiting_api.json
│ │ │ ├── objectInfo.json
│ │ │ ├── test.json
│ │ │ ├── text2imgapi.json
│ │ │ ├── workflow_api_faceswap.ts
│ │ │ ├── workflow_api_hdfix_4.ts
│ │ │ ├── workflow_api_image2img.json
│ │ │ ├── workflow_api_image2img.ts
│ │ │ ├── workflow_api_img2video.json
│ │ │ ├── workflow_api_img2video.ts
│ │ │ ├── workflow_api_inpainting.json
│ │ │ ├── workflow_api_inpainting.ts
│ │ │ ├── workflow_api_matting.json
│ │ │ ├── workflow_api_matting.ts
│ │ │ ├── workflow_api_model.ts
│ │ │ ├── workflow_api_removebg.json
│ │ │ ├── workflow_api_removebg.ts
│ │ │ ├── workflow_api_tagger.ts
│ │ │ ├── workflow_api_text2img.json
│ │ │ └── workflow_api_text2img.ts
│ │ ├── draw.controller.ts
│ │ ├── draw.module.ts
│ │ └── draw.service.ts
│ ├── file/
│ │ ├── file.controller.ts
│ │ ├── file.module.ts
│ │ └── file.service.ts
│ ├── main.ts
│ ├── middleware/
│ │ └── XML.middleware.ts
│ ├── oneapi/
│ │ ├── tokens/
│ │ │ ├── dto/
│ │ │ │ ├── create-token.dto.ts
│ │ │ │ └── update-token.dto.ts
│ │ │ ├── entities/
│ │ │ │ └── token.entity.ts
│ │ │ ├── tokens.controller.ts
│ │ │ ├── tokens.module.ts
│ │ │ └── tokens.service.ts
│ │ └── users/
│ │ ├── dto/
│ │ │ ├── create-user.dto.ts
│ │ │ └── update-user.dto.ts
│ │ ├── entities/
│ │ │ └── user.entity.ts
│ │ ├── users.controller.ts
│ │ ├── users.module.ts
│ │ └── users.service.ts
│ ├── tweet/
│ │ ├── dto/
│ │ │ ├── create-tweet.dto.ts
│ │ │ └── update-tweet.dto.ts
│ │ ├── entities/
│ │ │ └── tweet.schema.ts
│ │ ├── tweet.controller.ts
│ │ ├── tweet.module.ts
│ │ └── tweet.service.ts
│ ├── users/
│ │ ├── dto/
│ │ │ ├── create-user.dto.ts
│ │ │ └── update-user.dto.ts
│ │ ├── schema/
│ │ │ └── user.schema.ts
│ │ ├── users.controller.ts
│ │ ├── users.module.ts
│ │ └── users.service.ts
│ ├── wechat-auth/
│ │ ├── wechat-auth.controller.ts
│ │ ├── wechat-auth.module.ts
│ │ └── wechat-auth.service.ts
│ └── ws/
│ └── ws.gateway.ts
├── tsconfig.build.json
└── tsconfig.json
================================================
FILE CONTENTS
================================================
================================================
FILE: .eslintrc.js
================================================
module.exports = {
parser: '@typescript-eslint/parser',
parserOptions: {
project: 'tsconfig.json',
tsconfigRootDir: __dirname,
sourceType: 'module',
},
plugins: ['@typescript-eslint/eslint-plugin'],
extends: [
'plugin:@typescript-eslint/recommended',
'plugin:prettier/recommended',
],
root: true,
env: {
node: true,
jest: true,
},
ignorePatterns: ['.eslintrc.js'],
rules: {
'@typescript-eslint/interface-name-prefix': 'off',
'@typescript-eslint/explicit-function-return-type': 'off',
'@typescript-eslint/explicit-module-boundary-types': 'off',
'@typescript-eslint/no-explicit-any': 'off',
"prettier/prettier": ["error", { "endOfLine": "auto" }]
},
};
================================================
FILE: .gitignore
================================================
# compiled output
/dist
/node_modules
# Logs
logs
*.log
npm-debug.log*
pnpm-debug.log*
yarn-debug.log*
yarn-error.log*
lerna-debug.log*
# OS
.DS_Store
# Tests
/coverage
/.nyc_output
# IDEs and editors
/.idea
.project
.classpath
.c9/
*.launch
.settings/
*.sublime-workspace
# IDE - VSCode
.vscode/*
!.vscode/settings.json
!.vscode/tasks.json
!.vscode/launch.json
!.vscode/extensions.json
#配置文件
docker-compose.yml
.env.development
.env.production
temp.png
================================================
FILE: .prettierrc
================================================
{
"singleQuote": true,
"trailingComma": "all",
"endOfline":"auto"
}
================================================
FILE: .run/Comfy-server.run.xml
================================================
================================================
FILE: Dockerfile
================================================
# 第二阶段
FROM node:20.12.1 AS runtime-stage
#2、作者
MAINTAINER bobovinch
# 创建工作目录
RUN mkdir -p /app
WORKDIR /app
# 复制构建阶段生成的输出到运行时阶段
COPY ./dist /app/dist
COPY ./dist /app/dist
#COPY ./node_modules /app/node_modules
COPY ./package.json /app/
COPY ./.env.* /app/
RUN npm install --force
# 设置环境变量
ENV NODE_ENV=production
ENV CONFIG_COMFYUI_QUENE_REDIS_HOST=172.17.0.4
ENV CONFIG_COMFYUI_QUENE_REDIS_PORT=6379
ENV CONFIG_COMFYUI_SERVER_URL=http://127.0.0.1:8188
# 暴露端口
EXPOSE 3001
EXPOSE 3002
# 设置入口点为启动脚本
ENTRYPOINT ["npm", "run", "start:prod"]
================================================
FILE: README.md
================================================
[circleci-image]: https://img.shields.io/circleci/build/github/nestjs/nest/master?token=abc123def456
[circleci-url]: https://circleci.com/gh/nestjs/nest
A progressive Node.js framework for building efficient and scalable server-side applications.
## Description
## 开始使用前必读！！！！
开始使用前请先安装redis，并配置.env .end.development .env.production环境变量中密码，如果有的话，没有密码则将密码配置删除
然后检查环境变量中ComfyUI的http路径和websocket路径
## 功能介绍
0502更新，修复Redis相关问题，增加容器一键部署方式
最新0411更新，重大更新，增加大量商业化能力，运营AI绘画商业网站必备后台服务器
核心功能1：ComfyUI的绘画API服务和websocket转发，客户端必须使用socketIO链接，WS无法连接，注意版本
核心功能2：方便将任意comfyui工作转换为在线API，向外提供AI能力
ComfyUI server之间可以共享AI绘画能力
天然支持利用nginx直接实现负载均衡
增加注册，登录，微信登录，鉴权，黑名单等常用运营功能
支持任务队列，支持API提交任务的时候指定队列
支持黑名单管理
一键接入微信公众号，并且支持利用别人的API接入微信绘画，支持多轮指令记忆，能够区分绘画指令和提示词
## 如何使用
☆推荐使用docker-compose一键部署
1.下载docker-compose.yml.example和init.sql两个文件到一个文件夹中
2.取消.example后缀，修改docker-compose.yml中环境变量，主要修改ComfyUI的服务器地址和端口
3.运行:docker-compose pull拉取镜像
4.拉取完成后，运行docker-compose up -d启动容器
5.打开127.0.0.3001/api-docs可以访问API地址和进行在线API调试
开发部署方式
1.先按照如下如下方式启动服务器
2.客户端通过socketIO链接服务器，默认为3002端口，如果冲突在src/ws/ws.gateway.ts中修改
3.以websocket消息形式提交，提交绘画任务,事件名称为draw,消息格式：{client_id:"userid", prompt:"comfyui API", api:"define a API name" }
4、使用微信公众号绘画功能需要配置APPID和Secret
教程地址：https://www.bilibili.com/video/BV1AE42137Gn?t=40.6
## Installation
```bash
$ npm install
```
## Running the app
```bash
# development
$ npm run start
# watch mode
$ npm run start:dev
# production mode
$ npm run start:prod
```
## Test
```bash
# unit tests
$ npm run test
# e2e tests
$ npm run test:e2e
# test coverage
$ npm run test:cov
```
## Support
Nest is an MIT-licensed open source project. It can grow thanks to the sponsors and support by the amazing backers. If you'd like to join them, please [read more here](https://docs.nestjs.com/support).
## Stay in touch
- Author - [Kamil Myśliwiec](https://kamilmysliwiec.com)
- Website - [https://nestjs.com](https://nestjs.com/)
- Twitter - [@nestframework](https://twitter.com/nestframework)
## License
Nest is [MIT licensed](LICENSE).
================================================
FILE: docker-compose.yml.example
================================================
# 非 host 版本, 不使用本机代理
# (不懂 Docker 的，只需要关心 OPENAI_BASE_URL 和 CHAT_API_KEY 即可！)
version: '3'
services:
comfy-server:
container_name: comfy-server
#image: ghcr.io/labring/fastgpt:latest # git
image: registry.cn-shanghai.aliyuncs.com/comfy-ai/comfy-server:latest # 阿里云
ports:
- "3001:3001" #http端口
- "3002:3002" #websocket端口
networks:
comfyai:
ipv4_address: 172.20.0.6
depends_on:
- mysql
- mongo
- redis
restart: always
environment:
# 必须！！重要配置！！！- Comfy UI服务器地址，注意不能是127.0.0.1，容器内无法访问，查看网络获取
- CONFIG_COMFYUI_SERVER_URL=http://192.168.0.107:8188
# 必须配置，MongoDB配置，用户管理
- CONFIG_DB_MONGO_URI=mongodb://172.20.0.3:27017
- CONFIG_DB_MONGO_USERNAME=username
- CONFIG_DB_MONGO_PASSWORD=password
# 必须配置，Redis配置，队列缓存
- CONFIG_COMFYUI_QUENE_REDIS_HOST=172.20.0.4
- CONFIG_COMFYUI_QUENE_REDIS_PORT=6379
- CONFIG_COMFYUI_QUENE_REDIS_PASSWORD=
#必须配置，大模型ONEAPI数据库配置
- ONEAPI_MYSQL_HOST=172.20.0.2
- ONEAPI_MYSQL_USERNAME=root
- ONEAPI_MYSQL_PASSWORD=test123
- ONEAPI_MYSQL_DATABASE=oneapi
#可选配置，微信开放平台，微信登录功能
- CONFIG_AUTH_WECHAT_APPID=
- CONFIG_AUTH_WECHAT_SECRET=
#可选配置，公众号配置，实现公众号绘图配置这里
- CONFIG_OFFICIAL_WECHAT_APPID=
- CONFIG_OFFICIAL_WECHAT_SECRET=
#可选配置，使用阿里云OSS存储绘画结果，OSS配置,如果需要将绘画的图片上传到阿里云oss,则配置这里
- OSS_REGION=
- OSS_ACCESSKEYID=
- OSS_ACCESSKEYSECRET=
- OSS_BUCKET=
#SD3 key,使用SD3 需要配置
- CONFIG_SD3_APIKEY=
#可选配置，接别人的Comfy-server API
- CONFIG_COMFYUI_SERVER_REMOTE_URL=
- CONFIG_COMFUI_SERVER_REMOTE_AUTH_USERNAME=
- CONFIG_COMFUI_SERVER_REMOTE_AUTH_PASSWORD=
# 使用大预言模型的后端接口
oneapi:
image: justsong/one-api
container_name: oneapi-mysql
restart: always
ports:
- "3000:3000"
networks:
comfyai:
ipv4_address: 172.20.0.5
depends_on:
- mysql
environment:
- SQL_DSN=root:test123@tcp(172.20.0.2:3306)/oneapi
- TZ=Asia/Shanghai
mongo:
image: registry.cn-shanghai.aliyuncs.com/comfy-ai/mongo-aliyun:latest
container_name: mongo
restart: always
# 生产环境建议不要暴露
ports:
- "27017:27017"
networks:
comfyai:
ipv4_address: 172.20.0.3
environment:
# 这里的配置只有首次运行生效。修改后，重启镜像是不会生效的。需要把持久化数据删除再重启，才有效果
- MONGO_INITDB_ROOT_USERNAME=username
- MONGO_INITDB_ROOT_PASSWORD=password
volumes:
- /usr/mongo/data:/data/db
# 大预言模型数据库
mysql:
image: registry.cn-shanghai.aliyuncs.com/comfy-ai/mysql-aliyun:latest
container_name: mysql
restart: always
ports:
- "3306:3306"
networks:
comfyai:
ipv4_address: 172.20.0.2
environment:
- MYSQL_ROOT_PASSWORD=test123
volumes:
- ./init.sql:/docker-entrypoint-initdb.d/init.sql
- /usr/mysql/log:/var/log/mysql
- /usr/mysql/data:/var/lib/mysql
- /usr/mysql/conf:/etc/mysql/conf.d
# command: bash -c "mysqld --init-file=/docker-entrypoint-initdb.d/init.sql"
redis:
image: redis:latest
container_name: redis
restart: always
ports:
- "6379:6379"
networks:
comfyai:
ipv4_address: 172.20.0.4
networks:
comfyai:
driver: bridge
ipam:
config:
- subnet: 172.20.0.0/16
gateway: 172.20.0.1
================================================
FILE: init.sql
================================================
CREATE DATABASE oneapi;
================================================
FILE: nest-cli.json
================================================
{
"$schema": "https://json.schemastore.org/nest-cli",
"collection": "@nestjs/schematics",
"sourceRoot": "src",
"compilerOptions": {
"deleteOutDir": true
}
}
================================================
FILE: package.json
================================================
{
"name": "myserver",
"version": "0.0.1",
"description": "\r ![\"Nest](\"https://nestjs.com/img/logo-small.svg\")
\r
",
"author": "",
"private": true,
"license": "UNLICENSED",
"scripts": {
"build": "cross-env NODE_ENV=production nest build",
"format": "prettier --write \"src/**/*.ts\" \"test/**/*.ts\"",
"start": "cross-env NODE_ENV=production nest start",
"start:dev": "cross-env NODE_ENV=development nest start --watch",
"start:debug": "cross-env NODE_ENV=development nest start --debug --watch",
"start:prod": "node dist/main",
"lint": "eslint \"{src,apps,libs,test}/**/*.ts\" --fix",
"test": "jest",
"test:watch": "jest --watch",
"test:cov": "jest --coverage",
"test:debug": "node --inspect-brk -r tsconfig-paths/register -r ts-node/register node_modules/.bin/jest --runInBand",
"test:e2e": "jest --config ./test/jest-e2e.json"
},
"dependencies": {
"@nestjs-modules/ioredis": "^2.0.2",
"@nestjs/bull": "^10.0.1",
"@nestjs/common": "^10.3.6",
"@nestjs/config": "^3.2.0",
"@nestjs/core": "^10.3.6",
"@nestjs/jwt": "^10.2.0",
"@nestjs/mapped-types": "*",
"@nestjs/mongoose": "^10.0.4",
"@nestjs/platform-express": "^10.0.0",
"@nestjs/platform-socket.io": "^10.3.1",
"@nestjs/schedule": "^4.0.1",
"@nestjs/swagger": "^7.3.0",
"@nestjs/typeorm": "^10.0.2",
"@nestjs/websockets": "^10.3.1",
"@types/ali-oss": "^6.16.11",
"@types/socket.io": "^3.0.2",
"@types/socket.io-client": "^3.0.0",
"ali-oss": "^6.20.0",
"axios": "^1.6.7",
"bcrypt": "^5.1.1",
"bull": "^4.12.2",
"cross-env": "^7.0.3",
"fast-xml-parser": "^4.3.6",
"ioredis": "^5.4.1",
"mongoose": "^8.1.3",
"mysql2": "^3.9.1",
"nanoid": "^3.0.0",
"reflect-metadata": "^0.1.13",
"rxjs": "^7.8.1",
"socket.io": "^4.7.4",
"socket.io-client": "^4.7.4",
"typeorm": "^0.3.20",
"ws": "^8.16.0",
"xml2js": "^0.6.2",
"xmlbuilder2": "^3.1.1"
},
"devDependencies": {
"@nestjs/cli": "^10.0.0",
"@nestjs/schematics": "^10.0.0",
"@nestjs/testing": "^10.0.0",
"@types/bcrypt": "^5.0.2",
"@types/bull": "^4.10.0",
"@types/express": "^4.17.21",
"@types/jest": "^29.5.2",
"@types/node": "^20.3.1",
"@types/supertest": "^6.0.0",
"@types/ws": "^8.5.10",
"@typescript-eslint/eslint-plugin": "^6.0.0",
"@typescript-eslint/parser": "^6.0.0",
"eslint": "^8.42.0",
"eslint-config-prettier": "^9.0.0",
"eslint-plugin-prettier": "^5.0.0",
"jest": "^29.5.0",
"prettier": "^3.0.0",
"source-map-support": "^0.5.21",
"supertest": "^6.3.3",
"ts-jest": "^29.1.0",
"ts-loader": "^9.4.3",
"ts-node": "^10.9.1",
"tsconfig-paths": "^4.2.0",
"typescript": "^5.1.3"
},
"jest": {
"moduleFileExtensions": [
"js",
"json",
"ts"
],
"rootDir": "src",
"testRegex": ".*\\.spec\\.ts$",
"transform": {
"^.+\\.(t|j)s$": "ts-jest"
},
"collectCoverageFrom": [
"**/*.(t|j)s"
],
"coverageDirectory": "../coverage",
"testEnvironment": "node"
},
"main": ".eslintrc.js"
}
================================================
FILE: pm2.config.js
================================================
/**
* this is a pm2 starter config file
* @type {{apps: [{watch: boolean, name: string, env: {NODE_ENV: string}, script: string}]}}
*/
module.exports = {
apps: [
{
name: 'comfy-server',
script: './dist/main.js',
watch: true,
env: {
NODE_ENV: 'production',
},
},
],
};
================================================
FILE: src/app.controller.ts
================================================
import { Controller, Get, Inject } from '@nestjs/common';
import { AppService } from './app.service';
import { DrawService } from './draw/draw.service';
@Controller()
export class AppController {
constructor(
private readonly appService: AppService,
private readonly drawService: DrawService,
) {}
@Get()
getHello(): string {
return this.appService.getHello();
}
@Get('test')
test(): string {
return process.env.REDIS_HOST;
}
}
================================================
FILE: src/app.module.ts
================================================
import {
MiddlewareConsumer,
Module,
NestModule,
RequestMethod,
} from '@nestjs/common';
import { AppController } from './app.controller';
import { AppService } from './app.service';
import { WsGateway } from './ws/ws.gateway';
import { DrawConsumer } from './draw/DrawCosumer';
import { ConfigModule } from '@nestjs/config';
//链接mongodb
import { MongooseModule } from '@nestjs/mongoose';
//链接mysql
import { TypeOrmModule } from '@nestjs/typeorm';
import { OneAPIUsersModule } from 'src/oneapi/users/users.module';
import { TokensModule } from './oneapi/tokens/tokens.module';
import { ConfigService } from '@nestjs/config/dist';
//定时任务
import { WechatAuthModule } from './wechat-auth/wechat-auth.module';
import { XMLMiddleware } from './middleware/XML.middleware';
import { DrawModule } from './draw/draw.module';
import { UsersModule } from './users/users.module';
import { AuthModule } from './auth/auth.module';
import { CacheModule } from './cache/cache.module';
import { FileModule } from './file/file.module';
import { TweetModule } from './tweet/tweet.module';
//'mongodb://username:password@localhost:27017/nest'
@Module({
imports: [
ConfigModule.forRoot({
isGlobal: true,
envFilePath: [`.env.${process.env.NODE_ENV}`],
}),
//OneAPI数据库,如果不启动ONEAPI的大模型，删除此处的模块引入和oneapi模块
TypeOrmModule.forRootAsync({
useFactory: (config: ConfigService) => ({
type: 'mysql',
host: config.get('ONEAPI_MYSQL_HOST'),
port: config.get('ONEAPI_MYSQL_PORT'),
username: config.get('ONEAPI_MYSQL_USERNAME'),
password: config.get('ONEAPI_MYSQL_PASSWORD'),
database: config.get('ONEAPI_MYSQL_DATABASE'),
// entities: [User],
autoLoadEntities: true,
synchronize: false, //是否同步，正式环境设置为false
}),
inject: [ConfigService],
}),
MongooseModule.forRootAsync({
useFactory: (config: ConfigService) => ({
uri: config.get('CONFIG_DB_MONGO_URI'),
user: config.get('CONFIG_DB_MONGO_USERNAME'),
pass: config.get('CONFIG_DB_MONGO_PASSWORD'),
dbName: 'aidraw',
}),
inject: [ConfigService],
}),
WechatAuthModule,
OneAPIUsersModule,
TokensModule,
DrawModule,
ConfigModule,
UsersModule,
AuthModule,
CacheModule,
FileModule,
TweetModule,
],
controllers: [AppController],
providers: [AppService, WsGateway, DrawConsumer],
})
/**
* 注册中间件,微信消息处理中间件
*/
export class AppModule implements NestModule {
configure(consumer: MiddlewareConsumer) {
consumer.apply(XMLMiddleware).forRoutes({
path: 'wechatauth/handleMessage',
method: RequestMethod.POST,
});
}
}
================================================
FILE: src/app.service.ts
================================================
import { Injectable } from '@nestjs/common';
@Injectable()
export class AppService {
// 是否启用保存绘画历史记录
public Draw_SaveHistory = false;
getHello(): string {
return 'Welcome to my comfyui server'.toUpperCase();
}
}
================================================
FILE: src/auth/auth.controller.ts
================================================
import { AuthService } from './auth.service';
import {
Get,
Query,
Body,
Controller,
Post,
HttpCode,
HttpStatus,
} from '@nestjs/common';
import { ApiOperation } from '@nestjs/swagger';
@Controller('auth')
export class AuthController {
constructor(private authService: AuthService) {}
@ApiOperation({
summary: '鉴权',
description: 'Sign in with username and password,returning a token',
parameters: [
{
name: 'username',
in: 'query',
required: true,
description: 'username',
schema: {
type: 'string',
},
},
{
name: 'password',
in: 'query',
required: true,
description: 'password',
schema: {
type: 'string',
},
},
],
})
@HttpCode(HttpStatus.OK)
@Get('signin')
signIn(
@Query('username') username: string,
@Query('password') password: string,
) {
return this.authService.authToken(username, password);
}
}
================================================
FILE: src/auth/auth.guard.ts
================================================
import {
CanActivate,
ExecutionContext,
Injectable,
UnauthorizedException,
} from '@nestjs/common';
import { JwtService } from '@nestjs/jwt';
import { jwtConstants } from './constants';
import { Request } from 'express';
/**
* 🔒 This guard is used to protect routes that require authentication.
* It will check if the user is authenticated and if so, it will attach the user's payload to the request object.
* If the user is not authenticated, it will throw an UnauthorizedException.
*
*/
@Injectable()
export class AuthGuard implements CanActivate {
constructor(private jwtService: JwtService) {}
async canActivate(context: ExecutionContext): Promise {
const request = context.switchToHttp().getRequest();
const token = this.extractTokenFromHeader(request);
if (!token) {
throw new UnauthorizedException();
}
try {
// 💡 We're assigning the payload to the request object here
// so that we can access it in our route handlers
request['user'] = await this.jwtService.verifyAsync(token, {
secret: jwtConstants.secret,
});
} catch {
throw new UnauthorizedException();
}
return true;
}
private extractTokenFromHeader(request: Request): string | undefined {
const [type, token] = request.headers.authorization?.split(' ') ?? [];
return type === 'Bearer' ? token : undefined;
}
}
================================================
FILE: src/auth/auth.module.ts
================================================
import { Module } from '@nestjs/common';
import { AuthController } from './auth.controller';
import { AuthService } from './auth.service';
import { UsersModule } from '../users/users.module';
import { JwtModule } from '@nestjs/jwt';
import { jwtConstants } from './constants';
@Module({
imports: [
UsersModule,
JwtModule.register({
global: true,
secret: jwtConstants.secret,
signOptions: { expiresIn: '300s' },
}),
],
controllers: [AuthController],
providers: [AuthService],
exports: [AuthService],
})
export class AuthModule {}
================================================
FILE: src/auth/auth.service.ts
================================================
import { Injectable, UnauthorizedException } from '@nestjs/common';
import { UsersService } from '../users/users.service';
import { JwtService } from '@nestjs/jwt';
@Injectable()
export class AuthService {
constructor(
private usersService: UsersService,
private jwtService: JwtService,
) {}
async authToken(username: string, pass: string): Promise {
const user = await this.usersService.findByUsername(username);
if (
!user ||
!(await this.usersService.comparePassword(pass, user?.password))
) {
throw new UnauthorizedException();
}
return {
access_token: await this.usersService.createToken(
user._id + '',
user.username,
),
};
}
}
================================================
FILE: src/auth/constants.ts
================================================
export const jwtConstants = {
secret: 'this is a very secret secret',
};
================================================
FILE: src/cache/cache.controller.ts
================================================
import { Controller } from '@nestjs/common';
import { CacheService } from './cache.service';
@Controller('cache')
export class CacheController {
constructor(private readonly cacheService: CacheService) {}
}
================================================
FILE: src/cache/cache.module.ts
================================================
import { Module, Global } from '@nestjs/common';
import { CacheService } from './cache.service';
import { CacheController } from './cache.controller';
import { ConfigService } from '@nestjs/config';
import { RedisModule } from '@nestjs-modules/ioredis';
@Global()
@Module({
imports: [
RedisModule.forRootAsync({
useFactory: (config: ConfigService) => ({
type: 'single',
url: `redis://${config.get('CONFIG_COMFYUI_QUENE_REDIS_HOST')}:${config.get('CONFIG_COMFYUI_QUENE_REDIS_PORT')}`,
options: {
password: config.get('CONFIG_COMFYUI_QUENE_REDIS_PASSWORD'),
},
}),
inject: [ConfigService],
}),
],
providers: [CacheService],
exports: [CacheService],
})
export class CacheModule {}
================================================
FILE: src/cache/cache.service.ts
================================================
import { Injectable } from '@nestjs/common';
import Redis from 'ioredis';
import { ConfigService } from '@nestjs/config';
import { InjectRedis } from '@nestjs-modules/ioredis';
@Injectable()
export class CacheService {
// private redisClient: Redis;
constructor(
private readonly configService: ConfigService,
@InjectRedis() private readonly redis: Redis,
// private readonly redisService: RedisService,
) {
console.log('环境变量', this.configService);
// this.redisClient = this.redisService.getClient();
}
async root(): Promise {
// this.redisClient = await this.redisService.getClient();
return true;
}
//获取值
async get(key: string): Promise {
let value = await this.redis.get(key);
try {
value = JSON.parse(value);
} catch (error) {}
return value;
}
/**
* 设置值
* @param key {string} key
* @param value 值
* @param second 过期时间 秒
* @returns Promise
*/
async set(key: string, value: any, second?: number) {
value = JSON.stringify(value);
return this.redis.set(key, value);
}
//删除值
async del(key: string) {
return this.redis.del(key);
}
//清除缓存
async flushall() {
return this.redis.flushall();
}
}
================================================
FILE: src/draw/DrawCosumer.ts
================================================
import {
Process,
Processor,
OnQueueActive,
OnQueueCompleted,
} from '@nestjs/bull';
import { Job } from 'bull';
import { WsGateway } from 'src/ws/ws.gateway';
import { Logger } from '@nestjs/common';
import { DrawService } from './draw.service';
import { WechatAuthService } from '../wechat-auth/wechat-auth.service';
import { AppService } from '../app.service';
import Websocket = require('ws');
@Processor('draw')
export class DrawConsumer {
constructor(
private readonly drawService: DrawService,
private readonly wechatauthService: WechatAuthService,
private readonly appSevice: AppService,
) {}
private readonly logger = new Logger(DrawConsumer.name);
private readonly clientId = 'admin9527'; //id可以随意
public ws_client: Websocket;
@Process('drawtask')
async drawtask(job: Job): Promise {
// const output = await this.testTask(); //测试
// 绘画任务
const output = await this.drawTaskExcu(job, this.drawService.local_comfyui);
// this.logger.debug(`任务完成，${job.id}`);
//广播给所有人排队情况
const message = {
type: 'receive',
queue_remaining: await this.drawService.getQueueLength(),
};
WsGateway.server.emit('message', JSON.stringify(message));
return output + '';
}
/**
*
* @param job
* @param server_url
*/
async drawTaskExcu(job: Job, server_url: string) {
return new Promise(async (resolve, reject) => {
//client_id为用户id
// this.logger.debug(
// `初始化websocket链接的结构${await this.websocketInit()}`,
// );
if (!(await this.websocketInit(server_url))) {
reject({ status: 'error', data: job.data });
return;
}
const {
data: { source, client_id, prompt, socket_id, api },
} = job;
const params = {
client_id: this.clientId, //固定值
prompt: prompt,
};
const response = await this.drawService.sendTackprompt(
server_url,
params,
);
this.logger.debug(`发送绘画任务后的响应${response}`);
if (response) {
//将获取到的任务id保存到data中 方便后续在websocket失效的情况下也能取回绘画结果
job.data.prompt_id = response;
//将数据重新存给job
await job.update(job.data);
//监听服务器消息
this.logger.debug(`发送绘画任务成功`);
this.ws_client.onmessage = async (event: any) => {
//转发
this.logger.debug(event.data);
//如果存在并且socket处于连接状态
const target_socket =
WsGateway.server.sockets?.sockets?.get(socket_id);
if (target_socket) {
this.logger.debug(`发送给${socket_id},${event.data}`);
target_socket.emit('message', event.data);
}
try {
const { type, data } = JSON.parse(event.data + '');
if (type === 'execution_error') {
reject({ status: 'error', data: job.data });
}
if (type === 'executed') {
//如果API是反推提示词输出文本结果
if (api === '图片反推提示词') {
const {
output: { tags },
} = data;
resolve(tags[0]);
}
// 解析视频
const {
data: {
output: { gifs },
},
} = JSON.parse(event.data + '');
if (gifs && gifs[0]?.filename.includes('final')) {
let videoUrl = '';
//如果是微信消息
const { filename, subfolder, type } = gifs[0];
if (subfolder) {
videoUrl = `${server_url}/view?subfolder=${subfolder}&filename=${filename}&type=${type}`;
} else {
videoUrl = `${server_url}/view?filename=${filename}&type=${type}`;
}
// 微信公众号回复
if (source === 'wechat') {
const mediaId =
await this.wechatauthService.getMediaId(videoUrl);
await this.wechatauthService.sendServiceImageMessge(
mediaId,
client_id,
);
}
resolve(videoUrl);
}
// 解析图片
const {
data: {
output: { images },
},
} = JSON.parse(event.data + '');
if (images && images[0]?.filename.includes('final')) {
let imageUrl = '';
//如果是微信消息
const { filename, subfolder, type } = images[0];
if (subfolder) {
imageUrl = `${server_url}/view?subfolder=${subfolder}&filename=${filename}&type=${type}`;
} else {
imageUrl = `${server_url}/view?filename=${filename}&type=${type}`;
}
// 微信公众号回复
// if (source === 'wechat') {
// const mediaId =
// await this.wechatauthService.getMediaId(imageUrl);
// await this.wechatauthService.sendServiceImageMessge(
// mediaId,
// client_id,
// );
// }
resolve(imageUrl);
}
//保存到数据库
if (this.appSevice.Draw_SaveHistory) {
// todo 保存到数据库
}
}
} catch (e) {
this.logger.error(e);
}
};
} else {
reject({ status: 'error', data: job.data });
}
});
}
/**
* 初始化与绘画服务端的链接
*/
async websocketInit(server_url: string): Promise {
return new Promise(async (resolve) => {
if (!this.validateWsconnect()) {
const url = server_url.split('//')[1];
this.ws_client = new Websocket(
`${server_url.includes('https') ? 'wss' : 'ws'}://${url}/ws?clientId=${this.clientId}`,
);
this.ws_client.onopen = () => {
this.logger.debug(
'链接绘画服务器成功,链接状态：',
this.ws_client.readyState,
);
resolve(true);
};
this.ws_client.onerror = () => {
this.logger.error('链接绘画服务器失败');
resolve(false);
};
setTimeout(() => {
if (this.ws_client.readyState !== 1) {
this.logger.error('链接绘画服务器超时');
resolve(false);
}
}, 500);
} else {
resolve(true);
}
});
}
/**
* 验证链接状态
*/
validateWsconnect() {
if (this.ws_client === undefined || this.ws_client.readyState !== 1) {
this.logger.error('链接不存在,尝试重新连接');
return false;
} else {
return true;
}
}
@OnQueueActive()
async onActive(job: Job) {
const remain = await this.drawService.getQueueLength();
//广播队列任务信息
WsGateway.server.sockets?.emit('remain', {
remain,
});
this.logger.debug(
`onActive job ${job.id} of type ${job.name} with data ${job.data}...队列：：`,
remain,
);
}
@OnQueueCompleted()
OnQueueCompleted(job: Job) {
console.log(
`Processing job ${job.id} of type ${job.name} with data ${job.returnvalue}...finished`,
);
}
private async testTask() {
return new Promise((resolve) => {
setTimeout(() => {
resolve('ok');
}, 10000);
});
}
}
================================================
FILE: src/draw/data/AImodel_api.json
================================================
{
"42": {
"inputs": {
"upscale_method": "nearest-exact",
"scale_by": 1,
"image": [
"170",
0
]
},
"class_type": "ImageScaleBy"
},
"54": {
"inputs": {
"cloth_type": "None",
"return_form": "mask",
"image": [
"42",
0
]
},
"class_type": "ALY_Seg_Cloth"
},
"62": {
"inputs": {
"mask": [
"115",
0
]
},
"class_type": "InvertMask"
},
"63": {
"inputs": {
"samples": [
"64",
0
],
"mask": [
"62",
0
]
},
"class_type": "SetLatentNoiseMask"
},
"64": {
"inputs": {
"pixels": [
"42",
0
],
"vae": [
"143",
4
]
},
"class_type": "VAEEncode"
},
"74": {
"inputs": {
"channel": "red",
"image": [
"54",
1
]
},
"class_type": "ImageToMask"
},
"87": {
"inputs": {
"text": "(吊带:1.5)"
},
"class_type": "Text Multiline"
},
"88": {
"inputs": {
"from_translate": "auto",
"to_translate": "english",
"add_proxies": "disable",
"proxies": "",
"auth_data": "",
"service": "GoogleTranslator",
"text": [
"87",
0
],
"Show proxy": "proxy_hide",
"Show authorization": "authorization_hide"
},
"class_type": "DeepTranslatorTextNode"
},
"95": {
"inputs": {
"delimiter": ",",
"clean_whitespace": "true",
"text_a": [
"88",
0
],
"text_b": [
"96",
0
]
},
"class_type": "Text Concatenate"
},
"96": {
"inputs": {
"text": "NSFW,lowrs,blurry,deformed, distorted, disfigured:1.3, stacked torsos:1.2, totem pole:1.1, poorly drawn, bad anatomy, wrong anatomy, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck,extra limb, missing limb, floating limbs, mutated hands and fingers:1.4, disconnected limbs, mutation, mutated, ugly, disgusting, blurry, amputation, extra fingers:1.2, worst quality, low quality:1.3out of frame, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, mutation, deformed, blurry, dehydrated, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs"
},
"class_type": "Text Multiline"
},
"115": {
"inputs": {
"expand": 1,
"tapered_corners": false,
"mask": [
"74",
0
]
},
"class_type": "GrowMask"
},
"122": {
"inputs": {
"x": 0,
"y": 0,
"resize_source": false,
"destination": [
"128",
0
],
"source": [
"42",
0
],
"mask": [
"74",
0
]
},
"class_type": "ImageCompositeMasked"
},
"127": {
"inputs": {
"value": [
"42",
0
]
},
"class_type": "ImpactImageInfo"
},
"128": {
"inputs": {
"width": [
"127",
2
],
"height": [
"127",
1
],
"red": 255,
"green": 255,
"blue": 255
},
"class_type": "Image Blank"
},
"129": {
"inputs": {
"filename_prefix": "aimodel_mask",
"images": [
"122",
0
]
},
"class_type": "SaveImage"
},
"130": {
"inputs": {
"guide_size": 256,
"guide_size_for": true,
"max_size": 768,
"seed": 255582749293796,
"steps": 5,
"cfg": 1.5,
"sampler_name": "lcm",
"scheduler": "sgm_uniform",
"denoise": 0.5,
"feather": 5,
"noise_mask": true,
"force_inpaint": true,
"bbox_threshold": 0.5,
"bbox_dilation": 10,
"bbox_crop_factor": 3,
"sam_detection_hint": "center-1",
"sam_dilation": 0,
"sam_threshold": 0.93,
"sam_bbox_expansion": 0,
"sam_mask_hint_threshold": 0.7,
"sam_mask_hint_use_negative": "False",
"drop_size": 10,
"wildcard": "",
"cycle": 1,
"image": [
"142",
5
],
"model": [
"142",
0
],
"clip": [
"143",
5
],
"vae": [
"142",
4
],
"positive": [
"142",
1
],
"negative": [
"142",
2
],
"bbox_detector": [
"138",
0
]
},
"class_type": "FaceDetailer"
},
"138": {
"inputs": {
"model_name": "bbox/face_yolov8m.pt"
},
"class_type": "UltralyticsDetectorProvider"
},
"142": {
"inputs": {
"seed": 255582749293796,
"steps": 5,
"cfg": 1.5,
"sampler_name": "lcm",
"scheduler": "sgm_uniform",
"denoise": 1,
"preview_method": "none",
"vae_decode": "true",
"model": [
"143",
0
],
"positive": [
"143",
1
],
"negative": [
"143",
2
],
"latent_image": [
"63",
0
],
"optional_vae": [
"143",
4
]
},
"class_type": "KSampler (Efficient)"
},
"143": {
"inputs": {
"ckpt_name": "极氪写实MAX-极氪白系列模型_V6.safetensors",
"vae_name": "Baked VAE",
"clip_skip": -2,
"lora_name": "None",
"lora_model_strength": 1,
"lora_clip_strength": 1,
"positive": [
"159",
0
],
"negative": [
"95",
0
],
"token_normalization": "none",
"weight_interpretation": "A1111",
"empty_latent_width": 512,
"empty_latent_height": 512,
"batch_size": 1,
"lora_stack": [
"147",
0
],
"cnet_stack": [
"153",
0
]
},
"class_type": "Efficient Loader"
},
"147": {
"inputs": {
"input_mode": "simple",
"lora_count": 3,
"lora_name_1": "lcm\\lcm_sd1.5_pytorch_lora_weights.safetensors",
"lora_wt_1": 1,
"model_str_1": 1,
"clip_str_1": 1,
"lora_name_2": "None",
"lora_wt_2": 1,
"model_str_2": 1,
"clip_str_2": 1,
"lora_name_3": "None",
"lora_wt_3": 1,
"model_str_3": 1,
"clip_str_3": 1,
"lora_name_4": "None",
"lora_wt_4": 1,
"model_str_4": 1,
"clip_str_4": 1,
"lora_name_5": "None",
"lora_wt_5": 1,
"model_str_5": 1,
"clip_str_5": 1,
"lora_name_6": "None",
"lora_wt_6": 1,
"model_str_6": 1,
"clip_str_6": 1,
"lora_name_7": "None",
"lora_wt_7": 1,
"model_str_7": 1,
"clip_str_7": 1,
"lora_name_8": "None",
"lora_wt_8": 1,
"model_str_8": 1,
"clip_str_8": 1,
"lora_name_9": "None",
"lora_wt_9": 1,
"model_str_9": 1,
"clip_str_9": 1,
"lora_name_10": "None",
"lora_wt_10": 1,
"model_str_10": 1,
"clip_str_10": 1,
"lora_name_11": "None",
"lora_wt_11": 1,
"model_str_11": 1,
"clip_str_11": 1,
"lora_name_12": "None",
"lora_wt_12": 1,
"model_str_12": 1,
"clip_str_12": 1,
"lora_name_13": "None",
"lora_wt_13": 1,
"model_str_13": 1,
"clip_str_13": 1,
"lora_name_14": "None",
"lora_wt_14": 1,
"model_str_14": 1,
"clip_str_14": 1,
"lora_name_15": "None",
"lora_wt_15": 1,
"model_str_15": 1,
"clip_str_15": 1,
"lora_name_16": "None",
"lora_wt_16": 1,
"model_str_16": 1,
"clip_str_16": 1,
"lora_name_17": "None",
"lora_wt_17": 1,
"model_str_17": 1,
"clip_str_17": 1,
"lora_name_18": "None",
"lora_wt_18": 1,
"model_str_18": 1,
"clip_str_18": 1,
"lora_name_19": "None",
"lora_wt_19": 1,
"model_str_19": 1,
"clip_str_19": 1,
"lora_name_20": "None",
"lora_wt_20": 1,
"model_str_20": 1,
"clip_str_20": 1,
"lora_name_21": "None",
"lora_wt_21": 1,
"model_str_21": 1,
"clip_str_21": 1,
"lora_name_22": "None",
"lora_wt_22": 1,
"model_str_22": 1,
"clip_str_22": 1,
"lora_name_23": "None",
"lora_wt_23": 1,
"model_str_23": 1,
"clip_str_23": 1,
"lora_name_24": "None",
"lora_wt_24": 1,
"model_str_24": 1,
"clip_str_24": 1,
"lora_name_25": "None",
"lora_wt_25": 1,
"model_str_25": 1,
"clip_str_25": 1,
"lora_name_26": "None",
"lora_wt_26": 1,
"model_str_26": 1,
"clip_str_26": 1,
"lora_name_27": "None",
"lora_wt_27": 1,
"model_str_27": 1,
"clip_str_27": 1,
"lora_name_28": "None",
"lora_wt_28": 1,
"model_str_28": 1,
"clip_str_28": 1,
"lora_name_29": "None",
"lora_wt_29": 1,
"model_str_29": 1,
"clip_str_29": 1,
"lora_name_30": "None",
"lora_wt_30": 1,
"model_str_30": 1,
"clip_str_30": 1,
"lora_name_31": "None",
"lora_wt_31": 1,
"model_str_31": 1,
"clip_str_31": 1,
"lora_name_32": "None",
"lora_wt_32": 1,
"model_str_32": 1,
"clip_str_32": 1,
"lora_name_33": "None",
"lora_wt_33": 1,
"model_str_33": 1,
"clip_str_33": 1,
"lora_name_34": "None",
"lora_wt_34": 1,
"model_str_34": 1,
"clip_str_34": 1,
"lora_name_35": "None",
"lora_wt_35": 1,
"model_str_35": 1,
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"clip_str_47": 1,
"lora_name_48": "None",
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"clip_str_48": 1,
"lora_name_49": "None",
"lora_wt_49": 1,
"model_str_49": 1,
"clip_str_49": 1
},
"class_type": "LoRA Stacker"
},
"153": {
"inputs": {
"switch_1": "On",
"controlnet_1": "control_v11p_sd15_openpose.pth",
"controlnet_strength_1": 1,
"start_percent_1": 0,
"end_percent_1": 1,
"switch_2": "Off",
"controlnet_2": "None",
"controlnet_strength_2": 1,
"start_percent_2": 0,
"end_percent_2": 1,
"switch_3": "Off",
"controlnet_3": "None",
"controlnet_strength_3": 1,
"start_percent_3": 0,
"end_percent_3": 1,
"image_1": [
"166",
0
]
},
"class_type": "CR Multi-ControlNet Stack"
},
"159": {
"inputs": {
"from_translate": "auto",
"to_translate": "english",
"add_proxies": "disable",
"proxies": "",
"auth_data": "",
"service": "GoogleTranslator",
"text": "一个女孩，正面视角，灰色背景，光着肩膀，光着腿，坐在凳子上,室内场景,露腰",
"Show proxy": "proxy_hide",
"Show authorization": "authorization_hide"
},
"class_type": "DeepTranslatorTextNode"
},
"166": {
"inputs": {
"detect_hand": "enable",
"detect_body": "enable",
"detect_face": "enable",
"resolution": 512,
"bbox_detector": "yolox_l.onnx",
"pose_estimator": "dw-ll_ucoco_384.onnx",
"image": [
"42",
0
]
},
"class_type": "DWPreprocessor"
},
"169": {
"inputs": {
"output_path": "[time(%Y-%m-%d)]",
"filename_prefix": "aimodel_output_final_",
"filename_delimiter": "_",
"filename_number_padding": 4,
"filename_number_start": "false",
"extension": "png",
"quality": 100,
"lossless_webp": "false",
"overwrite_mode": "false",
"show_history": "false",
"show_history_by_prefix": "true",
"embed_workflow": "true",
"show_previews": "true",
"images": [
"130",
0
]
},
"class_type": "Image Save"
},
"170": {
"inputs": {
"image_path": "https://wangbo0808.oss-cn-shanghai.aliyuncs.com/aidraw/img2video_static.jpg",
"RGBA": "false",
"filename_text_extension": "false"
},
"class_type": "Image Load"
}
}
================================================
FILE: src/draw/data/DrawConfig.ts
================================================
/**
* 绘画模块配置参数
*/
export const drawConfig={
defaultTimeOut: 60, //默认任务超时时间
};
export const ApiTimeOut = [
{
type: '文生图',
timeout: 60,
},
{
type: '图生图',
timeout: 60,
},
{
type: '图生视频',
timeout: 120,
},
{
type: 'AI模特',
timeout: 120,
},
{
type: 'AI写真',
timeout: 240,
},
{
type: '放大1',
timeout: 120,
},
{
type: '放大2',
timeout: 180,
},
{
type: '图片反推提示词',
timeout: 20,
},
] as ComfyAPIType[];
export interface ComfyAPIType {
type:
| '文生图'
| '图生图'
| '图生视频'
| 'AI模特'
| 'AI写真'
| '放大1'
| '放大2'
| 'AI推文'
| '换脸'
| '图片反推提示词';
timeout: number;
}
export interface DrawTask {
source: 'wechat' | 'web';
client_id: string;
prompt: any;
api: string;
socket_id?: string;
lifo?: boolean;
prompt_id?: string;
}
================================================
FILE: src/draw/data/aistudio_api.json
================================================
{
"9": {
"inputs": {
"ipadapter_file": "ip-adapter-faceid-plusv2_sd15.bin"
},
"class_type": "IPAdapterModelLoader"
},
"31": {
"inputs": {
"clip_name": "model.safetensors"
},
"class_type": "CLIPVisionLoader"
},
"32": {
"inputs": {
"provider": "CUDA"
},
"class_type": "InsightFaceLoader"
},
"33": {
"inputs": {
"weight": 0.9,
"noise": 0,
"weight_type": "original",
"start_at": 0,
"end_at": 1,
"faceid_v2": true,
"weight_v2": 1,
"unfold_batch": false,
"ipadapter": [
"9",
0
],
"clip_vision": [
"31",
0
],
"insightface": [
"32",
0
],
"image": [
"92",
0
],
"model": [
"80",
0
]
},
"class_type": "IPAdapterApplyFaceID"
},
"50": {
"inputs": {
"model": "wd-v1-4-moat-tagger-v2",
"threshold": 0.35,
"character_threshold": 0.85,
"exclude_tags": "",
"tags": "1girl, solo, short_hair, brown_hair, shirt, brown_eyes, standing, white_shirt, short_sleeves, midriff, pants, lips, hand_on_hip, contrapposto, realistic, high-waist_pants",
"image": [
"63",
0
]
},
"class_type": "WD14Tagger|pysssss"
},
"63": {
"inputs": {
"enabled": true,
"swap_model": "inswapper_128.onnx",
"facedetection": "retinaface_resnet50",
"face_restore_model": "none",
"face_restore_visibility": 1,
"codeformer_weight": 0.5,
"detect_gender_source": "no",
"detect_gender_input": "no",
"source_faces_index": "0",
"input_faces_index": "0",
"console_log_level": 1,
"input_image": [
"91",
0
],
"source_image": [
"92",
0
]
},
"class_type": "ReActorFaceSwap"
},
"66": {
"inputs": {
"guide_size": 256,
"guide_size_for": true,
"max_size": 768,
"seed": 337795391108149,
"steps": 20,
"cfg": 7.5,
"sampler_name": "dpmpp_2m",
"scheduler": "karras",
"denoise": 0.25,
"feather": 5,
"noise_mask": true,
"force_inpaint": true,
"bbox_threshold": 0.5,
"bbox_dilation": 10,
"bbox_crop_factor": 3,
"sam_detection_hint": "center-1",
"sam_dilation": 0,
"sam_threshold": 0.93,
"sam_bbox_expansion": 0,
"sam_mask_hint_threshold": 0.7,
"sam_mask_hint_use_negative": "False",
"drop_size": 10,
"wildcard": "",
"cycle": 1,
"image": [
"79",
5
],
"model": [
"79",
0
],
"clip": [
"80",
5
],
"vae": [
"79",
4
],
"positive": [
"79",
1
],
"negative": [
"79",
2
],
"bbox_detector": [
"86",
0
]
},
"class_type": "FaceDetailer"
},
"79": {
"inputs": {
"seed": 337795391108149,
"steps": 20,
"cfg": 7.5,
"sampler_name": "dpmpp_2m",
"scheduler": "karras",
"denoise": 0.3,
"preview_method": "none",
"vae_decode": "true",
"model": [
"33",
0
],
"positive": [
"80",
1
],
"negative": [
"80",
2
],
"latent_image": [
"87",
0
],
"optional_vae": [
"80",
4
]
},
"class_type": "KSampler (Efficient)"
},
"80": {
"inputs": {
"ckpt_name": "极氪写实MAX-极氪白系列模型_V6.safetensors",
"vae_name": "Baked VAE",
"clip_skip": -2,
"lora_name": "None",
"lora_model_strength": 1,
"lora_clip_strength": 1,
"positive": [
"50",
0
],
"negative": [
"82",
0
],
"token_normalization": "none",
"weight_interpretation": "A1111",
"empty_latent_width": 512,
"empty_latent_height": 512,
"batch_size": 1,
"lora_stack": [
"89",
0
]
},
"class_type": "Efficient Loader"
},
"82": {
"inputs": {
"action": "append",
"tidy_tags": "yes",
"text_a": "blurry, malformed, distorted, naked",
"text_b": "",
"text_c": "",
"result": "blurry, malformed, distorted, naked"
},
"class_type": "StringFunction|pysssss"
},
"86": {
"inputs": {
"model_name": "bbox/face_yolov8m.pt"
},
"class_type": "UltralyticsDetectorProvider"
},
"87": {
"inputs": {
"pixels": [
"63",
0
],
"vae": [
"80",
4
]
},
"class_type": "VAEEncode"
},
"89": {
"inputs": {
"input_mode": "simple",
"lora_count": 3,
"lora_name_1": "None",
"lora_wt_1": 1,
"model_str_1": 1,
"clip_str_1": 1,
"lora_name_2": "None",
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"model_str_2": 1,
"clip_str_2": 1,
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"lora_name_21": "None",
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"lora_name_24": "None",
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"lora_name_25": "None",
"lora_wt_25": 1,
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"clip_str_25": 1,
"lora_name_26": "None",
"lora_wt_26": 1,
"model_str_26": 1,
"clip_str_26": 1,
"lora_name_27": "None",
"lora_wt_27": 1,
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"lora_wt_30": 1,
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"clip_str_30": 1,
"lora_name_31": "None",
"lora_wt_31": 1,
"model_str_31": 1,
"clip_str_31": 1,
"lora_name_32": "None",
"lora_wt_32": 1,
"model_str_32": 1,
"clip_str_32": 1,
"lora_name_33": "None",
"lora_wt_33": 1,
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"lora_name_34": "None",
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"clip_str_34": 1,
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"clip_str_41": 1,
"lora_name_42": "None",
"lora_wt_42": 1,
"model_str_42": 1,
"clip_str_42": 1,
"lora_name_43": "None",
"lora_wt_43": 1,
"model_str_43": 1,
"clip_str_43": 1,
"lora_name_44": "None",
"lora_wt_44": 1,
"model_str_44": 1,
"clip_str_44": 1,
"lora_name_45": "None",
"lora_wt_45": 1,
"model_str_45": 1,
"clip_str_45": 1,
"lora_name_46": "None",
"lora_wt_46": 1,
"model_str_46": 1,
"clip_str_46": 1,
"lora_name_47": "None",
"lora_wt_47": 1,
"model_str_47": 1,
"clip_str_47": 1,
"lora_name_48": "None",
"lora_wt_48": 1,
"model_str_48": 1,
"clip_str_48": 1,
"lora_name_49": "None",
"lora_wt_49": 1,
"model_str_49": 1,
"clip_str_49": 1
},
"class_type": "LoRA Stacker"
},
"91": {
"inputs": {
"image_path": "./ComfyUI/input/example.png",
"RGBA": "false",
"filename_text_extension": "true"
},
"class_type": "Image Load"
},
"92": {
"inputs": {
"image_path": "./ComfyUI/input/example.png",
"RGBA": "false",
"filename_text_extension": "true"
},
"class_type": "Image Load"
},
"93": {
"inputs": {
"output_path": "[time(%Y-%m-%d)]",
"filename_prefix": "aistudio_output_final_",
"filename_delimiter": "_",
"filename_number_padding": 4,
"filename_number_start": "false",
"extension": "png",
"quality": 100,
"lossless_webp": "false",
"overwrite_mode": "false",
"show_history": "false",
"show_history_by_prefix": "true",
"embed_workflow": "true",
"show_previews": "true",
"images": [
"66",
0
]
},
"class_type": "Image Save"
},
"94": {
"inputs": {
"output_path": "[time(%Y-%m-%d)]",
"filename_prefix": "aistudio_output_2_",
"filename_delimiter": "_",
"filename_number_padding": 4,
"filename_number_start": "false",
"extension": "png",
"quality": 100,
"lossless_webp": "false",
"overwrite_mode": "false",
"show_history": "false",
"show_history_by_prefix": "true",
"embed_workflow": "true",
"show_previews": "true",
"images": [
"79",
5
]
},
"class_type": "Image Save"
},
"95": {
"inputs": {
"output_path": "[time(%Y-%m-%d)]",
"filename_prefix": "aistudio_output_1_",
"filename_delimiter": "_",
"filename_number_padding": 4,
"filename_number_start": "false",
"extension": "png",
"quality": 100,
"lossless_webp": "false",
"overwrite_mode": "false",
"show_history": "false",
"show_history_by_prefix": "true",
"embed_workflow": "true",
"show_previews": "true",
"images": [
"63",
0
]
},
"class_type": "Image Save"
}
}
================================================
FILE: src/draw/data/api_matting.json
================================================
{
"5": {
"inputs": {
"prompt": [
"19",
0
],
"threshold": 0.31,
"sam_model": [
"6",
0
],
"grounding_dino_model": [
"7",
0
],
"image": [
"62",
0
]
},
"class_type": "GroundingDinoSAMSegment (segment anything)"
},
"6": {
"inputs": {
"model_name": "sam_vit_h_4b8939.pth",
"device_mode": "Prefer GPU"
},
"class_type": "SAMLoader"
},
"7": {
"inputs": {
"model_name": "GroundingDINO_SwinB (938MB)"
},
"class_type": "GroundingDinoModelLoader (segment anything)"
},
"19": {
"inputs": {
"from_translate": "auto",
"to_translate": "english",
"add_proxies": "disable",
"proxies": "",
"auth_data": "",
"service": "GoogleTranslator",
"text": "人",
"Show proxy": "proxy_hide",
"Show authorization": "authorization_hide"
},
"class_type": "DeepTranslatorTextNode"
},
"44": {
"inputs": {
"mask": [
"5",
1
]
},
"class_type": "InvertMask"
},
"59": {
"inputs": {
"image": [
"5",
0
],
"alpha": [
"44",
0
]
},
"class_type": "JoinImageWithAlpha"
},
"62": {
"inputs": {
"image_path": "C:\\Users\\wangb\\Downloads\\image1 - 2024-03-19T221914.324.png",
"RGBA": "false",
"filename_text_extension": "true"
},
"class_type": "Image Load"
},
"63": {
"inputs": {
"output_path": "[time(%Y-%m-%d)]",
"filename_prefix": [
"66",
0
],
"filename_delimiter": "_",
"filename_number_padding": 4,
"filename_number_start": "false",
"extension": "png",
"quality": 100,
"lossless_webp": "false",
"overwrite_mode": "false",
"show_history": "false",
"show_history_by_prefix": "true",
"embed_workflow": "true",
"show_previews": "true",
"images": [
"59",
0
]
},
"class_type": "Image Save"
},
"66": {
"inputs": {
"delimiter": "-",
"clean_whitespace": "true",
"text_a": [
"19",
0
],
"text_b": "output_matting_final_"
},
"class_type": "Text Concatenate"
}
}
================================================
FILE: src/draw/data/api_removebg.json
================================================
{
"62": {
"inputs": {
"image_path": "E:\\ComfyUI_windows_portable\\ComfyUI\\input\\canvasimage (35).png",
"RGBA": "false",
"filename_text_extension": "true"
},
"class_type": "Image Load"
},
"63": {
"inputs": {
"output_path": "[time(%Y-%m-%d)]",
"filename_prefix": [
"66",
0
],
"filename_delimiter": "_",
"filename_number_padding": 4,
"filename_number_start": "false",
"extension": "png",
"quality": 100,
"lossless_webp": "false",
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"show_history": "false",
"show_history_by_prefix": "true",
"embed_workflow": "true",
"show_previews": "true",
"images": [
"72",
0
]
},
"class_type": "Image Save"
},
"66": {
"inputs": {
"delimiter": "-",
"clean_whitespace": "true",
"text_a": [
"62",
2
],
"text_b": "output_rmbg_final_"
},
"class_type": "Text Concatenate"
},
"72": {
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FILE: src/draw/data/api_wechat_img2imgi.json
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FILE: src/draw/data/impaiting_api.json
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FILE: src/draw/data/objectInfo.json
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"INT",
{
"default": 67108864,
"min": 0,
"max": 9223372036854775807,
"step": 1
}
]
}
},
"output": [
"DETAILER_HOOK"
],
"output_is_list": [
false
],
"output_name": [
"DETAILER_HOOK"
],
"name": "SEGSRangeFilterDetailerHookProvider",
"display_name": "SEGSRangeFilterDetailerHookProvider",
"description": "",
"category": "ImpactPack/Util",
"output_node": false
},
"SEGSLabelFilterDetailerHookProvider": {
"input": {
"required": {
"segs": [
"SEGS"
],
"preset": [
[
"all",
"hand",
"face",
"mouth",
"eyes",
"eyebrows",
"pupils",
"left_eyebrow",
"left_eye",
"left_pupil",
"right_eyebrow",
"right_eye",
"right_pupil",
"short_sleeved_shirt",
"long_sleeved_shirt",
"short_sleeved_outwear",
"long_sleeved_outwear",
"vest",
"sling",
"shorts",
"trousers",
"skirt",
"short_sleeved_dress",
"long_sleeved_dress",
"vest_dress",
"sling_dress",
"person",
"bicycle",
"car",
"motorcycle",
"airplane",
"bus",
"train",
"truck",
"boat",
"traffic light",
"fire hydrant",
"stop sign",
"parking meter",
"bench",
"bird",
"cat",
"dog",
"horse",
"sheep",
"cow",
"elephant",
"bear",
"zebra",
"giraffe",
"backpack",
"umbrella",
"handbag",
"tie",
"suitcase",
"frisbee",
"skis",
"snowboard",
"sports ball",
"kite",
"baseball bat",
"baseball glove",
"skateboard",
"surfboard",
"tennis racket",
"bottle",
"wine glass",
"cup",
"fork",
"knife",
"spoon",
"bowl",
"banana",
"apple",
"sandwich",
"orange",
"broccoli",
"carrot",
"hot dog",
"pizza",
"donut",
"cake",
"chair",
"couch",
"potted plant",
"bed",
"dining table",
"toilet",
"tv",
"laptop",
"mouse",
"remote",
"keyboard",
"cell phone",
"microwave",
"oven",
"toaster",
"sink",
"refrigerator",
"book",
"clock",
"vase",
"scissors",
"teddy bear",
"hair drier",
"toothbrush"
]
],
"labels": [
"STRING",
{
"multiline": true,
"placeholder": "List the types of segments to be allowed, separated by commas"
}
]
}
},
"output": [
"DETAILER_HOOK"
],
"output_is_list": [
false
],
"output_name": [
"DETAILER_HOOK"
],
"name": "SEGSLabelFilterDetailerHookProvider",
"display_name": "SEGSLabelFilterDetailerHookProvider",
"description": "",
"category": "ImpactPack/Util",
"output_node": false
},
"BitwiseAndMask": {
"input": {
"required": {
"mask1": [
"MASK"
],
"mask2": [
"MASK"
]
}
},
"output": [
"MASK"
],
"output_is_list": [
false
],
"output_name": [
"MASK"
],
"name": "BitwiseAndMask",
"display_name": "Bitwise(MASK & MASK)",
"description": "",
"category": "ImpactPack/Operation",
"output_node": false
},
"SubtractMask": {
"input": {
"required": {
"mask1": [
"MASK"
],
"mask2": [
"MASK"
]
}
},
"output": [
"MASK"
],
"output_is_list": [
false
],
"output_name": [
"MASK"
],
"name": "SubtractMask",
"display_name": "Bitwise(MASK - MASK)",
"description": "",
"category": "ImpactPack/Operation",
"output_node": false
},
"AddMask": {
"input": {
"required": {
"mask1": [
"MASK"
],
"mask2": [
"MASK"
]
}
},
"output": [
"MASK"
],
"output_is_list": [
false
],
"output_name": [
"MASK"
],
"name": "AddMask",
"display_name": "Bitwise(MASK + MASK)",
"description": "",
"category": "ImpactPack/Operation",
"output_node": false
},
"ImpactSegsAndMask": {
"input": {
"required": {
"segs": [
"SEGS"
],
"mask": [
"MASK"
]
}
},
"output": [
"SEGS"
],
"output_is_list": [
false
],
"output_name": [
"SEGS"
],
"name": "ImpactSegsAndMask",
"display_name": "Bitwise(SEGS & MASK)",
"description": "",
"category": "ImpactPack/Operation",
"output_node": false
},
"ImpactSegsAndMaskForEach": {
"input": {
"required": {
"segs": [
"SEGS"
],
"masks": [
"MASK"
]
}
},
"output": [
"SEGS"
],
"output_is_list": [
false
],
"output_name": [
"SEGS"
],
"name": "ImpactSegsAndMaskForEach",
"display_name": "Bitwise(SEGS & MASKS ForEach)",
"description": "",
"category": "ImpactPack/Operation",
"output_node": false
},
"EmptySegs": {
"input": {
"required": {}
},
"output": [
"SEGS"
],
"output_is_list": [
false
],
"output_name": [
"SEGS"
],
"name": "EmptySegs",
"display_name": "EmptySegs",
"description": "",
"category": "ImpactPack/Util",
"output_node": false
},
"MediaPipeFaceMeshToSEGS": {
"input": {
"required": {
"image": [
"IMAGE"
],
"crop_factor": [
"FLOAT",
{
"default": 3.0,
"min": 1.0,
"max": 100,
"step": 0.1
}
],
"bbox_fill": [
"BOOLEAN",
{
"default": false,
"label_on": "enabled",
"label_off": "disabled"
}
],
"crop_min_size": [
"INT",
{
"min": 10,
"max": 16384,
"step": 1,
"default": 50
}
],
"drop_size": [
"INT",
{
"min": 1,
"max": 16384,
"step": 1,
"default": 1
}
],
"dilation": [
"INT",
{
"default": 0,
"min": -512,
"max": 512,
"step": 1
}
],
"face": [
"BOOLEAN",
{
"default": true,
"label_on": "Enabled",
"label_off": "Disabled"
}
],
"mouth": [
"BOOLEAN",
{
"default": false,
"label_on": "Enabled",
"label_off": "Disabled"
}
],
"left_eyebrow": [
"BOOLEAN",
{
"default": false,
"label_on": "Enabled",
"label_off": "Disabled"
}
],
"left_eye": [
"BOOLEAN",
{
"default": false,
"label_on": "Enabled",
"label_off": "Disabled"
}
],
"left_pupil": [
"BOOLEAN",
{
"default": false,
"label_on": "Enabled",
"label_off": "Disabled"
}
],
"right_eyebrow": [
"BOOLEAN",
{
"default": false,
"label_on": "Enabled",
"label_off": "Disabled"
}
],
"right_eye": [
"BOOLEAN",
{
"default": false,
"label_on": "Enabled",
"label_off": "Disabled"
}
],
"right_pupil": [
"BOOLEAN",
{
"default": false,
"label_on": "Enabled",
"label_off": "Disabled"
}
]
}
},
"output": [
"SEGS"
],
"output_is_list": [
false
],
"output_name": [
"SEGS"
],
"name": "MediaPipeFaceMeshToSEGS",
"display_name": "MediaPipe FaceMesh to SEGS",
"description": "",
"category": "ImpactPack/Operation",
"output_node": false
},
"MaskToSEGS": {
"input": {
"required": {
"mask": [
"MASK"
],
"combined": [
"BOOLEAN",
{
"default": false,
"label_on": "True",
"label_off": "False"
}
],
"crop_factor": [
"FLOAT",
{
"default": 3.0,
"min": 1.0,
"max": 100,
"step": 0.1
}
],
"bbox_fill": [
"BOOLEAN",
{
"default": false,
"label_on": "enabled",
"label_off": "disabled"
}
],
"drop_size": [
"INT",
{
"min": 1,
"max": 16384,
"step": 1,
"default": 10
}
],
"contour_fill": [
"BOOLEAN",
{
"default": false,
"label_on": "enabled",
"label_off": "disabled"
}
]
}
},
"output": [
"SEGS"
],
"output_is_list": [
false
],
"output_name": [
"SEGS"
],
"name": "MaskToSEGS",
"display_name": "MASK to SEGS",
"description": "",
"category": "ImpactPack/Operation",
"output_node": false
},
"MaskToSEGS_for_AnimateDiff": {
"input": {
"required": {
"mask": [
"MASK"
],
"combined": [
"BOOLEAN",
{
"default": false,
"label_on": "True",
"label_off": "False"
}
],
"crop_factor": [
"FLOAT",
{
"default": 3.0,
"min": 1.0,
"max": 100,
"step": 0.1
}
],
"bbox_fill": [
"BOOLEAN",
{
"default": false,
"label_on": "enabled",
"label_off": "disabled"
}
],
"drop_size": [
"INT",
{
"min": 1,
"max": 16384,
"step": 1,
"default": 10
}
],
"contour_fill": [
"BOOLEAN",
{
"default": false,
"label_on": "enabled",
"label_off": "disabled"
}
]
}
},
"output": [
"SEGS"
],
"output_is_list": [
false
],
"output_name": [
"SEGS"
],
"name": "MaskToSEGS_for_AnimateDiff",
"display_name": "MASK to SEGS for AnimateDiff",
"description": "",
"category": "ImpactPack/Operation",
"output_node": false
},
"ToBinaryMask": {
"input": {
"required": {
"mask": [
"MASK"
],
"threshold": [
"INT",
{
"default": 20,
"min": 1,
"max": 255
}
]
}
},
"output": [
"MASK"
],
"output_is_list": [
false
],
"output_name": [
"MASK"
],
"name": "ToBinaryMask",
"display_name": "ToBinaryMask",
"description": "",
"category": "ImpactPack/Operation",
"output_node": false
},
"MasksToMaskList": {
"input": {
"required": {
"masks": [
"MASK"
]
}
},
"output": [
"MASK"
],
"output_is_list": [
true
],
"output_name": [
"MASK"
],
"name": "MasksToMaskList",
"display_name": "Masks to Mask List",
"description": "",
"category": "ImpactPack/Operation",
"output_node": false
},
"MaskListToMaskBatch": {
"input": {
"required": {
"mask": [
"MASK"
]
}
},
"output": [
"MASK"
],
"output_is_list": [
false
],
"output_name": [
"MASK"
],
"name": "MaskListToMaskBatch",
"display_name": "Mask List to Masks",
"description": "",
"category": "ImpactPack/Operation",
"output_node": false
},
"ImageListToImageBatch": {
"input": {
"required": {
"images": [
"IMAGE"
]
}
},
"output": [
"IMAGE"
],
"output_is_list": [
false
],
"output_name": [
"IMAGE"
],
"name": "ImageListToImageBatch",
"display_name": "Image List to Image Batch",
"description": "",
"category": "ImpactPack/Operation",
"output_node": false
},
"SetDefaultImageForSEGS": {
"input": {
"required": {
"segs": [
"SEGS"
],
"image": [
"IMAGE"
],
"override": [
"BOOLEAN",
{
"default": true
}
]
}
},
"output": [
"SEGS"
],
"output_is_list": [
false
],
"output_name": [
"SEGS"
],
"name": "SetDefaultImageForSEGS",
"display_name": "Set Default Image for SEGS",
"description": "",
"category": "ImpactPack/Util",
"output_node": false
},
"RemoveImageFromSEGS": {
"input": {
"required": {
"segs": [
"SEGS"
]
}
},
"output": [
"SEGS"
],
"output_is_list": [
false
],
"output_name": [
"SEGS"
],
"name": "RemoveImageFromSEGS",
"display_name": "Remove Image from SEGS",
"description": "",
"category": "ImpactPack/Util",
"output_node": false
},
"BboxDetectorSEGS": {
"input": {
"required": {
"bbox_detector": [
"BBOX_DETECTOR"
],
"image": [
"IMAGE"
],
"threshold": [
"FLOAT",
{
"default": 0.5,
"min": 0.0,
"max": 1.0,
"step": 0.01
}
],
"dilation": [
"INT",
{
"default": 10,
"min": -512,
"max": 512,
"step": 1
}
],
"crop_factor": [
"FLOAT",
{
"default": 3.0,
"min": 1.0,
"max": 100,
"step": 0.1
}
],
"drop_size": [
"INT",
{
"min": 1,
"max": 8192,
"step": 1,
"default": 10
}
],
"labels": [
"STRING",
{
"multiline": true,
"default": "all",
"placeholder": "List the types of segments to be allowed, separated by commas"
}
]
},
"optional": {
"detailer_hook": [
"DETAILER_HOOK"
]
}
},
"output": [
"SEGS"
],
"output_is_list": [
false
],
"output_name": [
"SEGS"
],
"name": "BboxDetectorSEGS",
"display_name": "BBOX Detector (SEGS)",
"description": "",
"category": "ImpactPack/Detector",
"output_node": false
},
"SegmDetectorSEGS": {
"input": {
"required": {
"segm_detector": [
"SEGM_DETECTOR"
],
"image": [
"IMAGE"
],
"threshold": [
"FLOAT",
{
"default": 0.5,
"min": 0.0,
"max": 1.0,
"step": 0.01
}
],
"dilation": [
"INT",
{
"default": 10,
"min": -512,
"max": 512,
"step": 1
}
],
"crop_factor": [
"FLOAT",
{
"default": 3.0,
"min": 1.0,
"max": 100,
"step": 0.1
}
],
"drop_size": [
"INT",
{
"min": 1,
"max": 8192,
"step": 1,
"default": 10
}
],
"labels": [
"STRING",
{
"multiline": true,
"default": "all",
"placeholder": "List the types of segments to be allowed, separated by commas"
}
]
},
"optional": {
"detailer_hook": [
"DETAILER_HOOK"
]
}
},
"output": [
"SEGS"
],
"output_is_list": [
false
],
"output_name": [
"SEGS"
],
"name": "SegmDetectorSEGS",
"display_name": "SEGM Detector (SEGS)",
"description": "",
"category": "ImpactPack/Detector",
"output_node": false
},
"ONNXDetectorSEGS": {
"input": {
"required": {
"bbox_detector": [
"BBOX_DETECTOR"
],
"image": [
"IMAGE"
],
"threshold": [
"FLOAT",
{
"default": 0.5,
"min": 0.0,
"max": 1.0,
"step": 0.01
}
],
"dilation": [
"INT",
{
"default": 10,
"min": -512,
"max": 512,
"step": 1
}
],
"crop_factor": [
"FLOAT",
{
"default": 3.0,
"min": 1.0,
"max": 100,
"step": 0.1
}
],
"drop_size": [
"INT",
{
"min": 1,
"max": 8192,
"step": 1,
"default": 10
}
],
"labels": [
"STRING",
{
"multiline": true,
"default": "all",
"placeholder": "List the types of segments to be allowed, separated by commas"
}
]
},
"optional": {
"detailer_hook": [
"DETAILER_HOOK"
]
}
},
"output": [
"SEGS"
],
"output_is_list": [
false
],
"output_name": [
"SEGS"
],
"name": "ONNXDetectorSEGS",
"display_name": "ONNX Detector (SEGS/legacy) - use BBOXDetector",
"description": "",
"category": "ImpactPack/Detector",
"output_node": false
},
"ImpactSimpleDetectorSEGS_for_AD": {
"input": {
"required": {
"bbox_detector": [
"BBOX_DETECTOR"
],
"image_frames": [
"IMAGE"
],
"bbox_threshold": [
"FLOAT",
{
"default": 0.5,
"min": 0.0,
"max": 1.0,
"step": 0.01
}
],
"bbox_dilation": [
"INT",
{
"default": 0,
"min": -255,
"max": 255,
"step": 1
}
],
"crop_factor": [
"FLOAT",
{
"default": 3.0,
"min": 1.0,
"max": 100,
"step": 0.1
}
],
"drop_size": [
"INT",
{
"min": 1,
"max": 8192,
"step": 1,
"default": 10
}
],
"sub_threshold": [
"FLOAT",
{
"default": 0.5,
"min": 0.0,
"max": 1.0,
"step": 0.01
}
],
"sub_dilation": [
"INT",
{
"default": 0,
"min": -255,
"max": 255,
"step": 1
}
],
"sub_bbox_expansion": [
"INT",
{
"default": 0,
"min": 0,
"max": 1000,
"step": 1
}
],
"sam_mask_hint_threshold": [
"FLOAT",
{
"default": 0.7,
"min": 0.0,
"max": 1.0,
"step": 0.01
}
]
},
"optional": {
"masking_mode": [
[
"Pivot SEGS",
"Combine neighboring frames",
"Don't combine"
]
],
"segs_pivot": [
[
"Combined mask",
"1st frame mask"
]
],
"sam_model_opt": [
"SAM_MODEL"
],
"segm_detector_opt": [
"SEGM_DETECTOR"
]
}
},
"output": [
"SEGS"
],
"output_is_list": [
false
],
"output_name": [
"SEGS"
],
"name": "ImpactSimpleDetectorSEGS_for_AD",
"display_name": "Simple Detector for AnimateDiff (SEGS)",
"description": "",
"category": "ImpactPack/Detector",
"output_node": false
},
"ImpactSimpleDetectorSEGS": {
"input": {
"required": {
"bbox_detector": [
"BBOX_DETECTOR"
],
"image": [
"IMAGE"
],
"bbox_threshold": [
"FLOAT",
{
"default": 0.5,
"min": 0.0,
"max": 1.0,
"step": 0.01
}
],
"bbox_dilation": [
"INT",
{
"default": 0,
"min": -512,
"max": 512,
"step": 1
}
],
"crop_factor": [
"FLOAT",
{
"default": 3.0,
"min": 1.0,
"max": 100,
"step": 0.1
}
],
"drop_size": [
"INT",
{
"min": 1,
"max": 8192,
"step": 1,
"default": 10
}
],
"sub_threshold": [
"FLOAT",
{
"default": 0.5,
"min": 0.0,
"max": 1.0,
"step": 0.01
}
],
"sub_dilation": [
"INT",
{
"default": 0,
"min": -512,
"max": 512,
"step": 1
}
],
"sub_bbox_expansion": [
"INT",
{
"default": 0,
"min": 0,
"max": 1000,
"step": 1
}
],
"sam_mask_hint_threshold": [
"FLOAT",
{
"default": 0.7,
"min": 0.0,
"max": 1.0,
"step": 0.01
}
]
},
"optional": {
"post_dilation": [
"INT",
{
"default": 0,
"min": -512,
"max": 512,
"step": 1
}
],
"sam_model_opt": [
"SAM_MODEL"
],
"segm_detector_opt": [
"SEGM_DETECTOR"
]
}
},
"output": [
"SEGS"
],
"output_is_list": [
false
],
"output_name": [
"SEGS"
],
"name": "ImpactSimpleDetectorSEGS",
"display_name": "Simple Detector (SEGS)",
"description": "",
"category": "ImpactPack/Detector",
"output_node": false
},
"ImpactSimpleDetectorSEGSPipe": {
"input": {
"required": {
"detailer_pipe": [
"DETAILER_PIPE"
],
"image": [
"IMAGE"
],
"bbox_threshold": [
"FLOAT",
{
"default": 0.5,
"min": 0.0,
"max": 1.0,
"step": 0.01
}
],
"bbox_dilation": [
"INT",
{
"default": 0,
"min": -512,
"max": 512,
"step": 1
}
],
"crop_factor": [
"FLOAT",
{
"default": 3.0,
"min": 1.0,
"max": 100,
"step": 0.1
}
],
"drop_size": [
"INT",
{
"min": 1,
"max": 8192,
"step": 1,
"default": 10
}
],
"sub_threshold": [
"FLOAT",
{
"default": 0.5,
"min": 0.0,
"max": 1.0,
"step": 0.01
}
],
"sub_dilation": [
"INT",
{
"default": 0,
"min": -512,
"max": 512,
"step": 1
}
],
"sub_bbox_expansion": [
"INT",
{
"default": 0,
"min": 0,
"max": 1000,
"step": 1
}
],
"sam_mask_hint_threshold": [
"FLOAT",
{
"default": 0.7,
"min": 0.0,
"max": 1.0,
"step": 0.01
}
]
},
"optional": {
"post_dilation": [
"INT",
{
"default": 0,
"min": -512,
"max": 512,
"step": 1
}
]
}
},
"output": [
"SEGS"
],
"output_is_list": [
false
],
"output_name": [
"SEGS"
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"ImpactSEGSToMaskList": {
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}
},
"output": [
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"output_is_list": [
true
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"output_name": [
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"name": "ImpactSEGSToMaskList",
"display_name": "SEGS to Mask List",
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"category": "ImpactPack/Util",
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},
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}
},
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"output_is_list": [
false
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"name": "ImpactSEGSToMaskBatch",
"display_name": "SEGS to Mask Batch",
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"category": "ImpactPack/Util",
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}
},
"output": [
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"output_is_list": [
false
],
"output_name": [
"SEGS"
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"name": "ImpactSEGSConcat",
"display_name": "SEGS Concat",
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"category": "ImpactPack/Util",
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},
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"STRING",
{
"multiline": true,
"dynamicPrompts": false,
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],
"segs": [
"SEGS"
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},
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"IMAGE"
]
},
"hidden": {
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},
"output": [
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],
"output_is_list": [
false
],
"output_name": [
"SEGS"
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"name": "ImpactSEGSPicker",
"display_name": "Picker (SEGS)",
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"category": "ImpactPack/Util",
"output_node": true
},
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"bbox_size": [
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{
"default": 512,
"min": 64,
"max": 4096,
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],
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{
"default": 3.0,
"min": 1.0,
"max": 10,
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],
"min_overlap": [
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{
"default": 5,
"min": 0,
"max": 512,
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],
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{
"default": 20,
"min": -255,
"max": 255,
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],
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{
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"step": 0.01
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],
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[
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"All random quality"
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},
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"filter_out_segs_opt": [
"SEGS"
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}
},
"output": [
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false
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"output_name": [
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"name": "ImpactMakeTileSEGS",
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"category": "ImpactPack/__for_testing",
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},
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"IMAGE"
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"SEGS"
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{
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"min": 64,
"max": 16384,
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],
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{
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],
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{
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],
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{
"default": 0,
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"max": 18446744073709551615
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{
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"min": 1,
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],
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{
"default": 8.0,
"min": 0.0,
"max": 100.0
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],
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"dpm_2_ancestral",
"lms",
"dpm_fast",
"dpm_adaptive",
"dpmpp_2s_ancestral",
"dpmpp_sde",
"dpmpp_sde_gpu",
"dpmpp_2m",
"dpmpp_2m_alt",
"dpmpp_2m_sde",
"dpmpp_2m_sde_gpu",
"dpmpp_3m_sde",
"dpmpp_3m_sde_gpu",
"ddpm",
"lcm",
"ddim",
"uni_pc",
"uni_pc_bh2"
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],
"scheduler": [
[
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"karras",
"exponential",
"sgm_uniform",
"simple",
"ddim_uniform"
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],
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{
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"min": 0.0001,
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],
"basic_pipe": [
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],
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{
"default": 0.2,
"min": 0.0,
"max": 1.0
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]
},
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}
},
"output": [
"SEGS",
"IMAGE"
],
"output_is_list": [
false,
true
],
"output_name": [
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"cnet_images"
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"name": "SEGSDetailerForAnimateDiff",
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"category": "ImpactPack/Detailer",
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},
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{
"default": 0,
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],
"steps": [
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],
"cfg": [
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],
"sampler_name": [
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"dpm_2_ancestral",
"lms",
"dpm_fast",
"dpm_adaptive",
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"dpmpp_sde_gpu",
"dpmpp_2m",
"dpmpp_2m_alt",
"dpmpp_2m_sde",
"dpmpp_2m_sde_gpu",
"dpmpp_3m_sde",
"dpmpp_3m_sde_gpu",
"ddpm",
"lcm",
"ddim",
"uni_pc",
"uni_pc_bh2"
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],
"scheduler": [
[
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"karras",
"exponential",
"sgm_uniform",
"simple",
"ddim_uniform"
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],
"latent_image": [
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"denoise": [
"FLOAT",
{
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"min": 0.0,
"max": 1.0,
"step": 0.01
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]
}
},
"output": [
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"LATENT",
"VAE"
],
"output_is_list": [
false,
false,
false
],
"output_name": [
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"LATENT",
"VAE"
],
"name": "ImpactKSamplerBasicPipe",
"display_name": "KSampler (pipe)",
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"category": "sampling",
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},
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"BASIC_PIPE"
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{
"default": true,
"label_on": "enable",
"label_off": "disable"
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],
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{
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],
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{
"default": 20,
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],
"cfg": [
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{
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"min": 0.0,
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],
"sampler_name": [
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"dpm_2_ancestral",
"lms",
"dpm_fast",
"dpm_adaptive",
"dpmpp_2s_ancestral",
"dpmpp_sde",
"dpmpp_sde_gpu",
"dpmpp_2m",
"dpmpp_2m_alt",
"dpmpp_2m_sde",
"dpmpp_2m_sde_gpu",
"dpmpp_3m_sde",
"dpmpp_3m_sde_gpu",
"ddpm",
"lcm",
"ddim",
"uni_pc",
"uni_pc_bh2"
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],
"scheduler": [
[
"normal",
"karras",
"exponential",
"sgm_uniform",
"simple",
"ddim_uniform"
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],
"latent_image": [
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],
"start_at_step": [
"INT",
{
"default": 0,
"min": 0,
"max": 10000
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],
"end_at_step": [
"INT",
{
"default": 10000,
"min": 0,
"max": 10000
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],
"return_with_leftover_noise": [
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{
"default": false,
"label_on": "enable",
"label_off": "disable"
}
]
}
},
"output": [
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"VAE"
],
"output_is_list": [
false,
false,
false
],
"output_name": [
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"LATENT",
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"name": "ImpactKSamplerAdvancedBasicPipe",
"display_name": "KSampler (Advanced/pipe)",
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"category": "sampling",
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},
"ReencodeLatent": {
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[
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"Both",
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"Encode(output) only"
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],
"input_vae": [
"VAE"
],
"output_vae": [
"VAE"
],
"tile_size": [
"INT",
{
"default": 512,
"min": 320,
"max": 4096,
"step": 64
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]
}
},
"output": [
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],
"output_is_list": [
false
],
"output_name": [
"LATENT"
],
"name": "ReencodeLatent",
"display_name": "Reencode Latent",
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"category": "ImpactPack/Util",
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},
"ReencodeLatentPipe": {
"input": {
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],
"tile_mode": [
[
"None",
"Both",
"Decode(input) only",
"Encode(output) only"
]
],
"input_basic_pipe": [
"BASIC_PIPE"
],
"output_basic_pipe": [
"BASIC_PIPE"
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}
},
"output": [
"LATENT"
],
"output_is_list": [
false
],
"output_name": [
"LATENT"
],
"name": "ReencodeLatentPipe",
"display_name": "Reencode Latent (pipe)",
"description": "",
"category": "ImpactPack/Util",
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},
"ImpactImageBatchToImageList": {
"input": {
"required": {
"image": [
"IMAGE"
]
}
},
"output": [
"IMAGE"
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"output_is_list": [
true
],
"output_name": [
"IMAGE"
],
"name": "ImpactImageBatchToImageList",
"display_name": "Image batch to Image List",
"description": "",
"category": "ImpactPack/Util",
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},
"ImpactMakeImageList": {
"input": {
"required": {
"image1": [
"IMAGE"
]
}
},
"output": [
"IMAGE"
],
"output_is_list": [
true
],
"output_name": [
"IMAGE"
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"name": "ImpactMakeImageList",
"display_name": "Make Image List",
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"category": "ImpactPack/Util",
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},
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"IMAGE"
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}
},
"output": [
"IMAGE"
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"output_is_list": [
false
],
"output_name": [
"IMAGE"
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"name": "ImpactMakeImageBatch",
"display_name": "Make Image Batch",
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"category": "ImpactPack/Util",
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},
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],
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],
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],
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{
"default": 1.0,
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],
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{
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],
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],
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{
"default": "ratio between"
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],
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],
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{
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]
},
"hidden": {
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},
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"name": "RegionalSampler",
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},
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{
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],
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],
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],
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],
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{
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],
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{
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],
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{
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],
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{
"default": "ratio between"
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],
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"euler",
"heun",
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],
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{
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]
},
"hidden": {
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},
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false
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"output_name": [
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"name": "RegionalSamplerAdvanced",
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"category": "ImpactPack/Regional",
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},
"CombineRegionalPrompts": {
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}
},
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false
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"name": "CombineRegionalPrompts",
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"category": "ImpactPack/Regional",
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},
"RegionalPrompt": {
"input": {
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"MASK"
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"advanced_sampler": [
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}
},
"output": [
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],
"output_is_list": [
false
],
"output_name": [
"REGIONAL_PROMPTS"
],
"name": "RegionalPrompt",
"display_name": "RegionalPrompt",
"description": "",
"category": "ImpactPack/Regional",
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},
"ImpactCombineConditionings": {
"input": {
"required": {
"conditioning1": [
"CONDITIONING"
]
}
},
"output": [
"CONDITIONING"
],
"output_is_list": [
false
],
"output_name": [
"CONDITIONING"
],
"name": "ImpactCombineConditionings",
"display_name": "Combine Conditionings",
"description": "",
"category": "ImpactPack/Util",
"output_node": false
},
"ImpactConcatConditionings": {
"input": {
"required": {
"conditioning1": [
"CONDITIONING"
]
}
},
"output": [
"CONDITIONING"
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"output_is_list": [
false
],
"output_name": [
"CONDITIONING"
],
"name": "ImpactConcatConditionings",
"display_name": "Concat Conditionings",
"description": "",
"category": "ImpactPack/Util",
"output_node": false
},
"ImpactSEGSLabelAssign": {
"input": {
"required": {
"segs": [
"SEGS"
],
"labels": [
"STRING",
{
"multiline": true,
"placeholder": "List the label to be assigned in order of segs, separated by commas"
}
]
}
},
"output": [
"SEGS"
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"output_is_list": [
false
],
"output_name": [
"SEGS"
],
"name": "ImpactSEGSLabelAssign",
"display_name": "SEGS Assign (label)",
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"category": "ImpactPack/Util",
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},
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"SEGS"
],
"preset": [
[
"all",
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"eyebrows",
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"left_eyebrow",
"left_eye",
"left_pupil",
"right_eyebrow",
"right_eye",
"right_pupil",
"short_sleeved_shirt",
"long_sleeved_shirt",
"short_sleeved_outwear",
"long_sleeved_outwear",
"vest",
"sling",
"shorts",
"trousers",
"skirt",
"short_sleeved_dress",
"long_sleeved_dress",
"vest_dress",
"sling_dress",
"person",
"bicycle",
"car",
"motorcycle",
"airplane",
"bus",
"train",
"truck",
"boat",
"traffic light",
"fire hydrant",
"stop sign",
"parking meter",
"bench",
"bird",
"cat",
"dog",
"horse",
"sheep",
"cow",
"elephant",
"bear",
"zebra",
"giraffe",
"backpack",
"umbrella",
"handbag",
"tie",
"suitcase",
"frisbee",
"skis",
"snowboard",
"sports ball",
"kite",
"baseball bat",
"baseball glove",
"skateboard",
"surfboard",
"tennis racket",
"bottle",
"wine glass",
"cup",
"fork",
"knife",
"spoon",
"bowl",
"banana",
"apple",
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"orange",
"broccoli",
"carrot",
"hot dog",
"pizza",
"donut",
"cake",
"chair",
"couch",
"potted plant",
"bed",
"dining table",
"toilet",
"tv",
"laptop",
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"remote",
"keyboard",
"cell phone",
"microwave",
"oven",
"toaster",
"sink",
"refrigerator",
"book",
"clock",
"vase",
"scissors",
"teddy bear",
"hair drier",
"toothbrush"
]
],
"labels": [
"STRING",
{
"multiline": true,
"placeholder": "List the types of segments to be allowed, separated by commas"
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]
}
},
"output": [
"SEGS",
"SEGS"
],
"output_is_list": [
false,
false
],
"output_name": [
"filtered_SEGS",
"remained_SEGS"
],
"name": "ImpactSEGSLabelFilter",
"display_name": "SEGS Filter (label)",
"description": "",
"category": "ImpactPack/Util",
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},
"ImpactSEGSRangeFilter": {
"input": {
"required": {
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"SEGS"
],
"target": [
[
"area(=w*h)",
"width",
"height",
"x1",
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"x2",
"y2",
"length_percent"
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],
"mode": [
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{
"default": true,
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},
"BindImageListPromptList //Inspire": {
"input": {
"required": {
"images": [
"IMAGE"
],
"zipped_prompts": [
"ZIPPED_PROMPT"
],
"default_positive": [
"STRING",
{
"multiline": true,
"placeholder": "default positive"
}
],
"default_negative": [
"STRING",
{
"multiline": true,
"placeholder": "default negative"
}
]
}
},
"output": [
"IMAGE",
"STRING",
"STRING",
"STRING"
],
"output_is_list": [
true,
true,
true
],
"output_name": [
"image",
"positive",
"negative",
"prompt_label"
],
"name": "BindImageListPromptList //Inspire",
"display_name": "Bind [ImageList, PromptList] (Inspire)",
"description": "",
"category": "InspirePack/Prompt",
"output_node": false
},
"WildcardEncode //Inspire": {
"input": {
"required": {
"model": [
"MODEL"
],
"clip": [
"CLIP"
],
"token_normalization": [
[
"none",
"mean",
"length",
"length+mean"
]
],
"weight_interpretation": [
[
"comfy",
"A1111",
"compel",
"comfy++",
"down_weight"
],
{
"default": "comfy++"
}
],
"wildcard_text": [
"STRING",
{
"multiline": true,
"dynamicPrompts": false,
"placeholder": "Wildcard Prompt (User Input)"
}
],
"populated_text": [
"STRING",
{
"multiline": true,
"dynamicPrompts": false,
"placeholder": "Populated Prompt (Will be generated automatically)"
}
],
"mode": [
"BOOLEAN",
{
"default": true,
"label_on": "Populate",
"label_off": "Fixed"
}
],
"Select to add LoRA": [
[
"Select the LoRA to add to the text",
"ip-adapter-faceid-plus_sd15_lora.safetensors",
"ip-adapter-faceid-plusv2_sd15_lora.safetensors",
"ip-adapter-faceid-plusv2_sdxl_lora.safetensors",
"ip-adapter-faceid_sd15_lora.safetensors",
"ip-adapter-faceid_sdxl_lora.safetensors",
"sd15_lcm_lora_rank1.safetensors",
"sdxl_LCM_lora_rank1.safetensors"
]
],
"Select to add Wildcard": [
[
"Select the Wildcard to add to the text"
]
],
"seed": [
"INT",
{
"default": 0,
"min": 0,
"max": 18446744073709551615
}
]
}
},
"output": [
"MODEL",
"CLIP",
"CONDITIONING",
"STRING"
],
"output_is_list": [
false,
false,
false,
false
],
"output_name": [
"model",
"clip",
"conditioning",
"populated_text"
],
"name": "WildcardEncode //Inspire",
"display_name": "Wildcard Encode (Inspire)",
"description": "",
"category": "InspirePack/Prompt",
"output_node": false
},
"PromptBuilder //Inspire": {
"input": {
"required": {
"category": [
[
"Angle of View",
"Artists",
"Character Types",
"Colors",
"Composition",
"Composition Form",
"Lighting",
"Negative",
"Picture Effect",
"Picture Quality",
"Setting",
"Shot",
"Style",
"#PLACEHOLDER"
]
],
"preset": [
[
"#PRESET"
]
],
"text": [
"STRING",
{
"multiline": true
}
]
}
},
"output": [
"STRING"
],
"output_is_list": [
false
],
"output_name": [
"STRING"
],
"name": "PromptBuilder //Inspire",
"display_name": "Prompt Builder (Inspire)",
"description": "",
"category": "InspirePack/Prompt",
"output_node": false
},
"SeedExplorer //Inspire": {
"input": {
"required": {
"latent": [
"LATENT"
],
"seed_prompt": [
"STRING",
{
"multiline": true,
"dynamicPrompts": false,
"pysssss.autocomplete": false
}
],
"enable_additional": [
"BOOLEAN",
{
"default": true,
"label_on": "true",
"label_off": "false"
}
],
"additional_seed": [
"INT",
{
"default": 0,
"min": 0,
"max": 18446744073709551615
}
],
"additional_strength": [
"FLOAT",
{
"default": 0.0,
"min": 0.0,
"max": 1.0,
"step": 0.01
}
],
"noise_mode": [
[
"GPU(=A1111)",
"CPU"
]
],
"initial_batch_seed_mode": [
[
"incremental",
"comfy"
]
]
}
},
"output": [
"NOISE"
],
"output_is_list": [
false
],
"output_name": [
"NOISE"
],
"name": "SeedExplorer //Inspire",
"display_name": "Seed Explorer (Inspire)",
"description": "",
"category": "InspirePack/Prompt",
"output_node": false
},
"ListCounter //Inspire": {
"input": {
"required": {
"signal": [
"*"
],
"base_value": [
"INT",
{
"default": 0,
"min": 0,
"max": 18446744073709551615
}
]
},
"hidden": {
"unique_id": "UNIQUE_ID"
}
},
"output": [
"INT"
],
"output_is_list": [
false
],
"output_name": [
"INT"
],
"name": "ListCounter //Inspire",
"display_name": "List Counter (Inspire)",
"description": "",
"category": "InspirePack/Util",
"output_node": false
},
"CLIPTextEncodeWithWeight //Inspire": {
"input": {
"required": {
"text": [
"STRING",
{
"multiline": true
}
],
"clip": [
"CLIP"
],
"strength": [
"FLOAT",
{
"default": 1.0,
"min": 0.0,
"max": 10.0,
"step": 0.01
}
],
"add_weight": [
"FLOAT",
{
"default": 0.0,
"min": -10.0,
"max": 10.0,
"step": 0.01
}
]
}
},
"output": [
"CONDITIONING"
],
"output_is_list": [
false
],
"output_name": [
"CONDITIONING"
],
"name": "CLIPTextEncodeWithWeight //Inspire",
"display_name": "CLIPTextEncodeWithWeight (Inspire)",
"description": "",
"category": "InspirePack/Util",
"output_node": false
},
"RandomGeneratorForList //Inspire": {
"input": {
"required": {
"signal": [
"*"
],
"seed": [
"INT",
{
"default": 0,
"min": 0,
"max": 18446744073709551615
}
]
},
"hidden": {
"unique_id": "UNIQUE_ID"
}
},
"output": [
"*",
"INT"
],
"output_is_list": [
false,
false
],
"output_name": [
"signal",
"random_value"
],
"name": "RandomGeneratorForList //Inspire",
"display_name": "Random Generator for List (Inspire)",
"description": "",
"category": "InspirePack/Util",
"output_node": false
},
"MakeBasicPipe //Inspire": {
"input": {
"required": {
"ckpt_name": [
[
"ghostmix_v20Bakedvae.safetensors",
"juggernautXL_v9Rundiffusionphoto2.safetensors",
"majicmixRealistic_v7.safetensors",
"svd-fp16.safetensors"
]
],
"ckpt_key_opt": [
"STRING",
{
"multiline": false,
"placeholder": "If empty, use 'ckpt_name' as the key."
}
],
"positive_wildcard_text": [
"STRING",
{
"multiline": true,
"dynamicPrompts": false,
"placeholder": "Positive Prompt (User Input)"
}
],
"negative_wildcard_text": [
"STRING",
{
"multiline": true,
"dynamicPrompts": false,
"placeholder": "Negative Prompt (User Input)"
}
],
"Add selection to": [
"BOOLEAN",
{
"default": true,
"label_on": "Positive",
"label_off": "Negative"
}
],
"Select to add LoRA": [
[
"Select the LoRA to add to the text",
"ip-adapter-faceid-plus_sd15_lora.safetensors",
"ip-adapter-faceid-plusv2_sd15_lora.safetensors",
"ip-adapter-faceid-plusv2_sdxl_lora.safetensors",
"ip-adapter-faceid_sd15_lora.safetensors",
"ip-adapter-faceid_sdxl_lora.safetensors",
"sd15_lcm_lora_rank1.safetensors",
"sdxl_LCM_lora_rank1.safetensors"
]
],
"Select to add Wildcard": [
[
"Select the Wildcard to add to the text"
]
],
"wildcard_mode": [
"BOOLEAN",
{
"default": true,
"label_on": "Populate",
"label_off": "Fixed"
}
],
"positive_populated_text": [
"STRING",
{
"multiline": true,
"dynamicPrompts": false,
"placeholder": "Populated Positive Prompt (Will be generated automatically)"
}
],
"negative_populated_text": [
"STRING",
{
"multiline": true,
"dynamicPrompts": false,
"placeholder": "Populated Negative Prompt (Will be generated automatically)"
}
],
"token_normalization": [
[
"none",
"mean",
"length",
"length+mean"
]
],
"weight_interpretation": [
[
"comfy",
"A1111",
"compel",
"comfy++",
"down_weight"
],
{
"default": "comfy++"
}
],
"stop_at_clip_layer": [
"INT",
{
"default": -2,
"min": -24,
"max": -1,
"step": 1
}
],
"seed": [
"INT",
{
"default": 0,
"min": 0,
"max": 18446744073709551615
}
]
},
"optional": {
"vae_opt": [
"VAE"
]
}
},
"output": [
"BASIC_PIPE",
"STRING"
],
"output_is_list": [
false,
false
],
"output_name": [
"basic_pipe",
"cache_key"
],
"name": "MakeBasicPipe //Inspire",
"display_name": "Make Basic Pipe (Inspire)",
"description": "",
"category": "InspirePack/Prompt",
"output_node": false
},
"RemoveControlNet //Inspire": {
"input": {
"required": {
"conditioning": [
"CONDITIONING"
]
}
},
"output": [
"CONDITIONING"
],
"output_is_list": [
false
],
"output_name": [
"CONDITIONING"
],
"name": "RemoveControlNet //Inspire",
"display_name": "Remove ControlNet (Inspire)",
"description": "",
"category": "InspirePack/Util",
"output_node": false
},
"RemoveControlNetFromRegionalPrompts //Inspire": {
"input": {
"required": {
"regional_prompts": [
"REGIONAL_PROMPTS"
]
}
},
"output": [
"REGIONAL_PROMPTS"
],
"output_is_list": [
false
],
"output_name": [
"REGIONAL_PROMPTS"
],
"name": "RemoveControlNetFromRegionalPrompts //Inspire",
"display_name": "Remove ControlNet [RegionalPrompts] (Inspire)",
"description": "",
"category": "InspirePack/Util",
"output_node": false
},
"LoadImagesFromDir //Inspire": {
"input": {
"required": {
"directory": [
"STRING",
{
"default": ""
}
]
},
"optional": {
"image_load_cap": [
"INT",
{
"default": 0,
"min": 0,
"step": 1
}
],
"start_index": [
"INT",
{
"default": 0,
"min": 0,
"step": 1
}
],
"load_always": [
"BOOLEAN",
{
"default": false,
"label_on": "enabled",
"label_off": "disabled"
}
]
}
},
"output": [
"IMAGE",
"MASK",
"INT"
],
"output_is_list": [
false,
false,
false
],
"output_name": [
"IMAGE",
"MASK",
"INT"
],
"name": "LoadImagesFromDir //Inspire",
"display_name": "Load Image Batch From Dir (Inspire)",
"description": "",
"category": "image",
"output_node": false
},
"LoadImageListFromDir //Inspire": {
"input": {
"required": {
"directory": [
"STRING",
{
"default": ""
}
]
},
"optional": {
"image_load_cap": [
"INT",
{
"default": 0,
"min": 0,
"step": 1
}
],
"start_index": [
"INT",
{
"default": 0,
"min": 0,
"step": 1
}
],
"load_always": [
"BOOLEAN",
{
"default": false,
"label_on": "enabled",
"label_off": "disabled"
}
]
}
},
"output": [
"IMAGE",
"MASK"
],
"output_is_list": [
true,
true
],
"output_name": [
"IMAGE",
"MASK"
],
"name": "LoadImageListFromDir //Inspire",
"display_name": "Load Image List From Dir (Inspire)",
"description": "",
"category": "image",
"output_node": false
},
"LoadImage //Inspire": {
"input": {
"required": {
"image": [
[
"2022-06-23 19-46-52-280417 (1).png",
"2022-06-23 19-46-52-280417.png",
"ChMkJ13cwnOIK4FoAAXGX559JJgAAvbPAImQfAABcZ3473 (1).jpg",
"IMG_20230624_115309.jpg",
"R-C.jpg",
"example.png",
"u=1090338134,2696420997&fm=253&fmt=auto&app=138&f=JPEG (1).webp",
"u=1090338134,2696420997&fm=253&fmt=auto&app=138&f=JPEG (2).webp",
"u=1090338134,2696420997&fm=253&fmt=auto&app=138&f=JPEG.webp",
"#DATA"
],
{
"image_upload": true
}
],
"image_data": [
"STRING",
{
"multiline": false
}
]
}
},
"output": [
"IMAGE",
"MASK"
],
"output_is_list": [
false,
false
],
"output_name": [
"IMAGE",
"MASK"
],
"name": "LoadImage //Inspire",
"display_name": "Load Image (Inspire)",
"description": "",
"category": "InspirePack/image",
"output_node": false
},
"ChangeImageBatchSize //Inspire": {
"input": {
"required": {
"image": [
"IMAGE"
],
"batch_size": [
"INT",
{
"default": 1,
"min": 1,
"max": 4096,
"step": 1
}
],
"mode": [
[
"simple"
]
]
}
},
"output": [
"IMAGE"
],
"output_is_list": [
false
],
"output_name": [
"IMAGE"
],
"name": "ChangeImageBatchSize //Inspire",
"display_name": "Change Image Batch Size (Inspire)",
"description": "",
"category": "InspirePack/Util",
"output_node": false
},
"ChangeLatentBatchSize //Inspire": {
"input": {
"required": {
"latent": [
"LATENT"
],
"batch_size": [
"INT",
{
"default": 1,
"min": 1,
"max": 4096,
"step": 1
}
],
"mode": [
[
"simple"
]
]
}
},
"output": [
"LATENT"
],
"output_is_list": [
false
],
"output_name": [
"LATENT"
],
"name": "ChangeLatentBatchSize //Inspire",
"display_name": "Change Latent Batch Size (Inspire)",
"description": "",
"category": "InspirePack/Util",
"output_node": false
},
"ImageBatchSplitter //Inspire": {
"input": {
"required": {
"images": [
"IMAGE"
],
"split_count": [
"INT",
{
"default": 4,
"min": 0,
"max": 50,
"step": 1
}
]
}
},
"output": [
"IMAGE"
],
"output_is_list": [
false
],
"output_name": [
"IMAGE"
],
"name": "ImageBatchSplitter //Inspire",
"display_name": "Image Batch Splitter (Inspire)",
"description": "",
"category": "InspirePack/Util",
"output_node": false
},
"LatentBatchSplitter //Inspire": {
"input": {
"required": {
"latent": [
"LATENT"
],
"split_count": [
"INT",
{
"default": 4,
"min": 0,
"max": 50,
"step": 1
}
]
}
},
"output": [
"LATENT"
],
"output_is_list": [
false
],
"output_name": [
"LATENT"
],
"name": "LatentBatchSplitter //Inspire",
"display_name": "Latent Batch Splitter (Inspire)",
"description": "",
"category": "InspirePack/Util",
"output_node": false
},
"RegionalPromptSimple //Inspire": {
"input": {
"required": {
"basic_pipe": [
"BASIC_PIPE"
],
"mask": [
"MASK"
],
"cfg": [
"FLOAT",
{
"default": 8.0,
"min": 0.0,
"max": 100.0
}
],
"sampler_name": [
[
"euler",
"euler_ancestral",
"heun",
"heunpp2",
"dpm_2",
"dpm_2_ancestral",
"lms",
"dpm_fast",
"dpm_adaptive",
"dpmpp_2s_ancestral",
"dpmpp_sde",
"dpmpp_sde_gpu",
"dpmpp_2m",
"dpmpp_2m_alt",
"dpmpp_2m_sde",
"dpmpp_2m_sde_gpu",
"dpmpp_3m_sde",
"dpmpp_3m_sde_gpu",
"ddpm",
"lcm",
"ddim",
"uni_pc",
"uni_pc_bh2"
]
],
"scheduler": [
[
"normal",
"karras",
"exponential",
"sgm_uniform",
"simple",
"ddim_uniform"
]
],
"wildcard_prompt": [
"STRING",
{
"multiline": true,
"dynamicPrompts": false,
"placeholder": "wildcard prompt"
}
],
"controlnet_in_pipe": [
"BOOLEAN",
{
"default": false,
"label_on": "Keep",
"label_off": "Override"
}
],
"sigma_factor": [
"FLOAT",
{
"default": 1.0,
"min": 0.0,
"max": 10.0,
"step": 0.01
}
]
}
},
"output": [
"REGIONAL_PROMPTS"
],
"output_is_list": [
false
],
"output_name": [
"REGIONAL_PROMPTS"
],
"name": "RegionalPromptSimple //Inspire",
"display_name": "Regional Prompt Simple (Inspire)",
"description": "",
"category": "InspirePack/Regional",
"output_node": false
},
"RegionalPromptColorMask //Inspire": {
"input": {
"required": {
"basic_pipe": [
"BASIC_PIPE"
],
"color_mask": [
"IMAGE"
],
"mask_color": [
"STRING",
{
"multiline": false,
"default": "#FFFFFF"
}
],
"cfg": [
"FLOAT",
{
"default": 8.0,
"min": 0.0,
"max": 100.0
}
],
"sampler_name": [
[
"euler",
"euler_ancestral",
"heun",
"heunpp2",
"dpm_2",
"dpm_2_ancestral",
"lms",
"dpm_fast",
"dpm_adaptive",
"dpmpp_2s_ancestral",
"dpmpp_sde",
"dpmpp_sde_gpu",
"dpmpp_2m",
"dpmpp_2m_alt",
"dpmpp_2m_sde",
"dpmpp_2m_sde_gpu",
"dpmpp_3m_sde",
"dpmpp_3m_sde_gpu",
"ddpm",
"lcm",
"ddim",
"uni_pc",
"uni_pc_bh2"
]
],
"scheduler": [
[
"normal",
"karras",
"exponential",
"sgm_uniform",
"simple",
"ddim_uniform"
]
],
"wildcard_prompt": [
"STRING",
{
"multiline": true,
"dynamicPrompts": false,
"placeholder": "wildcard prompt"
}
],
"controlnet_in_pipe": [
"BOOLEAN",
{
"default": false,
"label_on": "Keep",
"label_off": "Override"
}
],
"sigma_factor": [
"FLOAT",
{
"default": 1.0,
"min": 0.0,
"max": 10.0,
"step": 0.01
}
]
}
},
"output": [
"REGIONAL_PROMPTS",
"MASK"
],
"output_is_list": [
false,
false
],
"output_name": [
"REGIONAL_PROMPTS",
"MASK"
],
"name": "RegionalPromptColorMask //Inspire",
"display_name": "Regional Prompt By Color Mask (Inspire)",
"description": "",
"category": "InspirePack/Regional",
"output_node": false
},
"RegionalConditioningSimple //Inspire": {
"input": {
"required": {
"clip": [
"CLIP"
],
"mask": [
"MASK"
],
"strength": [
"FLOAT",
{
"default": 1.0,
"min": 0.0,
"max": 10.0,
"step": 0.01
}
],
"set_cond_area": [
[
"default",
"mask bounds"
]
],
"prompt": [
"STRING",
{
"multiline": true,
"placeholder": "prompt"
}
]
}
},
"output": [
"CONDITIONING"
],
"output_is_list": [
false
],
"output_name": [
"CONDITIONING"
],
"name": "RegionalConditioningSimple //Inspire",
"display_name": "Regional Conditioning Simple (Inspire)",
"description": "",
"category": "InspirePack/Regional",
"output_node": false
},
"RegionalConditioningColorMask //Inspire": {
"input": {
"required": {
"clip": [
"CLIP"
],
"color_mask": [
"IMAGE"
],
"mask_color": [
"STRING",
{
"multiline": false,
"default": "#FFFFFF"
}
],
"strength": [
"FLOAT",
{
"default": 1.0,
"min": 0.0,
"max": 10.0,
"step": 0.01
}
],
"set_cond_area": [
[
"default",
"mask bounds"
]
],
"prompt": [
"STRING",
{
"multiline": true,
"placeholder": "prompt"
}
]
}
},
"output": [
"CONDITIONING",
"MASK"
],
"output_is_list": [
false,
false
],
"output_name": [
"CONDITIONING",
"MASK"
],
"name": "RegionalConditioningColorMask //Inspire",
"display_name": "Regional Conditioning By Color Mask (Inspire)",
"description": "",
"category": "InspirePack/Regional",
"output_node": false
},
"RegionalIPAdapterMask //Inspire": {
"input": {
"required": {
"mask": [
"MASK"
],
"image": [
"IMAGE"
],
"weight": [
"FLOAT",
{
"default": 0.7,
"min": -1,
"max": 3,
"step": 0.05
}
],
"noise": [
"FLOAT",
{
"default": 0.5,
"min": 0.0,
"max": 1.0,
"step": 0.01
}
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],
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{
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"max": 1.0,
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],
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{
"default": 1.0,
"min": 0.0,
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],
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{
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]
},
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{
"default": false
}
],
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{
"default": 1.0,
"min": -1,
"max": 3,
"step": 0.05
}
],
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"add",
"subtract",
"average",
"norm average"
]
],
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]
}
},
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],
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false
],
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"REGIONAL_IPADAPTER"
],
"name": "RegionalIPAdapterMask //Inspire",
"display_name": "Regional IPAdapter Mask (Inspire)",
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"category": "InspirePack/Regional",
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},
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"input": {
"required": {
"color_mask": [
"IMAGE"
],
"mask_color": [
"STRING",
{
"multiline": false,
"default": "#FFFFFF"
}
],
"image": [
"IMAGE"
],
"weight": [
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{
"default": 0.7,
"min": -1,
"max": 3,
"step": 0.05
}
],
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{
"default": 0.5,
"min": 0.0,
"max": 1.0,
"step": 0.01
}
],
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[
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"linear",
"channel penalty"
]
],
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{
"default": 0.0,
"min": 0.0,
"max": 1.0,
"step": 0.001
}
],
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{
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"min": 0.0,
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"step": 0.001
}
],
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{
"default": false
}
]
},
"optional": {
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{
"default": false
}
],
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{
"default": 1.0,
"min": -1,
"max": 3,
"step": 0.05
}
],
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[
"concat",
"add",
"subtract",
"average",
"norm average"
]
],
"neg_image": [
"IMAGE"
]
}
},
"output": [
"REGIONAL_IPADAPTER",
"MASK"
],
"output_is_list": [
false,
false
],
"output_name": [
"REGIONAL_IPADAPTER",
"MASK"
],
"name": "RegionalIPAdapterColorMask //Inspire",
"display_name": "Regional IPAdapter By Color Mask (Inspire)",
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"category": "InspirePack/Regional",
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},
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"input": {
"required": {
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"MASK"
],
"embeds": [
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],
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{
"default": 0.7,
"min": -1,
"max": 3,
"step": 0.05
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],
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"linear",
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],
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],
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{
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"min": 0.0,
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"step": 0.001
}
],
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{
"default": false
}
]
},
"optional": {
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"EMBEDS"
]
}
},
"output": [
"REGIONAL_IPADAPTER"
],
"output_is_list": [
false
],
"output_name": [
"REGIONAL_IPADAPTER"
],
"name": "RegionalIPAdapterEncodedMask //Inspire",
"display_name": "Regional IPAdapter Encoded Mask (Inspire)",
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"category": "InspirePack/Regional",
"output_node": false
},
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"input": {
"required": {
"color_mask": [
"IMAGE"
],
"mask_color": [
"STRING",
{
"multiline": false,
"default": "#FFFFFF"
}
],
"embeds": [
"EMBEDS"
],
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{
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"min": -1,
"max": 3,
"step": 0.05
}
],
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[
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"linear",
"channel penalty"
]
],
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{
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"min": 0.0,
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}
],
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{
"default": 1.0,
"min": 0.0,
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}
],
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{
"default": false
}
]
},
"optional": {
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"EMBEDS"
]
}
},
"output": [
"REGIONAL_IPADAPTER",
"MASK"
],
"output_is_list": [
false,
false
],
"output_name": [
"REGIONAL_IPADAPTER",
"MASK"
],
"name": "RegionalIPAdapterEncodedColorMask //Inspire",
"display_name": "Regional IPAdapter Encoded By Color Mask (Inspire)",
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"category": "InspirePack/Regional",
"output_node": false
},
"RegionalSeedExplorerMask //Inspire": {
"input": {
"required": {
"mask": [
"MASK"
],
"noise": [
"NOISE"
],
"seed_prompt": [
"STRING",
{
"multiline": true,
"dynamicPrompts": false,
"pysssss.autocomplete": false
}
],
"enable_additional": [
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{
"default": true,
"label_on": "true",
"label_off": "false"
}
],
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{
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],
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{
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"min": 0.0,
"max": 1.0,
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}
],
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[
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"CPU"
]
]
}
},
"output": [
"NOISE"
],
"output_is_list": [
false
],
"output_name": [
"NOISE"
],
"name": "RegionalSeedExplorerMask //Inspire",
"display_name": "Regional Seed Explorer By Mask (Inspire)",
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"category": "InspirePack/Regional",
"output_node": false
},
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"input": {
"required": {
"color_mask": [
"IMAGE"
],
"mask_color": [
"STRING",
{
"multiline": false,
"default": "#FFFFFF"
}
],
"noise": [
"NOISE"
],
"seed_prompt": [
"STRING",
{
"multiline": true,
"dynamicPrompts": false,
"pysssss.autocomplete": false
}
],
"enable_additional": [
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{
"default": true,
"label_on": "true",
"label_off": "false"
}
],
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{
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"min": 0,
"max": 18446744073709551615
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],
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{
"default": 0.0,
"min": 0.0,
"max": 1.0,
"step": 0.01
}
],
"noise_mode": [
[
"GPU(=A1111)",
"CPU"
]
]
}
},
"output": [
"NOISE",
"MASK"
],
"output_is_list": [
false,
false
],
"output_name": [
"NOISE",
"MASK"
],
"name": "RegionalSeedExplorerColorMask //Inspire",
"display_name": "Regional Seed Explorer By Color Mask (Inspire)",
"description": "",
"category": "InspirePack/Regional",
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},
"ToIPAdapterPipe //Inspire": {
"input": {
"required": {
"ipadapter": [
"IPADAPTER"
],
"model": [
"MODEL"
]
},
"optional": {
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"CLIP_VISION"
],
"insightface": [
"INSIGHTFACE"
]
}
},
"output": [
"IPADAPTER_PIPE"
],
"output_is_list": [
false
],
"output_name": [
"IPADAPTER_PIPE"
],
"name": "ToIPAdapterPipe //Inspire",
"display_name": "ToIPAdapterPipe (Inspire)",
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"category": "InspirePack/Util",
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},
"FromIPAdapterPipe //Inspire": {
"input": {
"required": {
"ipadapter_pipe": [
"IPADAPTER_PIPE"
]
}
},
"output": [
"IPADAPTER",
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"CLIP_VISION",
"INSIGHTFACE"
],
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false,
false,
false
],
"output_name": [
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"model",
"clip_vision",
"insight_face"
],
"name": "FromIPAdapterPipe //Inspire",
"display_name": "FromIPAdapterPipe (Inspire)",
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"category": "InspirePack/Util",
"output_node": false
},
"ApplyRegionalIPAdapters //Inspire": {
"input": {
"required": {
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"IPADAPTER_PIPE"
],
"regional_ipadapter1": [
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]
}
},
"output": [
"MODEL"
],
"output_is_list": [
false
],
"output_name": [
"MODEL"
],
"name": "ApplyRegionalIPAdapters //Inspire",
"display_name": "Apply Regional IPAdapters (Inspire)",
"description": "",
"category": "InspirePack/Regional",
"output_node": false
},
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"input": {
"required": {
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"MODEL"
],
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{
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"max": 18446744073709551615
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],
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{
"default": 20,
"min": 1,
"max": 10000
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"dpmpp_2s_ancestral",
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"dpmpp_2m",
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"dpmpp_2m_sde",
"dpmpp_2m_sde_gpu",
"dpmpp_3m_sde",
"dpmpp_3m_sde_gpu",
"ddpm",
"lcm",
"ddim",
"uni_pc",
"uni_pc_bh2"
]
],
"scheduler": [
[
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"karras",
"exponential",
"sgm_uniform",
"simple",
"ddim_uniform"
]
],
"positive": [
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],
"negative": [
"CONDITIONING"
],
"latent_image": [
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],
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{
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"max": 1.0,
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}
],
"noise_mode": [
[
"GPU(=A1111)",
"CPU"
]
],
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{
"default": 1,
"min": 1,
"max": 10000
}
],
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{
"default": true,
"label_on": "True",
"label_off": "False"
}
]
}
},
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"LATENT"
],
"output_is_list": [
false,
false
],
"output_name": [
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"progress_latent"
],
"name": "KSamplerProgress //Inspire",
"display_name": "KSampler Progress (Inspire)",
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"category": "InspirePack/analysis",
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},
"KSamplerAdvancedProgress //Inspire": {
"input": {
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"MODEL"
],
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{
"default": true,
"label_on": "enable",
"label_off": "disable"
}
],
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{
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],
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{
"default": 20,
"min": 1,
"max": 10000
}
],
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{
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"min": 0.0,
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"round": 0.01
}
],
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"dpm_adaptive",
"dpmpp_2s_ancestral",
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"dpmpp_sde_gpu",
"dpmpp_2m",
"dpmpp_2m_alt",
"dpmpp_2m_sde",
"dpmpp_2m_sde_gpu",
"dpmpp_3m_sde",
"dpmpp_3m_sde_gpu",
"ddpm",
"lcm",
"ddim",
"uni_pc",
"uni_pc_bh2"
]
],
"scheduler": [
[
"normal",
"karras",
"exponential",
"sgm_uniform",
"simple",
"ddim_uniform"
]
],
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],
"negative": [
"CONDITIONING"
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"latent_image": [
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],
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{
"default": 0,
"min": 0,
"max": 10000
}
],
"end_at_step": [
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{
"default": 10000,
"min": 0,
"max": 10000
}
],
"noise_mode": [
[
"GPU(=A1111)",
"CPU"
]
],
"return_with_leftover_noise": [
"BOOLEAN",
{
"default": false,
"label_on": "enable",
"label_off": "disable"
}
],
"interval": [
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{
"default": 1,
"min": 1,
"max": 10000
}
],
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{
"default": false,
"label_on": "True",
"label_off": "False"
}
]
},
"optional": {
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]
}
},
"output": [
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"LATENT"
],
"output_is_list": [
false,
false
],
"output_name": [
"latent",
"progress_latent"
],
"name": "KSamplerAdvancedProgress //Inspire",
"display_name": "KSampler Advanced Progress (Inspire)",
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"category": "InspirePack/analysis",
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},
"CacheBackendData //Inspire": {
"input": {
"required": {
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"STRING",
{
"multiline": false,
"placeholder": "Input data key (e.g. 'model a', 'chunli lora', 'girl latent 3', ...)"
}
],
"tag": [
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{
"multiline": false,
"placeholder": "Tag: short description"
}
],
"data": [
"*"
]
}
},
"output": [
"*"
],
"output_is_list": [
false
],
"output_name": [
"data opt"
],
"name": "CacheBackendData //Inspire",
"display_name": "Cache Backend Data (Inspire)",
"description": "",
"category": "InspirePack/Backend",
"output_node": true
},
"CacheBackendDataNumberKey //Inspire": {
"input": {
"required": {
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"INT",
{
"default": 0,
"min": 0,
"max": 18446744073709551615
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],
"tag": [
"STRING",
{
"multiline": false,
"placeholder": "Tag: short description"
}
],
"data": [
"*"
]
}
},
"output": [
"*"
],
"output_is_list": [
false
],
"output_name": [
"data opt"
],
"name": "CacheBackendDataNumberKey //Inspire",
"display_name": "Cache Backend Data [NumberKey] (Inspire)",
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"category": "InspirePack/Backend",
"output_node": true
},
"CacheBackendDataList //Inspire": {
"input": {
"required": {
"key": [
"STRING",
{
"multiline": false,
"placeholder": "Input data key (e.g. 'model a', 'chunli lora', 'girl latent 3', ...)"
}
],
"tag": [
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{
"multiline": false,
"placeholder": "Tag: short description"
}
],
"data": [
"*"
]
}
},
"output": [
"*"
],
"output_is_list": [
true
],
"output_name": [
"data opt"
],
"name": "CacheBackendDataList //Inspire",
"display_name": "Cache Backend Data List (Inspire)",
"description": "",
"category": "InspirePack/Backend",
"output_node": true
},
"CacheBackendDataNumberKeyList //Inspire": {
"input": {
"required": {
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"INT",
{
"default": 0,
"min": 0,
"max": 18446744073709551615
}
],
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"STRING",
{
"multiline": false,
"placeholder": "Tag: short description"
}
],
"data": [
"*"
]
}
},
"output": [
"*"
],
"output_is_list": [
true
],
"output_name": [
"data opt"
],
"name": "CacheBackendDataNumberKeyList //Inspire",
"display_name": "Cache Backend Data List [NumberKey] (Inspire)",
"description": "",
"category": "InspirePack/Backend",
"output_node": true
},
"RetrieveBackendData //Inspire": {
"input": {
"required": {
"key": [
"STRING",
{
"multiline": false,
"placeholder": "Input data key (e.g. 'model a', 'chunli lora', 'girl latent 3', ...)"
}
]
}
},
"output": [
"*"
],
"output_is_list": [
true
],
"output_name": [
"data"
],
"name": "RetrieveBackendData //Inspire",
"display_name": "Retrieve Backend Data (Inspire)",
"description": "",
"category": "InspirePack/Backend",
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},
"RetrieveBackendDataNumberKey //Inspire": {
"input": {
"required": {
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"INT",
{
"default": 0,
"min": 0,
"max": 18446744073709551615
}
]
}
},
"output": [
"*"
],
"output_is_list": [
true
],
"output_name": [
"data"
],
"name": "RetrieveBackendDataNumberKey //Inspire",
"display_name": "Retrieve Backend Data [NumberKey] (Inspire)",
"description": "",
"category": "InspirePack/Backend",
"output_node": false
},
"RemoveBackendData //Inspire": {
"input": {
"required": {
"key": [
"STRING",
{
"multiline": false,
"placeholder": "Input data key ('*' = clear all)"
}
]
},
"optional": {
"signal_opt": [
"*"
]
}
},
"output": [
"*"
],
"output_is_list": [
false
],
"output_name": [
"signal"
],
"name": "RemoveBackendData //Inspire",
"display_name": "Remove Backend Data (Inspire)",
"description": "",
"category": "InspirePack/Backend",
"output_node": true
},
"RemoveBackendDataNumberKey //Inspire": {
"input": {
"required": {
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"INT",
{
"default": 0,
"min": 0,
"max": 18446744073709551615
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]
},
"optional": {
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]
}
},
"output": [
"*"
],
"output_is_list": [
false
],
"output_name": [
"signal"
],
"name": "RemoveBackendDataNumberKey //Inspire",
"display_name": "Remove Backend Data [NumberKey] (Inspire)",
"description": "",
"category": "InspirePack/Backend",
"output_node": true
},
"ShowCachedInfo //Inspire": {
"input": {
"required": {
"cache_info": [
"STRING",
{
"multiline": true,
"default": ""
}
],
"key": [
"STRING",
{
"multiline": false,
"default": ""
}
]
},
"hidden": {
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}
},
"output": [],
"output_is_list": [],
"output_name": [],
"name": "ShowCachedInfo //Inspire",
"display_name": "Show Cached Info (Inspire)",
"description": "",
"category": "InspirePack/Backend",
"output_node": true
},
"CheckpointLoaderSimpleShared //Inspire": {
"input": {
"required": {
"ckpt_name": [
[
"ghostmix_v20Bakedvae.safetensors",
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]
],
"key_opt": [
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{
"multiline": false,
"placeholder": "If empty, use 'ckpt_name' as the key."
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]
},
"optional": {
"mode": [
[
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"Override Cache",
"Read Only"
]
]
}
},
"output": [
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"CLIP",
"VAE",
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],
"output_is_list": [
false,
false,
false,
false
],
"output_name": [
"model",
"clip",
"vae",
"cache key"
],
"name": "CheckpointLoaderSimpleShared //Inspire",
"display_name": "Shared Checkpoint Loader (Inspire)",
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"category": "InspirePack/Backend",
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},
"StableCascade_CheckpointLoader //Inspire": {
"input": {
"required": {
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[
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],
{
"default": "ghostmix_v20Bakedvae.safetensors"
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],
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{
"multiline": false,
"placeholder": "If empty, use 'stage_b' as the key."
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],
"stage_c": [
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"min": 0.0,
"max": 1000.0,
"step": 0.01
}
],
"width": [
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{
"default": 1024.0,
"min": 0,
"max": 16384
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],
"height": [
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"default": 1024.0,
"min": 0,
"max": 16384
}
],
"crop_w": [
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],
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"target_width": [
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"min": 0,
"max": 16384
}
],
"target_height": [
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{
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"min": 0,
"max": 16384
}
],
"text_g": [
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{
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"placeholder": "CLIP_G"
}
],
"text_l": [
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{
"multiline": true,
"placeholder": "CLIP_L"
}
]
},
"optional": {
"smZ_steps": [
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{
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"min": 1,
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}
]
}
},
"output": [
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"output_is_list": [
false
],
"output_name": [
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],
"name": "smZ CLIPTextEncode",
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},
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{
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},
"optional": {
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}
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"\u3164": [
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{
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"min": 0,
"max": 74,
"step": 1
}
],
"enable_emphasis": [
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{
"default": true
}
],
"info_RNG": [
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{
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"placeholder": "Random number generator source.\nchanges seeds drastically; use CPU to produce the same picture across different videocard vendors; use NV to produce same picture as on NVidia videocards"
}
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"RNG": [
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{
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],
"\u3164\u3164": [
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}
],
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"placeholder": "Disable NaN check in produced images/latent spaces. Only for CFGDenoiser."
}
],
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{
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"\u3164\u3164\u3164": [
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"default": "Sampler parameters",
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}
],
"info_eta_ancestral": [
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{
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"placeholder": "Eta for k-diffusion samplers\nnoise multiplier; currently only applies to ancestral samplers (i.e. Euler a) and SDE samplers"
}
],
"eta": [
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{
"default": 1.0,
"min": 0.0,
"max": 1.0,
"step": 0.01
}
],
"info_s_churn": [
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{
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"placeholder": "Sigma churn\namount of stochasticity; only applies to Euler, Heun, Heun++2, and DPM2"
}
],
"s_churn": [
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{
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"min": 0.0,
"max": 100.0,
"step": 0.01
}
],
"info_s_tmin": [
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{
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"placeholder": "Sigma tmin\nenable stochasticity; start value of the sigma range; only applies to Euler, Heun, Heun++2, and DPM2'"
}
],
"s_tmin": [
"FLOAT",
{
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"min": 0.0,
"max": 10.0,
"step": 0.01
}
],
"info_s_tmax": [
"STRING",
{
"multiline": true,
"placeholder": "Sigma tmax\n0 = inf; end value of the sigma range; only applies to Euler, Heun, Heun++2, and DPM2"
}
],
"s_tmax": [
"FLOAT",
{
"default": 0.0,
"min": 0.0,
"max": 999.0,
"step": 0.01
}
],
"info_s_noise": [
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{
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"placeholder": "Sigma noise\namount of additional noise to counteract loss of detail during sampling"
}
],
"s_noise": [
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{
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"min": 0.0,
"max": 1.1,
"step": 0.001
}
],
"info_eta_noise_seed_delta": [
"STRING",
{
"multiline": true,
"placeholder": "Eta noise seed delta\ndoes not improve anything, just produces different results for ancestral samplers - only useful for reproducing images"
}
],
"ENSD": [
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{
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"min": 0,
"max": 18446744073709551615,
"step": 1
}
],
"info_sgm_noise_multiplier": [
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{
"multiline": true,
"placeholder": "SGM noise multiplier\nmatch initial noise to official SDXL implementation - only useful for reproducing images\nsee https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12818"
}
],
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{
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}
],
"info_upcast_sampling": [
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{
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}
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{
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}
],
"\u3164\u3164\u3164\u3164": [
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}
],
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{
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],
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}
],
"info_batch_cond_uncond": [
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}
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"\u3164\u3164\u3164\u3164\u3164": [
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}
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"info_use_prev_scheduling": [
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{
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"placeholder": "Previous prompt editing timelines\nFor [red:green:N]; previous: If N < 1, it's a fraction of steps (and hires fix uses range from 0 to 1), if N >= 1, it's an absolute number of steps; new: If N has a decimal point in it, it's a fraction of steps (and hires fix uses range from 1 to 2), othewrwise it's an absolute number of steps"
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}
],
"info_use_CFGDenoiser": [
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"placeholder": "CFGDenoiser\nAn experimental option to use stable-diffusion-webui's denoiser. It allows you to use the 'Optimizations' settings listed here."
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{
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}
],
"info_debug": [
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{
"multiline": true,
"placeholder": "Debugging messages in the console."
}
],
"debug": [
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{
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"label_on": "on",
"label_off": "off"
}
]
}
},
"output": [
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],
"output_is_list": [
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],
"output_name": [
"*"
],
"name": "smZ Settings",
"display_name": "Settings (smZ)",
"description": "",
"category": "advanced",
"output_node": false
},
"SaveImageWebsocket": {
"input": {
"required": {
"images": [
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]
}
},
"output": [],
"output_is_list": [],
"output_name": [],
"name": "SaveImageWebsocket",
"display_name": "SaveImageWebsocket",
"description": "",
"category": "api/image",
"output_node": true
},
"NNLatentUpscale": {
"input": {
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"latent": [
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"version": [
[
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"SD 1.x"
]
],
"upscale": [
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{
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"min": 1.0,
"max": 2.0,
"step": 0.01,
"display": "number"
}
]
}
},
"output": [
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],
"output_is_list": [
false
],
"output_name": [
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],
"name": "NNLatentUpscale",
"display_name": "NNLatentUpscale",
"description": "",
"category": "latent",
"output_node": false
},
"SDXLPromptStylerAll": {
"input": {
"required": {
"text_positive": [
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{
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"multiline": true
}
],
"text_negative": [
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}
],
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"Allen Williams",
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"Allison Bechdel",
"Alma Thomas",
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"Alphonse Mucha",
"Alphonse Osbert",
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"Alvin Langdon Coburn",
"Alyssa Monks",
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"Ann Stookey",
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"Anna Bocek",
"Anna Dittmann",
"Anna Razumovskaya",
"Anna and Elena Balbusso",
"Anne Bachelier",
"Anne Brigman",
"Anne Dewailly",
"Anne Mccaffrey",
"Anne Packard",
"Anne Rothenstein",
"Anne Stokes",
"Anne Sudworth",
"Anne Truitt",
"Anne-Louis Girodet",
"Anni Albers",
"Annibale Carracci",
"Annick Bouvattier",
"Annie Soudain",
"Annie Swynnerton",
"Ansel Adams",
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"Antanas Sutkus",
"Anthony Gerace",
"Anthony Thieme",
"Anthony van Dyck",
"Anto Carte",
"Antoine Blanchard",
"Antoine Verney-Carron",
"Anton Corbijn",
"Anton Domenico Gabbiani",
"Anton Fadeev",
"Anton Mauve",
"Anton Otto Fischer",
"Anton Pieck",
"Anton Raphael Mengs",
"Anton Semenov",
"Antonello da Messina",
"Antoni Gaudi",
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"Cicely Mary Barker",
"Cindy Sherman",
"Claire Hummel",
"Clara Miller Burd",
"Clara Peeters",
"Clarence Holbrook Carter",
"Claude Cahun",
"Claude Monet",
"Clemens Ascher",
"Cliff Chiang",
"Clive Madgwick",
"Clovis Trouille",
"Clyde Caldwell",
"Coby Whitmore",
"Coles Phillips",
"Colin Geller",
"Conor Harrington",
"Conrad Roset",
"Constant Permeke",
"Constantin Brancusi",
"Cory Arcangel",
"Cory Loftis",
"Costa Dvorezky",
"Craig Davison",
"Craig Mullins",
"Craig Wylie",
"Craola",
"Cuno Amiet",
"Cyril Rolando",
"Dain Yoon",
"Dale Chihuly",
"Damien Hirst",
"Dan Flavin",
"Dan Mumford",
"Dan Witz",
"Daniel Buren",
"Daniel Clowes",
"Daniel Garber",
"Daniel Merriam",
"Daniel Ridgway Knight",
"Daniela Uhlig",
"Daniele Afferni",
"Dante Gabriel Rossetti",
"Dao Le Trong",
"Dariusz Zawadzki",
"Darren Bacon",
"Darwyn Cooke",
"Daryl Mandryk",
"Dave Dorman",
"Dave Gibbons",
"Dave McKean",
"David A. Hardy",
"David Aja",
"David B. Mattingly",
"David Bomberg",
"David Bowie",
"David Burdeny",
"David Burliuk",
"David Choe",
"David Driskell",
"David Finch",
"David Hockney",
"David Inshaw",
"David Ligare",
"David Lynch",
"David McClellan",
"David Palumbo",
"David Shrigley",
"David Spriggs",
"David Teniers the Younger",
"David Wiesner",
"Dean Cornwell",
"Dean Ellis",
"Death Burger",
"Debbie Criswell",
"Derek Boshier",
"Desmond Morris",
"Diane Arbus",
"Diane Dillon",
"Dick Bickenbach",
"Diego Dayer",
"Diego Rivera",
"Diego Vel\u00e1zquez",
"Dmitry Kustanovich",
"Don Bluth",
"Don Maitz",
"Donald Judd",
"Donato Giancola",
"Dora Carrington",
"Dora Maar",
"Dorina Costras",
"Dorothea Tanning",
"Dorothy Lathrop",
"Doug Chiang",
"Douglas Smith",
"Dr. Seuss",
"Dunkelbunt Hundertwasser\u201d",
"Dustin Nguyen",
"Duy Huynh",
"E. H. Shepard",
"Earl Norem",
"Ed Benedict",
"Ed Binkley",
"Ed Brubaker",
"Ed Emshwiller",
"Ed Freeman",
"Ed Mell",
"Ed Roth",
"Eddie Campbell",
"Eddie Mendoza",
"Edgar Degas",
"Edmund Dulac",
"Edmund Leighton",
"Edouard Manet",
"Edouard Riou",
"Eduardo Kobra",
"Edvard Munch",
"Edward Atkinson Hornel",
"Edward Burne-Jones",
"Edward Gorey",
"Edward Hopper",
"Edward John Poynter",
"Edward Julius Detmold",
"Edward Lear",
"Edward Robert Hughes",
"Edward Steichen",
"Edward Weston",
"Edwin Austin Abbey",
"Edwin Deakin",
"Edwin Henry Landseer",
"Eero Saarinen",
"Egon Schiele",
"Eiichiro Oda",
"Eileen Agar",
"El Greco",
"Elaine de Kooning",
"Eleanor Vere Boyle",
"Elenore Abbott",
"Eliott Lilly",
"Elizabeth Gadd",
"Elizabeth Shippen Green",
"Ellen Gallagher",
"Ellen Jewett",
"Elliot Lilly",
"Elsa Beskow",
"Emil Alzamora",
"Emil Ferris",
"Emil Nolde",
"Emilia Wilk",
"Emily Kame Kngwarreye",
"Emma Geary",
"Emmanuel Shiu",
"Emmanuelle Moureaux",
"Emory Douglas",
"Enki Bilal",
"Ephraim Moses Lilien",
"Eric Fischl",
"Eric Wallis",
"Eric Zener",
"Erich Heckel",
"Erin Hanson",
"Ernest Crichlow",
"Ernie Barnes",
"Ernst Fuchs",
"Ernst Haas",
"Ernst Haeckel",
"Ernst Ludwig Kirchner",
"Esao Andrews",
"Etel Adnan",
"Ethan Van Sciver",
"Etienne Hebinger",
"Ettore Sottsass",
"Ettore Tito",
"Eugene Delacroix",
"Eugene von Guerard",
"Eug\u00e8ne Grasset",
"Evelyn De Morgan",
"Everett Raymond Kinstler",
"Evgeni Gordiets",
"Ewald R\u00fcbsamen",
"Eyvind Earle",
"F Scott Hess",
"Fabian Perez",
"Fabio Hurtado",
"Fairfield Porter",
"Faith 47",
"Faith Ringgold",
"Fang Lijun",
"Farel Dalrymple",
"Fenghua Zhong",
"Ferdinand Hodler",
"Ferdinand Knab",
"Ferdinand Van Kessel",
"Fernand Khnopff",
"Fernand Toussaint",
"Fernando Herenu",
"Filip Hodas",
"Filippino Lippi",
"Filippo Balbi",
"Flora Borsi",
"Ford Madox Brown",
"Francesca Woodman",
"Francis Bacon",
"Francis Coates Jones",
"Francis Picabia",
"Francisco De Goya",
"Francisco Mart\u00edn",
"Frank Auerbach",
"Frank Frazetta",
"Frank Gehry",
"Frank Holl",
"Frank Lloyd Wright",
"Frank Miller",
"Frank Stella",
"Frank Tinsley",
"Frank Xavier Leyendecker",
"Franklin Booth",
"Franz Marc",
"Franz Sedlacek",
"Franz Xaver Winterhalter",
"Frederic Church",
"Frederic Remington",
"Frederick Lord Leighton",
"Frederick McCubbin",
"Frida Kahlo",
"Frits Van den Berghe",
"Gabriel Dawe",
"Gabriele M\u00fcnter",
"Gaetano Pesce",
"Galan Pang",
"Gareth Pugh",
"Gary Larson",
"Gaston Bussi\u00e8re",
"Gediminas Pranckevicius",
"Genndy Tartakovsky",
"Geof Darrow",
"Georg Jensen",
"Georg Karl Pfahler",
"George Ault",
"George Cruikshank",
"George Dionysus Ehret",
"George Frederic Watts",
"George French Angas",
"George Grosz",
"George Herriman",
"George Inness",
"George Lucas",
"George Luks",
"George Stubbs",
"George Tooker",
"Georges Rouault",
"Georges Seurat",
"Georges de La Tour",
"Georgia O\u2019Keeffe",
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"Gerda Wegener",
"Gerhard Munthe",
"Gerhard Richter",
"Gertrude Abercrombie",
"Giacomo Balla",
"Gianluca Foli",
"Gifford Beal",
"Gil Elvgren",
"Gilbert Stuart",
"Giorgio De Chirico",
"Giotto Di Bondone",
"Giovanni Battista Bracelli",
"Giovanni Battista Gaulli",
"Giovanni Battista Piranesi",
"Giovanni Battista Venanzi",
"Giovanni da Udina",
"Giuseppe Arcimboldo",
"Giuseppe de Nittis",
"Gjon Mili",
"Glen Orbik",
"Glenn Fabry",
"Gloria Stoll Karn",
"Go Nagai",
"Gordon Browne",
"Gordon Parks",
"Goro Fujita",
"Grace Cossington Smith",
"Grace Popp",
"Grandma Moses",
"Grant Wood",
"Grayson Perry",
"Greg Girard",
"Greg Hildebrandt",
"Greg Rutkowski",
"Greg Simkins",
"Gregory Crewdson",
"Guerrilla Girls",
"Guido Borelli Da Caluso",
"Guido Crepax",
"Guillermo del Toro",
"Guo Pei",
"Gustaf Tenggren",
"Gustav Klimt",
"Gustave Buchet",
"Gustave Courbet",
"Gustave Dor\u00e9",
"Gustave Moreau",
"Gustave Van de Woestijne",
"Guy Billout",
"Gwen John",
"Gwenda Morgan",
"H. R. (Hans Ruedi) Giger",
"H.P. Lovecraft",
"Haddon Sundblom",
"Hajime Sorayama",
"Hal Foster",
"Hale Woodruff",
"Hanna-Barbera",
"Hannah Hoch",
"Hans Arnold",
"Hans Baldung",
"Hans Baluschek",
"Hans Bellmer",
"Harold McCauley",
"Haroon Mirza",
"Harriet Backer",
"Harry Clarke",
"Hasui Kawase",
"Hayao Miyazaki",
"Hayv Kahraman",
"Hein Gorny",
"Heinrich Kley",
"Heinrich Lefler",
"Heinz Edelmann",
"Helen Frankenthaler",
"Helene Knoop",
"Helene Schjerfbeck",
"Helio Oiticica",
"Helmut Newton",
"Hendrick Avercamp",
"Hendrick Cornelisz Vroom",
"Hendrick Goltzius",
"Hendrik Kerstens",
"Henri De Toulouse Lautrec",
"Henri Fantin Latour",
"Henri Matisse",
"Henri Rousseau",
"Henri-Edmond Cross",
"Henriette Grindat",
"Henry Asencio",
"Henry Fuseli",
"Henry Moore",
"Henry Moret",
"Henry Ossawa Tanner",
"Henry Raleigh",
"Herbert List",
"Herve Groussin",
"Herv\u00e9 Guibert",
"Hethe Srodawa",
"Hieronymus Bosch",
"Hikari Shimoda",
"Hilma AF Klint",
"Hirohiko Araki",
"Hiroshi Nagai",
"Hiroshi Sugimoto",
"Hiroshi Yoshida",
"Honor C. Appleton",
"Honor\u00e9 Daumier",
"Hope Gangloff",
"Horace Vernet",
"Hou China",
"Howard Chandler Christy",
"Howard Finster",
"Howard Hodgkin",
"Howard Pyle",
"Hsiao-Ron Cheng",
"Hubert Robert",
"Hugh Ferriss",
"Hugh Kretschmer",
"Hyacinthe Rigaud",
"Iain Faulkner",
"Ian McQue",
"Ian Miller",
"Ida Rentoul Outhwaite",
"Igor Morski",
"Igor Wolski",
"Igor Zenin",
"Ilya Kuvshinov",
"Ilya Repin",
"Incarcerated Jerkfaces",
"Ingrid Baars",
"Inio Asano",
"Irma Stern",
"Iryna Yermolova",
"Isaac Cordal",
"Isaac Levitan",
"Ismail Inceoglu",
"Issac Levitan",
"Istvan Banyai",
"It\u014d Jakuch\u016b",
"Ivan Aivazovsky",
"Ivan Albright",
"Ivan Bilibin",
"Ivan Shishkin",
"Iwan Baan",
"J. J. Grandville",
"J.C. Leyendecker",
"J.M.W. Turner",
"JC Leyendecker",
"Jacek Yerka",
"Jack Butler Yeats",
"Jack Davis",
"Jack Gaughan",
"Jack Kirby",
"Jackson Pollock",
"Jacob Hashimoto",
"Jacob Lawrence",
"Jacob van Ruisdael",
"Jacques Le Moyne",
"Jacques Nathan-Garamond",
"Jake Parker",
"Jakub R\u00f3\u017calski",
"James Abbott McNeill Whistler",
"James C Christensen",
"James Ensor",
"James Gilleard",
"James Gillray",
"James Gurney",
"James Jean",
"James Montgomery Flagg",
"James Paick",
"James Stokoe",
"James Thomas Watts",
"James Tissot",
"James Turrell",
"Jamie Baldridge",
"Jamie Hawkesworth",
"Jamie Hewlett",
"Jamie McKelvie",
"Jamini Roy",
"Jan Brett",
"Jan Luyken",
"Jan Pietersz Saenredam",
"Jan Van Eyck",
"Jan van Kessel the Elder",
"Jane Graverol",
"Jane Newland",
"Janek Sedlar",
"Jasmine Becket-Griffith",
"Jason A. Engle",
"Jason Chan",
"Jason Edmiston",
"Jasper Johns",
"Jaume Plensa",
"Jaya Suberg",
"Jean Arp",
"Jean Auguste Dominique Ingres",
"Jean Bourdichon",
"Jean Delville",
"Jean Dubuffet",
"Jean Fouquet",
"Jean Giraud",
"Jean Jullien",
"Jean Marc Nattier",
"Jean Metzinger",
"Jean Nouvel",
"Jean-Antoine Watteau",
"Jean-Baptiste Monge",
"Jean-Fran\u00e7ois Millet",
"Jean-Honor\u00e9 Fragonard",
"Jean-Louis Prevost",
"Jean-L\u00e9on G\u00e9r\u00f4me",
"Jean-Michel Basquiat",
"Jean-Paul Riopelle",
"Jeanloup Sieff",
"Jeannette Guichard-Bunel",
"Jed Henry",
"Jef Wu",
"Jeff Easley",
"Jeff Goldblum",
"Jeff Kinney",
"Jeff Koons",
"Jeff Legg",
"Jeff Lemire",
"Jeff Simpson",
"Jeff Wall",
"Jeffrey Catherine Jones",
"Jeffrey Smith art",
"Jeffrey T. Larson",
"Jenny Saville",
"JennyBird Alcantara",
"Jeremiah Ketner",
"Jeremy Geddes",
"Jeremy Lipking",
"Jeremy Mann",
"Jerry Pinkney",
"Jerry Siegel",
"Jerzy Duda-Gracz",
"Jesper Ejsing",
"Jessica Rossier",
"Jessica Woulfe",
"Jessie Willcox Smith",
"Jhonen Vasquez",
"Jillian Tamaki",
"Jim Burns",
"Jim Davis",
"Jim Lee",
"Jim Mahfood",
"Jim Woodring",
"Jimmy Ernst",
"Jimmy Lawlor",
"Joachim Brohm",
"Joan Mir\u00f3",
"Joan Tuset",
"Joanna Kustra",
"Joao Ruas",
"Joaqu\u00edn Sorolla",
"Joe Bowler",
"Joe De Mers",
"Joe Fenton",
"Joe Jusko",
"Joe Madureira",
"Joe Webb",
"Joel Meyerowitz",
"Joel Sternfeld",
"Joey Chou",
"Johann Wolfgang von Goethe",
"Johannes Itten",
"Johannes Vermeer",
"Johannes Voss",
"Johfra Bosschart",
"John Anster Fitzgerald",
"John Atherton",
"John Atkinson Grimshaw",
"John Bauer",
"John Berkey",
"John Blanche",
"John Bratby",
"John Cassaday",
"John Constable",
"John Currin",
"John Duncan",
"John Frederick Kensett",
"John French Sloan",
"John Harris",
"John Howe",
"John Hoyland",
"John James Audubon",
"John Kenn Mortensen",
"John La Farge",
"John Lavery",
"John Martin",
"John Perceval",
"John Philip Falter",
"John Salminen",
"John Singer Sargent",
"John Singleton Copley",
"John Stezaker",
"John Totleben",
"John Wayne Gacy",
"John Whitcomb",
"John Wilhelm",
"John William Waterhouse",
"Jon Klassen",
"Jon McCoy",
"Jon Whitcomb",
"Jordan Grimmer",
"Jorge Jacinto",
"Josan Gonzalez",
"Josef Albers",
"Joseph Cornell",
"Joseph Ducreux",
"Joseph Lorusso",
"Joseph Mallord William Turner",
"Joseph Stella",
"Josephine Wall",
"Josh Kao",
"Josh Keyes",
"Jos\u00e9 Clemente Orozco",
"Jovana Rikalo",
"Juan Gris",
"Judy Chicago",
"Juergen Teller",
"Jules Bastien-Lepage",
"Julia Contacessi",
"Julian Calle",
"Juliana Huxtable",
"Julie Bell",
"Julie Blackmon",
"Julie Mehretu",
"Julien Delval",
"Julius Horsthuis",
"Jun Kaneko",
"Junji Ito",
"Justin Gerard",
"J\u00f3zef Mehoffer",
"Kadir Nelson",
"Kaethe Butcher",
"Kapwani Kiwanga",
"Karel Appel",
"Karel Thole",
"Karen Wallis",
"Karl Blossfeldt",
"Karl Schmidt-Rottluff",
"Karol Bak",
"Kasia Nowowiejska",
"Kate Beaton",
"Kate Greenaway",
"Kathryn Morris Trotter",
"Kati Horna",
"Katsuhiro Otomo",
"Katsushika Hokusai",
"Kawanabe Ky\u014dsai",
"Kaws",
"Kay Nielsen",
"Kay Sage",
"Kazimir Malevich",
"Kazuo Koike",
"Kehinde Wiley",
"Keith Haring",
"Keith Negley",
"Keith Parkinson",
"Kelly Freas",
"Kelly Mckernan",
"Kelly Sue Deconnick",
"Kelly Vivanco",
"Ken Fairclough",
"Ken Kelly",
"Ken Sugimori",
"Kengo Kuma",
"Kenne Gregoire",
"Kent Monkman",
"Kentaro Miura",
"Kevin Gnutzmans",
"Kevin Sloan",
"Kieron Gillen",
"Kilian Eng",
"Kim Jung Gi",
"Kim Keever",
"Kitagawa Utamaro",
"Kitty Lange Kielland",
"Klaus Burgle",
"Klaus Janson",
"Klaus Wittmann",
"Kobayashi Kiyochika",
"Konstantin Korovin",
"Konstantin Yuon",
"Koson Ohara",
"Krenz Cushart",
"Kris Kuksi",
"Kuang Hong",
"Kunisada",
"Kuno Veeber",
"Kurzgesagt",
"K\u00e4the Kollwitz",
"L. Birge Harrison",
"Lady Pink",
"Larry Elmore",
"Larry Poons",
"Larry Sultan",
"Laurel Burch",
"Laurent Grasso",
"Laurie Greasley",
"Laurie Lipton",
"Lawren Harris",
"Lee Krasner",
"Lee Madgwick",
"Lee Quinones",
"Leiji Matsumoto",
"Leon Kossoff",
"Leonardo Da Vinci",
"Leonetto Cappiello",
"Leonid Afremov",
"Leonora Carrington",
"Les Edwards",
"Lesley Vance",
"Leticia Gillett",
"Liam Wong",
"Liang Mark",
"Lisa Frank",
"Lisa Keene",
"Liu Ye",
"Liubov Sergeevna Popova",
"Lois van Baarle",
"Loish",
"Lorena Alvarez G\u00f3mez",
"Lorenz Hideyoshi",
"Loretta Lux",
"Lori Earley",
"Louis Comfort Tiffany",
"Louis Glackens",
"Louis Icart",
"Louis Janmot",
"Louis Rhead",
"Louis Wain",
"Louise Bourgeois",
"Louise Dahl-Wolfe",
"Lovis Corinth",
"Luca Boni",
"Lucas Cranach the Elder",
"Lucian Freud",
"Lucy Madox Brown",
"Ludwig Mies van der Rohe",
"Luis Royo",
"Luisa Russo",
"Lynd Ward",
"Lynda Barry",
"Lynda Benglis",
"Lyonel Feininger",
"Lyubov Popova",
"L\u00e1szl\u00f3 Moholy-Nagy",
"M.C. Escher",
"M.W. Kaluta",
"Mab Graves",
"Maginel Wright Enright Barney",
"Magnus Enckell",
"Makoto Shinkai",
"Malcolm Liepke",
"Man Ray",
"Mandy Disher",
"Mao Hamaguchi",
"Marat Latypov",
"Marc Chagall",
"Marc Davis",
"Marc Samson",
"Marc Simonetti",
"Marcin Jakubowski",
"Marco Mazzoni",
"Marcus Selmer",
"Marek Okon",
"Margaret Brundage",
"Margaret Macdonald Mackintosh",
"Margaret Mee",
"Margaux Valonia",
"Maria Kreyn",
"Maria Pascual Alberich",
"Maria Sibylla Merian",
"Marianne North",
"Marianne von Werefkin",
"Marie Guillemine Benoist",
"Marie Spartali Stillman",
"Marina Abramovi\u0107",
"Marius Borgeaud",
"Marjane Satrapi",
"Mark Arian",
"Mark Briscoe",
"Mark Brooks",
"Mark Keathley",
"Mark Lovett",
"Mark Rothko",
"Mark Ryden",
"Mark Seliger",
"Marsden Hartley",
"Martin Ansin",
"Martin Deschambault",
"Martin John Heade",
"Martin Johnson Heade",
"Martin Kippenberger",
"Martine Johanna",
"Martiros Saryan",
"Mary Anning",
"Mary Blair",
"Mary Cassatt",
"Masaaki Sasamoto",
"Masamune Shirow",
"Mat Collishaw",
"Mati Klarwein",
"Matias Hannecke",
"Matt Bors",
"Matt Fraction",
"Matt Groening",
"Matthias Gr\u00fcnewald",
"Matthias Jung",
"Matti Suuronen",
"Maurice Sendak",
"Max Beckmann",
"Max Dupain",
"Max Ernst",
"Max Pechstein",
"Max Weber",
"Maxfield Parrish",
"Maximilian Pirner",
"Maximilien Luce",
"Maxwell Boas",
"Mead Schaeffer",
"Meryl McMaster",
"Michael Carson",
"Michael Cheval",
"Michael Deforge",
"Michael Heizer",
"Michael Hutter",
"Michael Parkes",
"Michael Sowa",
"Michael Whelan",
"Michal Karcz",
"Michal Lisowski",
"Michelangelo Buonarroti",
"Michelangelo Merisi Da Caravaggio",
"Mickalene Thomas",
"Miho Hirano",
"Mikalojus Konstantinas Ciurlionis",
"Mike Campau",
"Mike Deodato",
"Mike Mayhew",
"Mike Mignola",
"Mike Winkelmann (Beeple)",
"Mike Worrall",
"Mikhail Larionov",
"Mikhail Nesterov",
"Mikhail Vrubel",
"Mikko Lagerstedt",
"Milo Manara",
"Milton Avery",
"Milton Caniff",
"Milton Glaser",
"Miriam Schapiro",
"Moebius",
"Mordecai Ardon",
"Mort Kunstler",
"Muxxi",
"M\u00e9ret Oppenheim",
"NC Wyeth",
"NHK Animation",
"Nagel Patrick",
"Nan Goldin",
"Naoki Urasawa",
"Naoko Takeuchi",
"Naomi Okubo",
"Naoto Hattori",
"Natalia Goncharova",
"Nathan Coley",
"Nathan Wirth",
"Neil Boyle",
"Neil Welliver",
"Nele Zirnite",
"Ni Chuanjing",
"Nicholas Roerich",
"Nick Knight",
"Nick Sharratt",
"Nick Silva",
"Nicola Samori",
"Nicolas Delort",
"Nicolas Mignard",
"Nicolas de Stael",
"Nikolai Ge",
"Nikolina Petolas",
"Noah Bradley",
"Nobuyoshi Araki",
"Noelle Stevenson",
"Noriyoshi Ohrai",
"Norman Ackroyd",
"Norman Bluhm",
"Norman Foster",
"Norman Rockwell",
"OSGEMEOS",
"Octavio Ocampo",
"Odd Nerdrum",
"Odilon Redon",
"Ogawa Kazumasa",
"Ohara Koson",
"Olafur Eliasson",
"Oleg Oprisco",
"Olga Skomorokhova",
"Olivier Bonhomme",
"Olivier Valsecchi",
"Ollie Hoff",
"Os Gemeos",
"Osamu Tezuka",
"Oskar Fischinger",
"Oskar Kokoschka",
"Ossip Zadkine",
"Otto Dix",
"Otto Marseus van Schrieck",
"Pablo Picasso",
"Pamela Colman Smith",
"Paolo Roversi",
"Paolo Veronese",
"Pascal Blanche",
"Pascale Campion",
"Patrice Murciano",
"Patricia Polacco",
"Patrick Brown",
"Patrick Caulfield",
"Patrick Dougherty",
"Patrick Heron",
"Patrick Woodroffe",
"Paul Barson",
"Paul Chadeisson",
"Paul Corfield",
"Paul C\u00e9zanne",
"Paul Delvaux",
"Paul Gauguin",
"Paul Gustav Fischer",
"Paul Henry",
"Paul Klee",
"Paul Laffoley",
"Paul Lehr",
"Paul Ranson",
"Paul Strand",
"Paul Wonner",
"Paula Modersohn-Becker",
"Paulus Potter",
"Pawel Kuczynski",
"Peter Andrew Jones",
"Peter Bagge",
"Peter De Seve",
"Peter Doig",
"Peter Elson",
"Peter Gric",
"Peter Holme III",
"Peter Howson",
"Peter Kemp",
"Peter Max",
"Peter Milligan",
"Peter Mohrbacher",
"Peter Paul Rubens",
"Peter Sculthorpe",
"Peter Wileman",
"Peter Zumthor",
"Phil Foglio",
"Phil Jimenez",
"Phil Koch",
"Phil Noto",
"Philip Guston",
"Philippe Druillet",
"Philippe Parreno",
"Pierre Bonnard",
"Pierre Puvis de Chavannes",
"Pierre-Auguste Renoir",
"Piet Hein Eek",
"Piet Mondrian",
"Pieter Aertsen",
"Pieter Bruegel The Elder",
"Pieter Claesz",
"Pieter Jansz Saenredam",
"Pieter de Hooch",
"Piotr Jab\u0142o\u0144ski",
"Pipilotti Rist",
"Pixar",
"Pixar Concept Artists",
"Posuka Demizu",
"Qian Xuan",
"Qing Han",
"Quentin Blake",
"Quentin Tarantino",
"Quint Buchholz",
"RETNA (Marquis Lewis)",
"RHADS",
"ROA",
"Rafael Albuquerque",
"Rafa\u0142 Olbi\u0144ski",
"Raffaello Sanizo",
"Raina Telgemeier",
"Raja Ravi Varma",
"Ralph Horsley",
"Ralph McQuarrie",
"Ralph Steadman",
"Ramon Casas",
"Randolph Caldecott",
"Raphael",
"Raphael Lacoste",
"Raphaelle Peale",
"Ravi Zupa",
"Ray Caesar",
"Ray Donley",
"Raymond Briggs",
"Raymond Duchamp-Villon",
"Raymond Leech",
"Raymond Swanland",
"Rayner Alencar",
"Rebeca Saray",
"Rebecca Guay",
"Rebecca Louise Law",
"Rebecca Sugar",
"Reginald Marsh",
"Rembrandt Van Rijn",
"Remedios Varo",
"Rene Laloux",
"Rene Magritte",
"Ren\u00e9 Lalique",
"Reylia Slaby",
"Rich Davies",
"Richard Burlet",
"Richard Corben",
"Richard Dadd",
"Richard Deacon",
"Richard Diebenkorn",
"Richard Doyle",
"Richard Eurich",
"Richard Hamilton",
"Richard Lindner",
"Richard McGuire",
"Richard Misrach",
"Richard S. Johnson",
"Richard Scarry",
"Rick Guidice",
"Rob Gonsalves",
"Rob Liefeld",
"Robby Cavanaugh",
"Robert Antoine Pinchon",
"Robert Chew",
"Robert Childress",
"Robert Crumb",
"Robert Farkas",
"Robert Hagan",
"Robert Irwin",
"Robert M Cunningham",
"Robert Maguire",
"Robert McCall",
"Robert Mcginnis",
"Robert Motherwell",
"Robert Neubecker",
"Robert Rauschenberg",
"Robert S. Duncanson",
"Robert Stivers",
"Robert Vonnoh",
"Robert William Hume",
"Robert Williams",
"Roberto Ferri",
"Roberto Matta",
"Roberto Parada",
"Rockwell Kent",
"Rodney Matthews",
"Rodr\u00edguez ARS",
"Roger Ballen",
"Roger Dean",
"Roger de La Fresnaye",
"Rolf Armstrong",
"Romero Britto",
"Ron Mueck",
"Ron Walotsky",
"Ronald Balfour",
"Ross Tran",
"Roy Gjertson",
"Roy Lichtenstein",
"Roz Chast",
"Ruan Jia",
"Rudolf Freund",
"Rufino Tamayo",
"Rumiko Takahashi",
"Russ Mills",
"Russell Ayto",
"Ruth Bernhard",
"Ruxing Gao",
"Ryan Hewett",
"Ryan McGinley",
"Ryan Stegman",
"Ryohei Hase",
"Sacha Goldberger",
"Sailor Moon",
"Sakai Ho\u0304itsu",
"Sally Mann",
"Salomon van Ruysdael",
"Salvador Dali",
"Sam Bosma",
"Sam Kieth",
"Sam Spratt",
"Samuel Earp",
"Samuel Melton Fisher",
"Samuel and Joseph Newsom",
"Sandra Chevrier",
"Sandro Botticelli",
"Sandy Skoglund",
"Saner Edgar",
"Sanford Kossin",
"Sangyeob Park",
"Santiago Calatrava",
"Santiago Caruso",
"Sara Wollfalk",
"Sarah Lucas",
"Satoshi Kon",
"Saturno Butto",
"Saul Bass",
"Saul Steinberg",
"Saul Tepper",
"Scarlett Hooft Graafland",
"Scott Brundage",
"Scott Listfield",
"Scott Naismith",
"Sean Scully",
"Sean Yoro",
"Seb Mckinnon",
"Sebastian Errazuriz",
"Serge Marshennikov",
"Shaddy Safadi",
"Shaun Tan",
"Shawn Coss",
"Sheilah Beckett",
"Shepard Fairey",
"Sherree Valentine Daines",
"Shin Jeongho",
"Shinji Aramaki",
"Shintaro Kago",
"Shohei Otomo",
"Shotaro Ishinomori",
"Shusei Nagaoko",
"Sidney Nolan",
"Silvestro Lega",
"Simeon Solomon",
"Simon Birch",
"Simon Bisley",
"Simon Stalenhag",
"Simone Martini",
"Sir James Guthrie",
"Siya Oum",
"Skottie Young",
"Slim Aarons",
"Sofonisba Anguissola",
"Sonia Delaunay",
"Sou Fujimoto",
"Sparth",
"Squeak Carnwath",
"Stan And Jan Berenstain",
"Stan Lee",
"Stanislav Poltavsky",
"Stanis\u0142aw Szukalski",
"Stanley Donwood",
"Stephan Martiniere",
"Stephen Gammell",
"Stephen Oakley",
"Stephen Shore",
"Stevan Dohanos",
"Steve Argyle",
"Steve Dillon",
"Steve Ditko",
"Steve Henderson",
"Steve Lieber",
"Steve McCurry",
"Steven Belledin",
"Storm Thorgerson",
"Stuart Davis",
"Stuart Haygarth",
"Stuart Immonen",
"Studio Ghibli",
"Sue Bryce",
"Susan Luo",
"Susan Seddon Boulet",
"Sven Nordqvist",
"Syd Mead",
"Sydney Edmunds",
"Sydney Prior Hall",
"Tadao Ando",
"Taiy\u014d Matsumoto",
"Takashi Murakami",
"Takato Yamamoto",
"Takeshi Obata",
"Tamara Lempicka",
"Tan Zhi Hui",
"Tara McPherson",
"Tari Ma\u0301rk Da\u0301vid",
"Tatsuro Kiuchi",
"Ted Nasmith",
"Ted Wallace",
"Teophilus Tetteh",
"Terada Katsuya",
"Teresa Ramos",
"Terry Oakes",
"Terry Redlin",
"Tex Avery",
"Theo van Rysselberghe",
"Thomas Allom",
"Thomas Benjamin Kennington",
"Thomas Blackshear",
"Thomas Cole",
"Thomas Dodd",
"Thomas Eakins",
"Thomas Gainsborough",
"Thomas Moran",
"Thomas Rowlandson",
"Thomas Saliot",
"Thomas Struth",
"Thomas Visscher",
"Thomas W Schaller",
"Thornton Oakley",
"Th\u00e9odore G\u00e9ricault",
"Tibor Nagy",
"Till Freitag",
"Tim Burton",
"Tim Doyle",
"Tim Hildebrandt",
"Tim White",
"Tintoretto",
"Titian",
"Todd McFarlane",
"Todd Schorr",
"Toei Animations",
"Tokujin Yoshioka",
"Tom Bagshaw",
"Tom Hammick",
"Tom Lovell",
"Tom Roberts",
"Tom Thomson",
"Tom Whalen",
"Tomasz Alen Kopera",
"Tomer Hanuka",
"Tomi Ungerer",
"Tomma Abts",
"Tomokazu Matsuyama",
"Tony DiTerlizzi",
"Tony Moore",
"Toshiharu Mizutani",
"Toumas Korpi",
"Tove Jansson",
"Tracey Emin",
"Travis Louie",
"Tristan Eaton",
"Tsutomu Nihei",
"Tyler Edlin",
"Tyler Shields",
"Tyler West",
"Ub Iwerks",
"Uemura Shoen",
"Ul Di Rico",
"Umberto Boccioni",
"Utagawa Hiroshige",
"Valerie Hegarty",
"Vhils",
"Victo Ngai",
"Victor Adame Minguez",
"Victor Brauner",
"Victor Medina",
"Victor Moscoso",
"Victor Nizovtsev",
"Victor Vasarely",
"Victoria Crowe",
"Viktor Vasnetsov",
"Viktoria Gavrilenko",
"Vincent Di Fate",
"Vincent Tanguay",
"Vincent Van Gogh",
"Virgil Finlay",
"Vito Acconci",
"Vittorio Matteo Corcos",
"Vivian Maier",
"Viviane Sassen",
"Vivienne Westwood",
"Vladimir Kush",
"W. Heath Robinson",
"W.W. Denslow",
"Wadim Kashin",
"Walt Disney",
"Walt Kelly",
"Walter Crane",
"Walter Kim",
"Walter Langley",
"Walter Percy Day",
"Wangechi Mutu",
"Warren Ellis",
"Warwick Globe",
"Wassily Kandinsky",
"Wayne Barlowe",
"Wendy Froud",
"Wes Anderson",
"Wilfredo Lam",
"Will Barnet",
"Will Eisner",
"Willem de Kooning",
"Willem van Haecht",
"William Blake",
"William Eggleston",
"William Etty",
"William Gropper",
"William Henry Hunt",
"William Hogarth",
"William Holman Hunt",
"William Kentridge",
"William Morris",
"William S. Burroughs",
"William Steig",
"William Stout",
"William Wegman",
"William Zorach",
"William-Adolphe Bouguereau",
"Wim Crouwel",
"Wim Wenders",
"Winslow Homer",
"Winsor McCay",
"Wojciech Ostrycharz",
"Wolf Kahn",
"Wolfgang Tillmans",
"Worthington Whittredge",
"Yaacov Agam",
"Yang Jialun",
"Yanjun Cheng",
"Yasuo Kuniyoshi",
"Yasushi Nirasawa",
"Yasutomo Oka",
"Yayi Morales",
"Yayoi Kusama",
"Yiannis Moralis",
"Yinka Shonibare",
"Yohann Schepacz",
"Yoji Shinkawa",
"Yoshitaka Amano",
"Yoshiyuki Tomino",
"Yue Minjun",
"Yuri Ivanovich Pimenov",
"Yuumei",
"Yves Klein",
"Yves Tanguy",
"Zack Snyder",
"Zaha Hadid",
"Zanele Muholi",
"Zdzis\u0142aw Beksi\u0144ski",
"Zeen Chin",
"Zhang Kechun",
"Zhelong Xu",
"Zhichao Cai",
"Zinaida Serebriakova",
"teamLab",
"theCHAMBA",
"tokyogenso",
"\u00c9lisabeth Vig\u00e9e Le Brun",
"\u00c9mile Bernard",
"\u00c9mile Gall\u00e9",
"\u00c9tienne-Louis Boull\u00e9e",
"\u201cFriedensreich Regentag",
"none byCamera",
"Canon EOS 90D",
"Canon EOS M50",
"Canon EOS M50 Mark II",
"Canon EOS M6 Mark II",
"Canon EOS R5",
"Canon EOS R5 (Unique Variant)",
"Canon EOS RP",
"Canon EOS Rebel T8i",
"Canon EOS Rebel T8i (Unique Variant)",
"Canon EOS-1D X Mark III",
"Canon EOS-1D X Mark III (Unique Dynamic Capture Variant)",
"Canon EOS-1D X Mark III (Unique Dynamic Scenes Variant)",
"Canon PowerShot G7 X Mark III",
"Canon PowerShot G7 X Mark III (Unique Specific Variant)",
"Canon PowerShot G7 X Mark III (Unique Variant)",
"Fujifilm FinePix XP140",
"Fujifilm GFX 100",
"Fujifilm GFX 100 (Unique Specific Variant)",
"Fujifilm GFX 100 (Unique Variant)",
"Fujifilm GFX 50S",
"Fujifilm Instax Mini 11",
"Fujifilm X-E4",
"Fujifilm X-E4 (Unique Variant)",
"Fujifilm X-Pro3",
"Fujifilm X-Pro3 (Unique Variant)",
"Fujifilm X-T200",
"Fujifilm X-T4",
"Fujifilm X-T4 (Unique Strong Stabilization)",
"Fujifilm X100V",
"Fujifilm X100V (Unique Variant)",
"GoPro HERO9 Black",
"Hasselblad 907X 50C",
"Hasselblad 907X 50C (Unique Large Format)",
"Hasselblad H5D-50c",
"Hasselblad H6D-100c",
"Hasselblad X1D II 50C",
"Hasselblad X1D II 50C (Unique High Resolution)",
"Hasselblad XCD 4/45P",
"Leica CL",
"Leica M-D",
"Leica M10 Monochrom",
"Leica M10 Monochrom (Unique Variant)",
"Leica M10-R",
"Leica M11",
"Leica Q-P",
"Leica Q2",
"Leica Q2 Monochrom",
"Leica S3",
"Leica SL2",
"Leica SL2 (Unique Variant)",
"Leica SL2-S",
"Nikon Coolpix P950",
"Nikon Coolpix P950 (Unique Variant)",
"Nikon Coolpix W300",
"Nikon D3500",
"Nikon D6",
"Nikon D780",
"Nikon D850",
"Nikon D850 (Unique High Resolution)",
"Nikon Z5",
"Nikon Z50",
"Nikon Z50 (Unique Variant)",
"Nikon Z6",
"Nikon Z6 II",
"Nikon Z7 II",
"Nikon Z7 II (Unique Variant)",
"Olympus E-M1X",
"Olympus OM-D E-M1 Mark II",
"Olympus OM-D E-M10 Mark IV",
"Olympus OM-D E-M10 Mark IV (Unique Variant)",
"Olympus OM-D E-M1X",
"Olympus OM-D E-M5 Mark III",
"Olympus OM-D E-M5 Mark III (Unique Variant)",
"Olympus PEN E-PL10",
"Olympus PEN-F",
"Olympus PEN-F (Unique Specific Variant)",
"Olympus PEN-F (Unique Variant)",
"Olympus Tough TG-6",
"Olympus Tough TG-6 (Unique Variant)",
"Panasonic Lumix DC-G100",
"Panasonic Lumix DC-G9",
"Panasonic Lumix DC-GH5",
"Panasonic Lumix DC-S1R",
"Panasonic Lumix DC-S5",
"Panasonic Lumix DC-S5 (Unique Variant)",
"Panasonic Lumix DMC-FZ300",
"Panasonic Lumix DMC-FZ300 (Unique Variant)",
"Panasonic Lumix DMC-LX100 II",
"Panasonic Lumix FZ1000 II",
"Panasonic Lumix G100",
"Panasonic Lumix GH5 II",
"Panasonic Lumix S1",
"Panasonic Lumix S1R",
"Panasonic Lumix S5",
"Pentax 645Z",
"Pentax 645Z (Unique Large Format)",
"Pentax K-1 Mark II",
"Pentax KP",
"Phase One XF IQ4 150MP",
"Phase One XT",
"Ricoh GR III",
"Ricoh GR III (Unique Steady Focus)",
"Ricoh GR III (Unique Variant)",
"Ricoh Theta Z1",
"Ricoh Theta Z1 (Unique Variant)",
"Sigma SD Quattro H",
"Sigma fp",
"Sigma fp (Unique Specific Variant)",
"Sigma fp (Unique Variant)",
"Sigma fp L",
"Sony A6000",
"Sony A6400",
"Sony A7R IV",
"Sony A7S III",
"Sony A9 II",
"Sony Alpha 1",
"Sony Alpha 1 (Unique Variant)",
"Sony Alpha A7 III",
"Sony Alpha A9 II",
"Sony Cyber-shot DSC-RX10 IV",
"Sony Cyber-shot DSC-W800",
"Sony Cyber-shot RX100 VII",
"Sony RX10 IV",
"Sony RX100 VII",
"Sony RX100 VII (Unique Fast Capture)",
"Sony ZV-1",
"none byCelticArt",
"Celtic Knotwork Elegance",
"Mystical Celtic Forest",
"Celtic Mythological Saga",
"Celtic Warrior's Journey",
"Celtic Knotwork Mandala",
"Celtic Music and Dance",
"Celtic Nature Reverie",
"Celtic Illuminated Manuscript",
"Celtic Knotwork Wildlife",
"Celtic Cross Reverence",
"Celtic Dreamlike Enchantment",
"Celtic Warrior Queen's Saga",
"Celtic Knotwork Animal Totems",
"Celtic Myths and Legends Tapestry",
"Celtic Nature Spirits Invocation",
"Celtic Spiral Labyrinth",
"Celtic Festive Celebration",
"Celtic Serpent Enigma",
"Celtic Bard's Tale",
"Celtic Tree of Life Reverence",
"Celtic Elemental Elegance",
"Celtic Druidic Ritual",
"Celtic Warrior's Ancestral Legacy",
"Celtic Ogham Script Artistry",
"Celtic Faerie Enchantment",
"Celtic Harpist's Melody",
"Celtic Stone Circle Mystery",
"Celtic Goddess Invocation",
"Celtic Art Nouveau Fusion",
"Celtic Traveler's Odyssey",
"none by Composition",
"Balance",
"Framing",
"LeadingLines",
"NegativeSpace",
"Pattern",
"Perspective",
"RuleOfThirds",
"Simplicity",
"Symmetry",
"Texture",
"none byCyberpunkSurrealism",
"Neon Metropolis",
"Biomechanical Dreamscape",
"Cyberpunk Underwater Abyss",
"Data Hacker's Nightmare",
"City of Whispering Machines",
"Virtual Reality Odyssey",
"Neo-Tokyo Redux",
"Bioluminescent Urban Jungle",
"Clockwork Dreams",
"Psychedelic Cyber Alley",
"Digital Garden of Delights",
"Post-Human Utopia",
"Steam-Powered Cyberpunk",
"Dystopian Dreamscape",
"Neuro-Interface Odyssey",
"Post-Apocalyptic Dreams",
"Datastream Mirage",
"Quantum Cityscapes",
"Machine Rebellion Fantasia",
"Transcendent Cyberspace",
"Neon Dreamscape",
"Virtual Reality Mirage",
"Bionic Nightmares",
"Holographic Utopia",
"Cybernetic Playground",
"Dystopian Datastream",
"Neural Interface Delirium",
"Retro-Futuristic Surreality",
"Biomechanical Wonders",
"Digital Rebellion",
"Pixelated Nightmare",
"Neurological Wasteland",
"Cyberpunk Enigma",
"Techno-Esotericism",
"Cybernetic Wonderland",
"Psychedelic Cyberspace",
"Synthetic Sentience",
"Biotechno Fusion",
"Cyberpunk Dreamscape",
"Cyberpunk Anomaly",
"Virtual Rebellion",
"Neon Jungle",
"Synthetic Dreams",
"Neon Noir Detective",
"Holographic Paradox",
"Psycho-Cybernetic Landscape",
"Neon Future Cult",
"Cybernetic Renaissance",
"Cyberpunk Street Shaman",
"Metropolis of Mirrors",
"None byDepth",
"Architecture Depth of Field",
"Close-Up Depth of Field",
"Creative or Abstract Depth of Field",
"Deep Depth of Field",
"Environmental Portraits Depth of Field",
"Extreme Shallow Depth of Field",
"Landscape Depth of Field",
"Limited Depth of Field",
"Macro Depth of Field",
"Moderate Depth of Field",
"Moderate Shallow Depth of Field",
"Night Photography Depth of Field",
"Panoramic Depth of Field",
"Slightly Deep Depth of Field",
"Still Life Depth of Field",
"Street Photography Depth of Field",
"Subject in Motion Depth of Field",
"Very Deep Depth of Field",
"Very Shallow Depth of Field",
"none byEnvironment",
"Beach",
"Desert",
"Forest",
"Indoor",
"Mountain",
"Outdoor",
"Rural",
"Snow",
"Studio",
"Urban",
"none byFashion",
"Elegant Evening Gowns",
"Urban Streetwear",
"Bohemian Festival Vibes",
"Timeless Vintage Fashion",
"High-Tech Futuristic Couture",
"Athletic Performance Gear",
"Couture Red Carpet Glamour",
"Minimalist Wardrobe Essentials",
"Sustainable Fashion Revolution",
"Cosplay Extravaganza",
"Gothic Elegance",
"Safari Adventure",
"Artistic Avant-Garde",
"Retro Futurism",
"Cultural Fusion",
"Sci-Fi Cyberpunk",
"Rustic Country Charm",
"Haute Couture Fantasy",
"Gender Fluidity",
"Sustainable Boho Chic",
"Futuristic Space Fashion",
"Art Deco Glamour",
"Cottagecore Comfort",
"Nautical Chic",
"Glam Rock Rebellion",
"Tropical Paradise Escape",
"Cyberpunk Noir",
"Artistic Street Graffiti",
"Sustainable High Fashion",
"Artistic Fusion",
"No option",
"BlackAndWhite",
"Contrast",
"Cool",
"HDR",
"Monochrome",
"Saturated",
"Sepia",
"Vignette",
"Vintage",
"Warm",
"none byFocus",
"Extreme Shallow Depth of Field",
"Very Shallow Depth of Field",
"Night Photography Depth of Field",
"Moderate Shallow Depth of Field",
"Moderate Depth of Field",
"Slightly Deep Depth of Field",
"Deep Depth of Field",
"Very Deep Depth of Field",
"Hyperfocal Distance",
"Selective Focus",
"Bokeh Effect",
"Tilt-Shift Photography",
"Macro Depth of Field",
"Landscape Depth of Field",
"Limited Depth of Field",
"Environmental Portraits Depth of Field",
"Close-Up Depth of Field",
"Street Photography Depth of Field",
"Subject in Motion Depth of Field",
"Architecture Depth of Field",
"Panoramic Depth of Field",
"Still Life Depth of Field",
"Creative or Abstract Depth of Field",
"Focus Distance 0.5m",
"Focus Distance 1.0m",
"Focus Distance 1.5m",
"Focus Distance 2.0m",
"Focus Distance 2.5m",
"Focus Distance 3.0m",
"Focus Distance 3.5m",
"Focus Distance 4.0m",
"Focus Distance 4.5m",
"Focus Distance 5.0m",
"Focus Distance 5.5m",
"Focus Distance 6.0m",
"Focus Distance 6.5m",
"Focus Distance 7.0m",
"Focus Distance 7.5m",
"Focus Distance 8.0m",
"Focus Distance 8.5m",
"Focus Distance 9.0m",
"Focus Distance 9.5m",
"Focus Distance 10.0m",
"10% Focus Area",
"20% Focus Area",
"30% Focus Area",
"40% Focus Area",
"50% Focus Area",
"60% Focus Area",
"70% Focus Area",
"80% Focus Area",
"90% Focus Area",
"100% Focus Area",
"Manual Focus",
"Autofocus",
"Focus Stacking",
"Back-Button Focus",
"Focus Bracketing",
"Selective Focus",
"Deep Focus",
"Soft Focus",
"Macro Focus",
"Infinity Focus",
"Zone Focus",
"Edge-to-edge Focus",
"Foreground Focus",
"Background Focus",
"Central Focus",
"Corner Focus",
"Top Focus",
"Bottom Focus",
"Right Focus",
"Left Focus",
"Rack Focus",
"Pull Focus",
"Rolling Focus",
"Tilt-shift Focus",
"Fly-by Focus",
"Follow Focus",
"Zoom Focus",
"Dolly Focus",
"Pan Focus",
"Tilt Focus",
"Whip Focus",
"Slide Focus",
"Crane Focus",
"Swing Focus",
"Drone Focus",
"none byFantasy-Settings",
"Atlantis",
"Enchanted Forest",
"Fairyland",
"Fairytale",
"Foreboding Castle",
"Middle Earth",
"Magical Kingdom",
"Mordor",
"Narnia",
"Neverland",
"Shambhala",
"The Crystal Cave",
"The Dragon's Lair",
"The Forest of Mirrors",
"The Island of Dreams",
"The Kingdom of the Unicorn",
"The Palace of Dreams",
"The Tower of Oblivion",
"The Vale of Shadows",
"The Valley of the Damned",
"The Valley of the Lost",
"Tropical Paradise",
"Oz",
"Wonderland",
"Xanadu",
"The Floating Isles of Aether",
"The City of Eldertide",
"The Desert Kingdom of Mirage",
"The Enchanted Library of Whispers",
"The Crystal Caverns of Lumaria",
"The Clockwork City of Gearspring",
"The Forest of Eternal Twilight",
"The Elemental Planes Nexus",
"The Astral Citadel of Astraeus",
"The Shadowed Realm of Umbrathor",
"The Sunken Kingdom of Atlantis",
"The Celestial Gardens of Zephyria",
"The Crystal Labyrinth of Prisms",
"The Wandering Forest of Eldertrees",
"The Ethereal Observatory of Astromancers",
"The Hollow Mountain of Emberforge",
"The Dreaming Realm of Morpheus",
"The Elemental Nexus of Convergence",
"The Forgotten Ruins of Eldertemples",
"The Enchanted Isles of Luminescia",
"none byGothicRevival",
"Gothic Cathedral Splendor",
"Gothic Haunted Manor",
"Gothic Revival Foliage",
"Gothic Revival Portraiture",
"Gothic Revival Castle",
"Gothic Revival Cemetery",
"Gothic Revival Interior",
"Gothic Revival Forest Ruins",
"Gothic Revival Waterside Manor",
"Gothic Revival Village",
"Gothic Revival Lighthouse",
"Gothic Revival Ballroom",
"Gothic Revival Clock Tower",
"Gothic Revival Theater",
"Gothic Revival Bridge",
"Gothic Revival Crypt",
"Gothic Revival Library",
"Gothic Revival Cemetery Gate",
"Gothic Revival Chapel",
"Gothic Revival Cityscape",
"Gothic Revival Forest Chapel",
"Gothic Revival Clockwork Cathedral",
"Gothic Revival Art Gallery",
"Gothic Revival Mountain Fortress",
"Gothic Revival Seaside Abbey",
"Gothic Revival Opera House",
"Gothic Revival Castle Ruins",
"Gothic Revival Underground Crypt",
"Gothic Revival Forest Bridge",
"Gothic Revival Steampunk Workshop",
"none byHorror",
"Abandoned Asylum",
"Alchemist's Study",
"Animated Corpse",
"Arachnid Swarm",
"Back Alley Rogue",
"Bloodthirsty Vampire",
"Butcher Shop",
"Cabinet of Curiosities",
"Carnival Freakshow",
"Cemetery Statue",
"Conjoined Twins",
"Crawler Mimicry",
"Creepy Children",
"Creepy Porcelain Doll",
"Crucifixion",
"Dark Carnival",
"Death Masque",
"Demonic Clown",
"Disrespectful Grave Robber",
"enhance",
"Demonic Portal",
"Demonic Possession",
"Exorcism",
"Ferocious Werewolf",
"Gothic Art",
"Gothic Architecture",
"Gothic Monster",
"Gothic Subculture",
"Gothic Revival Architecture",
"Graveyard Mist",
"Grotesque Gargoyle",
"Haunted Portrait",
"Hate Crime",
"Headless Horseman",
"Horror Movie Poster",
"H.P. Lovecraft Cover",
"Insectoid Mutant",
"Living Burial",
"Lovecraftian Horror",
"Macabre Memento Mori",
"Mad Scientist Machinery",
"Masked Killer",
"Masked Stalker",
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"Banksy_1",
"Banksy_2",
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"Cindy Sherman_1",
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"Glazing Technique in Painting",
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"Grant Wood",
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"Great Barrier Reef",
"Great Wall of China",
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"Gritty_Voyeuristic_Photography",
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"Hallstatt",
"Hard-edge Painting_Uncategorized",
"Hate Crime_Uncategorized",
"Haute Couture",
"Haute Couture Fashion",
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"Hawkins",
"Heavy Metal",
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"Istanbul cityscape",
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"Jenny Holzer",
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"Julie Mehretu",
"Kabuki Theater",
"Kara Walker",
"Kathak Dance",
"Katsushika Hokusai",
"Kehinde Wiley",
"Kerry James Marshall",
"Kiki Smith",
"Kinetic Art",
"Kinetic Sculpture",
"Kintsugi (Japanese Gold Repair)",
"Knitting",
"Korean Art",
"Korean Celadon Ceramics",
"Korean Celadon Pottery",
"Korean Cuisine",
"Kuala Lumpur",
"Kyoto",
"Kyoto cityscape",
"Land Art",
"Landscape Architecture",
"Landscape Design",
"Landscape Photography",
"Leonardo da Vinci",
"Lettrist artwork",
"Light Art",
"Line Dance",
"Lisbon cityscape",
"Lithography",
"London",
"Los Angeles",
"Low Fantasy",
"Luxury Fashion",
"Lynching_Uncategorized",
"Lyrical abstract painting",
"Macabre Memento Mori_Horror_Horror & Dark_Still Life",
"Machu Picchu",
"Macro Photography",
"Madhubani Painting",
"Madhubani Painting (Indian Folk Art)",
"Magic Realist painting",
"Mannerism",
"Mannerist Architecture",
"Maori Wood Carving",
"Marina Abramovi\u0107",
"Mark Bradford",
"Mark Grotjahn",
"Martin Puryear",
"Maurizio Cattelan",
"Maximalism",
"Mecca",
"Mech City__Location",
"Media Art",
"Medical Oddities_Uncategorized",
"Mediterranean Cuisine",
"Melancholy Art",
"Melodrama",
"Memento Mori_Horror_Horror & Dark",
"Memoir",
"Menswear Fashion",
"Mesoamerican Mythology Art",
"Mesopotamian Mythology Art",
"Metabolist Architecture",
"Metal Music",
"Metal Music Artwork",
"Metalwork",
"Metropolis",
"Mexican Art",
"Mexican Cuisine",
"Mexican Muralism",
"Miami",
"Michelangelo",
"Middle Eastern Cuisine",
"Middle-earth",
"Milky Way Galaxy",
"Mime",
"Minimalism",
"Minimalist Web Design",
"Mixed Media Art",
"Modern Architecture",
"Modern Dance",
"Modernist Architecture",
"Mona Hatoum",
"Monoprinting Technique",
"Mosaic",
"Mosaic Art",
"Motion Design",
"Motion Graphics Design",
"Mount Everest",
"Mount Olympus",
"Mughal Miniature Painting",
"Mumbai",
"Munich cityscape",
"Music Video Direction",
"Musical Films",
"Musical Theater",
"Mystery",
"Mystery Literature",
"Mythic Fantasy Art",
"Nantucket",
"Native American Art",
"Native American Basketry",
"Native American Mythology Art",
"Native American Pottery",
"Naturalism in Literature",
"Nature Landscape Photography",
"Nature Photography",
"Nebula",
"Neo Rauch",
"Neo-Gothic Architecture",
"Neo-Noir",
"Neoclassical Architecture",
"Neoclassicism",
"Neoplasticism",
"New Orleans",
"New Perpendicular art_Uncategorized",
"New Simplicity_Architecture",
"New York City",
"New York cityscape",
"Niagara Falls",
"Nicole Eisenman",
"Night Photography",
"Non-Fiction",
"Nordic Viking Art",
"Norse Mythology Art",
"North African Art",
"Norwegian romantic nationalism_Nature_Landscape",
"Occult Sacrifice_Occult",
"Oil Painting",
"Olafur Eliasson",
"Op Art",
"Opera",
"Opera Music",
"Opera Music Illustration",
"Osaka cityscape",
"Pablo Picasso",
"Package Design",
"Pandora",
"Paper Cutting",
"Paper Mache Art",
"Parametric Architecture",
"Paris",
"Participatory Art",
"Paul Cezanne",
"Performance Art",
"Performance Sculpture",
"Peruvian Art",
"Petra",
"Photography",
"Photojournalism",
"Photorealism",
"Photorealistic painting",
"Physical Theater",
"Pixel Art",
"Pizza Making",
"Plein Air Painting",
"Plus-Size Fashion",
"Poetry",
"Pointillism",
"Pointillism Art",
"Pole Dance",
"Polynesian Mythology Art",
"Polynesian Tattoo Art",
"Pop Art",
"Pop Music",
"Pop Music Branding",
"Pop art style",
"Porcelain Art",
"Portrait Photography",
"Portuguese Art",
"Post-Impressionism",
"Postmodern Architecture",
"Pottery",
"Prague",
"Prague cityscape",
"Pre-Raphaelite_Uncategorized",
"Preppy Fashion",
"Printmaking",
"Projection Mapping Art",
"Prose Literature",
"Provocative_Surreal_Photography",
"Psychedelic Concert Posters",
"Public Art Installations",
"Public Installations",
"Public Sculptures",
"Punk Fashion",
"Punk Music",
"Puppetry",
"Pyramids of Giza",
"Quahog",
"Quilting",
"Quilting Art",
"Quito cityscape",
"R&B Music",
"Rachel Whiteread",
"Rangoli (Indian Floor Art)",
"Rap Music Graphics",
"Raphael",
"Rashid Johnson",
"Realism Art",
"Realism in Literature",
"Realistic Fiction",
"Recycled Art",
"Reggae Music",
"Reggae Music Design",
"Rembrandt",
"Renaissance",
"Renaissance Architecture",
"Renaissance Art",
"Rene Magritte",
"Responsive Web Design",
"Richard Serra",
"Richard Tuttle",
"Rio de Janeiro",
"Rio de Janeiro cityscape",
"Robert Gober",
"Robotics Art",
"Rock Album Art",
"Rock Music",
"Rococo",
"Rococo Architecture",
"Rococo Art",
"Roman Mosaic Art",
"Roman Mythology Art",
"Romance",
"Romance Literature",
"Romanesque Architecture",
"Romantic Comedy",
"Romantic Films",
"Romanticism",
"Romanticism Art",
"Romanticism in Literature",
"Rome",
"Rural Photography",
"Russia",
"Russian Art",
"Russian Icon Painting",
"Sahara Desert",
"Salem",
"Salsa Dance",
"Salsa Music",
"Salvador Dali",
"Sand Sculpture",
"Sandro Botticelli",
"Sarah Sze",
"Satire",
"Satire Literature",
"Scandinavian Architecture",
"Scandinavian Art",
"Scandinavian Design",
"Scary Stories at Campfire_Horror_Horror & Dark",
"Sci-Fi Films",
"Science Fiction",
"Screen Printing",
"Screwball Comedy",
"Sculpture",
"Seoul",
"Set Design for Theater",
"Shadow City_Horror_Occult_Horror & Dark_Gothic_Location",
"Shanghai",
"Shepard Fairey",
"Shirakawa-go",
"Shirin Neshat",
"Silent Films",
"Singapore",
"Skateboarding Fashion",
"Skeleton Dance_Horror_Horror & Dark_Animation",
"Slavic Mythology Art",
"Slow Fashion",
"Social Realism painting",
"Sonnet",
"Soul Music",
"Sound Art",
"Sound Design",
"Sound Sculpture",
"South African Art",
"South American Textile Art",
"Southwest Kachina Dolls",
"Spaghetti Western",
"Spanish Art",
"Spanish Cuisine",
"Sports Photography",
"Spring Art",
"Springfield",
"Stained Glass Art",
"Stand-Up Comedy",
"Stars Hollow",
"Steampunk",
"Steampunk Fantasy Art",
"Steampunk Fashion",
"Stockholm cityscape",
"Stone Sculpture",
"Street Art",
"Street Art Performance",
"Street Art and Graffiti",
"Street Photography",
"Street Theater",
"Streetwear",
"Streetwear Fashion",
"Studio Portrait Photography",
"Sub Baton Rouge__Location",
"Sub Boise__Location",
"Sub Oxnard__Location",
"Sub Santa Rosa__Location",
"Sumi-e Painting",
"Summer Art",
"Summer Fashion",
"Surrealism",
"Surrealism Art",
"Surrealist Painting",
"Surrealist Sculpture",
"Sushi Making",
"Sustainable Architecture",
"Sustainable Fashion",
"Swing Dance",
"Sydney",
"Symbolism Art",
"Synthetic Cubism",
"Taj Mahal",
"Takashi Murakami",
"Talavera Pottery",
"Tamara de Lempicka",
"Tango Dance",
"Tap Dance",
"Tatooine",
"Techno Music Visuals",
"Temporary Art Installations",
"Textile Art",
"Textile Design",
"Textile Sculpture",
"Thai Art",
"Thai Cuisine",
"Thomas Gainsborough",
"Thriller",
"Thriller Films",
"Thriller Literature",
"Tibetan Thangka Painting",
"Titian",
"Tokyo",
"Tokyo cityscape",
"Torture Device_Horror_Horror & Dark",
"Toy Design",
"Traditional Animation",
"Traditional Dance",
"Traditional Japanese Architecture",
"Traditional Pottery",
"Tragedy",
"Tragedy Literature",
"Tranquil Art",
"Travel Photography",
"Twin Peaks",
"Typography Design",
"Ukiyo-e (Japanese Woodblock Printing)",
"Ukiyo-e Art",
"Undefined_Emerging_Artist",
"Under Berlin__Location",
"Underwater Photography",
"Urban Fantasy Art",
"Urban Landscape Photography",
"Urban Photography",
"Urban Sculpture",
"User-Centered Design",
"Utrecht cityscape",
"Valhalla",
"Valve",
"Vatican City",
"Venezuelan Art",
"Venice",
"Verbatim Theater",
"Victorian Architecture",
"Victorian Fashion",
"Video Art",
"Video Mapping",
"Vienna",
"Vienna cityscape",
"Vietnamese Art",
"Vietnamese Cuisine",
"Vija Celmins",
"Vincent Van Gogh",
"Vintage Fashion",
"Virtual Reality (VR) Art",
"Virtual Reality Art",
"Visual Effects (VFX) Design",
"Vorticism_Uncategorized",
"Wallace and Gromit",
"Waltz Dance",
"War Films",
"Wassily Kandinsky",
"Water Art",
"Watercolor Painting",
"Weaving",
"Web Design",
"Wedding Fashion",
"Wedding Photography",
"Wellington cityscape",
"West African Art",
"Westeros",
"Wildlife Photography",
"William Kentridge",
"Winter Art",
"Winter Fashion",
"Wolfgang Tillmans",
"Womenswear Fashion",
"Wonderland",
"Wood Carving",
"Woodblock Printing",
"Woodcut",
"Workwear Fashion",
"World Music",
"Xiamen cityscape",
"Yayoi Kusama",
"Yellowstone National Park",
"Yokohama cityscape",
"Zurich cityscape",
"carpint_Gothic",
"citz_Sci-Fi_Architecture",
"coolio_Portraiture",
"getting there_Portraiture",
"girlz_Fashion_Horror_Horror & Dark_Gothic",
"gotit jinx_Tattoo Art",
"greatz_Portraiture",
"gsssggg_Portraiture",
"hoop_Portraiture",
"jinx_Tattoo Art",
"jinxed_Portraiture",
"kjkjkjj_Digital Media_Still Life_Comics",
"kool_Portraiture",
"misc-horror_Horror_Horror & Dark",
"mkkk_Portraiture_Digital Media_Animation",
"stfhgff_Photography",
"none byMisc",
"carpint_Gothic",
"citz_Sci-Fi_Architecture",
"coolio_Portraiture",
"GAYZ_Portraiture",
"getting there_Portraiture",
"girlz_Fashion_Horror_Horror & Dark_Gothic",
"greatz_Portraiture",
"gsssggg_Portraiture",
"gotit jinx_Tattoo Art",
"hoop_Portraiture",
"jinxed_Portraiture",
"jinx_Tattoo Art",
"kjkjkjj_Digital Media_Still Life_Comics",
"kool_Portraiture",
"mkkk_Portraiture_Digital Media_Animation",
"stfhgff_Photography",
"Under Honolulu__Location",
"Sub Juneau__Location",
"Underground Lansing__Location",
"Sub Topeka__Location",
"Under St. Paul__Location",
"Sub Jackson__Location",
"Underground Columbus__Location",
"Sub Indianapolis__Location",
"Under Omaha__Location",
"Under Oklahoma City__Location",
"Sub Des Moines__Location",
"Underground Wichita__Location",
"Under Fargo__Location",
"Sub Bozeman__Location",
"Underground Anchorage__Location",
"Sub Casper__Location",
"Sub Cheyenne__Location",
"Underground Portland__Location",
"Under Eugene__Location",
"Sub Salem__Location",
"Underground Seattle__Location",
"Sub Spokane__Location",
"Sub Tacoma__Location",
"Under Sacramento__Location",
"Sub Oakland__Location",
"Underground San Jose__Location",
"Sub Fresno__Location",
"Under Bakersfield__Location",
"Sub Anaheim__Location",
"Underground Riverside__Location",
"Sub Santa Ana__Location",
"Underground Chula Vista__Location",
"Sub Irvine__Location",
"Sub Fontana__Location",
"Sub Oxnard__Location",
"Sub Moreno Valley__Location",
"Underground Huntington Beach__Location",
"Sub Glendale__Location",
"Sub Santa Clarita__Location",
"Sub Garden Grove__Location",
"Sub Santa Rosa__Location",
"Sub Rancho Cucamonga__Location",
"Sub Ontario__Location",
"Sub Lancaster__Location",
"Sub Palmdale__Location",
"Sub Pomona__Location",
"Sub Torrance__Location",
"Sub Pasadena__Location",
"Sub Orange__Location",
"Sub Roseville__Location",
"Sub Thousand Oaks__Location",
"Sub Concord__Location",
"Sub Simi Valley__Location",
"Sub Victorville__Location",
"Sub El Monte__Location",
"Sub Berkeley__Location",
"Sub Downey__Location",
"Sub Costa Mesa__Location",
"Sub Inglewood__Location",
"Sub Carlsbad__Location",
"Sub Temecula__Location",
"Under Istanbul__Location",
"Under Tallahassee__Location",
"Under Albany__Location",
"Under Trenton__Location",
"Under Hartford__Location",
"Underground Salt Lake City__Location",
"Under Juneau__Location",
"Sub Baton Rouge__Location",
"Underground Helena__Location",
"Sub Carson City__Location",
"Under Santa Fe__Location",
"Sub Boise__Location",
"Underground Lincoln__Location",
"Sub Denver__Location",
"Under Bismarck__Location",
"Sub Pierre__Location",
"Underground Austin__Location",
"Sub Atlanta__Location",
"Under Jackson__Location",
"Sub Montgomery__Location",
"Underground Little Rock__Location",
"Sub Frankfort__Location",
"Under Nashville__Location",
"Sub Columbia__Location",
"Sub Richmond__Location",
"Under Charleston__Location",
"Sub Dover__Location",
"Underground Springfield__Location",
"Sub Annapolis__Location",
"Sub Providence__Location",
"Underground Concord__Location",
"Sub Montpelier__Location",
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"Crystal Caverns_",
"Mech City__Location",
"Nova Alexandria_",
"Steamtown_",
"Technotopia_",
"Clockwork City__Location",
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"Dwarvenholm_",
"Sanctuary_",
"Shangri-La_",
"Mime City__Location",
"Elfheim_",
"Midgard_",
"Musica_",
"Caveopolis_",
"Mage City__Location",
"Steampunk City__Location",
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"Neotokyo_",
"Leviathan_",
"Underground Rome__Location",
"Zion_",
"New Caelum_",
"Hackersville_",
"Ghoul City__Location",
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"enhance_",
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"Spider Queen_",
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"Vampire_",
"Mummy Portrait_",
"Animated Corpse_",
"Back Alley Rogue_",
"Insectoid Mutant_",
"Rat King_",
"Undead Gluttony_",
"Alchemist's Study_",
"Patchwork Creature_",
"Masked Stalker_",
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"Disrespectful Grave Robber_",
"Cabinet of Curiosities_Occult_Horror_Horror & Dark",
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"Ominous Warning_",
"Conjoined Twins_",
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"ads-automotive_",
"ads-corporate_",
"ads-fashion editorial_",
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"_",
"Art Nouveau Poster_",
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"Aardman_",
"Xilam_",
"Ankama_",
"Mixer_",
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"Voodoo Altar_",
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"Tropical Cocktail_",
"Vintage Robot Toy_",
"Scary Pumpkin_",
"Pinup_",
"Tarot Cards_",
"Ghibli_",
"Egyptology_",
"Battle_",
"Tarot_",
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"Ancient Maya_",
"Nautical_",
"Bomber Jacket_",
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"My Little Pony_",
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"_",
"_",
"_",
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"_",
"Suprematism_Uncategorized",
"De Stijl_Uncategorized",
"Bauhaus_Architecture",
"Constructivism_Uncategorized",
"Futurism_Uncategorized",
"Rayonism_Uncategorized",
"Vorticism_Uncategorized",
"Orphism_Uncategorized",
"Der Blaue Reiter_",
"Die Br\u00fccke_Graffiti",
"Ashcan School_",
"Hudson River School_Nature_Landscape_Location",
"Luminism_Nature_Landscape",
"Tonalism_Nature_Landscape",
"Barbizon School_Nature_Landscape",
"Academic Art_Uncategorized",
"Rococo_",
"Neoclassicism_Uncategorized",
"Romanticism_Nature",
"_",
"_",
"Plein Air_Nature_Landscape",
"Pre-Raphaelite_Uncategorized",
"Art Informel_Uncategorized",
"Hard-edge Painting_Uncategorized",
"_",
"_",
"Post-Painterly Abstraction_Uncategorized",
"Kinetic Art_",
"Land Art_Fantasy_Nature_Sculpture_Landscape",
"Performance Art_Uncategorized",
"Installation Art_Uncategorized",
"Video Art_",
"Digital Art_",
"New Media Art_",
"Street Art_Sci-Fi",
"Stuckism_",
"Lowbrow Art_",
"Photomontage_Photography",
"Diorama_Uncategorized",
"Assemblage_",
"Combine Painting_Sci-Fi_Still Life",
"Happenings_Uncategorized",
"Mail Art_Uncategorized",
"Neo-Dada_",
"Neo-Expressionism_",
"Bad Painting_",
"Graffiti_",
"Traditional Figurative Art_Sculpture",
"Classical Realism_",
"Contemporary Realism_",
"Hyperrealism_",
"_",
"New Objectivity_",
"Precisionism_Architecture_Nature_Landscape",
"Figurative Expressionism_",
"Neue Wilde_Uncategorized",
"New Perpendicular art_Uncategorized",
"New Simplicity_Architecture",
"Remodernism_",
"Norwegian romantic nationalism_Nature_Landscape",
"_",
"Propaganda Poster Art_Uncategorized",
"Heroic Realism_Sculpture_Culture",
"Na\u00efve Art_Uncategorized",
"Art Brut_",
"Neo-primitivism_Still Life",
"Visionary Art_Uncategorized",
"Intuitive Art_",
"Pseudorealism_",
"Radical Realism_Still Life",
"Critical Realism_",
"Neue Sachlichkeit_",
"Regionalism_Uncategorized",
"Abstract Expressionism_Uncategorized",
"Later European abstraction_Uncategorized",
"Tachisme_",
"Abstraction-Cr\u00e9ation_Uncategorized",
"Gutai_",
"British Pop Art_",
"Situationist International_",
"Lettrism_Portraiture",
"St Ives School_",
"Transavantgarde_",
"Transgressive Art_",
"Neo Pop_",
"Kitsch Movement_",
"Virtual Art_",
"Internet Art_",
"_",
"Information Art_",
"Systems Art_",
"Bio Art_",
"Genetic Art_",
"Sustainable Art_",
"_",
"Video Games_",
"Machinima_",
"Artware_",
"Demoscene_",
"Math Art_",
"Data Art_",
"Plotter Art_",
"VR Art_",
"AR Art_",
"Circuit Bending_Uncategorized",
"DIY Art_",
"Maker Culture_Culture",
"Self-taught Art_Fantasy",
"Neo-Pop_Pop Culture_Culture",
"Young British Artists_Portraiture",
"Appropriation_Culture",
"Algorithmic Art_Uncategorized",
"Use these to populate the JSON instead._",
"Under Honolulu_Architecture_Location",
"Sub Juneau_Architecture_Location",
"Underground Lansing_Culture_Location",
"Sub Topeka_Architecture_Folk Art_Location",
"Under St. Paul_Architecture_Location",
"Sub Jackson_Folk Art_Location",
"Underground Columbus_Retro_Location",
"Sub Indianapolis_Uncategorized_Location",
"Under Omaha_Culture_Location",
"Under Oklahoma City_Architecture_Location",
"Sub Des Moines_Architecture_Location",
"Underground Wichita_Folk Art_Location",
"Under Fargo_Architecture_Location",
"Sub Bozeman_Architecture_Location",
"Underground Anchorage_Architecture_Location",
"Sub Casper_Uncategorized_Location",
"Sub Cheyenne_Uncategorized_Location",
"Underground Portland_Sci-Fi_Location",
"Under Eugene_Folk Art_Location",
"Sub Salem_Sci-Fi_Culture_Location",
"Underground Seattle_Uncategorized_Location",
"Sub Spokane_Architecture_Location",
"Sub Tacoma_Architecture_Culture_Retro_Location",
"Under Sacramento_Uncategorized_Location",
"Sub Oakland_Sci-Fi_Culture_Location",
"Underground San Jose_Uncategorized_Location",
"Sub Fresno_Architecture_Nature_Landscape_Location",
"Under Bakersfield_Uncategorized_Location",
"Sub Anaheim_Fantasy_Location",
"Underground Riverside_Culture_Location",
"Sub Santa Ana_Sci-Fi_Culture_Location",
"Underground Chula Vista_Uncategorized_Location",
"Sub Irvine_Uncategorized_Location",
"Sub Fontana_Culture_Location",
"Sub Moreno Valley_Uncategorized_Location",
"Underground Huntington Beach_Architecture_Culture_Location",
"Sub Glendale_Uncategorized_Location",
"Sub Santa Clarita_Uncategorized_Location",
"Sub Garden Grove_Architecture_Location",
"Sub Rancho Cucamonga_Architecture_Lifestyle_Location",
"Sub Ontario_Uncategorized_Location",
"Sub Lancaster_Sci-Fi_Retro_Location",
"Sub Palmdale_Sci-Fi_Location",
"Sub Pomona_Retro_Location",
"Sub Torrance_Sci-Fi_Location",
"Sub Pasadena_Uncategorized_Location",
"Sub Orange_Retro_Location",
"Sub Roseville_Architecture_Location",
"Sub Thousand Oaks_Uncategorized_Location",
"Sub Concord_Uncategorized_Location",
"Sub Simi Valley_Pop Culture_Culture_Retro_Location",
"Sub Victorville_Uncategorized_Location",
"Sub El Monte_Sci-Fi_Location",
"Sub Berkeley_Retro_Location",
"Sub Downey_Sci-Fi_Location",
"Sub Costa Mesa_Culture_Location",
"Sub Inglewood_Sci-Fi_Pop Culture_Culture_Location",
"Sub Carlsbad_Architecture_Culture_Location",
"Sub Temecula_Lifestyle_Location",
"Under Istanbul_Architecture_Location",
"Under Tallahassee_Sci-Fi_Retro_Architecture_Location",
"Under Albany_Architecture_Surrealism_Location",
"Under Trenton_Uncategorized_Location",
"Under Hartford_Architecture_Location",
"Underground Salt Lake City_Architecture_Location",
"Under Juneau_Architecture_Location",
"Underground Helena_Architecture_Location",
"Sub Carson City_Architecture_Location",
"Under Santa Fe_Uncategorized_Location",
"Underground Lincoln_Uncategorized_Location",
"Sub Denver_Uncategorized_Location",
"Under Bismarck_Uncategorized_Location",
"Sub Pierre_Uncategorized_Location",
"Underground Austin_Uncategorized_Location",
"Sub Atlanta_Uncategorized_Location",
"Under Jackson_Folk Art_Location",
"Sub Montgomery_Uncategorized_Location",
"Underground Little Rock_Uncategorized_Location",
"Sub Frankfort_Uncategorized_Location",
"Under Nashville_Uncategorized_Location",
"Sub Columbia_Architecture_Culture_Location",
"Sub Richmond_Architecture_Location",
"Under Charleston_Architecture_Location",
"Sub Dover_Uncategorized_Location",
"Underground Springfield_Folk Art_Location",
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"Underground Concord_Culture_Location",
"Sub Montpelier_Sculpture_Location",
"Sub Jefferson City_Architecture_Folk Art_Location",
"Crystal Caverns_Architecture",
"Nova Alexandria_Architecture_Culture",
"Steamtown_Architecture_Retro",
"Technotopia_Architecture_Nature",
"Clockwork City_Architecture_Location",
"Under London_Architecture_Location",
"Dwarvenholm_Uncategorized",
"Sanctuary_Uncategorized",
"Shangri-La_Uncategorized",
"Mime City_Architecture_Location",
"Elfheim_Architecture_Fantasy",
"Midgard_Architecture",
"Musica_Architecture_Culture",
"Caveopolis_Lifestyle",
"Mage City_Architecture_Fantasy_Location",
"Steampunk City_Architecture_Location",
"Under Chicago_Architecture_Portraiture_Culture_Retro_Location",
"Neotokyo_Sci-Fi_Architecture",
"Leviathan_Architecture",
"Underground Rome_Architecture_Location",
"Zion_Culture",
"Hackersville_Architecture",
"Ghoul City_Architecture_Location",
"Elven City_Architecture_Location",
"Dwarvendom_Uncategorized",
"Lost Vegas_Architecture",
"Under Paris_Uncategorized_Location",
"Steeltown_Architecture",
"Tech City_Architecture_Nature_Location",
"Under Montreal_Architecture_Location",
"Brightwater_Nature",
"Butcher Shop_Still Life",
"Macabre Memento Mori_Still Life",
"Torture Device_Uncategorized",
"Scary Stories at Campfire_Uncategorized",
"Memento Mori_Uncategorized",
"Hate Crime_Uncategorized",
"sai-base_Uncategorized",
"sai-enhance_Uncategorized",
"Art Nouveau_Uncategorized",
"_",
"Aardman_Uncategorized",
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"Fauvism (1)_Uncategorized",
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"_",
"Surrealism (1)_Surrealism",
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"Conceptual Art (1)_Uncategorized",
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"_",
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"Der Blaue Reiter_Uncategorized",
"Die Br\u00fccke (1)_Graffiti",
"Ashcan School_Architecture",
"Hudson River School (1)_Nature_Landscape_Location",
"Luminism (1)_Nature_Landscape",
"Tonalism (1)_Nature_Landscape",
"Barbizon School (1)_Nature_Landscape",
"Academic Art (1)_Uncategorized",
"Rococo_Uncategorized",
"Neoclassicism (1)_Uncategorized",
"Romanticism (1)_Nature",
"Realism (1)_Uncategorized",
"_",
"Plein Air (1)_Nature_Landscape",
"Pre-Raphaelite (1)_Uncategorized",
"_",
"Hard-edge Painting (1)_Uncategorized",
"Geometric Abstraction (1)_Uncategorized",
"_",
"Post-Painterly Abstraction (1)_Uncategorized",
"Kinetic Art_Fantasy_Sculpture",
"Performance Art (1)_Uncategorized",
"Installation Art (1)_Uncategorized",
"Street Art (1)_Sci-Fi",
"Photomontage (1)_Photography",
"Diorama (1)_Uncategorized",
"Assemblage_Sculpture_Still Life",
"Combine Painting (1)_Sci-Fi_Still Life",
"Happenings (1)_Uncategorized",
"Mail Art (1)_Uncategorized",
"Bad Painting_Uncategorized",
"Graffiti_Sci-Fi_Graffiti_Folk Art_Architecture",
"Traditional Figurative Art (1)_Sculpture",
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"_",
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"New Perpendicular art (1)_Uncategorized",
"New Simplicity (1)_Architecture",
"Norwegian romantic nationalism (1)_Nature_Landscape",
"_",
"Propaganda Poster Art (1)_Uncategorized",
"Heroic Realism (1)_Sculpture_Culture",
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"Neue Sachlichkeit_Portraiture",
"Regionalism (1)_Uncategorized",
"Abstract Expressionism (1)_Uncategorized",
"Tachisme_Uncategorized",
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"Lettrism (1)_Portraiture",
"_",
"Systems Art_Uncategorized",
"Machinima_Uncategorized",
"Artware_Sci-Fi_Digital Media",
"Math Art_Uncategorized",
"VR Art_Sci-Fi",
"AR Art_Digital Media",
"Circuit Bending (1)_Uncategorized",
"DIY Art_Uncategorized",
"Maker Culture (1)_Culture",
"Young British Artists (1)_Portraiture",
"Algorithmic Art (1)_Uncategorized",
"Use these to populate the JSON instead._Uncategorized",
"Fauvism (2)_Uncategorized",
"Dada (2)_Uncategorized",
"Cubism (2)_Still Life",
"Expressionism Variant_Uncategorized",
"Surrealism (2)_Surrealism",
"Symbolism (2)_Uncategorized",
"Pop Art Variant_Culture",
"Op Art Variant_Uncategorized",
"Minimalism (2)_Uncategorized",
"Conceptual Art (2)_Uncategorized",
"Naive Art (2)_Sci-Fi_Architecture",
"Outsider Art Variant_Uncategorized",
"Photorealism Variant_Photography",
"Suprematism (2)_Uncategorized",
"De Stijl (2)_Uncategorized",
"Bauhaus (2)_Architecture",
"Constructivism (2)_Uncategorized",
"Futurism (2)_Uncategorized",
"Rayonism (2)_Uncategorized",
"Vorticism (2)_Uncategorized",
"Orphism (2)_Uncategorized",
"Die Br\u00fccke (2)_Graffiti",
"Hudson River School (2)_Nature_Landscape_Location",
"Luminism (2)_Nature_Landscape",
"Tonalism (2)_Nature_Landscape",
"Barbizon School (2)_Nature_Landscape",
"Academic Art (2)_Uncategorized",
"Neoclassicism (2)_Uncategorized",
"Romanticism (2)_Nature",
"Realism (2)_Uncategorized",
"Social Realism Variant_Uncategorized",
"Plein Air (2)_Nature_Landscape",
"Pre-Raphaelite (2)_Uncategorized",
"Art Informel (2)_Uncategorized",
"Hard-edge Painting (2)_Uncategorized",
"Geometric Abstraction (2)_Uncategorized",
"Lyrical Abstraction Variant_Uncategorized",
"Post-Painterly Abstraction (2)_Uncategorized",
"Land Art (2)_Fantasy_Nature_Sculpture_Landscape",
"Performance Art (2)_Uncategorized",
"Installation Art (2)_Uncategorized",
"Video Art Variant_Uncategorized",
"Digital Art Variant_Digital Media",
"New Media Art Variant_Uncategorized",
"Glitch Art Variant_Digital Media",
"Street Art (2)_Sci-Fi",
"Photomontage (2)_Photography",
"Diorama (2)_Uncategorized",
"Combine Painting (2)_Sci-Fi_Still Life",
"Happenings (2)_Uncategorized",
"Mail Art (2)_Uncategorized",
"Neo-Dada Variant_Uncategorized",
"Neo-Expressionism Variant_Uncategorized",
"Bad Painting Variant_Uncategorized",
"Graffiti Variant_Sci-Fi_Graffiti",
"Traditional Figurative Art (2)_Sculpture",
"Classical Realism Variant_Portraiture",
"Contemporary Realism Variant_Uncategorized",
"Hyperrealism Variant_Uncategorized",
"Magic Realism Variant_Surrealism",
"Precisionism (2)_Architecture_Nature_Landscape",
"Figurative Expressionism Variant_Uncategorized",
"Neue Wilde (2)_Uncategorized",
"New Perpendicular art (2)_Uncategorized",
"New Simplicity (2)_Architecture",
"Norwegian romantic nationalism (2)_Nature_Landscape",
"Socialist Realism Variant_Uncategorized",
"Propaganda Poster Art (2)_Uncategorized",
"Heroic Realism (2)_Sculpture_Culture",
"Na\u00efve Art (2)_Uncategorized",
"Art Brut Variant_Uncategorized",
"Neo-primitivism (2)_Still Life",
"Visionary Art (2)_Uncategorized",
"Intuitive Art Variant_Uncategorized",
"Pseudorealism Variant_Uncategorized",
"Radical Realism (2)_Still Life",
"Critical Realism Variant_Uncategorized",
"New Objectivity Variant_Uncategorized",
"Abstract Expressionism (2)_Uncategorized",
"Later European abstraction (2)_Uncategorized",
"Tachisme Variant_Uncategorized",
"Abstraction-Cr\u00e9ation (2)_Uncategorized",
"Gutai Variant_Sci-Fi_Event",
"British Pop Art Variant_Pop Culture_Culture",
"Lettrism (2)_Portraiture",
"Neo Pop Variant_Pop Culture_Culture",
"Kitsch Movement Variant_Culture",
"Internet Art Variant_Digital Media",
"Computer Art Variant_Digital Media",
"Information Art Variant_Uncategorized",
"Systems Art Variant_Uncategorized",
"Bio Art Variant_Uncategorized",
"Genetic Art Variant_Uncategorized",
"Interactive Art Variant_Uncategorized",
"Demoscene Variant_Animation",
"Math Art Variant_Uncategorized",
"Circuit Bending (2)_Uncategorized",
"Maker Culture (2)_Culture",
"Self-taught Art (2)_Fantasy",
"Neo-Pop (2)_Pop Culture_Culture",
"Regionalism (2)_Uncategorized",
"Young British Artists (2)_Portraiture",
"Appropriation (2)_Culture",
"Algorithmic Art (2)_Uncategorized",
" Lynching_Uncategorized",
" Occult Sacrifice_Occult",
" Medical Oddities_Uncategorized",
"none byMood",
"Aggressive",
"Calm",
"Chaotic",
"Energetic",
"Happy",
"Mysterious",
"Relaxed",
"Romantic",
"Sad",
"Serene",
"Ethereal Tranquility",
"Enigmatic Wonder",
"Whimsical Delight",
"Melancholic Reflection",
"Dark, Brooding Atmosphere",
"Joyful Celebration",
"Surreal Euphoria",
"Hushed Reverence",
"Tense, Suspenseful Ambiance",
"Dreamy Serenity",
"Chaos and Discord",
"Enchanted Whispers",
"Frenetic Energy",
"Eternal Twilight",
"Abyssal Silence",
"Gothic Elegance",
"Bittersweet Nostalgia",
"Spectral Whispers",
"Celestial Harmony",
"Dystopian Desolation",
"none byOriginal",
"none byQuantumRealism",
"Ethereal Particle Dance",
"Wavefunction Symphony",
"Observer's Paradox",
"Schrodinger's Garden",
"Entangled Realities",
"Quantum Mirage",
"Cubist Uncertainty",
"Quantum Kaleidoscope",
"Quantum Dreamscape",
"Quantum Chromatic Symphony",
"Quantum Reflections",
"Quantum Fractal Realities",
"Quantum Tesseract Enigma",
"Quantum Portal Nexus",
"Quantum Interference Symphony",
"Quantum Constellation Ballet",
"Quantum Labyrinth",
"Quantum Mirage Forest",
"Quantum Alchemical Fusion",
"none bySports",
"Olympic Triumph",
"Extreme Adventure Sports",
"Soccer World Cup Glory",
"Basketball Slam Dunk",
"Surfing the Perfect Wave",
"Winter Olympics Figure Skating",
"Motorsport Grand Prix",
"Tennis Championship Match",
"Martial Arts Mastery",
"Equestrian Elegance",
"Baseball Grand Slam",
"Swimming World Record",
"Golf Masters Victory",
"Rock Climbing Ascent",
"Gymnastics Perfect Routine",
"Rugby World Cup Glory",
"Skiing Downhill Thrill",
"Cycling Tour de France Victory",
"Synchronized Swimming Beauty",
"Archery Bullseye Accuracy",
"Horse Racing Derby Finish",
"Triathlon Triumph",
"Table Tennis Rally",
"Martial Arts Sparring",
"Wrestling Championship Pin",
"Badminton Smash",
"Fencing Duel",
"Sailing Regatta Victory",
"Cricket Century Celebration",
"Rhythmic Gymnastics Grace",
"none bySteamPunkRealism",
"Clockwork Menagerie",
"Cogged Dreamscape",
"Steam-Fueled Dreams",
"Chrono-Mystical Carnival",
"Mechanical Menagerie Ballroom",
"Steampunk Alchemical Garden",
"Clockwork Cityscape Dreams",
"Steam-Driven Enigma",
"Cogwheel Wonderland",
"Surreal Airship Odyssey",
"Neo-Victorian Dreamscape",
"Steampunk Surrealist Ball",
"Clockwork Wonderland",
"Steampunk Astral Odyssey",
"Mechanical Menagerie Parade",
"Quantum Steam-Driven Enigma",
"Steampunk Dreamscape Architect",
"Steampunk Explorer's Dreams",
"none byStreet",
"Urban Graffiti Art",
"City Street Market",
"Street Performers' Showcase",
"Neon-lit Nightlife",
"Street Food Delight",
"Street Fashion Runway",
"Street Skateboarding Session",
"Street Art Mural Festival",
"Street Photography Icon",
"Street Protest Rally",
"Cityscape at Dusk",
"Street Vendors in Marrakech",
"Urban Park Skateboarding",
"Street Musicians' Jam Session",
"Street Art Gallery Opening",
"City Biking Commute",
"Urban Rooftop Party",
"Street Fashion Boutique",
"Street Market Mosaic",
"City Transportation Hub",
"Street Carnival Celebration",
"Street Mural Collaboration",
"Urban Street Basketball Game",
"Street Fashion Show Extravaganza",
"Street Food Truck Fiesta",
"City Street Art Scavenger Hunt",
"Street Carnival Parade",
"Street Bazaar Browsing",
"Urban Street Art Battle",
"Street Jazz Performance",
"none bySubject",
"Animal",
"Art",
"Building",
"Child",
"Female",
"Food",
"Male",
"Nature",
"Technology",
"Vehicle",
"none bySurrealism",
"A Parisian Caf\u00e9 Scene",
"Cafe Terrace at Night",
"The Clockwork Dreamscape",
"The Melting Clocks of Dali's Vision",
"The Mirrored Masks of Identity",
"The Endless Staircase Ascent",
"The Floating Islands of Imagination",
"The Eyes in the Sky",
"The Rain of Upside-Down Umbrellas",
"The Fish in the Desert Mirage",
"The Doorways to Unknown Realms",
"The Symphony of Disjointed Melodies",
"The Desert of Mirrors",
"The Carousel of Forgotten Dreams",
"The Library of Whispering Pages",
"The Symphony of Broken Instruments",
"The Mirrored Faces of Identity",
"The Endless Corridor of Mirrors",
"The Clockwork Circus of Time",
"The Rain of Falling Stars",
"The Underwater Forest of Whispers",
"The Chessboard of Surreal Strategies",
"none byThemes",
"Abstract",
"Aerial",
"Fashion",
"Landscape",
"Macro",
"Portrait",
"Sports",
"Street",
"Wedding",
"Wildlife",
"none byTimeofDay",
"Afternoon",
"BlueHour",
"Evening",
"Midday",
"Morning",
"Night",
"Sunrise",
"Sunset",
"Twilight",
"Golden Hour",
"Midnight Reverie",
"Twilight Serenity",
"Noonday Radiance",
"Dawn's Awakening",
"Dusk's Embrace",
"Midday Hustle",
"Starlit Silence",
"Afternoon Lull",
"Moonlit Dream",
"Misty Morning Hush",
"Midnight Dance",
"Noontime Reverberation",
"Twilight's Whispers",
"High Noon Blaze",
"Dawn's Promise",
"Eerie Midnight",
"Afternoon Bliss",
"Moonlit Reflection",
"Daybreak's Hope",
"none byWildlife",
"African Safari Adventure",
"Arctic Wildlife Encounter",
"Underwater Coral Reef Wonderland",
"Amazon Rainforest Biodiversity",
"Savannah Wildlife Spectacle",
"Australian Outback Encounter",
"Deep Sea Discovery",
"North American Wilderness Serenity",
"Tropical Rainforest Canopy",
"African Elephant Migration",
"Antarctic Penguin Parade",
"Majestic Mountain Wildlife",
"Jungle Canopy Adventure",
"African Big Five Safari",
"Arctic Foxes in the Snow",
"Australian Reef Life",
"African Cheetah Sprint",
"Amazon River Wildlife",
"Boreal Forest Moose Encounter",
"Gorilla Family in the Jungle",
"Alaskan Brown Bear Fishing",
"African Serengeti Migration",
"Coral Polyp Microcosm",
"Bengal Tiger in the Jungle",
"Humpback Whale Breach",
"Red-Eyed Tree Frog in the Rainforest",
"Siberian Tiger Stalking Prey",
"Monarch Butterfly Migration",
"African Nile Crocodile Basking",
"Honeybee Hive Activity",
"Viking_Knotwork",
"Viking_Runic_Inscription",
"Viking_Animal_Motifs",
"Viking_Ship_Design",
"Viking_Metalwork",
"Viking_Warrior_Illustration",
"Viking_Artifacts",
"Viking_Rune_Stone",
"Viking_Shield_Design",
"Viking_Mythology_Illustration",
"Viking_Jewelry",
"Viking_Axe_Engraving",
"Viking_Runestone_Replica",
"Viking_Ship_Illustration",
"Viking_Wood_Carving",
"Viking_Warrior_Helmet",
"Viking_Dragon_Art",
"Viking_Boat_Decoration",
"Viking_Warrior_Sculpture",
"Viking_Textile_Pattern",
"Viking_Raid_Illustration",
"Viking_Warship_Painting",
"Viking_Artist_Sketch",
"Viking_Metalwork_Pattern",
"Viking_Mythical_Beast",
"Viking_Armor_Design",
"Viking_Ship_Model",
"Viking_Folk_Art",
"Nationalist_Landscape_Painting",
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"Giorgio De Chirico",
"Giotto Di Bondone",
"Giovanni Battista Bracelli",
"Giovanni Battista Gaulli",
"Giovanni Battista Piranesi",
"Giovanni Battista Venanzi",
"Giovanni da Udina",
"Giuseppe Arcimboldo",
"Giuseppe de Nittis",
"Gjon Mili",
"Glen Orbik",
"Glenn Fabry",
"Gloria Stoll Karn",
"Go Nagai",
"Gordon Browne",
"Gordon Parks",
"Goro Fujita",
"Grace Cossington Smith",
"Grace Popp",
"Grandma Moses",
"Grant Wood",
"Grayson Perry",
"Greg Girard",
"Greg Hildebrandt",
"Greg Rutkowski",
"Greg Simkins",
"Gregory Crewdson",
"Guerrilla Girls",
"Guido Borelli Da Caluso",
"Guido Crepax",
"Guillermo del Toro",
"Guo Pei",
"Gustaf Tenggren",
"Gustav Klimt",
"Gustave Buchet",
"Gustave Courbet",
"Gustave Dor\u00e9",
"Gustave Moreau",
"Gustave Van de Woestijne",
"Guy Billout",
"Gwen John",
"Gwenda Morgan",
"H. R. (Hans Ruedi) Giger",
"H.P. Lovecraft",
"Haddon Sundblom",
"Hajime Sorayama",
"Hal Foster",
"Hale Woodruff",
"Hanna-Barbera",
"Hannah Hoch",
"Hans Arnold",
"Hans Baldung",
"Hans Baluschek",
"Hans Bellmer",
"Harold McCauley",
"Haroon Mirza",
"Harriet Backer",
"Harry Clarke",
"Hasui Kawase",
"Hayao Miyazaki",
"Hayv Kahraman",
"Hein Gorny",
"Heinrich Kley",
"Heinrich Lefler",
"Heinz Edelmann",
"Helen Frankenthaler",
"Helene Knoop",
"Helene Schjerfbeck",
"Helio Oiticica",
"Helmut Newton",
"Hendrick Avercamp",
"Hendrick Cornelisz Vroom",
"Hendrick Goltzius",
"Hendrik Kerstens",
"Henri De Toulouse Lautrec",
"Henri Fantin Latour",
"Henri Matisse",
"Henri Rousseau",
"Henri-Edmond Cross",
"Henriette Grindat",
"Henry Asencio",
"Henry Fuseli",
"Henry Moore",
"Henry Moret",
"Henry Ossawa Tanner",
"Henry Raleigh",
"Herbert List",
"Herve Groussin",
"Herv\u00e9 Guibert",
"Hethe Srodawa",
"Hieronymus Bosch",
"Hikari Shimoda",
"Hilma AF Klint",
"Hirohiko Araki",
"Hiroshi Nagai",
"Hiroshi Sugimoto",
"Hiroshi Yoshida",
"Honor C. Appleton",
"Honor\u00e9 Daumier",
"Hope Gangloff",
"Horace Vernet",
"Hou China",
"Howard Chandler Christy",
"Howard Finster",
"Howard Hodgkin",
"Howard Pyle",
"Hsiao-Ron Cheng",
"Hubert Robert",
"Hugh Ferriss",
"Hugh Kretschmer",
"Hyacinthe Rigaud",
"Iain Faulkner",
"Ian McQue",
"Ian Miller",
"Ida Rentoul Outhwaite",
"Igor Morski",
"Igor Wolski",
"Igor Zenin",
"Ilya Kuvshinov",
"Ilya Repin",
"Incarcerated Jerkfaces",
"Ingrid Baars",
"Inio Asano",
"Irma Stern",
"Iryna Yermolova",
"Isaac Cordal",
"Isaac Levitan",
"Ismail Inceoglu",
"Issac Levitan",
"Istvan Banyai",
"It\u014d Jakuch\u016b",
"Ivan Aivazovsky",
"Ivan Albright",
"Ivan Bilibin",
"Ivan Shishkin",
"Iwan Baan",
"J. J. Grandville",
"J.C. Leyendecker",
"J.M.W. Turner",
"JC Leyendecker",
"Jacek Yerka",
"Jack Butler Yeats",
"Jack Davis",
"Jack Gaughan",
"Jack Kirby",
"Jackson Pollock",
"Jacob Hashimoto",
"Jacob Lawrence",
"Jacob van Ruisdael",
"Jacques Le Moyne",
"Jacques Nathan-Garamond",
"Jake Parker",
"Jakub R\u00f3\u017calski",
"James Abbott McNeill Whistler",
"James C Christensen",
"James Ensor",
"James Gilleard",
"James Gillray",
"James Gurney",
"James Jean",
"James Montgomery Flagg",
"James Paick",
"James Stokoe",
"James Thomas Watts",
"James Tissot",
"James Turrell",
"Jamie Baldridge",
"Jamie Hawkesworth",
"Jamie Hewlett",
"Jamie McKelvie",
"Jamini Roy",
"Jan Brett",
"Jan Luyken",
"Jan Pietersz Saenredam",
"Jan Van Eyck",
"Jan van Kessel the Elder",
"Jane Graverol",
"Jane Newland",
"Janek Sedlar",
"Jasmine Becket-Griffith",
"Jason A. Engle",
"Jason Chan",
"Jason Edmiston",
"Jasper Johns",
"Jaume Plensa",
"Jaya Suberg",
"Jean Arp",
"Jean Auguste Dominique Ingres",
"Jean Bourdichon",
"Jean Delville",
"Jean Dubuffet",
"Jean Fouquet",
"Jean Giraud",
"Jean Jullien",
"Jean Marc Nattier",
"Jean Metzinger",
"Jean Nouvel",
"Jean-Antoine Watteau",
"Jean-Baptiste Monge",
"Jean-Fran\u00e7ois Millet",
"Jean-Honor\u00e9 Fragonard",
"Jean-Louis Prevost",
"Jean-L\u00e9on G\u00e9r\u00f4me",
"Jean-Michel Basquiat",
"Jean-Paul Riopelle",
"Jeanloup Sieff",
"Jeannette Guichard-Bunel",
"Jed Henry",
"Jef Wu",
"Jeff Easley",
"Jeff Goldblum",
"Jeff Kinney",
"Jeff Koons",
"Jeff Legg",
"Jeff Lemire",
"Jeff Simpson",
"Jeff Wall",
"Jeffrey Catherine Jones",
"Jeffrey Smith art",
"Jeffrey T. Larson",
"Jenny Saville",
"JennyBird Alcantara",
"Jeremiah Ketner",
"Jeremy Geddes",
"Jeremy Lipking",
"Jeremy Mann",
"Jerry Pinkney",
"Jerry Siegel",
"Jerzy Duda-Gracz",
"Jesper Ejsing",
"Jessica Rossier",
"Jessica Woulfe",
"Jessie Willcox Smith",
"Jhonen Vasquez",
"Jillian Tamaki",
"Jim Burns",
"Jim Davis",
"Jim Lee",
"Jim Mahfood",
"Jim Woodring",
"Jimmy Ernst",
"Jimmy Lawlor",
"Joachim Brohm",
"Joan Mir\u00f3",
"Joan Tuset",
"Joanna Kustra",
"Joao Ruas",
"Joaqu\u00edn Sorolla",
"Joe Bowler",
"Joe De Mers",
"Joe Fenton",
"Joe Jusko",
"Joe Madureira",
"Joe Webb",
"Joel Meyerowitz",
"Joel Sternfeld",
"Joey Chou",
"Johann Wolfgang von Goethe",
"Johannes Itten",
"Johannes Vermeer",
"Johannes Voss",
"Johfra Bosschart",
"John Anster Fitzgerald",
"John Atherton",
"John Atkinson Grimshaw",
"John Bauer",
"John Berkey",
"John Blanche",
"John Bratby",
"John Cassaday",
"John Constable",
"John Currin",
"John Duncan",
"John Frederick Kensett",
"John French Sloan",
"John Harris",
"John Howe",
"John Hoyland",
"John James Audubon",
"John Kenn Mortensen",
"John La Farge",
"John Lavery",
"John Martin",
"John Perceval",
"John Philip Falter",
"John Salminen",
"John Singer Sargent",
"John Singleton Copley",
"John Stezaker",
"John Totleben",
"John Wayne Gacy",
"John Whitcomb",
"John Wilhelm",
"John William Waterhouse",
"Jon Klassen",
"Jon McCoy",
"Jon Whitcomb",
"Jordan Grimmer",
"Jorge Jacinto",
"Josan Gonzalez",
"Josef Albers",
"Joseph Cornell",
"Joseph Ducreux",
"Joseph Lorusso",
"Joseph Mallord William Turner",
"Joseph Stella",
"Josephine Wall",
"Josh Kao",
"Josh Keyes",
"Jos\u00e9 Clemente Orozco",
"Jovana Rikalo",
"Juan Gris",
"Judy Chicago",
"Juergen Teller",
"Jules Bastien-Lepage",
"Julia Contacessi",
"Julian Calle",
"Juliana Huxtable",
"Julie Bell",
"Julie Blackmon",
"Julie Mehretu",
"Julien Delval",
"Julius Horsthuis",
"Jun Kaneko",
"Junji Ito",
"Justin Gerard",
"J\u00f3zef Mehoffer",
"Kadir Nelson",
"Kaethe Butcher",
"Kapwani Kiwanga",
"Karel Appel",
"Karel Thole",
"Karen Wallis",
"Karl Blossfeldt",
"Karl Schmidt-Rottluff",
"Karol Bak",
"Kasia Nowowiejska",
"Kate Beaton",
"Kate Greenaway",
"Kathryn Morris Trotter",
"Kati Horna",
"Katsuhiro Otomo",
"Katsushika Hokusai",
"Kawanabe Ky\u014dsai",
"Kaws",
"Kay Nielsen",
"Kay Sage",
"Kazimir Malevich",
"Kazuo Koike",
"Kehinde Wiley",
"Keith Haring",
"Keith Negley",
"Keith Parkinson",
"Kelly Freas",
"Kelly Mckernan",
"Kelly Sue Deconnick",
"Kelly Vivanco",
"Ken Fairclough",
"Ken Kelly",
"Ken Sugimori",
"Kengo Kuma",
"Kenne Gregoire",
"Kent Monkman",
"Kentaro Miura",
"Kevin Gnutzmans",
"Kevin Sloan",
"Kieron Gillen",
"Kilian Eng",
"Kim Jung Gi",
"Kim Keever",
"Kitagawa Utamaro",
"Kitty Lange Kielland",
"Klaus Burgle",
"Klaus Janson",
"Klaus Wittmann",
"Kobayashi Kiyochika",
"Konstantin Korovin",
"Konstantin Yuon",
"Koson Ohara",
"Krenz Cushart",
"Kris Kuksi",
"Kuang Hong",
"Kunisada",
"Kuno Veeber",
"Kurzgesagt",
"K\u00e4the Kollwitz",
"L. Birge Harrison",
"Lady Pink",
"Larry Elmore",
"Larry Poons",
"Larry Sultan",
"Laurel Burch",
"Laurent Grasso",
"Laurie Greasley",
"Laurie Lipton",
"Lawren Harris",
"Lee Krasner",
"Lee Madgwick",
"Lee Quinones",
"Leiji Matsumoto",
"Leon Kossoff",
"Leonardo Da Vinci",
"Leonetto Cappiello",
"Leonid Afremov",
"Leonora Carrington",
"Les Edwards",
"Lesley Vance",
"Leticia Gillett",
"Liam Wong",
"Liang Mark",
"Lisa Frank",
"Lisa Keene",
"Liu Ye",
"Liubov Sergeevna Popova",
"Lois van Baarle",
"Loish",
"Lorena Alvarez G\u00f3mez",
"Lorenz Hideyoshi",
"Loretta Lux",
"Lori Earley",
"Louis Comfort Tiffany",
"Louis Glackens",
"Louis Icart",
"Louis Janmot",
"Louis Rhead",
"Louis Wain",
"Louise Bourgeois",
"Louise Dahl-Wolfe",
"Lovis Corinth",
"Luca Boni",
"Lucas Cranach the Elder",
"Lucian Freud",
"Lucy Madox Brown",
"Ludwig Mies van der Rohe",
"Luis Royo",
"Luisa Russo",
"Lynd Ward",
"Lynda Barry",
"Lynda Benglis",
"Lyonel Feininger",
"Lyubov Popova",
"L\u00e1szl\u00f3 Moholy-Nagy",
"M.C. Escher",
"M.W. Kaluta",
"Mab Graves",
"Maginel Wright Enright Barney",
"Magnus Enckell",
"Makoto Shinkai",
"Malcolm Liepke",
"Man Ray",
"Mandy Disher",
"Mao Hamaguchi",
"Marat Latypov",
"Marc Chagall",
"Marc Davis",
"Marc Samson",
"Marc Simonetti",
"Marcin Jakubowski",
"Marco Mazzoni",
"Marcus Selmer",
"Marek Okon",
"Margaret Brundage",
"Margaret Macdonald Mackintosh",
"Margaret Mee",
"Margaux Valonia",
"Maria Kreyn",
"Maria Pascual Alberich",
"Maria Sibylla Merian",
"Marianne North",
"Marianne von Werefkin",
"Marie Guillemine Benoist",
"Marie Spartali Stillman",
"Marina Abramovi\u0107",
"Marius Borgeaud",
"Marjane Satrapi",
"Mark Arian",
"Mark Briscoe",
"Mark Brooks",
"Mark Keathley",
"Mark Lovett",
"Mark Rothko",
"Mark Ryden",
"Mark Seliger",
"Marsden Hartley",
"Martin Ansin",
"Martin Deschambault",
"Martin John Heade",
"Martin Johnson Heade",
"Martin Kippenberger",
"Martine Johanna",
"Martiros Saryan",
"Mary Anning",
"Mary Blair",
"Mary Cassatt",
"Masaaki Sasamoto",
"Masamune Shirow",
"Mat Collishaw",
"Mati Klarwein",
"Matias Hannecke",
"Matt Bors",
"Matt Fraction",
"Matt Groening",
"Matthias Gr\u00fcnewald",
"Matthias Jung",
"Matti Suuronen",
"Maurice Sendak",
"Max Beckmann",
"Max Dupain",
"Max Ernst",
"Max Pechstein",
"Max Weber",
"Maxfield Parrish",
"Maximilian Pirner",
"Maximilien Luce",
"Maxwell Boas",
"Mead Schaeffer",
"Meryl McMaster",
"Michael Carson",
"Michael Cheval",
"Michael Deforge",
"Michael Heizer",
"Michael Hutter",
"Michael Parkes",
"Michael Sowa",
"Michael Whelan",
"Michal Karcz",
"Michal Lisowski",
"Michelangelo Buonarroti",
"Michelangelo Merisi Da Caravaggio",
"Mickalene Thomas",
"Miho Hirano",
"Mikalojus Konstantinas Ciurlionis",
"Mike Campau",
"Mike Deodato",
"Mike Mayhew",
"Mike Mignola",
"Mike Winkelmann (Beeple)",
"Mike Worrall",
"Mikhail Larionov",
"Mikhail Nesterov",
"Mikhail Vrubel",
"Mikko Lagerstedt",
"Milo Manara",
"Milton Avery",
"Milton Caniff",
"Milton Glaser",
"Miriam Schapiro",
"Moebius",
"Mordecai Ardon",
"Mort Kunstler",
"Muxxi",
"M\u00e9ret Oppenheim",
"NC Wyeth",
"NHK Animation",
"Nagel Patrick",
"Nan Goldin",
"Naoki Urasawa",
"Naoko Takeuchi",
"Naomi Okubo",
"Naoto Hattori",
"Natalia Goncharova",
"Nathan Coley",
"Nathan Wirth",
"Neil Boyle",
"Neil Welliver",
"Nele Zirnite",
"Ni Chuanjing",
"Nicholas Roerich",
"Nick Knight",
"Nick Sharratt",
"Nick Silva",
"Nicola Samori",
"Nicolas Delort",
"Nicolas Mignard",
"Nicolas de Stael",
"Nikolai Ge",
"Nikolina Petolas",
"Noah Bradley",
"Nobuyoshi Araki",
"Noelle Stevenson",
"Noriyoshi Ohrai",
"Norman Ackroyd",
"Norman Bluhm",
"Norman Foster",
"Norman Rockwell",
"OSGEMEOS",
"Octavio Ocampo",
"Odd Nerdrum",
"Odilon Redon",
"Ogawa Kazumasa",
"Ohara Koson",
"Olafur Eliasson",
"Oleg Oprisco",
"Olga Skomorokhova",
"Olivier Bonhomme",
"Olivier Valsecchi",
"Ollie Hoff",
"Os Gemeos",
"Osamu Tezuka",
"Oskar Fischinger",
"Oskar Kokoschka",
"Ossip Zadkine",
"Otto Dix",
"Otto Marseus van Schrieck",
"Pablo Picasso",
"Pamela Colman Smith",
"Paolo Roversi",
"Paolo Veronese",
"Pascal Blanche",
"Pascale Campion",
"Patrice Murciano",
"Patricia Polacco",
"Patrick Brown",
"Patrick Caulfield",
"Patrick Dougherty",
"Patrick Heron",
"Patrick Woodroffe",
"Paul Barson",
"Paul Chadeisson",
"Paul Corfield",
"Paul C\u00e9zanne",
"Paul Delvaux",
"Paul Gauguin",
"Paul Gustav Fischer",
"Paul Henry",
"Paul Klee",
"Paul Laffoley",
"Paul Lehr",
"Paul Ranson",
"Paul Strand",
"Paul Wonner",
"Paula Modersohn-Becker",
"Paulus Potter",
"Pawel Kuczynski",
"Peter Andrew Jones",
"Peter Bagge",
"Peter De Seve",
"Peter Doig",
"Peter Elson",
"Peter Gric",
"Peter Holme III",
"Peter Howson",
"Peter Kemp",
"Peter Max",
"Peter Milligan",
"Peter Mohrbacher",
"Peter Paul Rubens",
"Peter Sculthorpe",
"Peter Wileman",
"Peter Zumthor",
"Phil Foglio",
"Phil Jimenez",
"Phil Koch",
"Phil Noto",
"Philip Guston",
"Philippe Druillet",
"Philippe Parreno",
"Pierre Bonnard",
"Pierre Puvis de Chavannes",
"Pierre-Auguste Renoir",
"Piet Hein Eek",
"Piet Mondrian",
"Pieter Aertsen",
"Pieter Bruegel The Elder",
"Pieter Claesz",
"Pieter Jansz Saenredam",
"Pieter de Hooch",
"Piotr Jab\u0142o\u0144ski",
"Pipilotti Rist",
"Pixar",
"Pixar Concept Artists",
"Posuka Demizu",
"Qian Xuan",
"Qing Han",
"Quentin Blake",
"Quentin Tarantino",
"Quint Buchholz",
"RETNA (Marquis Lewis)",
"RHADS",
"ROA",
"Rafael Albuquerque",
"Rafa\u0142 Olbi\u0144ski",
"Raffaello Sanizo",
"Raina Telgemeier",
"Raja Ravi Varma",
"Ralph Horsley",
"Ralph McQuarrie",
"Ralph Steadman",
"Ramon Casas",
"Randolph Caldecott",
"Raphael",
"Raphael Lacoste",
"Raphaelle Peale",
"Ravi Zupa",
"Ray Caesar",
"Ray Donley",
"Raymond Briggs",
"Raymond Duchamp-Villon",
"Raymond Leech",
"Raymond Swanland",
"Rayner Alencar",
"Rebeca Saray",
"Rebecca Guay",
"Rebecca Louise Law",
"Rebecca Sugar",
"Reginald Marsh",
"Rembrandt Van Rijn",
"Remedios Varo",
"Rene Laloux",
"Rene Magritte",
"Ren\u00e9 Lalique",
"Reylia Slaby",
"Rich Davies",
"Richard Burlet",
"Richard Corben",
"Richard Dadd",
"Richard Deacon",
"Richard Diebenkorn",
"Richard Doyle",
"Richard Eurich",
"Richard Hamilton",
"Richard Lindner",
"Richard McGuire",
"Richard Misrach",
"Richard S. Johnson",
"Richard Scarry",
"Rick Guidice",
"Rob Gonsalves",
"Rob Liefeld",
"Robby Cavanaugh",
"Robert Antoine Pinchon",
"Robert Chew",
"Robert Childress",
"Robert Crumb",
"Robert Farkas",
"Robert Hagan",
"Robert Irwin",
"Robert M Cunningham",
"Robert Maguire",
"Robert McCall",
"Robert Mcginnis",
"Robert Motherwell",
"Robert Neubecker",
"Robert Rauschenberg",
"Robert S. Duncanson",
"Robert Stivers",
"Robert Vonnoh",
"Robert William Hume",
"Robert Williams",
"Roberto Ferri",
"Roberto Matta",
"Roberto Parada",
"Rockwell Kent",
"Rodney Matthews",
"Rodr\u00edguez ARS",
"Roger Ballen",
"Roger Dean",
"Roger de La Fresnaye",
"Rolf Armstrong",
"Romero Britto",
"Ron Mueck",
"Ron Walotsky",
"Ronald Balfour",
"Ross Tran",
"Roy Gjertson",
"Roy Lichtenstein",
"Roz Chast",
"Ruan Jia",
"Rudolf Freund",
"Rufino Tamayo",
"Rumiko Takahashi",
"Russ Mills",
"Russell Ayto",
"Ruth Bernhard",
"Ruxing Gao",
"Ryan Hewett",
"Ryan McGinley",
"Ryan Stegman",
"Ryohei Hase",
"Sacha Goldberger",
"Sailor Moon",
"Sakai Ho\u0304itsu",
"Sally Mann",
"Salomon van Ruysdael",
"Salvador Dali",
"Sam Bosma",
"Sam Kieth",
"Sam Spratt",
"Samuel Earp",
"Samuel Melton Fisher",
"Samuel and Joseph Newsom",
"Sandra Chevrier",
"Sandro Botticelli",
"Sandy Skoglund",
"Saner Edgar",
"Sanford Kossin",
"Sangyeob Park",
"Santiago Calatrava",
"Santiago Caruso",
"Sara Wollfalk",
"Sarah Lucas",
"Satoshi Kon",
"Saturno Butto",
"Saul Bass",
"Saul Steinberg",
"Saul Tepper",
"Scarlett Hooft Graafland",
"Scott Brundage",
"Scott Listfield",
"Scott Naismith",
"Sean Scully",
"Sean Yoro",
"Seb Mckinnon",
"Sebastian Errazuriz",
"Serge Marshennikov",
"Shaddy Safadi",
"Shaun Tan",
"Shawn Coss",
"Sheilah Beckett",
"Shepard Fairey",
"Sherree Valentine Daines",
"Shin Jeongho",
"Shinji Aramaki",
"Shintaro Kago",
"Shohei Otomo",
"Shotaro Ishinomori",
"Shusei Nagaoko",
"Sidney Nolan",
"Silvestro Lega",
"Simeon Solomon",
"Simon Birch",
"Simon Bisley",
"Simon Stalenhag",
"Simone Martini",
"Sir James Guthrie",
"Siya Oum",
"Skottie Young",
"Slim Aarons",
"Sofonisba Anguissola",
"Sonia Delaunay",
"Sou Fujimoto",
"Sparth",
"Squeak Carnwath",
"Stan And Jan Berenstain",
"Stan Lee",
"Stanislav Poltavsky",
"Stanis\u0142aw Szukalski",
"Stanley Donwood",
"Stephan Martiniere",
"Stephen Gammell",
"Stephen Oakley",
"Stephen Shore",
"Stevan Dohanos",
"Steve Argyle",
"Steve Dillon",
"Steve Ditko",
"Steve Henderson",
"Steve Lieber",
"Steve McCurry",
"Steven Belledin",
"Storm Thorgerson",
"Stuart Davis",
"Stuart Haygarth",
"Stuart Immonen",
"Studio Ghibli",
"Sue Bryce",
"Susan Luo",
"Susan Seddon Boulet",
"Sven Nordqvist",
"Syd Mead",
"Sydney Edmunds",
"Sydney Prior Hall",
"Tadao Ando",
"Taiy\u014d Matsumoto",
"Takashi Murakami",
"Takato Yamamoto",
"Takeshi Obata",
"Tamara Lempicka",
"Tan Zhi Hui",
"Tara McPherson",
"Tari Ma\u0301rk Da\u0301vid",
"Tatsuro Kiuchi",
"Ted Nasmith",
"Ted Wallace",
"Teophilus Tetteh",
"Terada Katsuya",
"Teresa Ramos",
"Terry Oakes",
"Terry Redlin",
"Tex Avery",
"Theo van Rysselberghe",
"Thomas Allom",
"Thomas Benjamin Kennington",
"Thomas Blackshear",
"Thomas Cole",
"Thomas Dodd",
"Thomas Eakins",
"Thomas Gainsborough",
"Thomas Moran",
"Thomas Rowlandson",
"Thomas Saliot",
"Thomas Struth",
"Thomas Visscher",
"Thomas W Schaller",
"Thornton Oakley",
"Th\u00e9odore G\u00e9ricault",
"Tibor Nagy",
"Till Freitag",
"Tim Burton",
"Tim Doyle",
"Tim Hildebrandt",
"Tim White",
"Tintoretto",
"Titian",
"Todd McFarlane",
"Todd Schorr",
"Toei Animations",
"Tokujin Yoshioka",
"Tom Bagshaw",
"Tom Hammick",
"Tom Lovell",
"Tom Roberts",
"Tom Thomson",
"Tom Whalen",
"Tomasz Alen Kopera",
"Tomer Hanuka",
"Tomi Ungerer",
"Tomma Abts",
"Tomokazu Matsuyama",
"Tony DiTerlizzi",
"Tony Moore",
"Toshiharu Mizutani",
"Toumas Korpi",
"Tove Jansson",
"Tracey Emin",
"Travis Louie",
"Tristan Eaton",
"Tsutomu Nihei",
"Tyler Edlin",
"Tyler Shields",
"Tyler West",
"Ub Iwerks",
"Uemura Shoen",
"Ul Di Rico",
"Umberto Boccioni",
"Utagawa Hiroshige",
"Valerie Hegarty",
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"Environmental Portraits Depth of Field",
"Close-Up Depth of Field",
"Street Photography Depth of Field",
"Subject in Motion Depth of Field",
"Architecture Depth of Field",
"Panoramic Depth of Field",
"Still Life Depth of Field",
"Creative or Abstract Depth of Field",
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"Focus Distance 1.0m",
"Focus Distance 1.5m",
"Focus Distance 2.0m",
"Focus Distance 2.5m",
"Focus Distance 3.0m",
"Focus Distance 3.5m",
"Focus Distance 4.0m",
"Focus Distance 4.5m",
"Focus Distance 5.0m",
"Focus Distance 5.5m",
"Focus Distance 6.0m",
"Focus Distance 6.5m",
"Focus Distance 7.0m",
"Focus Distance 7.5m",
"Focus Distance 8.0m",
"Focus Distance 8.5m",
"Focus Distance 9.0m",
"Focus Distance 9.5m",
"Focus Distance 10.0m",
"10% Focus Area",
"20% Focus Area",
"30% Focus Area",
"40% Focus Area",
"50% Focus Area",
"60% Focus Area",
"70% Focus Area",
"80% Focus Area",
"90% Focus Area",
"100% Focus Area",
"Manual Focus",
"Autofocus",
"Focus Stacking",
"Back-Button Focus",
"Focus Bracketing",
"Selective Focus",
"Deep Focus",
"Soft Focus",
"Macro Focus",
"Infinity Focus",
"Zone Focus",
"Edge-to-edge Focus",
"Foreground Focus",
"Background Focus",
"Central Focus",
"Corner Focus",
"Top Focus",
"Bottom Focus",
"Right Focus",
"Left Focus",
"Rack Focus",
"Pull Focus",
"Rolling Focus",
"Tilt-shift Focus",
"Fly-by Focus",
"Follow Focus",
"Zoom Focus",
"Dolly Focus",
"Pan Focus",
"Tilt Focus",
"Whip Focus",
"Slide Focus",
"Crane Focus",
"Swing Focus",
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"Gothic Revival Cemetery",
"Gothic Revival Interior",
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"Gothic Revival Ballroom",
"Gothic Revival Clock Tower",
"Gothic Revival Theater",
"Gothic Revival Bridge",
"Gothic Revival Crypt",
"Gothic Revival Library",
"Gothic Revival Cemetery Gate",
"Gothic Revival Chapel",
"Gothic Revival Cityscape",
"Gothic Revival Forest Chapel",
"Gothic Revival Clockwork Cathedral",
"Gothic Revival Art Gallery",
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},
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"Arachnid Swarm",
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"Bloodthirsty Vampire",
"Butcher Shop",
"Cabinet of Curiosities",
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"Conjoined Twins",
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"Creepy Children",
"Creepy Porcelain Doll",
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"Disrespectful Grave Robber",
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"Exorcism",
"Ferocious Werewolf",
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"Gothic Architecture",
"Gothic Monster",
"Gothic Subculture",
"Gothic Revival Architecture",
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"Headless Horseman",
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"William Eggleston",
"---------------------",
"sai-base",
"sai-3d-model",
"sai-analog film",
"sai-anime",
"sai-cinematic",
"sai-comic book",
"sai-craft clay",
"sai-digital art",
"sai-enhance",
"sai-fantasy art",
"sai-isometric",
"sai-line art",
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"sai-neonpunk",
"sai-origami",
"sai-photographic",
"sai-pixel art",
"sai-texture",
"ads-advertising",
"ads-automotive",
"ads-corporate",
"ads-fashion editorial",
"ads-food photography",
"ads-luxury",
"ads-real estate",
"ads-retail",
"artstyle-abstract",
"artstyle-abstract expressionism",
"artstyle-art deco",
"artstyle-art nouveau",
"artstyle-constructivist",
"artstyle-cubist",
"artstyle-expressionist",
"artstyle-graffiti",
"artstyle-hyperrealism",
"artstyle-impressionist",
"artstyle-pointillism",
"artstyle-pop art",
"artstyle-psychedelic",
"artstyle-renaissance",
"artstyle-steampunk",
"artstyle-surrealist",
"artstyle-typography",
"artstyle-watercolor",
"futuristic-biomechanical",
"futuristic-biomechanical cyberpunk",
"futuristic-cybernetic",
"futuristic-cybernetic robot",
"futuristic-cyberpunk cityscape",
"futuristic-futuristic",
"futuristic-retro cyberpunk",
"futuristic-retro futurism",
"futuristic-sci-fi",
"futuristic-vaporwave",
"game-bubble bobble",
"game-cyberpunk game",
"game-fighting game",
"game-gta",
"game-mario",
"game-minecraft",
"game-pokemon",
"game-retro arcade",
"game-retro game",
"game-rpg fantasy game",
"game-strategy game",
"game-streetfighter",
"game-zelda",
"misc-architectural",
"misc-disco",
"misc-dreamscape",
"misc-dystopian",
"misc-fairy tale",
"misc-gothic",
"misc-grunge",
"misc-horror",
"misc-kawaii",
"misc-lovecraftian",
"misc-macabre",
"misc-manga",
"misc-metropolis",
"misc-minimalist",
"misc-monochrome",
"misc-nautical",
"misc-space",
"misc-stained glass",
"misc-techwear fashion",
"misc-tribal",
"misc-zentangle",
"papercraft-collage",
"papercraft-flat papercut",
"papercraft-kirigami",
"papercraft-paper mache",
"papercraft-paper quilling",
"papercraft-papercut collage",
"papercraft-papercut shadow box",
"papercraft-stacked papercut",
"papercraft-thick layered papercut",
"photo-alien",
"photo-film noir",
"photo-hdr",
"photo-long exposure",
"photo-neon noir",
"photo-silhouette",
"photo-tilt-shift",
"Vintage Travel Poster",
"Prismatic",
"Glamorous Portrait",
"Scientific Illustration",
"Stained Glass",
"Art Deco Architecture",
"Pop Surrealism",
"Vintage Tattoo Flash",
"Sports Card",
"Stop Motion",
"Bauhaus Design",
"Propaganda Poster",
"Art Nouveau",
"Glitch Art",
"Blueprint",
"Woodblock Print",
"Neon Lighting",
"Bas-Relief Sculpture",
"Pop Art",
"Art Nouveau Poster",
"Vector Portrait",
"Egyptian Hieroglyphs",
"Movie Storyboard",
"Disney Animation",
"Studio Ghibli",
"Aardman",
"Xilam",
"Ankama",
"Mixer",
"LAIKA",
"Toei",
"UPA",
"Vaporwave Retro",
"Central American",
"Vogue Cover",
"Satanic",
"Fortune Telling",
"Southern Gothic",
"Nouveau Circus",
"Rococo Interior",
"Mexican Skull Art",
"Psychedelic Pop Art",
"Voodoo",
"Vintage Baseball",
"Circus Performer",
"Tiki Bar",
"Apr\u00e8s-Ski",
"Kawaii Fashion",
"Tropical Luau",
"Voodoo Doll",
"Mardi Gras",
"Figurine Shelf",
"Terrarium",
"Tiki Cocktail",
"Cottagecore Fashion",
"Vintage Tattoo Print",
"Boudoir Photography",
"Vaporwave",
"Terrarium Bottle",
"Vaporwave City",
"Kawaii Character",
"Vintage Halloween Costume",
"Cassette Futurism",
"Punk Poster",
"Luchador",
"Tiki Totem",
"Cassette J-Card",
"Prairie Dress",
"Lounge Singer",
"Gongfu Tea",
"Aztec Calendar",
"Cassette Graphics",
"Laser Grid",
"Giant Robot",
"Vaporwave Sunset",
"Fortune Teller",
"Steampunk Portrait",
"Scary Stories",
"Woodblock Art",
"Vintage Halloween Mask",
"Grunge Flyer",
"Voodoo Altar",
"Cassette Collage",
"Tropical Cocktail",
"Vintage Robot Toy",
"Scary Pumpkin",
"Pinup",
"Tarot Cards",
"Ghibli",
"Egyptology",
"Battle",
"Tarot",
"Steampunk",
"Ancient Maya",
"Nautical",
"Bomber Jacket",
"Samurai",
"American Traditional",
"Propaganda Art",
"Glitchcore",
"Vaporgram",
"Desaturated",
"Dieselpunk",
"My Little Pony",
"Ballet",
"Galactic",
"Streamer Bike",
"Tropical Hotel",
"Tiki Mug",
"Neon Racer",
"Luau Fire Dancer",
"Day of the Dead",
"Sideshow Poster",
"Vaporwave Graphics",
"Voodoo Ceremony",
"Blacklight Poster",
"Teslapunk",
"Cassette Bedroom",
"Tropical Bathroom",
"Voodoo Shop",
"Vintage Halloween",
"Goth Boudoir",
"Island Luau",
"Tiki Outdoor Shower",
"Black Velvet Painting",
"Tattoo Print",
"Addams Family",
"Cassette Wall",
"Neon Tokyo",
"Voodoo Queen",
"Surf Wood Sign",
"Haunted Carnival",
"Tiki Idol",
"Mall Goth",
"Volcano Lair",
"Graffiti Style",
"Impressionism",
"Fauvism",
"Dada",
"Cubism",
"Expressionism",
"Surrealism",
"Symbolism",
"Op Art",
"Minimalism",
"Conceptual Art",
"Folk Art",
"Naive Art",
"Outsider Art",
"Photorealism",
"Suprematism",
"De Stijl",
"Bauhaus",
"Constructivism",
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"Rayonism",
"Vorticism",
"Orphism",
"Der Blaue Reiter",
"Die Br\u00fccke",
"Ashcan School",
"Hudson River School",
"Luminism",
"Tonalism",
"Barbizon School",
"Academic Art",
"Rococo",
"Neoclassicism",
"Romanticism",
"Realism",
"Social Realism",
"Plein Air",
"Pre-Raphaelite",
"Art Informel",
"",
"Hard-edge Painting",
"Geometric Abstraction",
"Lyrical Abstraction",
"Post-Painterly Abstraction",
"Kinetic Art",
"Land Art",
"Performance Art",
"Installation Art",
"Video Art",
"Digital Art",
"New Media Art",
"Street Art",
"Stuckism",
"Lowbrow Art",
"Photomontage",
"Diorama",
"Assemblage",
"Combine Painting",
"Happenings",
"Mail Art",
"Neo-Dada",
"Neo-Expressionism",
"Bad Painting",
"Graffiti",
"Traditional Figurative Art",
"Classical Realism",
"Contemporary Realism",
"Hyperrealism",
"Magic Realism",
"New Objectivity",
"Precisionism",
"Figurative Expressionism",
"Neue Wilde",
"New Perpendicular art",
"New Simplicity",
"Remodernism",
"Norwegian romantic nationalism",
"Socialist Realism",
"Propaganda Poster Art",
"Heroic Realism",
"Na\u00efve Art",
"Art Brut",
"Neo-primitivism",
"Visionary Art",
"Intuitive Art",
"Pseudorealism",
"Radical Realism",
"Critical Realism",
"Neue Sachlichkeit",
"Regionalism",
"Abstract Expressionism",
"Later European abstraction",
"Tachisme",
"Abstraction-Cr\u00e9ation",
"Gutai",
"British Pop Art",
"Situationist International",
"Lettrism",
"St Ives School",
"Transavantgarde",
"Transgressive Art",
"Neo Pop",
"Kitsch Movement",
"Virtual Art",
"Internet Art",
"Computer Art",
"Information Art",
"Systems Art",
"Bio Art",
"Genetic Art",
"Sustainable Art",
"Interactive Art",
"Video Games",
"Machinima",
"Artware",
"Demoscene",
"Math Art",
"Data Art",
"Plotter Art",
"VR Art",
"AR Art",
"Circuit Bending",
"DIY Art",
"Maker Culture",
"Self-taught Art",
"Neo-Pop",
"Young British Artists",
"Appropriation",
"Algorithmic Art",
"Use these to populate the JSON instead."
]
],
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}
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}
},
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},
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},
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"Animated Films",
"Animation",
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"Apr\u00e8s-Ski_Uncategorized",
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"Architectural Photography",
"Argentinian Art",
"Art Activism",
"Art Collaborations with Musicians",
"Art Collaborations with Writers",
"Art Conservation",
"Art Criticism",
"Art Curation",
"Art Deco",
"Art Deco Architecture",
"Art Deco Architecture_Architecture",
"Art Deco Design",
"Art Education",
"Art Education for Adults",
"Art Education for Children",
"Art Education for Remote Areas",
"Art Education for Special Needs",
"Art Gallery Management",
"Art Games",
"Art Historical Writing",
"Art History",
"Art History Research",
"Art Informatics",
"Art Informel (1)_Uncategorized",
"Art Inspired by Ancient Civilizations",
"Art Inspired by the Digital Age",
"Art Inspired by the Renaissance",
"Art Inspired by the Roaring Twenties",
"Art Inspired by the Victorian Era",
"Art Installations",
"Art Journalism",
"Art Marketing",
"Art Nouveau",
"Art Nouveau Architecture",
"Art Nouveau Design",
"Art Nouveau Poster_Uncategorized",
"Art Nouveau Variant_Uncategorized",
"Art Restoration",
"Art Sales and Auctions",
"Art Therapy",
"Art Therapy for Adults",
"Art Therapy for Children",
"Art Workshop Facilitation",
"Art and AI Collaboration",
"Art and Architecture Collaboration",
"Art and Cultural Heritage Preservation",
"Art and Environmental Sustainability",
"Art and Literature Collaboration",
"Art and Medical Collaboration",
"Art and Mental Health",
"Art and Music Collaboration",
"Art and Science Collaboration",
"Art and Social Justice Projects",
"Art and Technology Collaboration",
"Art and Urban Development",
"Art for Agricultural Industry",
"Art for Agricultural Sector",
"Art for Airports",
"Art for Animal Welfare Organizations",
"Art for Anniversaries",
"Art for Aquariums",
"Art for Architectural Visualization",
"Art for Asian Cultures",
"Art for Augmented Reality Experiences",
"Art for Automotive Design",
"Art for Automotive Industry",
"Art for Aviation Industry",
"Art for Baby Showers",
"Art for Birthdays",
"Art for Botanical Gardens",
"Art for Cafes and Restaurants",
"Art for Charity Fundraisers",
"Art for Children",
"Art for Children's Hospitals",
"Art for Climate Change Initiatives",
"Art for Construction Industry",
"Art for Corporate Spaces",
"Art for Cruise Ships",
"Art for Culinary Presentation",
"Art for E-Commerce Platforms",
"Art for Educational Institutions",
"Art for Educational Technology",
"Art for Elderly",
"Art for Emergency Services",
"Art for Energy Industry",
"Art for Entertainment Industry",
"Art for Environmental Activism",
"Art for Environmental Campaigns",
"Art for Factories and Workshops",
"Art for Fashion Industry",
"Art for Festivals and Events",
"Art for Financial Institutions",
"Art for Financial Sector",
"Art for Fitness Centers",
"Art for Funerals",
"Art for Gender Equality",
"Art for Government Entities",
"Art for Graduations",
"Art for Health Care Facilities",
"Art for Home Decor",
"Art for Hospitality Industry",
"Art for Hotels",
"Art for Human Anatomy Studies",
"Art for Human Rights Campaigns",
"Art for Indigenous Cultures",
"Art for LGBTQ+ Celebrations",
"Art for Libraries",
"Art for Marine Industry",
"Art for Maritime Industry",
"Art for Medical Illustrations",
"Art for Military and Defense Sector",
"Art for Military and Veterans",
"Art for Mobile Apps",
"Art for Museums",
"Art for Music Videos",
"Art for National Holidays",
"Art for Nautical Navigation",
"Art for Non-Profit Organizations",
"Art for Office Spaces",
"Art for Outdoor Advertising",
"Art for Packaging Design",
"Art for Pet Products",
"Art for Pharmaceutical Industry",
"Art for Political Campaigns",
"Art for Prisons",
"Art for Public Transportation",
"Art for Real Estate Marketing",
"Art for Religious Celebrations",
"Art for Religious Institutions",
"Art for Renewable Energy Sector",
"Art for Retail Spaces",
"Art for Retirement Parties",
"Art for Robotics",
"Art for Schools and Colleges",
"Art for Science Centers",
"Art for Scientific Exploration",
"Art for Security and Defense",
"Art for Seniors",
"Art for Shopping Malls",
"Art for Smart City Projects",
"Art for Social Media Platforms",
"Art for Social Networking Sites",
"Art for Spa and Wellness Centers",
"Art for Space Exploration",
"Art for Space Industry",
"Art for Spaceships and Aerospace",
"Art for Sports Industry",
"Art for Sports Venues",
"Art for Technical Manuals",
"Art for Teenagers",
"Art for Teens",
"Art for Television Shows",
"Art for Theme Parks",
"Art for Toddlers",
"Art for Train Stations",
"Art for Underwater Exploration",
"Art for Video Game Development",
"Art for Virtual Assistants and AI",
"Art for Virtual Events",
"Art for Virtual Reality Experiences",
"Art for Wearable Technology",
"Art for Wearables",
"Art for Web Platforms",
"Art for Weddings",
"Art for Zoos",
"Art in Public Transportation",
"Art with Light and Projection",
"Art with Metalwork",
"Art with Organic Materials",
"Art with Recycled Materials",
"Artist's Books",
"Artware Variant_Sci-Fi_Graffiti_Digital Media",
"Aspen",
"Assemblage Art",
"Astrophotography",
"Athens",
"Athleisure Fashion",
"Atlantis",
"Augmented Reality (AR) Art",
"Augmented Reality Art",
"Australian Aboriginal Art",
"Autobiography",
"Automotive Design",
"Autumn Art",
"Avant-Garde Fashion",
"Aztec Calendar_Uncategorized",
"Back Alley Rogue_Uncategorized",
"Ballet Dance",
"Ballet_Uncategorized",
"Ballroom Dance",
"Bangkok",
"Banksy_1",
"Banksy_2",
"Barbara Kruger",
"Barcelona",
"Baroque",
"Baroque Architecture",
"Baroque Art",
"Baroque Music",
"Bas-Relief Sculpture_Sculpture",
"Basket Weaving",
"Basket Weaving Art",
"Battle_Uncategorized",
"Bauhaus",
"Bauhaus (1)_Architecture",
"Bauhaus Architecture",
"Bauhaus Design",
"Bauhaus Design_Uncategorized",
"Beachwear Fashion",
"Beijing",
"Belly Dance",
"Berlin",
"Bharatanatyam Dance",
"Bikini Bottom",
"Bio Art",
"Bio Art_Nature",
"Biographical Films",
"Biographical Literature",
"Biography",
"Biomorphic Architecture",
"Black Hole",
"Black Velvet Painting_Portraiture",
"Black and White Photography",
"Blacklight Poster_Uncategorized",
"Blockbuster Films",
"Bloodthirsty Vampire_Horror",
"Bluegrass Music",
"Blueprint_Uncategorized",
"Blues Music",
"Blues Music Illustration",
"Body Art",
"Body Art Performance",
"Body Painting",
"Bohemian Fashion",
"Bomber Jacket_Retro",
"Bookbinding",
"Botanical Illustration",
"Boudoir Photography_Photography",
"Brazil",
"Brazilian Art",
"Brazilian Cuisine",
"Brazilian Graffiti Art",
"Breakdance",
"Bridal Fashion",
"Brightwater Variant_Nature",
"British Art",
"Bronze Sculpture",
"Bruce Nauman",
"Bruges",
"Brutalism",
"Brutalist Architecture",
"Budapest cityscape",
"Cabinet of Curiosities_Occult",
"Cai Guo-Qiang",
"Cake Decorating",
"Canada",
"Candid Portrait Photography",
"Caravaggio",
"Caribbean Carnival Art",
"Caribbean Cuisine",
"Caricature",
"Carnival Freakshow_Retro",
"Caspar David Friedrich",
"Cassette Bedroom_Retro",
"Cassette Collage_Sci-Fi_Surrealism_Retro",
"Cassette Futurism_Retro",
"Cassette Graphics_Retro_Surrealism",
"Cassette J-Card_Retro",
"Cassette Wall_Retro",
"Casual Fashion",
"Caveopolis Variant_Lifestyle",
"Cecily Brown",
"Celtic Knotwork Art",
"Celtic Mythology Art",
"Cemetery Statue_Uncategorized",
"Central African Art",
"Central American_Uncategorized",
"Ceramic Art",
"Ceramic Design",
"Ceramic Sculpture",
"Ceramics",
"Chalk Art",
"Charcoal Drawing",
"Charles Ray",
"Chicago",
"Children's Fashion",
"Children's Theater",
"Chilean Art",
"Chinese Architecture",
"Chinese Art",
"Chinese Calligraphy",
"Chinese Cuisine",
"Chinese Ink Painting",
"Chinese Jade Carving",
"Chinese Landscape Painting",
"Chinese Mythology Art",
"Chinese Paper Cutting",
"Chinese Scroll Painting",
"Chris Ofili",
"Cindy Sherman_1",
"Cindy Sherman_2",
"Cinematography",
"Cinque Terre",
"Circuit Bending_Uncategorized",
"Circus Arts",
"Circus Performer_Retro",
"Classic Western",
"Classical Architecture",
"Classical Art",
"Classical Music",
"Classical Music Illustration",
"Classical Realism",
"Classical Realism_Portraiture",
"Classical Theater",
"Claude Monet",
"Clockwork City Variant_Architecture_Location",
"Collaborative Art Projects",
"Collage",
"Collage Art",
"Colombian Art",
"Colonial Architecture",
"Colosseum",
"Combine Painting_Sci-Fi_Still Life",
"Comedy",
"Comedy Literature",
"Commercial Photography",
"Community Mural Projects",
"Computer art",
"Concept Art for Movies",
"Concept Art for Video Games",
"Conceptual Art",
"Concert Poster Design",
"Conjoined Twins_Uncategorized",
"Constructivism",
"Constructivism Art",
"Contemporary Ballet",
"Contemporary Dance",
"Copenhagen",
"Copenhagen cityscape",
"Corporate Identity Design",
"Cosplay Design",
"Cottagecore Fashion_Fashion",
"Country Music",
"Country Music Graphics",
"Crawler Mimicry_Uncategorized",
"Creepy Children_Portraiture",
"Creepy Porcelain Doll_Fashion_Portraiture",
"Crime Films",
"Critical Realism_Uncategorized",
"Cross-Disciplinary Art",
"Crucifixion_Uncategorized",
"Crystal Caverns Variant_Architecture",
"Cuban Art",
"Cuban Cuisine",
"Cubism",
"Cubism Art",
"Cult Films",
"Cyberpunk",
"Cyberpunk Fantasy Art",
"Dadaism",
"Dadaism Art",
"Damien Hirst_1",
"Damien Hirst_2",
"Dan Flavin",
"Dance Choreography",
"Dance Performance Art",
"Dark Carnival_Gothic",
"Dark Fantasy Art",
"Data Art Variant_Uncategorized",
"Data Art_Uncategorized",
"Data Visualization Art",
"Day of the Dead_Uncategorized",
"De Stijl_Uncategorized",
"Death Masque_Uncategorized",
"Deconstructivist Architecture",
"Demonic Clown_Uncategorized",
"Demonic Portal_Horror",
"Demonic Possession_Uncategorized",
"Demoscene_Animation",
"Desaturated_Uncategorized",
"Die Br\u00fccke_Graffiti",
"Diego Velazquez",
"Dieselpunk_Retro",
"Digital Animation",
"Digital Art",
"Digital Art_Digital Media",
"Digital Drawing Tablets",
"Digital Illustration",
"Digital Painting",
"Digital Sculpture",
"Digital Storytelling",
"Diorama_Uncategorized",
"Disco Music",
"Disney Animation_Animation",
"Disrespectful Grave Robber_Uncategorized",
"Documentary Films",
"Documentary Photography",
"Drama",
"Drama Films",
"Dubai",
"Dublin",
"Dublin cityscape",
"Dunder Mifflin",
"Dutch Art",
"Dwarvendom Variant_Uncategorized",
"Dwarvenholm Variant_Uncategorized",
"Earth Art",
"East African Art",
"Eco Art",
"Eco-Art",
"Ed Ruscha",
"Edgar Degas",
"Edinburgh cityscape",
"Editorial Design",
"Edvard Munch",
"Edward Hopper",
"Egyptian Hieroglyphs_Uncategorized",
"Egyptian Mythology Art",
"Egyptian Wall Art",
"Egyptology_Uncategorized",
"El Anatsui",
"Electronic Music",
"Electronic Music Visuals",
"Elegant_Erotic_Photography",
"Elfheim Variant_Architecture_Fantasy",
"Elven City Variant_Architecture_Location",
"Embroidery",
"Emerging_Artist",
"Engraving",
"Environmental Art",
"Environmental Design",
"Ephemeral Art",
"Etching",
"Eugene Delacroix",
"Exhibition Design",
"Exoplanet",
"Exorcism_Uncategorized",
"Experimental Art",
"Experimental Films",
"Experimental Music Video",
"Experimental Photography",
"Experimental Theater",
"Expressionism",
"Expressionist Architecture",
"Expressionist painting",
"Fairy Tale Art",
"Fantasy",
"Fantasy Films",
"Fantasy Literature",
"Farce",
"Fashion Design",
"Fashion Illustration",
"Fashion Photography",
"Fast Fashion",
"Fauvism",
"Fauvism Art",
"Ferocious Werewolf_Uncategorized",
"Festival Fashion",
"Fiction",
"Figurative Expressionism_Uncategorized",
"Figurine Shelf_Fantasy_Sculpture",
"Filipino Art",
"Film Direction",
"Film Editing",
"Film Noir",
"Fine Art Photography",
"Fine_Art_Black_and_White_Photography",
"Fire Art",
"Flamenco Dance",
"Folk Art Variant_Folk Art",
"Folk Art_Folk Art",
"Folk Dance",
"Folk Music",
"Folk Music Art",
"Food Art",
"Food Photography",
"Formal Fashion",
"Fortune Teller_Occult",
"Fortune Telling_Occult",
"France",
"Francisco Goya",
"Frankfurt cityscape",
"French Art",
"French Cuisine",
"French Impressionism",
"Fresco Painting Technique",
"Frida Kahlo",
"Funk Music",
"Furniture Design",
"Futurism",
"Futurist Architecture",
"GAYZ_Portraiture",
"Gabriel Orozco",
"Galactic_Sci-Fi",
"Game Design",
"Generative Art",
"Genetic Art_Uncategorized",
"Geometric Abstraction",
"Geometric abstract painting",
"Georg Baselitz",
"Georgia O'Keeffe",
"Gerhard Richter_1",
"Gerhard Richter_2",
"German Art",
"Ghibli_Surrealism",
"Ghoul City Variant_Architecture_Location",
"Giant Robot_Sci-Fi_Retro_Architecture",
"Glamorous Portrait_Fashion_Portraiture",
"Glamorous_Erotic_Photography",
"Glasgow cityscape",
"Glass Sculpture",
"Glassblowing",
"Glazing Technique in Painting",
"Glenn Ligon",
"Glitch Art_Uncategorized",
"Glitchcore_Digital Media",
"Gongfu Tea_Uncategorized",
"Gospel Music",
"Goth Boudoir_Gothic",
"Gotham City",
"Gothic Architecture",
"Gothic Architecture_Architecture_Gothic",
"Gothic Fashion",
"Gothic Literature",
"Gothic Monster_Architecture_Gothic",
"Gothic Revival Architecture",
"Gothic Revival Architecture_Architecture_Gothic",
"Graffiti Art",
"Graffiti Style_Graffiti",
"Grand Canyon",
"Grant Wood",
"Graphic Design",
"Graveyard Mist_Horror",
"Great Barrier Reef",
"Great Wall of China",
"Greek Art",
"Greek Classical Sculpture",
"Greek Mythology Art",
"Greek Pottery Art",
"Greendale",
"Gritty_Voyeuristic_Photography",
"Grotesque Gargoyle_Uncategorized",
"Grunge Flyer_Uncategorized",
"Gustav Klimt",
"Gutai_Sci-Fi_Event",
"H.P. Lovecraft Cover_Horror",
"Hackersville Variant_Architecture",
"Hallstatt",
"Hard-edge Painting_Uncategorized",
"Hate Crime_Uncategorized",
"Haunted Carnival_Horror",
"Haunted Portrait_Portraiture_Horror",
"Haute Couture",
"Haute Couture Fashion",
"Haute Cuisine",
"Hawkins",
"Headless Horseman_Uncategorized",
"Heavy Metal",
"Henri Matisse",
"Hieronymus Bosch",
"High Fantasy",
"High Fantasy Art",
"Hip-Hop Album Art",
"Hip-Hop Dance",
"Hip-Hop Fashion",
"Hip-Hop Music",
"Historical Fiction",
"Hogwarts",
"Hong Kong",
"Hong Kong cityscape",
"Horror",
"Horror Films",
"Horror Movie Poster_Horror_Gothic",
"Hyperrealism_Uncategorized",
"Ice Sculpture",
"Illustration Design",
"Illustration for Children's Books",
"Impressionism",
"Impressionism Art",
"Impressionist Landscape Painting",
"Impressionist Portrait Painting",
"Improvisational Theater",
"Inca Mythology Art",
"Indian Art",
"Indian Cuisine",
"Indian Miniature Painting",
"Indian Mythology Art",
"Indie Films",
"Indie Music Art",
"Indigenous Australian Art",
"Indigenous Painting",
"Indigenous Pottery",
"Indonesian Art",
"Industrial Architecture",
"Industrial Design",
"Information Art_Uncategorized",
"Ink Drawing",
"Insectoid Mutant_Portraiture",
"Installation Art",
"Interaction Design",
"Interactive Art",
"Interactive Art Installations",
"Interactive artwork",
"Interior Design",
"Internet Art_Sci-Fi_Digital Media",
"Intimate_Naturist_Photography",
"Intuitive Art_Uncategorized",
"Irish Art",
"Irish Dance",
"Islamic Architecture",
"Islamic Art",
"Islamic Calligraphy",
"Islamic Geometric Art",
"Islamic Geometric Patterns",
"Island Luau_Uncategorized_Location",
"Istanbul",
"Istanbul cityscape",
"Italian Art",
"Italian Cuisine",
"Italian Renaissance Art",
"J.M.W. Turner",
"Jackson Pollock",
"Jakarta cityscape",
"Japan",
"Japanese Architecture",
"Japanese Art",
"Japanese Cuisine",
"Japanese Mythology Art",
"Jazz Dance",
"Jazz Music",
"Jazz Poster Art",
"Jean-Honore Fragonard",
"Jeff Koons",
"Jenny Holzer",
"Jerusalem",
"Jewelry Design",
"Johannes Vermeer",
"John Baldessari",
"Joyful Art",
"Julie Mehretu",
"Kabuki Theater",
"Kara Walker",
"Kathak Dance",
"Katsushika Hokusai",
"Kawaii Character_Uncategorized",
"Kawaii Fashion_Fashion",
"Kehinde Wiley",
"Kerry James Marshall",
"Kiki Smith",
"Kinetic Art",
"Kinetic Sculpture",
"Kintsugi (Japanese Gold Repair)",
"Kitsch Movement_Uncategorized",
"Knitting",
"Korean Art",
"Korean Celadon Ceramics",
"Korean Celadon Pottery",
"Korean Cuisine",
"Kuala Lumpur",
"Kyoto",
"Kyoto cityscape",
"LAIKA_Animation",
"Land Art",
"Land Art (1)_Fantasy_Nature_Sculpture_Landscape",
"Landscape Architecture",
"Landscape Design",
"Landscape Photography",
"Laser Grid_Uncategorized",
"Later European abstraction (1)_Uncategorized",
"Leonardo da Vinci",
"Lettrist artwork",
"Leviathan Variant_Architecture",
"Light Art",
"Line Dance",
"Lisbon cityscape",
"Lithography",
"Living Burial_Uncategorized",
"London",
"Los Angeles",
"Lost Vegas Variant_Architecture",
"Lounge Singer_Retro",
"Lovecraftian Horror_Horror",
"Low Fantasy",
"Lowbrow Art Variant_Surrealism_Culture",
"Lowbrow Art_Culture",
"Luau Fire Dancer_Fashion",
"Luchador_Uncategorized",
"Luxury Fashion",
"Lynching_Uncategorized",
"Lyrical abstract painting",
"Macabre Memento Mori_Horror_Horror & Dark_Still Life",
"Machinima Variant_Uncategorized",
"Machu Picchu",
"Macro Photography",
"Mad Scientist Machinery_Uncategorized",
"Madhubani Painting",
"Madhubani Painting (Indian Folk Art)",
"Mage City Variant_Architecture_Fantasy_Location",
"Magic Realist painting",
"Mall Goth_Portraiture_Gothic",
"Mannerism",
"Mannerist Architecture",
"Maori Wood Carving",
"Mardi Gras_Uncategorized",
"Marina Abramovi\u0107",
"Mark Bradford",
"Mark Grotjahn",
"Martin Puryear",
"Masked Killer_Uncategorized",
"Masked Stalker_Uncategorized",
"Maurizio Cattelan",
"Maximalism",
"Mecca",
"Mech City Variant_Sci-Fi_Architecture_Location",
"Mech City_Sci-Fi_Architecture_Location",
"Mech City__Location",
"Media Art",
"Medical Oddities_Uncategorized",
"Mediterranean Cuisine",
"Melancholy Art",
"Melodrama",
"Melting Skull_Uncategorized",
"Memento Mori_Horror_Horror & Dark",
"Memoir",
"Menacing Scarecrow_Uncategorized",
"Menswear Fashion",
"Mesoamerican Mythology Art",
"Mesopotamian Mythology Art",
"Metabolist Architecture",
"Metal Music",
"Metal Music Artwork",
"Metalwork",
"Metropolis",
"Mexican Art",
"Mexican Cuisine",
"Mexican Muralism",
"Mexican Skull Art_Uncategorized",
"Miami",
"Michelangelo",
"Middle Eastern Cuisine",
"Middle-earth",
"Midgard Variant_Architecture",
"Milky Way Galaxy",
"Mime",
"Mime City Variant_Architecture_Location",
"Minimalism",
"Minimalist Web Design",
"Mixed Media Art",
"Mixer_Animation",
"Modern Architecture",
"Modern Dance",
"Modernist Architecture",
"Mona Hatoum",
"Monoprinting Technique",
"Mosaic",
"Mosaic Art",
"Motion Design",
"Motion Graphics Design",
"Mount Everest",
"Mount Olympus",
"Movie Storyboard_Uncategorized",
"Mughal Miniature Painting",
"Mumbai",
"Mummy Portrait_Portraiture",
"Munich cityscape",
"Music Video Direction",
"Musica Variant_Architecture_Culture",
"Musical Films",
"Musical Theater",
"Mutated Beast_Uncategorized",
"My Little Pony_Uncategorized",
"Mystery",
"Mystery Literature",
"Mythic Fantasy Art",
"Nantucket",
"Native American Art",
"Native American Basketry",
"Native American Mythology Art",
"Native American Pottery",
"Naturalism in Literature",
"Nature Landscape Photography",
"Nature Photography",
"Nautical_Retro",
"Na\u00efve Art (1)_Uncategorized",
"Nebula",
"Neo Pop_Pop Culture_Culture",
"Neo Rauch",
"Neo-Dada_Uncategorized",
"Neo-Expressionism_Uncategorized",
"Neo-Gothic Architecture",
"Neo-Noir",
"Neo-Pop (1)_Pop Culture_Culture",
"Neo-primitivism (1)_Still Life",
"Neoclassical Architecture",
"Neoclassicism",
"Neon Lighting_Uncategorized",
"Neon Racer_Sci-Fi",
"Neon Tokyo_Retro",
"Neoplasticism",
"Neotokyo Variant_Sci-Fi_Architecture",
"Neue Sachlichkeit Variant_Portraiture",
"Neue Wilde (1)_Uncategorized",
"New Caelum Variant_Architecture",
"New Caelum_Architecture",
"New Media Art_Digital Media",
"New Orleans",
"New Perpendicular art_Uncategorized",
"New Simplicity_Architecture",
"New York City",
"New York cityscape",
"Niagara Falls",
"Nicole Eisenman",
"Night Photography",
"Nightmare Beast_Uncategorized",
"Non-Fiction",
"Nordic Viking Art",
"Norse Mythology Art",
"North African Art",
"Norwegian romantic nationalism_Nature_Landscape",
"Nouveau Circus_Uncategorized",
"Nova Alexandria Variant_Architecture_Culture",
"Occult Ritual_Occult",
"Occult Sacrifice_Occult",
"Oil Painting",
"Olafur Eliasson",
"Ominous Fog_Uncategorized",
"Ominous Warning_Uncategorized",
"Op Art",
"Op Art_Uncategorized",
"Opera",
"Opera Music",
"Opera Music Illustration",
"Osaka cityscape",
"Outsider Art_Uncategorized",
"Pablo Picasso",
"Package Design",
"Pandora",
"Paper Cutting",
"Paper Mache Art",
"Parametric Architecture",
"Paris",
"Participatory Art",
"Patchwork Creature_Uncategorized",
"Paul Cezanne",
"Performance Art",
"Performance Sculpture",
"Peruvian Art",
"Petra",
"Photography",
"Photojournalism",
"Photorealism",
"Photorealistic painting",
"Physical Theater",
"Pinup_Retro",
"Pixel Art",
"Pizza Making",
"Plague Mass Grave_Uncategorized",
"Plein Air Painting",
"Plotter Art Variant_Uncategorized",
"Plotter Art_Uncategorized",
"Plus-Size Fashion",
"Poetry",
"Pointillism",
"Pointillism Art",
"Pole Dance",
"Polynesian Mythology Art",
"Polynesian Tattoo Art",
"Pop Art",
"Pop Music",
"Pop Music Branding",
"Pop Surrealism_Nature_Surrealism_Landscape_Still Life",
"Pop art style",
"Porcelain Art",
"Portrait Photography",
"Portuguese Art",
"Post-Impressionism",
"Postmodern Architecture",
"Pottery",
"Prague",
"Prague cityscape",
"Prairie Dress_Retro_Fashion",
"Pre-Raphaelite_Uncategorized",
"Preppy Fashion",
"Printmaking",
"Prismatic_Uncategorized",
"Projection Mapping Art",
"Propaganda Art_Retro",
"Propaganda Poster_Uncategorized",
"Prose Literature",
"Provocative_Surreal_Photography",
"Pseudorealism_Uncategorized",
"Psychedelic Concert Posters",
"Psychedelic Pop Art_Surrealism",
"Public Art Installations",
"Public Installations",
"Public Sculptures",
"Punk Fashion",
"Punk Music",
"Punk Poster_Uncategorized",
"Puppetry",
"Pyramids of Giza",
"Quahog",
"Quilting",
"Quilting Art",
"Quito cityscape",
"R&B Music",
"Rachel Whiteread",
"Radical Realism (1)_Still Life",
"Rangoli (Indian Floor Art)",
"Rap Music Graphics",
"Raphael",
"Rashid Johnson",
"Rat Infestation_Uncategorized",
"Rat King_Uncategorized",
"Realism Art",
"Realism in Literature",
"Realistic Fiction",
"Reanimated Corpse_Animation",
"Recycled Art",
"Reggae Music",
"Reggae Music Design",
"Rembrandt",
"Remodernism Variant_Uncategorized",
"Remodernism_Architecture",
"Renaissance",
"Renaissance Architecture",
"Renaissance Art",
"Rene Magritte",
"Responsive Web Design",
"Richard Serra",
"Richard Tuttle",
"Rio de Janeiro",
"Rio de Janeiro cityscape",
"Robert Gober",
"Robotics Art",
"Rock Album Art",
"Rock Music",
"Rococo",
"Rococo Architecture",
"Rococo Art",
"Rococo Interior_Uncategorized",
"Roman Mosaic Art",
"Roman Mythology Art",
"Romance",
"Romance Literature",
"Romanesque Architecture",
"Romantic Comedy",
"Romantic Films",
"Romanticism",
"Romanticism Art",
"Romanticism in Literature",
"Rome",
"Rural Photography",
"Russia",
"Russian Art",
"Russian Icon Painting",
"Sahara Desert",
"Salem",
"Salsa Dance",
"Salsa Music",
"Salvador Dali",
"Samurai_Uncategorized",
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"Lovecraftian Horror_Horror_Horror & Dark",
"Macabre Memento Mori_Horror_Horror & Dark_Still Life",
"Creepy Children_",
"Demonic Possession_",
"Undead Portrait_",
"Occult Ritual_",
"Nightmare Beast_",
"Gothic Architecture_Architecture_Horror_Horror & Dark_Gothic",
"Horror Movie Poster_Horror_Horror & Dark_Gothic",
"H.P. Lovecraft Cover_Horror_Horror & Dark",
"Carnival Freakshow_",
"Creepy Porcelain Doll_",
"Torture Device_Horror_Horror & Dark",
"Gothic Revival Architecture_Architecture_Horror_Horror & Dark_Gothic",
"Dark Carnival_Horror_Horror & Dark_Gothic",
"Cemetery Statue_",
"Spider Queen_",
"Skeleton Dance_Horror_Horror & Dark_Animation",
"Scary Stories at Campfire_Horror_Horror & Dark",
"Tortured Prisoner_",
"Victorian Laboratory_",
"Vampire_",
"Mummy Portrait_",
"Animated Corpse_",
"Back Alley Rogue_",
"Insectoid Mutant_",
"Rat King_",
"Undead Gluttony_",
"Alchemist's Study_",
"Patchwork Creature_",
"Masked Stalker_",
"Rat Infestation_",
"Disrespectful Grave Robber_",
"Cabinet of Curiosities_Occult_Horror_Horror & Dark",
"Plague Mass Grave_",
"Sinister Crone_",
"Crawler Mimicry_",
"Ominous Warning_",
"Conjoined Twins_",
"Living Burial_",
"Death Masque_",
"Memento Mori_Horror_Horror & Dark",
"Reanimated Corpse_",
"Hate Crime_",
"Mad Scientist Machinery_",
"Smothering Earth_",
"Vacuous Grimace_",
"Arachnid Swarm_",
"sai-base_",
"sai-3d-model_",
"sai-analog film_",
"sai-anime_",
"sai-cinematic_",
"sai-comic book_",
"sai-craft clay_",
"sai-digital art_",
"sai-enhance_",
"sai-fantasy art_",
"sai-isometric_",
"sai-line art_",
"sai-lowpoly_",
"sai-neonpunk_",
"sai-origami_",
"sai-photographic_",
"sai-pixel art_",
"sai-texture_",
"ads-advertising_",
"ads-automotive_",
"ads-corporate_",
"ads-fashion editorial_",
"ads-food photography_",
"ads-luxury_",
"ads-real estate_",
"ads-retail_",
"artstyle-abstract_",
"artstyle-abstract expressionism_",
"artstyle-art deco_",
"artstyle-art nouveau_",
"artstyle-constructivist_",
"artstyle-cubist_",
"artstyle-expressionist_",
"artstyle-graffiti_",
"artstyle-hyperrealism_",
"artstyle-impressionist_",
"artstyle-pointillism_",
"artstyle-pop art_",
"artstyle-psychedelic_",
"artstyle-renaissance_",
"artstyle-steampunk_",
"artstyle-surrealist_",
"artstyle-typography_",
"artstyle-watercolor_",
"futuristic-biomechanical_",
"futuristic-biomechanical cyberpunk_",
"futuristic-cybernetic_",
"futuristic-cybernetic robot_",
"futuristic-cyberpunk cityscape_",
"futuristic-futuristic_",
"futuristic-retro cyberpunk_",
"futuristic-retro futurism_",
"futuristic-sci-fi_",
"futuristic-vaporwave_",
"game-bubble bobble_",
"game-cyberpunk game_",
"game-fighting game_",
"game-gta_",
"game-mario_",
"game-minecraft_",
"game-pokemon_",
"game-retro arcade_",
"game-retro game_",
"game-rpg fantasy game_",
"game-strategy game_",
"game-streetfighter_",
"game-zelda_",
"misc-architectural_",
"misc-disco_",
"misc-dreamscape_",
"misc-dystopian_",
"misc-fairy tale_",
"misc-gothic_Horror_Horror & Dark_Gothic",
"misc-grunge_",
"misc-horror_Horror_Horror & Dark",
"misc-kawaii_",
"misc-lovecraftian_Surrealism_Horror & Dark_Horror",
"misc-macabre_Horror_Horror & Dark_Gothic",
"misc-manga_",
"misc-metropolis_",
"misc-minimalist_",
"misc-monochrome_",
"misc-nautical_",
"misc-space_",
"misc-stained glass_",
"misc-techwear fashion_",
"misc-tribal_",
"misc-zentangle_",
"papercraft-collage_",
"papercraft-flat papercut_",
"papercraft-kirigami_",
"papercraft-paper mache_",
"papercraft-paper quilling_",
"papercraft-papercut collage_",
"papercraft-papercut shadow box_",
"papercraft-stacked papercut_",
"papercraft-thick layered papercut_",
"photo-alien_",
"photo-film noir_",
"photo-hdr_",
"photo-long exposure_",
"photo-neon noir_",
"photo-silhouette_",
"photo-tilt-shift_",
"Vintage Travel Poster_",
"Prismatic_",
"Glamorous Portrait_",
"Scientific Illustration_",
"Stained Glass_",
"Art Deco Architecture_",
"Pop Surrealism_",
"Vintage Tattoo Flash_",
"Sports Card_",
"Stop Motion_",
"Bauhaus Design_",
"Propaganda Poster_",
"Art Nouveau_",
"Glitch Art_",
"Blueprint_",
"Woodblock Print_",
"Neon Lighting_",
"Bas-Relief Sculpture_",
"_",
"Art Nouveau Poster_",
"Vector Portrait_",
"Egyptian Hieroglyphs_",
"Movie Storyboard_",
"Disney Animation_",
"Studio Ghibli_",
"Aardman_",
"Xilam_",
"Ankama_",
"Mixer_",
"LAIKA_",
"Toei_",
"UPA_",
"Vaporwave Retro_",
"Central American_",
"Vogue Cover_",
"Satanic_Occult_Horror_Horror & Dark",
"Fortune Telling_",
"Southern Gothic_Horror_Horror & Dark_Gothic",
"Nouveau Circus_",
"Rococo Interior_",
"Mexican Skull Art_",
"Psychedelic Pop Art_",
"Voodoo_",
"Vintage Baseball_",
"Circus Performer_",
"Tiki Bar_",
"Apr\u00e8s-Ski_",
"Kawaii Fashion_",
"Tropical Luau_",
"Voodoo Doll_",
"Mardi Gras_",
"Figurine Shelf_",
"Terrarium_",
"Tiki Cocktail_",
"Cottagecore Fashion_",
"Vintage Tattoo Print_",
"Boudoir Photography_",
"Vaporwave_",
"Terrarium Bottle_",
"Vaporwave City__Location",
"Kawaii Character_",
"Vintage Halloween Costume_",
"Cassette Futurism_",
"Punk Poster_",
"Luchador_",
"Tiki Totem_",
"Cassette J-Card_",
"Prairie Dress_",
"Lounge Singer_",
"Gongfu Tea_",
"Aztec Calendar_",
"Cassette Graphics_",
"Laser Grid_",
"Giant Robot_",
"Vaporwave Sunset_",
"Fortune Teller_",
"Steampunk Portrait_",
"Scary Stories_",
"Woodblock Art_",
"Vintage Halloween Mask_",
"Grunge Flyer_",
"Voodoo Altar_",
"Cassette Collage_",
"Tropical Cocktail_",
"Vintage Robot Toy_",
"Scary Pumpkin_",
"Pinup_",
"Tarot Cards_",
"Ghibli_",
"Egyptology_",
"Battle_",
"Tarot_",
"Steampunk_",
"Ancient Maya_",
"Nautical_",
"Bomber Jacket_",
"Samurai_",
"American Traditional_",
"Propaganda Art_",
"Glitchcore_",
"Vaporgram_",
"Desaturated_",
"Dieselpunk_",
"My Little Pony_",
"Ballet_",
"Galactic_",
"Streamer Bike_",
"Tropical Hotel_",
"Tiki Mug_",
"Neon Racer_",
"Luau Fire Dancer_",
"Day of the Dead_",
"Sideshow Poster_",
"Vaporwave Graphics_",
"Voodoo Ceremony_",
"Blacklight Poster_",
"Teslapunk_",
"Cassette Bedroom_",
"Tropical Bathroom_",
"Voodoo Shop_",
"Vintage Halloween_",
"Goth Boudoir_Horror_Horror & Dark_Gothic",
"Island Luau__Location",
"Tiki Outdoor Shower_",
"Black Velvet Painting_",
"Tattoo Print_",
"Addams Family_Portraiture_Horror_Horror & Dark",
"Cassette Wall_",
"Neon Tokyo_",
"Voodoo Queen_",
"Surf Wood Sign_",
"Haunted Carnival_",
"Tiki Idol_",
"Mall Goth_",
"Volcano Lair_",
"Graffiti Style_",
"Impressionism_",
"Fauvism_Uncategorized",
"Dada_Uncategorized",
"Cubism_Still Life",
"_",
"_",
"_",
"Op Art_",
"_",
"Conceptual Art_Uncategorized",
"Folk Art_",
"Naive Art_Sci-Fi_Architecture",
"Outsider Art_",
"_",
"Suprematism_Uncategorized",
"De Stijl_Uncategorized",
"Bauhaus_Architecture",
"Constructivism_Uncategorized",
"Futurism_Uncategorized",
"Rayonism_Uncategorized",
"Vorticism_Uncategorized",
"Orphism_Uncategorized",
"Der Blaue Reiter_",
"Die Br\u00fccke_Graffiti",
"Ashcan School_",
"Hudson River School_Nature_Landscape_Location",
"Luminism_Nature_Landscape",
"Tonalism_Nature_Landscape",
"Barbizon School_Nature_Landscape",
"Academic Art_Uncategorized",
"Rococo_",
"Neoclassicism_Uncategorized",
"Romanticism_Nature",
"_",
"_",
"Plein Air_Nature_Landscape",
"Pre-Raphaelite_Uncategorized",
"Art Informel_Uncategorized",
"Hard-edge Painting_Uncategorized",
"_",
"_",
"Post-Painterly Abstraction_Uncategorized",
"Kinetic Art_",
"Land Art_Fantasy_Nature_Sculpture_Landscape",
"Performance Art_Uncategorized",
"Installation Art_Uncategorized",
"Video Art_",
"Digital Art_",
"New Media Art_",
"Street Art_Sci-Fi",
"Stuckism_",
"Lowbrow Art_",
"Photomontage_Photography",
"Diorama_Uncategorized",
"Assemblage_",
"Combine Painting_Sci-Fi_Still Life",
"Happenings_Uncategorized",
"Mail Art_Uncategorized",
"Neo-Dada_",
"Neo-Expressionism_",
"Bad Painting_",
"Graffiti_",
"Traditional Figurative Art_Sculpture",
"Classical Realism_",
"Contemporary Realism_",
"Hyperrealism_",
"_",
"New Objectivity_",
"Precisionism_Architecture_Nature_Landscape",
"Figurative Expressionism_",
"Neue Wilde_Uncategorized",
"New Perpendicular art_Uncategorized",
"New Simplicity_Architecture",
"Remodernism_",
"Norwegian romantic nationalism_Nature_Landscape",
"_",
"Propaganda Poster Art_Uncategorized",
"Heroic Realism_Sculpture_Culture",
"Na\u00efve Art_Uncategorized",
"Art Brut_",
"Neo-primitivism_Still Life",
"Visionary Art_Uncategorized",
"Intuitive Art_",
"Pseudorealism_",
"Radical Realism_Still Life",
"Critical Realism_",
"Neue Sachlichkeit_",
"Regionalism_Uncategorized",
"Abstract Expressionism_Uncategorized",
"Later European abstraction_Uncategorized",
"Tachisme_",
"Abstraction-Cr\u00e9ation_Uncategorized",
"Gutai_",
"British Pop Art_",
"Situationist International_",
"Lettrism_Portraiture",
"St Ives School_",
"Transavantgarde_",
"Transgressive Art_",
"Neo Pop_",
"Kitsch Movement_",
"Virtual Art_",
"Internet Art_",
"_",
"Information Art_",
"Systems Art_",
"Bio Art_",
"Genetic Art_",
"Sustainable Art_",
"_",
"Video Games_",
"Machinima_",
"Artware_",
"Demoscene_",
"Math Art_",
"Data Art_",
"Plotter Art_",
"VR Art_",
"AR Art_",
"Circuit Bending_Uncategorized",
"DIY Art_",
"Maker Culture_Culture",
"Self-taught Art_Fantasy",
"Neo-Pop_Pop Culture_Culture",
"Young British Artists_Portraiture",
"Appropriation_Culture",
"Algorithmic Art_Uncategorized",
"Use these to populate the JSON instead._",
"Under Honolulu_Architecture_Location",
"Sub Juneau_Architecture_Location",
"Underground Lansing_Culture_Location",
"Sub Topeka_Architecture_Folk Art_Location",
"Under St. Paul_Architecture_Location",
"Sub Jackson_Folk Art_Location",
"Underground Columbus_Retro_Location",
"Sub Indianapolis_Uncategorized_Location",
"Under Omaha_Culture_Location",
"Under Oklahoma City_Architecture_Location",
"Sub Des Moines_Architecture_Location",
"Underground Wichita_Folk Art_Location",
"Under Fargo_Architecture_Location",
"Sub Bozeman_Architecture_Location",
"Underground Anchorage_Architecture_Location",
"Sub Casper_Uncategorized_Location",
"Sub Cheyenne_Uncategorized_Location",
"Underground Portland_Sci-Fi_Location",
"Under Eugene_Folk Art_Location",
"Sub Salem_Sci-Fi_Culture_Location",
"Underground Seattle_Uncategorized_Location",
"Sub Spokane_Architecture_Location",
"Sub Tacoma_Architecture_Culture_Retro_Location",
"Under Sacramento_Uncategorized_Location",
"Sub Oakland_Sci-Fi_Culture_Location",
"Underground San Jose_Uncategorized_Location",
"Sub Fresno_Architecture_Nature_Landscape_Location",
"Under Bakersfield_Uncategorized_Location",
"Sub Anaheim_Fantasy_Location",
"Underground Riverside_Culture_Location",
"Sub Santa Ana_Sci-Fi_Culture_Location",
"Underground Chula Vista_Uncategorized_Location",
"Sub Irvine_Uncategorized_Location",
"Sub Fontana_Culture_Location",
"Sub Oxnard_Uncategorized_Location",
"Sub Moreno Valley_Uncategorized_Location",
"Underground Huntington Beach_Architecture_Culture_Location",
"Sub Glendale_Uncategorized_Location",
"Sub Santa Clarita_Uncategorized_Location",
"Sub Garden Grove_Architecture_Location",
"Sub Santa Rosa_Sci-Fi_Nature_Location",
"Sub Rancho Cucamonga_Architecture_Lifestyle_Location",
"Sub Ontario_Uncategorized_Location",
"Sub Lancaster_Sci-Fi_Retro_Location",
"Sub Palmdale_Sci-Fi_Location",
"Sub Pomona_Retro_Location",
"Sub Torrance_Sci-Fi_Location",
"Sub Pasadena_Uncategorized_Location",
"Sub Orange_Retro_Location",
"Sub Roseville_Architecture_Location",
"Sub Thousand Oaks_Uncategorized_Location",
"Sub Concord_Uncategorized_Location",
"Sub Simi Valley_Pop Culture_Culture_Retro_Location",
"Sub Victorville_Uncategorized_Location",
"Sub El Monte_Sci-Fi_Location",
"Sub Berkeley_Retro_Location",
"Sub Downey_Sci-Fi_Location",
"Sub Costa Mesa_Culture_Location",
"Sub Inglewood_Sci-Fi_Pop Culture_Culture_Location",
"Sub Carlsbad_Architecture_Culture_Location",
"Sub Temecula_Lifestyle_Location",
"Under Istanbul_Architecture_Location",
"Under Tallahassee_Sci-Fi_Retro_Architecture_Location",
"Under Albany_Architecture_Surrealism_Location",
"Under Trenton_Uncategorized_Location",
"Under Hartford_Architecture_Location",
"Underground Salt Lake City_Architecture_Location",
"Under Juneau_Architecture_Location",
"Sub Baton Rouge_Culture_Location",
"Underground Helena_Architecture_Location",
"Sub Carson City_Architecture_Location",
"Under Santa Fe_Uncategorized_Location",
"Sub Boise_Uncategorized_Location",
"Underground Lincoln_Uncategorized_Location",
"Sub Denver_Uncategorized_Location",
"Under Bismarck_Uncategorized_Location",
"Sub Pierre_Uncategorized_Location",
"Underground Austin_Uncategorized_Location",
"Sub Atlanta_Uncategorized_Location",
"Under Jackson_Folk Art_Location",
"Sub Montgomery_Uncategorized_Location",
"Underground Little Rock_Uncategorized_Location",
"Sub Frankfort_Uncategorized_Location",
"Under Nashville_Uncategorized_Location",
"Sub Columbia_Architecture_Culture_Location",
"Sub Richmond_Architecture_Location",
"Under Charleston_Architecture_Location",
"Sub Dover_Uncategorized_Location",
"Underground Springfield_Folk Art_Location",
"Sub Annapolis_Sculpture_Location",
"Sub Providence_Uncategorized_Location",
"Underground Concord_Culture_Location",
"Sub Montpelier_Sculpture_Location",
"Sub Jefferson City_Architecture_Folk Art_Location",
"Crystal Caverns_Architecture",
"Mech City_Sci-Fi_Architecture_Location",
"Nova Alexandria_Architecture_Culture",
"Steamtown_Architecture_Retro",
"Technotopia_Architecture_Nature",
"Clockwork City_Architecture_Location",
"Under London_Architecture_Location",
"Dwarvenholm_Uncategorized",
"Sanctuary_Uncategorized",
"Shangri-La_Uncategorized",
"Mime City_Architecture_Location",
"Elfheim_Architecture_Fantasy",
"Midgard_Architecture",
"Musica_Architecture_Culture",
"Caveopolis_Lifestyle",
"Mage City_Architecture_Fantasy_Location",
"Steampunk City_Architecture_Location",
"Shadow City_Architecture_Occult_Gothic_Location",
"Under Chicago_Architecture_Portraiture_Culture_Retro_Location",
"Neotokyo_Sci-Fi_Architecture",
"Leviathan_Architecture",
"Underground Rome_Architecture_Location",
"Zion_Culture",
"New Caelum_Architecture",
"Hackersville_Architecture",
"Ghoul City_Architecture_Location",
"Under Berlin_Retro_Surrealism_Location",
"Elven City_Architecture_Location",
"Dwarvendom_Uncategorized",
"Lost Vegas_Architecture",
"Under Paris_Uncategorized_Location",
"Steeltown_Architecture",
"Tech City_Architecture_Nature_Location",
"Under Montreal_Architecture_Location",
"Brightwater_Nature",
"enhance_Uncategorized",
"Bloodthirsty Vampire_Horror",
"Serial Killer_Horror",
"Exorcism_Uncategorized",
"Torture Chamber_Uncategorized",
"Mutated Beast_Uncategorized",
"Crucifixion_Uncategorized",
"Headless Horseman_Uncategorized",
"Sinister Ritual_Uncategorized",
"Ferocious Werewolf_Uncategorized",
"Abandoned Asylum_Uncategorized",
"Ominous Fog_Uncategorized",
"Demonic Portal_Horror",
"Scarecrow_Horror",
"Menacing Scarecrow_Uncategorized",
"Grotesque Gargoyle_Uncategorized",
"Masked Killer_Uncategorized",
"Graveyard Mist_Horror",
"Gothic Monster_Architecture_Gothic",
"Butcher Shop_Still Life",
"Melting Skull_Uncategorized",
"Tortured Soul_Uncategorized",
"Sinister Laboratory_Horror_Occult_Still Life",
"Demonic Clown_Uncategorized",
"Haunted Portrait_Portraiture_Horror",
"Lovecraftian Horror_Horror",
"Macabre Memento Mori_Still Life",
"Creepy Children_Portraiture",
"Demonic Possession_Uncategorized",
"Undead Portrait_Portraiture",
"Occult Ritual_Occult",
"Nightmare Beast_Uncategorized",
"Gothic Architecture_Architecture_Gothic",
"Horror Movie Poster_Horror_Gothic",
"H.P. Lovecraft Cover_Horror",
"Carnival Freakshow_Retro",
"Creepy Porcelain Doll_Fashion_Portraiture",
"Torture Device_Uncategorized",
"Gothic Revival Architecture_Architecture_Gothic",
"Dark Carnival_Gothic",
"Cemetery Statue_Uncategorized",
"Spider Queen_Uncategorized",
"Skeleton Dance_Animation",
"Scary Stories at Campfire_Uncategorized",
"Tortured Prisoner_Uncategorized",
"Victorian Laboratory_Occult_Still Life",
"Vampire_Portraiture_Horror",
"Mummy Portrait_Portraiture",
"Animated Corpse_Animation",
"Back Alley Rogue_Uncategorized",
"Insectoid Mutant_Portraiture",
"Rat King_Uncategorized",
"Undead Gluttony_Architecture",
"Alchemist's Study_Uncategorized",
"Patchwork Creature_Uncategorized",
"Masked Stalker_Uncategorized",
"Rat Infestation_Uncategorized",
"Disrespectful Grave Robber_Uncategorized",
"Cabinet of Curiosities_Occult",
"Plague Mass Grave_Uncategorized",
"Sinister Crone_Uncategorized",
"Crawler Mimicry_Uncategorized",
"Ominous Warning_Uncategorized",
"Conjoined Twins_Uncategorized",
"Living Burial_Uncategorized",
"Death Masque_Uncategorized",
"Memento Mori_Uncategorized",
"Reanimated Corpse_Animation",
"Hate Crime_Uncategorized",
"Mad Scientist Machinery_Uncategorized",
"Smothering Earth_Fantasy",
"Vacuous Grimace_Uncategorized",
"Arachnid Swarm_Uncategorized",
"sai-base_Uncategorized",
"sai-3d-model_Uncategorized",
"sai-analog film_Retro_Photography",
"sai-anime_Uncategorized",
"sai-cinematic_Uncategorized",
"sai-comic book_Uncategorized",
"sai-craft clay_Sculpture",
"sai-digital art_Digital Media",
"sai-enhance_Uncategorized",
"sai-fantasy art_Fantasy_Surrealism",
"sai-isometric_Uncategorized",
"sai-line art_Uncategorized",
"sai-lowpoly_Uncategorized",
"sai-neonpunk_Uncategorized",
"sai-origami_Uncategorized",
"sai-photographic_Photography",
"sai-pixel art_Uncategorized",
"sai-texture_Uncategorized",
"ads-advertising_Uncategorized",
"ads-automotive_Uncategorized",
"ads-corporate_Uncategorized",
"ads-fashion editorial_Fashion",
"ads-food photography_Photography",
"ads-luxury_Uncategorized",
"ads-real estate_Photography",
"ads-retail_Uncategorized",
"artstyle-abstract_Uncategorized",
"artstyle-abstract expressionism_Uncategorized",
"artstyle-art deco_Uncategorized",
"artstyle-art nouveau_Nature",
"artstyle-constructivist_Uncategorized",
"artstyle-cubist_Uncategorized",
"artstyle-expressionist_Uncategorized",
"artstyle-graffiti_Architecture_Graffiti",
"artstyle-hyperrealism_Photography",
"artstyle-impressionist_Uncategorized",
"artstyle-pointillism_Uncategorized",
"artstyle-pop art_Culture",
"artstyle-psychedelic_Surrealism",
"artstyle-renaissance_Uncategorized",
"artstyle-steampunk_Uncategorized",
"artstyle-surrealist_Surrealism",
"artstyle-typography_Uncategorized",
"artstyle-watercolor_Uncategorized",
"futuristic-biomechanical_Sci-Fi",
"futuristic-biomechanical cyberpunk_Sci-Fi_Dystopia",
"futuristic-cybernetic_Sci-Fi",
"futuristic-cybernetic robot_Sci-Fi",
"futuristic-cyberpunk cityscape_Sci-Fi_Architecture",
"futuristic-futuristic_Sci-Fi",
"futuristic-retro cyberpunk_Sci-Fi_Retro",
"futuristic-retro futurism_Sci-Fi_Retro",
"futuristic-sci-fi_Sci-Fi",
"futuristic-vaporwave_Sci-Fi_Retro",
"game-bubble bobble_Fantasy",
"game-cyberpunk game_Sci-Fi_Dystopia_Games_Digital Media",
"game-fighting game_Games",
"game-gta_Uncategorized",
"game-mario_Fantasy_Comics",
"game-minecraft_Still Life",
"game-pokemon_Fantasy",
"game-retro arcade_Retro_Games",
"game-retro game_Retro",
"game-rpg fantasy game_Fantasy_Games",
"game-strategy game_Games",
"game-streetfighter_Uncategorized",
"game-zelda_Fantasy",
"misc-architectural_Uncategorized",
"misc-disco_Retro",
"misc-dreamscape_Fantasy_Surrealism",
"misc-dystopian_Dystopia",
"misc-fairy tale_Fantasy",
"misc-gothic_Gothic",
"misc-grunge_Retro",
"misc-horror_Horror",
"misc-kawaii_Uncategorized",
"misc-lovecraftian_Surrealism_Horror",
"misc-macabre_Gothic",
"misc-manga_Uncategorized",
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"Folk Art Variant_Folk Art",
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"Suprematism (2)_Uncategorized",
"De Stijl (2)_Uncategorized",
"Bauhaus (2)_Architecture",
"Constructivism (2)_Uncategorized",
"Futurism (2)_Uncategorized",
"Rayonism (2)_Uncategorized",
"Vorticism (2)_Uncategorized",
"Orphism (2)_Uncategorized",
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"Hudson River School (2)_Nature_Landscape_Location",
"Luminism (2)_Nature_Landscape",
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"Barbizon School (2)_Nature_Landscape",
"Academic Art (2)_Uncategorized",
"Neoclassicism (2)_Uncategorized",
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"Realism (2)_Uncategorized",
"Social Realism Variant_Uncategorized",
"Plein Air (2)_Nature_Landscape",
"Pre-Raphaelite (2)_Uncategorized",
"Art Informel (2)_Uncategorized",
"Hard-edge Painting (2)_Uncategorized",
"Geometric Abstraction (2)_Uncategorized",
"Lyrical Abstraction Variant_Uncategorized",
"Post-Painterly Abstraction (2)_Uncategorized",
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"Installation Art (2)_Uncategorized",
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"Neue Wilde (2)_Uncategorized",
"New Perpendicular art (2)_Uncategorized",
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"Remodernism Variant_Uncategorized",
"Norwegian romantic nationalism (2)_Nature_Landscape",
"Socialist Realism Variant_Uncategorized",
"Propaganda Poster Art (2)_Uncategorized",
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"Na\u00efve Art (2)_Uncategorized",
"Art Brut Variant_Uncategorized",
"Neo-primitivism (2)_Still Life",
"Visionary Art (2)_Uncategorized",
"Intuitive Art Variant_Uncategorized",
"Pseudorealism Variant_Uncategorized",
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"Critical Realism Variant_Uncategorized",
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"Abstract Expressionism (2)_Uncategorized",
"Later European abstraction (2)_Uncategorized",
"Tachisme Variant_Uncategorized",
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"Transavantgarde Variant_Uncategorized",
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"Virtual Art Variant_Uncategorized",
"Internet Art Variant_Digital Media",
"Computer Art Variant_Digital Media",
"Information Art Variant_Uncategorized",
"Systems Art Variant_Uncategorized",
"Bio Art Variant_Uncategorized",
"Genetic Art Variant_Uncategorized",
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"Video Games Variant_Games",
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"Circuit Bending (2)_Uncategorized",
"Maker Culture (2)_Culture",
"Self-taught Art (2)_Fantasy",
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"Regionalism (2)_Uncategorized",
"Young British Artists (2)_Portraiture",
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"Algorithmic Art (2)_Uncategorized",
" Lynching_Uncategorized",
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"Clockwork City Variant_Architecture_Location",
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"Dwarvenholm Variant_Uncategorized",
"Sanctuary Variant_Uncategorized",
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"Elfheim Variant_Architecture_Fantasy",
"Midgard Variant_Architecture",
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"Caveopolis Variant_Lifestyle",
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"Steampunk City Variant_Architecture_Location",
"Shadow City Variant_Architecture_Occult_Gothic_Location",
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"Neotokyo Variant_Sci-Fi_Architecture",
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"Under Berlin Variant_Retro_Surrealism_Location",
"Under Istanbul Variant_Architecture_Location",
"Elven City Variant_Architecture_Location",
"Dwarvendom Variant_Uncategorized",
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"Tech City Variant_Architecture_Nature_Location",
"Under Montreal Variant_Architecture_Location",
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"Sub Des Moines Variant_Architecture_Location",
"Underground Salt Lake City Variant_Architecture_Location",
"Sub Cheyenne Variant_Uncategorized_Location",
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"Sub Baton Rouge Variant_Culture_Location",
"Underground Helena Variant_Architecture_Location",
"Sub Carson City Variant_Architecture_Location",
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"Underground Lincoln Variant_Uncategorized_Location",
"Sub Denver Variant_Uncategorized_Location",
"Under Bismarck Variant_Uncategorized_Location",
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"Underground Austin Variant_Uncategorized_Location",
"Sub Atlanta Variant_Uncategorized_Location",
"Under Jackson Variant_Folk Art_Location",
"Sub Montgomery Variant_Uncategorized_Location",
"Underground Little Rock Variant_Uncategorized_Location",
"Sub Frankfort Variant_Uncategorized_Location",
"Under Nashville Variant_Uncategorized_Location",
"Sub Columbia Variant_Architecture_Culture_Location",
"Sub Richmond Variant_Architecture_Location",
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"Sub Dover Variant_Uncategorized_Location",
"Underground Springfield Variant_Folk Art_Location",
"Sub Annapolis Variant_Sculpture_Location",
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"Underground Chula Vista Variant_Uncategorized_Location",
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"Sub Rancho Cucamonga Variant_Architecture_Lifestyle_Location",
"Sub Ontario Variant_Uncategorized_Location",
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{
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],
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{
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}
],
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"min": 0,
"max": 18446744073709551615
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],
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"step": 0.001
}
]
},
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]
}
},
"output": [
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],
"output_is_list": [
false
],
"output_name": [
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],
"name": "Noise Control Script",
"display_name": "Noise Control Script",
"description": "",
"category": "Efficiency Nodes/Scripts",
"output_node": false
},
"HighRes-Fix Script": {
"input": {
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],
"hires_ckpt_name": [
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],
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],
"pixel_upscaler": [
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],
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],
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}
],
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],
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}
],
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}
],
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],
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}
],
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"control_v11p_sd15_canny_fp16.safetensors",
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]
],
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{
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}
],
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"AnimeLineArtPreprocessor",
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"CannyEdgePreprocessor",
"Manga2Anime_LineArt_Preprocessor",
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"LineartStandardPreprocessor",
"SAMPreprocessor",
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"MiDaS-DepthMapPreprocessor",
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"Zoe_DepthAnythingPreprocessor",
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"ImageIntensityDetector",
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"FacialPartColoringFromPoseKps",
"LeReS-DepthMapPreprocessor",
"DWPreprocessor",
"AnimalPosePreprocessor",
"MeshGraphormer-DepthMapPreprocessor",
"PiDiNetPreprocessor",
"ScribblePreprocessor",
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],
{
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}
],
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{
"default": false
}
]
},
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]
},
"hidden": {
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}
},
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],
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],
"name": "HighRes-Fix Script",
"display_name": "HighRes-Fix Script",
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"category": "Efficiency Nodes/Scripts",
"output_node": false
},
"Tiled Upscaler Script": {
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{
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"min": 0.01,
"max": 8.0,
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}
],
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{
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"min": 256,
"max": 16384,
"step": 64
}
],
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"padded",
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]
],
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}
],
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],
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}
],
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}
],
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]
],
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"min": 0.0,
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}
]
},
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]
}
},
"output": [
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],
"output_is_list": [
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],
"output_name": [
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],
"name": "Tiled Upscaler Script",
"display_name": "Tiled Upscaler Script",
"description": "",
"category": "Efficiency Nodes/Scripts",
"output_node": false
},
"LoRA Stack to String converter": {
"input": {
"required": {
"lora_stack": [
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]
}
},
"output": [
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],
"output_is_list": [
false
],
"output_name": [
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],
"name": "LoRA Stack to String converter",
"display_name": "LoRA Stack to String converter",
"description": "",
"category": "Efficiency Nodes/Misc",
"output_node": false
},
"Evaluate Integers": {
"input": {
"required": {
"python_expression": [
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{
"default": "((a + b) - c) / 2",
"multiline": false
}
],
"print_to_console": [
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]
},
"optional": {
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{
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"min": -48000,
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}
],
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}
],
"c": [
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}
]
}
},
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"output_is_list": [
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false,
false
],
"output_name": [
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"FLOAT",
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],
"name": "Evaluate Integers",
"display_name": "Evaluate Integers",
"description": "",
"category": "Efficiency Nodes/Simple Eval",
"output_node": true
},
"Evaluate Floats": {
"input": {
"required": {
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{
"default": "((a + b) - c) / 2",
"multiline": false
}
],
"print_to_console": [
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]
]
},
"optional": {
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"min": -1.7976931348623157e+308,
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}
],
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]
}
},
"output": [
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"FLOAT",
"STRING"
],
"output_is_list": [
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false,
false
],
"output_name": [
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"FLOAT",
"STRING"
],
"name": "Evaluate Floats",
"display_name": "Evaluate Floats",
"description": "",
"category": "Efficiency Nodes/Simple Eval",
"output_node": true
},
"Evaluate Strings": {
"input": {
"required": {
"python_expression": [
"STRING",
{
"default": "a + b + c",
"multiline": false
}
],
"print_to_console": [
[
"False",
"True"
]
]
},
"optional": {
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{
"default": "Hello",
"multiline": false
}
],
"b": [
"STRING",
{
"default": " World",
"multiline": false
}
],
"c": [
"STRING",
{
"default": "!",
"multiline": false
}
]
}
},
"output": [
"STRING"
],
"output_is_list": [
false
],
"output_name": [
"STRING"
],
"name": "Evaluate Strings",
"display_name": "Evaluate Strings",
"description": "",
"category": "Efficiency Nodes/Simple Eval",
"output_node": true
},
"Simple Eval Examples": {
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"required": {
"models_text": [
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{
"default": "The Evaluate Integers, Floats, and Strings nodes \nnow employ the SimpleEval library, enabling secure \ncreation and execution of custom Python expressions.\n\n(https://github.com/danthedeckie/simpleeval)\n\nBelow is a short list of what is possible.\n______________________________________________\n\n\"EVALUATE INTEGERS/FLOATS\" NODE EXPRESSION EXAMPLES:\n\nAddition: a + b + c\nSubtraction: a - b - c\nMultiplication: a * b * c\nDivision: a / b / c\nModulo: a % b % c\nExponentiation: a ** b ** c\nFloor Division: a // b // c\nAbsolute Value: abs(a) + abs(b) + abs(c)\nMaximum: max(a, b, c)\nMinimum: min(a, b, c)\nSum of Squares: a**2 + b**2 + c**2\nBitwise And: a & b & c\nBitwise Or: a | b | c\nBitwise Xor: a ^ b ^ c\nLeft Shift: a << 1 + b << 1 + c << 1\nRight Shift: a >> 1 + b >> 1 + c >> 1\nGreater Than Comparison: a > b > c\nLess Than Comparison: a < b < c\nEqual To Comparison: a == b == c\nNot Equal To Comparison: a != b != c\n______________________________________________\n\n\"EVALUATE STRINGS\" NODE EXPRESSION EXAMPLES:\n\nConcatenate: a + b + c\nFormat: f'{a} {b} {c}'\nLength: len(a) + len(b) + len(c)\nUppercase: a.upper() + b.upper() + c.upper()\nLowercase: a.lower() + b.lower() + c.lower()\nCapitalize: a.capitalize() + b.capitalize() + c.capitalize()\nTitle Case: a.title() + b.title() + c.title()\nStrip: a.strip() + b.strip() + c.strip()\nFind Substring: a.find('sub') + b.find('sub') + c.find('sub')\nReplace Substring: a.replace('old', 'new') + b.replace('old', 'new') + c.replace('old', 'new')\nCount Substring: a.count('sub') + b.count('sub') + c.count('sub')\nCheck Numeric: a.isnumeric() + b.isnumeric() + c.isnumeric()\nCheck Alphabetic: a.isalpha() + b.isalpha() + c.isalpha()\nCheck Alphanumeric: a.isalnum() + b.isalnum() + c.isalnum()\nCheck Start: a.startswith('prefix') + b.startswith('prefix') + c.startswith('prefix')\nCheck End: a.endswith('suffix') + b.endswith('suffix') + c.endswith('suffix')\nSplit: a.split(' ') + b.split(' ') + c.split(' ')\nZero Fill: a.zfill(5) + b.zfill(5) + c.zfill(5)\nSlice: a[:5] + b[:5] + c[:5]\nReverse: a[::-1] + b[::-1] + c[::-1]\n______________________________________________",
"multiline": true
}
]
}
},
"output": [],
"output_is_list": [],
"output_name": [],
"name": "Simple Eval Examples",
"display_name": "Simple Eval Examples",
"description": "",
"category": "Efficiency Nodes/Simple Eval",
"output_node": false
},
"BNK_CLIPTextEncodeAdvanced": {
"input": {
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"text": [
"STRING",
{
"multiline": true
}
],
"clip": [
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],
"token_normalization": [
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"mean",
"length",
"length+mean"
]
],
"weight_interpretation": [
[
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"A1111",
"compel",
"comfy++",
"down_weight"
]
]
}
},
"output": [
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],
"output_is_list": [
false
],
"output_name": [
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],
"name": "BNK_CLIPTextEncodeAdvanced",
"display_name": "CLIP Text Encode (Advanced)",
"description": "",
"category": "conditioning/advanced",
"output_node": false
},
"BNK_CLIPTextEncodeSDXLAdvanced": {
"input": {
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{
"multiline": true
}
],
"text_g": [
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{
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],
"clip": [
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],
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],
"balance": [
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{
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"min": 0.0,
"max": 1.0,
"step": 0.01
}
]
}
},
"output": [
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],
"output_is_list": [
false
],
"output_name": [
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],
"name": "BNK_CLIPTextEncodeSDXLAdvanced",
"display_name": "CLIP Text Encode SDXL (Advanced)",
"description": "",
"category": "conditioning/advanced",
"output_node": false
},
"BNK_AddCLIPSDXLParams": {
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"required": {
"conditioning": [
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],
"width": [
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{
"default": 1024.0,
"min": 0,
"max": 16384
}
],
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}
],
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}
],
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}
],
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}
],
"target_height": [
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{
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"min": 0,
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}
]
}
},
"output": [
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],
"output_is_list": [
false
],
"output_name": [
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],
"name": "BNK_AddCLIPSDXLParams",
"display_name": "Add CLIP SDXL Params",
"description": "",
"category": "conditioning/advanced",
"output_node": false
},
"BNK_AddCLIPSDXLRParams": {
"input": {
"required": {
"conditioning": [
"CONDITIONING"
],
"width": [
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{
"default": 1024.0,
"min": 0,
"max": 16384
}
],
"height": [
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{
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"min": 0,
"max": 16384
}
],
"ascore": [
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{
"default": 6.0,
"min": 0.0,
"max": 1000.0,
"step": 0.01
}
]
}
},
"output": [
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],
"output_is_list": [
false
],
"output_name": [
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],
"name": "BNK_AddCLIPSDXLRParams",
"display_name": "Add CLIP SDXL Refiner Params",
"description": "",
"category": "conditioning/advanced",
"output_node": false
},
"LoadImageByUrl //Browser": {
"input": {
"required": {
"url": [
"STRING",
{}
]
},
"optional": {
"cache": [
"BOOLEAN",
{
"default": true
}
]
}
},
"output": [
"IMAGE"
],
"output_is_list": [
false
],
"output_name": [
"IMAGE"
],
"name": "LoadImageByUrl //Browser",
"display_name": "Load Image By URL",
"description": "",
"category": "Browser",
"output_node": false
},
"SelectInputs //Browser": {
"input": {
"required": {
"input_1": [
[
"none"
],
{}
],
"input_2": [
[
"none"
],
{}
],
"input_3": [
[
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],
{}
],
"input_4": [
[
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{}
],
"preview": [
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{
"multiline": true
}
]
}
},
"output": [
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"INPUT",
"INPUT",
"INPUT"
],
"output_is_list": [
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false,
false,
false
],
"output_name": [
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"input_2",
"input_3",
"input_4"
],
"name": "SelectInputs //Browser",
"display_name": "Select Node Inputs",
"description": "",
"category": "Browser",
"output_node": true
},
"XyzPlot //Browser": {
"input": {
"required": {
"images": [
"IMAGE",
{}
],
"input_x": [
"INPUT",
{}
],
"input_y": [
"INPUT",
{}
],
"value_x": [
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{
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"placeholder": "X values split by semicolon such as \"1girl; 1boy\""
}
],
"value_y": [
"STRING",
{
"multiline": true,
"placeholder": "Y values split by semicolon such as \"1girl; 1boy\""
}
],
"value_z": [
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{
"multiline": true,
"placeholder": "Z values split by semicolon such as \"1girl; 1boy\""
}
],
"output_folder_name": [
"STRING",
{
"default": "xyz_plot"
}
]
},
"optional": {
"input_z": [
"INPUT",
{}
]
},
"hidden": {
"prompt": "PROMPT",
"unique_id": "UNIQUE_ID",
"extra_pnginfo": "EXTRA_PNGINFO"
}
},
"output": [],
"output_is_list": [],
"output_name": [],
"name": "XyzPlot //Browser",
"display_name": "XYZ Plot",
"description": "",
"category": "Browser",
"output_node": true
},
"DifyTextGenerator //Browser": {
"input": {
"required": {
"dify_api_endpoint": [
"STRING",
{}
],
"api_key": [
"STRING",
{}
]
},
"optional": {
"query": [
"STRING",
{
"multiline": true,
"placeholder": "Input as the Query field."
}
],
"inputs_json_str": [
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{
"multiline": true,
"placeholder": "JSON format. It will overwrite the query field above."
}
]
}
},
"output": [
"STRING"
],
"output_is_list": [
false
],
"output_name": [
"STRING"
],
"name": "DifyTextGenerator //Browser",
"display_name": "Dify Text Generator",
"description": "",
"category": "Browser",
"output_node": true
},
"UploadToRemote //Browser": {
"input": {
"required": {
"remote_url": [
"STRING",
{}
],
"extension": [
[
"jpeg",
"webp",
"png",
"jpg",
"gif"
]
],
"quality": [
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{
"default": 85,
"min": 1,
"max": 100,
"step": 1
}
],
"embed_workflow": [
[
"false",
"true"
]
]
},
"optional": {
"images": [
"IMAGE",
{}
],
"extra": [
"STRING",
{
"forceInput": true
}
],
"track_id": [
"STRING",
{
"placeholder": "Optional. Post it as the track_id field."
}
]
},
"hidden": {
"unique_id": "UNIQUE_ID",
"prompt": "PROMPT"
}
},
"output": [],
"output_is_list": [],
"output_name": [],
"name": "UploadToRemote //Browser",
"display_name": "Upload To Remote",
"description": "",
"category": "Browser",
"output_node": true
},
"ADE_AnimateDiffLoRALoader": {
"input": {
"required": {
"lora_name": [
[
"v2_lora_PanLeft.ckpt",
"v2_lora_PanRight.ckpt",
"v2_lora_RollingAnticlockwise.ckpt",
"v2_lora_RollingClockwise.ckpt",
"v2_lora_TiltDown.ckpt",
"v2_lora_TiltUp.ckpt",
"v2_lora_ZoomIn.ckpt",
"v2_lora_ZoomOut.ckpt",
"v3_sd15_adapter.ckpt"
]
],
"strength": [
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{
"default": 1.0,
"min": 0.0,
"max": 10.0,
"step": 0.001
}
]
},
"optional": {
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]
}
},
"output": [
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],
"output_is_list": [
false
],
"output_name": [
"MOTION_LORA"
],
"name": "ADE_AnimateDiffLoRALoader",
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"placeholder": "Proxies list (http=proxy), example:\nhttps=34.195.196.27:8080\nhttp=34.195.196.27:8080"
}
],
"auth_data": [
"STRING",
{
"multiline": true,
"placeholder": "Authorization data...\nExample:\napi_key=your_api_key\ndetect_lang_api_key=your_api_key\nclient_id=your_client_id\nsecret_key=your_secret_key\nappid=your-appid\nappkey=your-appkey"
}
],
"service": [
[
"GoogleTranslator [free]",
"MyMemoryTranslator [free]",
"LingueeTranslator [free - word(s) only]",
"PonsTranslator [free - word(s) only]",
"LibreTranslator [free or api_key]",
"QcriTranslator [free or api_key]"
],
{
"default": "GoogleTranslator"
}
],
"text": [
"STRING",
{
"multiline": true,
"placeholder": "Input text"
}
],
"clip": [
"CLIP"
]
}
},
"output": [
"CONDITIONING",
"STRING"
],
"output_is_list": [
false,
false
],
"output_name": [
"CONDITIONING",
"STRING"
],
"name": "DeepTranslatorCLIPTextEncodeNode",
"display_name": "Deep Translator CLIP Text Encode Node",
"description": "",
"category": "AlekPet Nodes/conditioning",
"output_node": false
},
"DeepTranslatorTextNode": {
"input": {
"required": {
"from_translate": [
[
"auto",
"afrikaans",
"albanian",
"amharic",
"arabic",
"armenian",
"assamese",
"aymara",
"azerbaijani",
"bambara",
"basque",
"belarusian",
"bengali",
"bhojpuri",
"bosnian",
"bulgarian",
"catalan",
"cebuano",
"chichewa",
"chinese (simplified)",
"chinese (traditional)",
"corsican",
"croatian",
"czech",
"danish",
"dhivehi",
"dogri",
"dutch",
"english",
"esperanto",
"estonian",
"ewe",
"filipino",
"finnish",
"french",
"frisian",
"galician",
"georgian",
"german",
"greek",
"guarani",
"gujarati",
"haitian creole",
"hausa",
"hawaiian",
"hebrew",
"hindi",
"hmong",
"hungarian",
"icelandic",
"igbo",
"ilocano",
"indonesian",
"irish",
"italian",
"japanese",
"javanese",
"kannada",
"kazakh",
"khmer",
"kinyarwanda",
"konkani",
"korean",
"krio",
"kurdish (kurmanji)",
"kurdish (sorani)",
"kyrgyz",
"lao",
"latin",
"latvian",
"lingala",
"lithuanian",
"luganda",
"luxembourgish",
"macedonian",
"maithili",
"malagasy",
"malay",
"malayalam",
"maltese",
"maori",
"marathi",
"meiteilon (manipuri)",
"mizo",
"mongolian",
"myanmar",
"nepali",
"norwegian",
"odia (oriya)",
"oromo",
"pashto",
"persian",
"polish",
"portuguese",
"punjabi",
"quechua",
"romanian",
"russian",
"samoan",
"sanskrit",
"scots gaelic",
"sepedi",
"serbian",
"sesotho",
"shona",
"sindhi",
"sinhala",
"slovak",
"slovenian",
"somali",
"spanish",
"sundanese",
"swahili",
"swedish",
"tajik",
"tamil",
"tatar",
"telugu",
"thai",
"tigrinya",
"tsonga",
"turkish",
"turkmen",
"twi",
"ukrainian",
"urdu",
"uyghur",
"uzbek",
"vietnamese",
"welsh",
"xhosa",
"yiddish",
"yoruba",
"zulu"
],
{
"default": "auto"
}
],
"to_translate": [
[
"afrikaans",
"albanian",
"amharic",
"arabic",
"armenian",
"assamese",
"aymara",
"azerbaijani",
"bambara",
"basque",
"belarusian",
"bengali",
"bhojpuri",
"bosnian",
"bulgarian",
"catalan",
"cebuano",
"chichewa",
"chinese (simplified)",
"chinese (traditional)",
"corsican",
"croatian",
"czech",
"danish",
"dhivehi",
"dogri",
"dutch",
"english",
"esperanto",
"estonian",
"ewe",
"filipino",
"finnish",
"french",
"frisian",
"galician",
"georgian",
"german",
"greek",
"guarani",
"gujarati",
"haitian creole",
"hausa",
"hawaiian",
"hebrew",
"hindi",
"hmong",
"hungarian",
"icelandic",
"igbo",
"ilocano",
"indonesian",
"irish",
"italian",
"japanese",
"javanese",
"kannada",
"kazakh",
"khmer",
"kinyarwanda",
"konkani",
"korean",
"krio",
"kurdish (kurmanji)",
"kurdish (sorani)",
"kyrgyz",
"lao",
"latin",
"latvian",
"lingala",
"lithuanian",
"luganda",
"luxembourgish",
"macedonian",
"maithili",
"malagasy",
"malay",
"malayalam",
"maltese",
"maori",
"marathi",
"meiteilon (manipuri)",
"mizo",
"mongolian",
"myanmar",
"nepali",
"norwegian",
"odia (oriya)",
"oromo",
"pashto",
"persian",
"polish",
"portuguese",
"punjabi",
"quechua",
"romanian",
"russian",
"samoan",
"sanskrit",
"scots gaelic",
"sepedi",
"serbian",
"sesotho",
"shona",
"sindhi",
"sinhala",
"slovak",
"slovenian",
"somali",
"spanish",
"sundanese",
"swahili",
"swedish",
"tajik",
"tamil",
"tatar",
"telugu",
"thai",
"tigrinya",
"tsonga",
"turkish",
"turkmen",
"twi",
"ukrainian",
"urdu",
"uyghur",
"uzbek",
"vietnamese",
"welsh",
"xhosa",
"yiddish",
"yoruba",
"zulu"
],
{
"default": "english"
}
],
"add_proxies": [
[
"enable",
"disable"
],
{
"default": "disable"
}
],
"proxies": [
"STRING",
{
"multiline": true,
"placeholder": "Proxies list (http=proxy), example:\nhttps=34.195.196.27:8080\nhttp=34.195.196.27:8080"
}
],
"auth_data": [
"STRING",
{
"multiline": true,
"placeholder": "Authorization data...\nExample:\napi_key=your_api_key\ndetect_lang_api_key=your_api_key\nclient_id=your_client_id\nsecret_key=your_secret_key\nappid=your-appid\nappkey=your-appkey"
}
],
"service": [
[
"GoogleTranslator [free]",
"MyMemoryTranslator [free]",
"LingueeTranslator [free - word(s) only]",
"PonsTranslator [free - word(s) only]",
"LibreTranslator [free or api_key]",
"QcriTranslator [free or api_key]"
],
{
"default": "GoogleTranslator"
}
],
"text": [
"STRING",
{
"multiline": true,
"placeholder": "Input text"
}
]
}
},
"output": [
"STRING"
],
"output_is_list": [
false
],
"output_name": [
"text"
],
"name": "DeepTranslatorTextNode",
"display_name": "Deep Translator Text Node",
"description": "",
"category": "AlekPet Nodes/text",
"output_node": false
},
"PreviewTextNode": {
"input": {
"required": {
"text": [
"STRING",
{
"forceInput": true
}
]
},
"hidden": {
"prompt": "PROMPT",
"extra_pnginfo": "EXTRA_PNGINFO"
}
},
"output": [
"STRING"
],
"output_is_list": [
false
],
"output_name": [
"STRING"
],
"name": "PreviewTextNode",
"display_name": "Preview Text Node",
"description": "",
"category": "AlekPet Nodes/extras",
"output_node": true
},
"GoogleTranslateCLIPTextEncodeNode": {
"input": {
"required": {
"from_translate": [
[
"auto",
"af",
"sq",
"am",
"ar",
"hy",
"az",
"eu",
"be",
"bn",
"bs",
"bg",
"ca",
"ceb",
"ny",
"zh-cn",
"zh-tw",
"co",
"hr",
"cs",
"da",
"nl",
"en",
"eo",
"et",
"tl",
"fi",
"fr",
"fy",
"gl",
"ka",
"de",
"el",
"gu",
"ht",
"ha",
"haw",
"iw",
"he",
"hi",
"hmn",
"hu",
"is",
"ig",
"id",
"ga",
"it",
"ja",
"jw",
"kn",
"kk",
"km",
"ko",
"ku",
"ky",
"lo",
"la",
"lv",
"lt",
"lb",
"mk",
"mg",
"ms",
"ml",
"mt",
"mi",
"mr",
"mn",
"my",
"ne",
"no",
"or",
"ps",
"fa",
"pl",
"pt",
"pa",
"ro",
"ru",
"sm",
"gd",
"sr",
"st",
"sn",
"sd",
"si",
"sk",
"sl",
"so",
"es",
"su",
"sw",
"sv",
"tg",
"ta",
"te",
"th",
"tr",
"uk",
"ur",
"ug",
"uz",
"vi",
"cy",
"xh",
"yi",
"yo",
"zu"
],
{
"default": "auto"
}
],
"to_translate": [
[
"af",
"sq",
"am",
"ar",
"hy",
"az",
"eu",
"be",
"bn",
"bs",
"bg",
"ca",
"ceb",
"ny",
"zh-cn",
"zh-tw",
"co",
"hr",
"cs",
"da",
"nl",
"en",
"eo",
"et",
"tl",
"fi",
"fr",
"fy",
"gl",
"ka",
"de",
"el",
"gu",
"ht",
"ha",
"haw",
"iw",
"he",
"hi",
"hmn",
"hu",
"is",
"ig",
"id",
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"jw",
"kn",
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"ml",
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"es",
"su",
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"tr",
"uk",
"ur",
"ug",
"uz",
"vi",
"cy",
"xh",
"yi",
"yo",
"zu"
],
{
"default": "en"
}
],
"manual_translate": [
[
true,
false
]
],
"text": [
"STRING",
{
"multiline": true,
"placeholder": "Input prompt"
}
],
"clip": [
"CLIP"
]
}
},
"output": [
"CONDITIONING",
"STRING"
],
"output_is_list": [
false,
false
],
"output_name": [
"CONDITIONING",
"STRING"
],
"name": "GoogleTranslateCLIPTextEncodeNode",
"display_name": "Google Translate CLIP Text Encode Node",
"description": "",
"category": "AlekPet Nodes/conditioning",
"output_node": false
},
"GoogleTranslateTextNode": {
"input": {
"required": {
"from_translate": [
[
"auto",
"af",
"sq",
"am",
"ar",
"hy",
"az",
"eu",
"be",
"bn",
"bs",
"bg",
"ca",
"ceb",
"ny",
"zh-cn",
"zh-tw",
"co",
"hr",
"cs",
"da",
"nl",
"en",
"eo",
"et",
"tl",
"fi",
"fr",
"fy",
"gl",
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"gu",
"ht",
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"haw",
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"he",
"hi",
"hmn",
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"gd",
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"es",
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"th",
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"uk",
"ur",
"ug",
"uz",
"vi",
"cy",
"xh",
"yi",
"yo",
"zu"
],
{
"default": "auto"
}
],
"to_translate": [
[
"af",
"sq",
"am",
"ar",
"hy",
"az",
"eu",
"be",
"bn",
"bs",
"bg",
"ca",
"ceb",
"ny",
"zh-cn",
"zh-tw",
"co",
"hr",
"cs",
"da",
"nl",
"en",
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"fr",
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"kn",
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"km",
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"ku",
"ky",
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"lv",
"lt",
"lb",
"mk",
"mg",
"ms",
"ml",
"mt",
"mi",
"mr",
"mn",
"my",
"ne",
"no",
"or",
"ps",
"fa",
"pl",
"pt",
"pa",
"ro",
"ru",
"sm",
"gd",
"sr",
"st",
"sn",
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"sk",
"sl",
"so",
"es",
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"sw",
"sv",
"tg",
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"th",
"tr",
"uk",
"ur",
"ug",
"uz",
"vi",
"cy",
"xh",
"yi",
"yo",
"zu"
],
{
"default": "en"
}
],
"manual_translate": [
[
true,
false
]
],
"text": [
"STRING",
{
"multiline": true,
"placeholder": "Input prompt"
}
]
}
},
"output": [
"STRING"
],
"output_is_list": [
false
],
"output_name": [
"text"
],
"name": "GoogleTranslateTextNode",
"display_name": "Google Translate Text Node",
"description": "",
"category": "AlekPet Nodes/text",
"output_node": false
},
"PainterNode": {
"input": {
"required": {
"image": [
[
"2022-06-23 19-46-52-280417 (1).png",
"2022-06-23 19-46-52-280417.png",
"ChMkJ13cwnOIK4FoAAXGX559JJgAAvbPAImQfAABcZ3473 (1).jpg",
"IMG_20230624_115309.jpg",
"R-C.jpg",
"example.png",
"u=1090338134,2696420997&fm=253&fmt=auto&app=138&f=JPEG (1).webp",
"u=1090338134,2696420997&fm=253&fmt=auto&app=138&f=JPEG (2).webp",
"u=1090338134,2696420997&fm=253&fmt=auto&app=138&f=JPEG.webp"
]
]
}
},
"output": [
"IMAGE",
"MASK"
],
"output_is_list": [
false,
false
],
"output_name": [
"IMAGE",
"MASK"
],
"name": "PainterNode",
"display_name": "Painter Node",
"description": "",
"category": "AlekPet Nodes/image",
"output_node": false
},
"PoseNode": {
"input": {
"required": {
"image": [
[
"metadata.txt"
]
]
}
},
"output": [
"IMAGE"
],
"output_is_list": [
false
],
"output_name": [
"IMAGE"
],
"name": "PoseNode",
"display_name": "Pose Node",
"description": "",
"category": "AlekPet Nodes/image",
"output_node": false
}
}
================================================
FILE: src/draw/data/test.json
================================================
{
"source": "web",
"prompt": {
"18": {
"inputs": {
"text": "paintings, sketches, (worst quality:2), (low quality:2), (normal quality:2), lowres, normal quality, ((monochrome)), ((grayscale)), skin spots, acnes, skin blemishes, age spot, manboobs,(ugly:1.331), (duplicate:1.331), (morbid:1.21), (mutilated:1.21), (tranny:1.331), mutated hands, (poorly drawn hands:1.331), blurry, (bad anatomy:1.21), (bad proportions:1.331), extra limbs, (disfigured:1.331), (more than 2 nipples:1.331), (missing arms:1.331), (extra legs:1.331), (fused fingers:1.61051), (too many fingers:1.61051), (unclear eyes:1.331), bad hands, missing fingers, extra digit, (futa:1.1), bad body, NG_DeepNegative_V1_75T,"
},
"class_type": "ttN text",
"_meta": {
"title": "TTN文本"
}
},
"19": {
"inputs": {
"text": "(8k, best quality, masterpiece:1.2)"
},
"class_type": "ttN text",
"_meta": {
"title": "TTN文本"
}
},
"25": {
"inputs": {
"delimiter": ",",
"clean_whitespace": "true",
"text_a": [
"55",
0
],
"text_b": [
"19",
0
]
},
"class_type": "Text Concatenate",
"_meta": {
"title": "文本连锁"
}
},
"26": {
"inputs": {
"delimiter": "",
"clean_whitespace": "true",
"text_a": [
"58",
0
],
"text_b": [
"18",
0
]
},
"class_type": "Text Concatenate",
"_meta": {
"title": "文本连锁"
}
},
"44": {
"inputs": {
"upscale_type": "latent",
"hires_ckpt_name": "(use same)",
"latent_upscaler": "nearest-exact",
"pixel_upscaler": "4x-UltraSharp.pth",
"upscale_by": 1,
"use_same_seed": true,
"seed": -1,
"hires_steps": 12,
"denoise": 0.3,
"iterations": 1,
"use_controlnet": false,
"control_net_name": "control_sd15_random_color.pth",
"strength": 1,
"preprocessor": "CannyEdgePreprocessor",
"preprocessor_imgs": false
},
"class_type": "HighRes-Fix Script",
"_meta": {
"title": "高清修复"
}
},
"45": {
"inputs": {
"input_mode": "simple",
"lora_count": 5,
"lora_name_1": "lcm\\lcm_sd1.5_pytorch_lora_weights.safetensors",
"lora_wt_1": 1,
"model_str_1": 1,
"clip_str_1": 1,
"lora_name_2": "None",
"lora_wt_2": 0.5,
"model_str_2": 1,
"clip_str_2": 1,
"lora_name_3": "None",
"lora_wt_3": 0.5,
"model_str_3": 1,
"clip_str_3": 1,
"lora_name_4": "None",
"lora_wt_4": 1,
"model_str_4": 1,
"clip_str_4": 1,
"lora_name_5": "None",
"lora_wt_5": 1,
"model_str_5": 1,
"clip_str_5": 1,
"lora_name_6": "None",
"lora_wt_6": 1,
"model_str_6": 1,
"clip_str_6": 1,
"lora_name_7": "None",
"lora_wt_7": 1,
"model_str_7": 1,
"clip_str_7": 1,
"lora_name_8": "None",
"lora_wt_8": 1,
"model_str_8": 1,
"clip_str_8": 1,
"lora_name_9": "None",
"lora_wt_9": 1,
"model_str_9": 1,
"clip_str_9": 1,
"lora_name_10": "None",
"lora_wt_10": 1,
"model_str_10": 1,
"clip_str_10": 1,
"lora_name_11": "None",
"lora_wt_11": 1,
"model_str_11": 1,
"clip_str_11": 1,
"lora_name_12": "None",
"lora_wt_12": 1,
"model_str_12": 1,
"clip_str_12": 1,
"lora_name_13": "None",
"lora_wt_13": 1,
"model_str_13": 1,
"clip_str_13": 1,
"lora_name_14": "None",
"lora_wt_14": 1,
"model_str_14": 1,
"clip_str_14": 1,
"lora_name_15": "None",
"lora_wt_15": 1,
"model_str_15": 1,
"clip_str_15": 1,
"lora_name_16": "None",
"lora_wt_16": 1,
"model_str_16": 1,
"clip_str_16": 1,
"lora_name_17": "None",
"lora_wt_17": 1,
"model_str_17": 1,
"clip_str_17": 1,
"lora_name_18": "None",
"lora_wt_18": 1,
"model_str_18": 1,
"clip_str_18": 1,
"lora_name_19": "None",
"lora_wt_19": 1,
"model_str_19": 1,
"clip_str_19": 1,
"lora_name_20": "None",
"lora_wt_20": 1,
"model_str_20": 1,
"clip_str_20": 1,
"lora_name_21": "None",
"lora_wt_21": 1,
"model_str_21": 1,
"clip_str_21": 1,
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"lora_wt_29": 1,
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"clip_str_30": 1,
"lora_name_31": "None",
"lora_wt_31": 1,
"model_str_31": 1,
"clip_str_31": 1,
"lora_name_32": "None",
"lora_wt_32": 1,
"model_str_32": 1,
"clip_str_32": 1,
"lora_name_33": "None",
"lora_wt_33": 1,
"model_str_33": 1,
"clip_str_33": 1,
"lora_name_34": "None",
"lora_wt_34": 1,
"model_str_34": 1,
"clip_str_34": 1,
"lora_name_35": "None",
"lora_wt_35": 1,
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"clip_str_35": 1,
"lora_name_36": "None",
"lora_wt_36": 1,
"model_str_36": 1,
"clip_str_36": 1,
"lora_name_37": "None",
"lora_wt_37": 1,
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"clip_str_37": 1,
"lora_name_38": "None",
"lora_wt_38": 1,
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"clip_str_38": 1,
"lora_name_39": "None",
"lora_wt_39": 1,
"model_str_39": 1,
"clip_str_39": 1,
"lora_name_40": "None",
"lora_wt_40": 1,
"model_str_40": 1,
"clip_str_40": 1,
"lora_name_41": "None",
"lora_wt_41": 1,
"model_str_41": 1,
"clip_str_41": 1,
"lora_name_42": "None",
"lora_wt_42": 1,
"model_str_42": 1,
"clip_str_42": 1,
"lora_name_43": "None",
"lora_wt_43": 1,
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"clip_str_43": 1,
"lora_name_44": "None",
"lora_wt_44": 1,
"model_str_44": 1,
"clip_str_44": 1,
"lora_name_45": "None",
"lora_wt_45": 1,
"model_str_45": 1,
"clip_str_45": 1,
"lora_name_46": "None",
"lora_wt_46": 1,
"model_str_46": 1,
"clip_str_46": 1,
"lora_name_47": "None",
"lora_wt_47": 1,
"model_str_47": 1,
"clip_str_47": 1,
"lora_name_48": "None",
"lora_wt_48": 1,
"model_str_48": 1,
"clip_str_48": 1,
"lora_name_49": "None",
"lora_wt_49": 1,
"model_str_49": 1,
"clip_str_49": 1
},
"class_type": "LoRA Stacker",
"_meta": {
"title": "LoRA堆"
}
},
"51": {
"inputs": {
"output_path": "[time(%Y-%m-%d)]",
"filename_prefix": "undefined_text2img_output_final_",
"filename_delimiter": "_",
"filename_number_padding": 4,
"filename_number_start": "false",
"extension": "png",
"quality": 100,
"lossless_webp": "false",
"overwrite_mode": "false",
"show_history": "false",
"show_history_by_prefix": "true",
"embed_workflow": "true",
"show_previews": "true",
"images": [
"54",
5
]
},
"class_type": "Image Save",
"_meta": {
"title": "图像保存"
}
},
"52": {
"inputs": {
"vae_name": "Baked VAE",
"clip_skip": -2,
"lora_name": "None",
"lora_model_strength": 1,
"lora_clip_strength": 1,
"positive": [
"25",
0
],
"negative": [
"26",
0
],
"token_normalization": "none",
"weight_interpretation": "A1111",
"empty_latent_width": 512,
"empty_latent_height": 768,
"batch_size": 1
},
"class_type": "Efficient Loader",
"_meta": {
"title": "效率加载器"
}
},
"54": {
"inputs": {
"seed": 517745000437515,
"steps": 20,
"cfg": 7,
"sampler_name": "dpmpp_2m",
"scheduler": "karras",
"denoise": 1,
"preview_method": "none",
"vae_decode": "true",
"model": [
"52",
0
],
"positive": [
"52",
1
],
"negative": [
"52",
2
],
"latent_image": [
"52",
3
],
"optional_vae": [
"52",
4
]
},
"class_type": "KSampler (Efficient)",
"_meta": {
"title": "K采样器(效率)"
}
},
"55": {
"inputs": {
"text": "个绝美的职业装女孩",
"platform": "alibaba",
"source": "auto",
"target": "en"
},
"class_type": "ZFTextTranslation",
"_meta": {
"title": "文本翻译"
}
},
"58": {
"inputs": {
"text": "",
"platform": "alibaba",
"source": "auto",
"target": "en"
},
"class_type": "ZFTextTranslation",
"_meta": {
"title": "文本翻译"
}
}
},
"api": "文生图",
"lifo": false
}
================================================
FILE: src/draw/data/text2imgapi.json
================================================
{
"18": {
"inputs": {
"text": "paintings, sketches, (worst quality:2), (low quality:2), (normal quality:2), lowres, normal quality, ((monochrome)), ((grayscale)), skin spots, acnes, skin blemishes, age spot, manboobs,(ugly:1.331), (duplicate:1.331), (morbid:1.21), (mutilated:1.21), (tranny:1.331), mutated hands, (poorly drawn hands:1.331), blurry, (bad anatomy:1.21), (bad proportions:1.331), extra limbs, (disfigured:1.331), (more than 2 nipples:1.331), (missing arms:1.331), (extra legs:1.331), (fused fingers:1.61051), (too many fingers:1.61051), (unclear eyes:1.331), bad hands, missing fingers, extra digit, (futa:1.1), bad body, NG_DeepNegative_V1_75T,"
},
"class_type": "ttN text"
},
"19": {
"inputs": {
"text": "(8k, best quality, masterpiece:1.2)"
},
"class_type": "ttN text"
},
"23": {
"inputs": {
"from_translate": "auto",
"to_translate": "english",
"add_proxies": "disable",
"proxies": "",
"auth_data": "",
"service": "GoogleTranslator",
"text": "",
"Show proxy": "proxy_hide",
"Show authorization": "authorization_hide"
},
"class_type": "DeepTranslatorTextNode"
},
"24": {
"inputs": {
"from_translate": "auto",
"to_translate": "english",
"add_proxies": "disable",
"proxies": "",
"auth_data": "",
"service": "GoogleTranslator",
"text": "aichang,sdmai,fanhua,1girl\\(Eyes\\(Deep amber,crystal clear,long and delicate eyelashes\\),Nose\\(Elevated,with a slightly upturned nose tip\\),Lips\\(Rosy color, with a defined lip line\\),Hairstyle\\(Black hair,smooth and shiny,slightly wavy at the ends\\),Skin\\(Fair,blemish-free,as delicate as porcelain\\),Clothing\\(chinese traditional clothing,Fabric\\(Lightweight silk,smooth and shiny\\),Posture\\(Straight back,walking confidently and gracefully,light and graceful gait\\),cyberpunk,Chinese New Year,dragon dance,(look at viewer,floating hair,outdoor,upper body):1.5\\),\nBackground\\(snow,mountains,sunshine,cloud,grassland,sky,forest,lake\\),\nmasterpiece,best quality,unreal engine 5 rendering,movie light,movie lens,movie special effects,detailed details,HDR,UHD,8K,CG wallpaper",
"Show proxy": "proxy_hide",
"Show authorization": "authorization_hide"
},
"class_type": "DeepTranslatorTextNode"
},
"25": {
"inputs": {
"delimiter": ",",
"clean_whitespace": "true",
"text_a": [
"24",
0
],
"text_b": [
"19",
0
]
},
"class_type": "Text Concatenate"
},
"26": {
"inputs": {
"delimiter": "",
"clean_whitespace": "true",
"text_a": [
"23",
0
],
"text_b": [
"18",
0
]
},
"class_type": "Text Concatenate"
},
"32": {
"inputs": {
"add_noise": "enable",
"noise_seed": 830784415360923,
"steps": 5,
"cfg": 1.5,
"sampler_name": "lcm",
"scheduler": "normal",
"start_at_step": 0,
"end_at_step": 10000,
"return_with_leftover_noise": "disable",
"preview_method": "none",
"vae_decode": "true",
"model": [
"47",
0
],
"positive": [
"47",
1
],
"negative": [
"47",
2
],
"latent_image": [
"47",
3
],
"optional_vae": [
"47",
4
],
"script": [
"44",
0
]
},
"class_type": "KSampler Adv. (Efficient)"
},
"44": {
"inputs": {
"upscale_type": "latent",
"hires_ckpt_name": "(use same)",
"latent_upscaler": "nearest-exact",
"pixel_upscaler": "4x-UltraSharp.pth",
"upscale_by": 1.5,
"use_same_seed": true,
"seed": -1,
"hires_steps": 12,
"denoise": 0.3,
"iterations": 1,
"use_controlnet": false,
"control_net_name": "control_sd15_random_color.pth",
"strength": 1,
"preprocessor": "CannyEdgePreprocessor",
"preprocessor_imgs": false
},
"class_type": "HighRes-Fix Script"
},
"45": {
"inputs": {
"input_mode": "simple",
"lora_count": 5,
"lora_name_1": "lcm\\lcm_sd1.5_pytorch_lora_weights.safetensors",
"lora_wt_1": 1,
"model_str_1": 1,
"clip_str_1": 1,
"lora_name_2": "None",
"lora_wt_2": 0.5,
"model_str_2": 1,
"clip_str_2": 1,
"lora_name_3": "None",
"lora_wt_3": 0.5,
"model_str_3": 1,
"clip_str_3": 1,
"lora_name_4": "None",
"lora_wt_4": 1,
"model_str_4": 1,
"clip_str_4": 1,
"lora_name_5": "None",
"lora_wt_5": 1,
"model_str_5": 1,
"clip_str_5": 1,
"lora_name_6": "None",
"lora_wt_6": 1,
"model_str_6": 1,
"clip_str_6": 1,
"lora_name_7": "None",
"lora_wt_7": 1,
"model_str_7": 1,
"clip_str_7": 1,
"lora_name_8": "None",
"lora_wt_8": 1,
"model_str_8": 1,
"clip_str_8": 1,
"lora_name_9": "None",
"lora_wt_9": 1,
"model_str_9": 1,
"clip_str_9": 1,
"lora_name_10": "None",
"lora_wt_10": 1,
"model_str_10": 1,
"clip_str_10": 1,
"lora_name_11": "None",
"lora_wt_11": 1,
"model_str_11": 1,
"clip_str_11": 1,
"lora_name_12": "None",
"lora_wt_12": 1,
"model_str_12": 1,
"clip_str_12": 1,
"lora_name_13": "None",
"lora_wt_13": 1,
"model_str_13": 1,
"clip_str_13": 1,
"lora_name_14": "None",
"lora_wt_14": 1,
"model_str_14": 1,
"clip_str_14": 1,
"lora_name_15": "None",
"lora_wt_15": 1,
"model_str_15": 1,
"clip_str_15": 1,
"lora_name_16": "None",
"lora_wt_16": 1,
"model_str_16": 1,
"clip_str_16": 1,
"lora_name_17": "None",
"lora_wt_17": 1,
"model_str_17": 1,
"clip_str_17": 1,
"lora_name_18": "None",
"lora_wt_18": 1,
"model_str_18": 1,
"clip_str_18": 1,
"lora_name_19": "None",
"lora_wt_19": 1,
"model_str_19": 1,
"clip_str_19": 1,
"lora_name_20": "None",
"lora_wt_20": 1,
"model_str_20": 1,
"clip_str_20": 1,
"lora_name_21": "None",
"lora_wt_21": 1,
"model_str_21": 1,
"clip_str_21": 1,
"lora_name_22": "None",
"lora_wt_22": 1,
"model_str_22": 1,
"clip_str_22": 1,
"lora_name_23": "None",
"lora_wt_23": 1,
"model_str_23": 1,
"clip_str_23": 1,
"lora_name_24": "None",
"lora_wt_24": 1,
"model_str_24": 1,
"clip_str_24": 1,
"lora_name_25": "None",
"lora_wt_25": 1,
"model_str_25": 1,
"clip_str_25": 1,
"lora_name_26": "None",
"lora_wt_26": 1,
"model_str_26": 1,
"clip_str_26": 1,
"lora_name_27": "None",
"lora_wt_27": 1,
"model_str_27": 1,
"clip_str_27": 1,
"lora_name_28": "None",
"lora_wt_28": 1,
"model_str_28": 1,
"clip_str_28": 1,
"lora_name_29": "None",
"lora_wt_29": 1,
"model_str_29": 1,
"clip_str_29": 1,
"lora_name_30": "None",
"lora_wt_30": 1,
"model_str_30": 1,
"clip_str_30": 1,
"lora_name_31": "None",
"lora_wt_31": 1,
"model_str_31": 1,
"clip_str_31": 1,
"lora_name_32": "None",
"lora_wt_32": 1,
"model_str_32": 1,
"clip_str_32": 1,
"lora_name_33": "None",
"lora_wt_33": 1,
"model_str_33": 1,
"clip_str_33": 1,
"lora_name_34": "None",
"lora_wt_34": 1,
"model_str_34": 1,
"clip_str_34": 1,
"lora_name_35": "None",
"lora_wt_35": 1,
"model_str_35": 1,
"clip_str_35": 1,
"lora_name_36": "None",
"lora_wt_36": 1,
"model_str_36": 1,
"clip_str_36": 1,
"lora_name_37": "None",
"lora_wt_37": 1,
"model_str_37": 1,
"clip_str_37": 1,
"lora_name_38": "None",
"lora_wt_38": 1,
"model_str_38": 1,
"clip_str_38": 1,
"lora_name_39": "None",
"lora_wt_39": 1,
"model_str_39": 1,
"clip_str_39": 1,
"lora_name_40": "None",
"lora_wt_40": 1,
"model_str_40": 1,
"clip_str_40": 1,
"lora_name_41": "None",
"lora_wt_41": 1,
"model_str_41": 1,
"clip_str_41": 1,
"lora_name_42": "None",
"lora_wt_42": 1,
"model_str_42": 1,
"clip_str_42": 1,
"lora_name_43": "None",
"lora_wt_43": 1,
"model_str_43": 1,
"clip_str_43": 1,
"lora_name_44": "None",
"lora_wt_44": 1,
"model_str_44": 1,
"clip_str_44": 1,
"lora_name_45": "None",
"lora_wt_45": 1,
"model_str_45": 1,
"clip_str_45": 1,
"lora_name_46": "None",
"lora_wt_46": 1,
"model_str_46": 1,
"clip_str_46": 1,
"lora_name_47": "None",
"lora_wt_47": 1,
"model_str_47": 1,
"clip_str_47": 1,
"lora_name_48": "None",
"lora_wt_48": 1,
"model_str_48": 1,
"clip_str_48": 1,
"lora_name_49": "None",
"lora_wt_49": 1,
"model_str_49": 1,
"clip_str_49": 1
},
"class_type": "LoRA Stacker"
},
"47": {
"inputs": {
"ckpt_name": "极氪写实MAX-极氪白系列模型_V6.safetensors",
"vae_name": "Baked VAE",
"clip_skip": -2,
"lora_name": "None",
"lora_model_strength": 1,
"lora_clip_strength": 1,
"positive": [
"25",
0
],
"negative": [
"26",
0
],
"token_normalization": "none",
"weight_interpretation": "A1111",
"empty_latent_width": 512,
"empty_latent_height": 768,
"batch_size": 1,
"lora_stack": [
"45",
0
]
},
"class_type": "Efficient Loader"
},
"51": {
"inputs": {
"output_path": "[time(%Y-%m-%d)]",
"filename_prefix": "text2img_output_final_",
"filename_delimiter": "_",
"filename_number_padding": 4,
"filename_number_start": "false",
"extension": "png",
"quality": 100,
"lossless_webp": "false",
"overwrite_mode": "false",
"show_history": "false",
"show_history_by_prefix": "true",
"embed_workflow": "true",
"show_previews": "true",
"images": [
"32",
5
]
},
"class_type": "Image Save"
}
}
================================================
FILE: src/draw/data/workflow_api_faceswap.ts
================================================
export const FaceSwap = {
'63': {
inputs: {
enabled: true,
swap_model: 'inswapper_128.onnx',
facedetection: 'retinaface_resnet50',
face_restore_model: 'GFPGANv1.4.pth',
face_restore_visibility: 1,
codeformer_weight: 0.5,
detect_gender_input: 'no',
detect_gender_source: 'no',
input_faces_index: '0',
source_faces_index: '0',
console_log_level: 1,
input_image: ['91', 0],
source_image: ['92', 0],
},
class_type: 'ReActorFaceSwap',
_meta: {
title: 'ReActor换脸',
},
},
'91': {
inputs: {
image_path:
'https://wangbo0808.oss-cn-shanghai.aliyuncs.com/aidraw/t2i_1.jpg',
RGBA: 'false',
filename_text_extension: 'true',
},
class_type: 'Image Load',
_meta: {
title: '图像加载',
},
},
'92': {
inputs: {
image_path:
'https://wangbo0808.oss-cn-shanghai.aliyuncs.com/aidraw/R-C.jpg',
RGBA: 'false',
filename_text_extension: 'true',
},
class_type: 'Image Load',
_meta: {
title: '图像加载',
},
},
'93': {
inputs: {
output_path: '[time(%Y-%m-%d)]',
filename_prefix: 'aistudio_output_final_',
filename_delimiter: '_',
filename_number_padding: 4,
filename_number_start: 'false',
extension: 'png',
quality: 100,
lossless_webp: 'false',
overwrite_mode: 'false',
show_history: 'false',
show_history_by_prefix: 'true',
embed_workflow: 'true',
show_previews: 'true',
},
class_type: 'Image Save',
_meta: {
title: '图像保存',
},
},
'94': {
inputs: {
output_path: '[time(%Y-%m-%d)]',
filename_prefix: 'aistudio_output_2_',
filename_delimiter: '_',
filename_number_padding: 4,
filename_number_start: 'false',
extension: 'png',
quality: 100,
lossless_webp: 'false',
overwrite_mode: 'false',
show_history: 'false',
show_history_by_prefix: 'true',
embed_workflow: 'true',
show_previews: 'true',
},
class_type: 'Image Save',
_meta: {
title: '图像保存',
},
},
'95': {
inputs: {
output_path: '[time(%Y-%m-%d)]',
filename_prefix: 'faceswap_output_final_',
filename_delimiter: '_',
filename_number_padding: 4,
filename_number_start: 'false',
extension: 'png',
quality: 100,
lossless_webp: 'false',
overwrite_mode: 'false',
show_history: 'false',
show_history_by_prefix: 'true',
embed_workflow: 'true',
show_previews: 'true',
images: ['63', 0],
},
class_type: 'Image Save',
_meta: {
title: '图像保存',
},
},
};
================================================
FILE: src/draw/data/workflow_api_hdfix_4.ts
================================================
export const workflowApiHdfix4 = {
'5': {
inputs: {
output_path: '[time(%Y-%m-%d)]',
filename_prefix: ['16', 0],
filename_delimiter: '_',
filename_number_padding: 4,
filename_number_start: 'false',
extension: 'png',
quality: 100,
lossless_webp: 'false',
overwrite_mode: 'false',
show_history: 'false',
show_history_by_prefix: 'true',
embed_workflow: 'true',
show_previews: 'true',
images: ['11', 0],
},
class_type: 'Image Save',
_meta: {
title: '图像保存',
},
},
'11': {
inputs: {
upscale_model: ['12', 0],
image: ['15', 0],
},
class_type: 'ImageUpscaleWithModel',
_meta: {
title: '图像通过模型放大',
},
},
'12': {
inputs: {
model_name: '4x-UltraSharp.pth',
},
class_type: 'UpscaleModelLoader',
_meta: {
title: '放大模型加载器',
},
},
'15': {
inputs: {
image_path: 'E:\\ComfyUI-aki-v1.2\\output\\ComfyUI_00001_.png',
RGBA: 'false',
filename_text_extension: 'false',
},
class_type: 'Image Load',
_meta: {
title: '图像加载',
},
},
'16': {
inputs: {
delimiter: '_',
clean_whitespace: 'true',
text_a: ['15', 2],
text_b: ['18', 0],
},
class_type: 'Text Concatenate',
_meta: {
title: '文本连锁',
},
},
'18': {
inputs: {
Text: 'hdifx_output_final_',
},
class_type: 'Text box',
_meta: {
title: '文本框',
},
},
};
================================================
FILE: src/draw/data/workflow_api_image2img.json
================================================
{
"18": {
"inputs": {
"text": "paintings, sketches, (worst quality:2), (low quality:2), (normal quality:2), lowres, normal quality, ((monochrome)), ((grayscale)), skin spots, acnes, skin blemishes, age spot, manboobs,(ugly:1.331), (duplicate:1.331), (morbid:1.21), (mutilated:1.21), (tranny:1.331), mutated hands, (poorly drawn hands:1.331), blurry, (bad anatomy:1.21), (bad proportions:1.331), extra limbs, (disfigured:1.331), (more than 2 nipples:1.331), (missing arms:1.331), (extra legs:1.331), (fused fingers:1.61051), (too many fingers:1.61051), (unclear eyes:1.331), bad hands, missing fingers, extra digit, (futa:1.1), bad body, NG_DeepNegative_V1_75T,"
},
"class_type": "ttN text",
"_meta": {
"title": "TTN文本"
}
},
"19": {
"inputs": {
"text": "(8k, best quality, masterpiece:1.2)"
},
"class_type": "ttN text",
"_meta": {
"title": "TTN文本"
}
},
"25": {
"inputs": {
"delimiter": ",",
"clean_whitespace": "true",
"text_a": [
"98",
0
],
"text_b": [
"19",
0
]
},
"class_type": "Text Concatenate",
"_meta": {
"title": "文本连锁"
}
},
"26": {
"inputs": {
"delimiter": "",
"clean_whitespace": "true",
"text_b": [
"18",
0
]
},
"class_type": "Text Concatenate",
"_meta": {
"title": "文本连锁"
}
},
"51": {
"inputs": {
"output_path": "[time(%Y-%m-%d)]",
"filename_prefix": "img2img_output_final_",
"filename_delimiter": "_",
"filename_number_padding": 4,
"filename_number_start": "false",
"extension": "png",
"quality": 100,
"lossless_webp": "false",
"overwrite_mode": "false",
"show_history": "false",
"show_history_by_prefix": "true",
"embed_workflow": "true",
"show_previews": "true",
"images": [
"85",
5
]
},
"class_type": "Image Save",
"_meta": {
"title": "图像保存"
}
},
"70": {
"inputs": {
"image_path": "./ComfyUI/input/example.png",
"RGBA": "false",
"filename_text_extension": "false"
},
"class_type": "Image Load",
"_meta": {
"title": "图像加载"
}
},
"71": {
"inputs": {
"image": [
"70",
0
]
},
"class_type": "Image Size to Number",
"_meta": {
"title": "图像尺寸到数字"
}
},
"72": {
"inputs": {
"number_type": "integer",
"number": 512
},
"class_type": "Constant Number",
"_meta": {
"title": "常数"
}
},
"73": {
"inputs": {
"operation": "division",
"number_a": [
"72",
0
],
"number_b": [
"71",
0
]
},
"class_type": "Number Operation",
"_meta": {
"title": "数字运算"
}
},
"74": {
"inputs": {
"upscale_method": "nearest-exact",
"scale_by": [
"73",
1
],
"image": [
"70",
0
]
},
"class_type": "ImageScaleBy",
"_meta": {
"title": "图像按系数缩放"
}
},
"85": {
"inputs": {
"seed": 385975464625457,
"steps": 20,
"cfg": 7,
"sampler_name": "dpmpp_2m",
"scheduler": "karras",
"denoise": 1,
"preview_method": "none",
"vae_decode": "true",
"model": [
"92",
0
],
"positive": [
"92",
1
],
"negative": [
"92",
2
],
"latent_image": [
"86",
0
],
"optional_vae": [
"92",
4
]
},
"class_type": "KSampler (Efficient)",
"_meta": {
"title": "K采样器(效率)"
}
},
"86": {
"inputs": {
"pixels": [
"74",
0
],
"vae": [
"92",
4
]
},
"class_type": "VAEEncode",
"_meta": {
"title": "VAE编码"
}
},
"87": {
"inputs": {
"operation": "division",
"number_a": [
"88",
0
],
"number_b": [
"73",
0
]
},
"class_type": "Number Operation",
"_meta": {
"title": "数字运算"
}
},
"88": {
"inputs": {
"number_type": "integer",
"number": 1
},
"class_type": "Constant Number",
"_meta": {
"title": "常数"
}
},
"89": {
"inputs": {
"image": "R-C (1).jpg",
"upload": "image"
},
"class_type": "LoadImage",
"_meta": {
"title": "加载图像"
}
},
"92": {
"inputs": {
"ckpt_name": "majicmixRealistic_v7.safetensors",
"vae_name": "Baked VAE",
"clip_skip": -2,
"lora_name": "None",
"lora_model_strength": 1,
"lora_clip_strength": 1,
"positive": [
"25",
0
],
"negative": [
"26",
0
],
"token_normalization": "none",
"weight_interpretation": "A1111",
"empty_latent_width": 512,
"empty_latent_height": 512,
"batch_size": 1,
"lora_stack": [
"96",
0
]
},
"class_type": "Efficient Loader",
"_meta": {
"title": "效率加载器"
}
},
"94": {
"inputs": {
"text": [
"95",
0
]
},
"class_type": "ShowText|pysssss",
"_meta": {
"title": "展示文本"
}
},
"95": {
"inputs": {
"number": [
"87",
0
]
},
"class_type": "Number to Text",
"_meta": {
"title": "数字到文本"
}
},
"96": {
"inputs": {
"input_mode": "simple",
"lora_count": 4,
"lora_name_1": "sd15_lcm_lora_rank1.safetensors",
"lora_wt_1": 1,
"model_str_1": 1,
"clip_str_1": 1,
"lora_name_2": "None",
"lora_wt_2": 1,
"model_str_2": 1,
"clip_str_2": 1,
"lora_name_3": "None",
"lora_wt_3": 1,
"model_str_3": 1,
"clip_str_3": 1,
"lora_name_4": "None",
"lora_wt_4": 1,
"model_str_4": 1,
"clip_str_4": 1,
"lora_name_5": "None",
"lora_wt_5": 1,
"model_str_5": 1,
"clip_str_5": 1,
"lora_name_6": "None",
"lora_wt_6": 1,
"model_str_6": 1,
"clip_str_6": 1,
"lora_name_7": "None",
"lora_wt_7": 1,
"model_str_7": 1,
"clip_str_7": 1,
"lora_name_8": "None",
"lora_wt_8": 1,
"model_str_8": 1,
"clip_str_8": 1,
"lora_name_9": "None",
"lora_wt_9": 1,
"model_str_9": 1,
"clip_str_9": 1,
"lora_name_10": "None",
"lora_wt_10": 1,
"model_str_10": 1,
"clip_str_10": 1,
"lora_name_11": "None",
"lora_wt_11": 1,
"model_str_11": 1,
"clip_str_11": 1,
"lora_name_12": "None",
"lora_wt_12": 1,
"model_str_12": 1,
"clip_str_12": 1,
"lora_name_13": "None",
"lora_wt_13": 1,
"model_str_13": 1,
"clip_str_13": 1,
"lora_name_14": "None",
"lora_wt_14": 1,
"model_str_14": 1,
"clip_str_14": 1,
"lora_name_15": "None",
"lora_wt_15": 1,
"model_str_15": 1,
"clip_str_15": 1,
"lora_name_16": "None",
"lora_wt_16": 1,
"model_str_16": 1,
"clip_str_16": 1,
"lora_name_17": "None",
"lora_wt_17": 1,
"model_str_17": 1,
"clip_str_17": 1,
"lora_name_18": "None",
"lora_wt_18": 1,
"model_str_18": 1,
"clip_str_18": 1,
"lora_name_19": "None",
"lora_wt_19": 1,
"model_str_19": 1,
"clip_str_19": 1,
"lora_name_20": "None",
"lora_wt_20": 1,
"model_str_20": 1,
"clip_str_20": 1,
"lora_name_21": "None",
"lora_wt_21": 1,
"model_str_21": 1,
"clip_str_21": 1,
"lora_name_22": "None",
"lora_wt_22": 1,
"model_str_22": 1,
"clip_str_22": 1,
"lora_name_23": "None",
"lora_wt_23": 1,
"model_str_23": 1,
"clip_str_23": 1,
"lora_name_24": "None",
"lora_wt_24": 1,
"model_str_24": 1,
"clip_str_24": 1,
"lora_name_25": "None",
"lora_wt_25": 1,
"model_str_25": 1,
"clip_str_25": 1,
"lora_name_26": "None",
"lora_wt_26": 1,
"model_str_26": 1,
"clip_str_26": 1,
"lora_name_27": "None",
"lora_wt_27": 1,
"model_str_27": 1,
"clip_str_27": 1,
"lora_name_28": "None",
"lora_wt_28": 1,
"model_str_28": 1,
"clip_str_28": 1,
"lora_name_29": "None",
"lora_wt_29": 1,
"model_str_29": 1,
"clip_str_29": 1,
"lora_name_30": "None",
"lora_wt_30": 1,
"model_str_30": 1,
"clip_str_30": 1,
"lora_name_31": "None",
"lora_wt_31": 1,
"model_str_31": 1,
"clip_str_31": 1,
"lora_name_32": "None",
"lora_wt_32": 1,
"model_str_32": 1,
"clip_str_32": 1,
"lora_name_33": "None",
"lora_wt_33": 1,
"model_str_33": 1,
"clip_str_33": 1,
"lora_name_34": "None",
"lora_wt_34": 1,
"model_str_34": 1,
"clip_str_34": 1,
"lora_name_35": "None",
"lora_wt_35": 1,
"model_str_35": 1,
"clip_str_35": 1,
"lora_name_36": "None",
"lora_wt_36": 1,
"model_str_36": 1,
"clip_str_36": 1,
"lora_name_37": "None",
"lora_wt_37": 1,
"model_str_37": 1,
"clip_str_37": 1,
"lora_name_38": "None",
"lora_wt_38": 1,
"model_str_38": 1,
"clip_str_38": 1,
"lora_name_39": "None",
"lora_wt_39": 1,
"model_str_39": 1,
"clip_str_39": 1,
"lora_name_40": "None",
"lora_wt_40": 1,
"model_str_40": 1,
"clip_str_40": 1,
"lora_name_41": "None",
"lora_wt_41": 1,
"model_str_41": 1,
"clip_str_41": 1,
"lora_name_42": "None",
"lora_wt_42": 1,
"model_str_42": 1,
"clip_str_42": 1,
"lora_name_43": "None",
"lora_wt_43": 1,
"model_str_43": 1,
"clip_str_43": 1,
"lora_name_44": "None",
"lora_wt_44": 1,
"model_str_44": 1,
"clip_str_44": 1,
"lora_name_45": "None",
"lora_wt_45": 1,
"model_str_45": 1,
"clip_str_45": 1,
"lora_name_46": "None",
"lora_wt_46": 1,
"model_str_46": 1,
"clip_str_46": 1,
"lora_name_47": "None",
"lora_wt_47": 1,
"model_str_47": 1,
"clip_str_47": 1,
"lora_name_48": "None",
"lora_wt_48": 1,
"model_str_48": 1,
"clip_str_48": 1,
"lora_name_49": "None",
"lora_wt_49": 1,
"model_str_49": 1,
"clip_str_49": 1
},
"class_type": "LoRA Stacker",
"_meta": {
"title": "LoRA堆"
}
},
"98": {
"inputs": {
"model": "wd-v1-4-moat-tagger-v2",
"threshold": 0.35,
"character_threshold": 0.85,
"replace_underscore": false,
"trailing_comma": false,
"exclude_tags": "",
"tags": "1girl, solo, looking_at_viewer, brown_hair, dress, bare_shoulders, brown_eyes, jewelry, earrings, bracelet, lips, realistic",
"image": [
"70",
0
]
},
"class_type": "WD14Tagger|pysssss",
"_meta": {
"title": "WD14反推提示词"
}
}
}
================================================
FILE: src/draw/data/workflow_api_image2img.ts
================================================
export const image2img = {
'18': {
inputs: {
text: 'paintings, sketches, (worst quality:2), (low quality:2), (normal quality:2), lowres, normal quality, ((monochrome)), ((grayscale)), skin spots, acnes, skin blemishes, age spot, manboobs,(ugly:1.331), (duplicate:1.331), (morbid:1.21), (mutilated:1.21), (tranny:1.331), mutated hands, (poorly drawn hands:1.331), blurry, (bad anatomy:1.21), (bad proportions:1.331), extra limbs, (disfigured:1.331), (more than 2 nipples:1.331), (missing arms:1.331), (extra legs:1.331), (fused fingers:1.61051), (too many fingers:1.61051), (unclear eyes:1.331), bad hands, missing fingers, extra digit, (futa:1.1), bad body, NG_DeepNegative_V1_75T,',
},
class_type: 'ttN text',
_meta: {
title: 'TTN文本',
},
},
'19': {
inputs: {
text: '(8k, best quality, masterpiece:1.2)',
},
class_type: 'ttN text',
_meta: {
title: 'TTN文本',
},
},
'25': {
inputs: {
delimiter: ',',
clean_whitespace: 'true',
text_a: ['98', 0],
text_b: ['19', 0],
},
class_type: 'Text Concatenate',
_meta: {
title: '文本连锁',
},
},
'26': {
inputs: {
delimiter: '',
clean_whitespace: 'true',
text_b: ['18', 0],
},
class_type: 'Text Concatenate',
_meta: {
title: '文本连锁',
},
},
'51': {
inputs: {
output_path: '[time(%Y-%m-%d)]',
filename_prefix: 'img2img_output_final_',
filename_delimiter: '_',
filename_number_padding: 4,
filename_number_start: 'false',
extension: 'png',
quality: 100,
lossless_webp: 'false',
overwrite_mode: 'false',
show_history: 'false',
show_history_by_prefix: 'true',
embed_workflow: 'true',
show_previews: 'true',
images: ['85', 5],
},
class_type: 'Image Save',
_meta: {
title: '图像保存',
},
},
'70': {
inputs: {
image_path: './ComfyUI/input/example.png',
RGBA: 'false',
filename_text_extension: 'false',
},
class_type: 'Image Load',
_meta: {
title: '图像加载',
},
},
'71': {
inputs: {
image: ['70', 0],
},
class_type: 'Image Size to Number',
_meta: {
title: '图像尺寸到数字',
},
},
'72': {
inputs: {
number_type: 'integer',
number: 512,
},
class_type: 'Constant Number',
_meta: {
title: '常数',
},
},
'73': {
inputs: {
operation: 'division',
number_a: ['72', 0],
number_b: ['71', 0],
},
class_type: 'Number Operation',
_meta: {
title: '数字运算',
},
},
'74': {
inputs: {
upscale_method: 'nearest-exact',
scale_by: ['73', 1],
image: ['70', 0],
},
class_type: 'ImageScaleBy',
_meta: {
title: '图像按系数缩放',
},
},
'85': {
inputs: {
seed: 116235324919319,
steps: 20,
cfg: 7,
sampler_name: 'dpmpp_2m',
scheduler: 'karras',
denoise: 1,
preview_method: 'none',
vae_decode: 'true',
model: ['101', 0],
positive: ['101', 1],
negative: ['101', 2],
latent_image: ['86', 0],
optional_vae: ['101', 4],
},
class_type: 'KSampler (Efficient)',
_meta: {
title: 'K采样器(效率)',
},
},
'86': {
inputs: {
pixels: ['74', 0],
vae: ['101', 4],
},
class_type: 'VAEEncode',
_meta: {
title: 'VAE编码',
},
},
'89': {
inputs: {
image: 'ComfyUI_00001_.png',
upload: 'image',
},
class_type: 'LoadImage',
_meta: {
title: '加载图像',
},
},
'98': {
inputs: {
model: 'wd-v1-4-moat-tagger-v2',
threshold: 0.35,
character_threshold: 0.85,
replace_underscore: false,
trailing_comma: false,
exclude_tags: '',
tags: 'flower, outdoors, sky, cloud, water, no_humans, night, star_\\(sky\\), night_sky, scenery, starry_sky, reflection, sunset, mountain, horizon, landscape, mountainous_horizon',
image: ['70', 0],
},
class_type: 'WD14Tagger|pysssss',
_meta: {
title: 'WD14反推提示词',
},
},
'101': {
inputs: {
ckpt_name: 'anything-v5-PrtRE.safetensors',
vae_name: 'Baked VAE',
clip_skip: -1,
lora_name: 'None',
lora_model_strength: 1,
lora_clip_strength: 1,
positive: ['25', 0],
negative: ['26', 0],
token_normalization: 'none',
weight_interpretation: 'A1111',
empty_latent_width: 512,
empty_latent_height: 512,
batch_size: 1,
},
class_type: 'Efficient Loader',
_meta: {
title: '效率加载器',
},
},
};
================================================
FILE: src/draw/data/workflow_api_img2video.json
================================================
{
"8": {
"inputs": {
"width": [
"36",
2
],
"height": [
"36",
1
],
"video_frames": 14,
"motion_bucket_id": 127,
"fps": 6,
"augmentation_level": 0,
"clip_vision": [
"35",
3
],
"init_image": [
"37",
0
],
"vae": [
"35",
2
]
},
"class_type": "SVD_img2vid_Conditioning",
"_meta": {
"title": "SVD_图像到视频_条件"
}
},
"10": {
"inputs": {
"image": "u=1090338134,2696420997&fm=253&fmt=auto&app=138&f=JPEG (3).webp",
"upload": "image"
},
"class_type": "LoadImage",
"_meta": {
"title": "加载图像"
}
},
"18": {
"inputs": {
"min_cfg": 1,
"model": [
"35",
0
]
},
"class_type": "VideoLinearCFGGuidance",
"_meta": {
"title": "线性CFG引导"
}
},
"19": {
"inputs": {
"seed": 526114517786088,
"steps": 20,
"cfg": 3,
"sampler_name": "dpmpp_2m",
"scheduler": "karras",
"denoise": 1,
"model": [
"18",
0
],
"positive": [
"8",
0
],
"negative": [
"8",
1
],
"latent_image": [
"8",
2
]
},
"class_type": "KSampler",
"_meta": {
"title": "K采样器"
}
},
"20": {
"inputs": {
"samples": [
"19",
0
],
"vae": [
"35",
2
]
},
"class_type": "VAEDecode",
"_meta": {
"title": "VAE解码"
}
},
"23": {
"inputs": {
"frame_rate": 8,
"loop_count": 0,
"filename_prefix": "img2video_output_final_",
"format": "video/h264-mp4",
"pix_fmt": "yuv420p",
"crf": 19,
"save_metadata": true,
"pingpong": false,
"save_output": true,
"images": [
"24",
0
]
},
"class_type": "VHS_VideoCombine",
"_meta": {
"title": "合并为视频"
}
},
"24": {
"inputs": {
"ckpt_name": "rife47.pth",
"clear_cache_after_n_frames": 10,
"multiplier": 2,
"fast_mode": true,
"ensemble": true,
"scale_factor": 1,
"frames": [
"20",
0
]
},
"class_type": "RIFE VFI",
"_meta": {
"title": "RIFE VFI"
}
},
"35": {
"inputs": {
"ckpt_name": "svd-fp16.safetensors"
},
"class_type": "unCLIPCheckpointLoader",
"_meta": {
"title": "unCLIPCheckpoint加载器"
}
},
"36": {
"inputs": {
"value": [
"37",
0
]
},
"class_type": "ImpactImageInfo",
"_meta": {
"title": "图像信息"
}
},
"37": {
"inputs": {
"image_path": "./ComfyUI/input/example.png",
"RGBA": "false",
"filename_text_extension": "true"
},
"class_type": "Image Load",
"_meta": {
"title": "图像加载"
}
}
}
================================================
FILE: src/draw/data/workflow_api_img2video.ts
================================================
export const img2video = {
'8': {
inputs: {
width: ['36', 2],
height: ['36', 1],
video_frames: 14,
motion_bucket_id: 127,
fps: 6,
augmentation_level: 0,
clip_vision: ['35', 3],
init_image: ['37', 0],
vae: ['35', 2],
},
class_type: 'SVD_img2vid_Conditioning',
_meta: {
title: 'SVD_图像到视频_条件',
},
},
'10': {
inputs: {
image: 'u=1090338134,2696420997&fm=253&fmt=auto&app=138&f=JPEG (3).webp',
upload: 'image',
},
class_type: 'LoadImage',
_meta: {
title: '加载图像',
},
},
'18': {
inputs: {
min_cfg: 1,
model: ['35', 0],
},
class_type: 'VideoLinearCFGGuidance',
_meta: {
title: '线性CFG引导',
},
},
'19': {
inputs: {
seed: 526114517786088,
steps: 20,
cfg: 3,
sampler_name: 'dpmpp_2m',
scheduler: 'karras',
denoise: 1,
model: ['18', 0],
positive: ['8', 0],
negative: ['8', 1],
latent_image: ['8', 2],
},
class_type: 'KSampler',
_meta: {
title: 'K采样器',
},
},
'20': {
inputs: {
samples: ['19', 0],
vae: ['35', 2],
},
class_type: 'VAEDecode',
_meta: {
title: 'VAE解码',
},
},
'23': {
inputs: {
frame_rate: 8,
loop_count: 0,
filename_prefix: 'img2video_output_final_',
format: 'video/h264-mp4',
pix_fmt: 'yuv420p',
crf: 19,
save_metadata: true,
pingpong: false,
save_output: true,
images: ['24', 0],
},
class_type: 'VHS_VideoCombine',
_meta: {
title: '合并为视频',
},
},
'24': {
inputs: {
ckpt_name: 'rife47.pth',
clear_cache_after_n_frames: 10,
multiplier: 2,
fast_mode: true,
ensemble: true,
scale_factor: 1,
frames: ['20', 0],
},
class_type: 'RIFE VFI',
_meta: {
title: 'RIFE VFI',
},
},
'35': {
inputs: {
ckpt_name: 'svd-fp16.safetensors',
},
class_type: 'unCLIPCheckpointLoader',
_meta: {
title: 'unCLIPCheckpoint加载器',
},
},
'36': {
inputs: {
value: ['37', 0],
},
class_type: 'ImpactImageInfo',
_meta: {
title: '图像信息',
},
},
'37': {
inputs: {
image_path: './ComfyUI/input/example.png',
RGBA: 'false',
filename_text_extension: 'true',
},
class_type: 'Image Load',
_meta: {
title: '图像加载',
},
},
};
================================================
FILE: src/draw/data/workflow_api_inpainting.json
================================================
{
"18": {
"inputs": {
"text": "paintings, sketches, (worst quality:2), (low quality:2), (normal quality:2), lowres, normal quality, ((monochrome)), ((grayscale)), skin spots, acnes, skin blemishes, age spot, manboobs,(ugly:1.331), (duplicate:1.331), (morbid:1.21), (mutilated:1.21), (tranny:1.331), mutated hands, (poorly drawn hands:1.331), blurry, (bad anatomy:1.21), (bad proportions:1.331), extra limbs, (disfigured:1.331), (more than 2 nipples:1.331), (missing arms:1.331), (extra legs:1.331), (fused fingers:1.61051), (too many fingers:1.61051), (unclear eyes:1.331), bad hands, missing fingers, extra digit, (futa:1.1), bad body, NG_DeepNegative_V1_75T,"
},
"class_type": "ttN text",
"_meta": {
"title": "TTN文本"
}
},
"19": {
"inputs": {
"text": "(8k, best quality, masterpiece:1.2)"
},
"class_type": "ttN text",
"_meta": {
"title": "TTN文本"
}
},
"25": {
"inputs": {
"delimiter": ",",
"clean_whitespace": "true",
"text_a": [
"95",
0
],
"text_b": [
"19",
0
]
},
"class_type": "Text Concatenate",
"_meta": {
"title": "文本连锁"
}
},
"26": {
"inputs": {
"delimiter": "",
"clean_whitespace": "true",
"text_a": [
"94",
0
],
"text_b": [
"18",
0
]
},
"class_type": "Text Concatenate",
"_meta": {
"title": "文本连锁"
}
},
"51": {
"inputs": {
"output_path": "[time(%Y-%m-%d)]",
"filename_prefix": [
"93",
0
],
"filename_delimiter": "_",
"filename_number_padding": 4,
"filename_number_start": "false",
"extension": "png",
"quality": 100,
"lossless_webp": "false",
"overwrite_mode": "false",
"show_history": "false",
"show_history_by_prefix": "true",
"embed_workflow": "true",
"show_previews": "true",
"images": [
"85",
5
]
},
"class_type": "Image Save",
"_meta": {
"title": "图像保存"
}
},
"70": {
"inputs": {
"image_path": "C:\\Users\\wangb\\Downloads\\image1 - 2024-03-20T180727.831.png",
"RGBA": "false",
"filename_text_extension": "false"
},
"class_type": "Image Load",
"_meta": {
"title": "图像加载"
}
},
"85": {
"inputs": {
"seed": 205149528245215,
"steps": 20,
"cfg": 7,
"sampler_name": "dpmpp_2m",
"scheduler": "karras",
"denoise": 0.5,
"preview_method": "none",
"vae_decode": "true",
"model": [
"98",
0
],
"positive": [
"98",
1
],
"negative": [
"98",
2
],
"latent_image": [
"88",
0
],
"optional_vae": [
"98",
4
]
},
"class_type": "KSampler (Efficient)",
"_meta": {
"title": "K采样器(效率)"
}
},
"86": {
"inputs": {
"pixels": [
"70",
0
],
"vae": [
"98",
4
]
},
"class_type": "VAEEncode",
"_meta": {
"title": "VAE编码"
}
},
"87": {
"inputs": {
"image_path": "C:\\Users\\wangb\\Downloads\\mask (21).png",
"RGBA": "false",
"filename_text_extension": "false"
},
"class_type": "Image Load",
"_meta": {
"title": "图像加载"
}
},
"88": {
"inputs": {
"samples": [
"86",
0
],
"mask": [
"87",
1
]
},
"class_type": "SetLatentNoiseMask",
"_meta": {
"title": "设置Latent噪波遮罩"
}
},
"93": {
"inputs": {
"delimiter": "_",
"clean_whitespace": "true",
"text_a": [
"87",
2
],
"text_b": [
"100",
0
]
},
"class_type": "Text Concatenate",
"_meta": {
"title": "文本连锁"
}
},
"94": {
"inputs": {
"text": "",
"platform": "alibaba",
"source": "auto",
"target": "en"
},
"class_type": "ZFTextTranslation",
"_meta": {
"title": "文本翻译"
}
},
"95": {
"inputs": {
"text": "",
"platform": "alibaba",
"source": "auto",
"target": "en"
},
"class_type": "ZFTextTranslation",
"_meta": {
"title": "文本翻译"
}
},
"98": {
"inputs": {
"ckpt_name": "majicmixRealistic_v7.safetensors",
"vae_name": "Baked VAE",
"clip_skip": -2,
"lora_name": "None",
"lora_model_strength": 1,
"lora_clip_strength": 1,
"positive": [
"25",
0
],
"negative": [
"26",
0
],
"token_normalization": "none",
"weight_interpretation": "A1111",
"empty_latent_width": 512,
"empty_latent_height": 512,
"batch_size": 1
},
"class_type": "Efficient Loader",
"_meta": {
"title": "效率加载器"
}
},
"100": {
"inputs": {
"text": "inpaiting_output_final_"
},
"class_type": "ttN text",
"_meta": {
"title": "TTN文本"
}
}
}
================================================
FILE: src/draw/data/workflow_api_inpainting.ts
================================================
export const inpainting = {
'18': {
inputs: {
text: 'paintings, sketches, (worst quality:2), (low quality:2), (normal quality:2), lowres, normal quality, ((monochrome)), ((grayscale)), skin spots, acnes, skin blemishes, age spot, manboobs,(ugly:1.331), (duplicate:1.331), (morbid:1.21), (mutilated:1.21), (tranny:1.331), mutated hands, (poorly drawn hands:1.331), blurry, (bad anatomy:1.21), (bad proportions:1.331), extra limbs, (disfigured:1.331), (more than 2 nipples:1.331), (missing arms:1.331), (extra legs:1.331), (fused fingers:1.61051), (too many fingers:1.61051), (unclear eyes:1.331), bad hands, missing fingers, extra digit, (futa:1.1), bad body, NG_DeepNegative_V1_75T,',
},
class_type: 'ttN text',
_meta: {
title: 'TTN文本',
},
},
'19': {
inputs: {
text: '(8k, best quality, masterpiece:1.2)',
},
class_type: 'ttN text',
_meta: {
title: 'TTN文本',
},
},
'25': {
inputs: {
delimiter: ',',
clean_whitespace: 'true',
text_a: ['95', 0],
text_b: ['19', 0],
},
class_type: 'Text Concatenate',
_meta: {
title: '文本连锁',
},
},
'26': {
inputs: {
delimiter: '',
clean_whitespace: 'true',
text_a: ['94', 0],
text_b: ['18', 0],
},
class_type: 'Text Concatenate',
_meta: {
title: '文本连锁',
},
},
'51': {
inputs: {
output_path: '[time(%Y-%m-%d)]',
filename_prefix: ['93', 0],
filename_delimiter: '_',
filename_number_padding: 4,
filename_number_start: 'false',
extension: 'png',
quality: 100,
lossless_webp: 'false',
overwrite_mode: 'false',
show_history: 'false',
show_history_by_prefix: 'true',
embed_workflow: 'true',
show_previews: 'true',
images: ['85', 5],
},
class_type: 'Image Save',
_meta: {
title: '图像保存',
},
},
'70': {
inputs: {
image_path:
'C:\\Users\\wangb\\Downloads\\image1 - 2024-03-20T180727.831.png',
RGBA: 'false',
filename_text_extension: 'false',
},
class_type: 'Image Load',
_meta: {
title: '图像加载',
},
},
'85': {
inputs: {
seed: 205149528245215,
steps: 20,
cfg: 7,
sampler_name: 'dpmpp_2m',
scheduler: 'karras',
denoise: 0.5,
preview_method: 'none',
vae_decode: 'true',
model: ['98', 0],
positive: ['98', 1],
negative: ['98', 2],
latent_image: ['88', 0],
optional_vae: ['98', 4],
},
class_type: 'KSampler (Efficient)',
_meta: {
title: 'K采样器(效率)',
},
},
'86': {
inputs: {
pixels: ['70', 0],
vae: ['98', 4],
},
class_type: 'VAEEncode',
_meta: {
title: 'VAE编码',
},
},
'87': {
inputs: {
image_path: 'C:\\Users\\wangb\\Downloads\\mask (21).png',
RGBA: 'false',
filename_text_extension: 'false',
},
class_type: 'Image Load',
_meta: {
title: '图像加载',
},
},
'88': {
inputs: {
samples: ['86', 0],
mask: ['87', 1],
},
class_type: 'SetLatentNoiseMask',
_meta: {
title: '设置Latent噪波遮罩',
},
},
'93': {
inputs: {
delimiter: '_',
clean_whitespace: 'true',
text_a: ['87', 2],
text_b: ['100', 0],
},
class_type: 'Text Concatenate',
_meta: {
title: '文本连锁',
},
},
'94': {
inputs: {
text: '',
platform: 'alibaba',
source: 'auto',
target: 'en',
},
class_type: 'ZFTextTranslation',
_meta: {
title: '文本翻译',
},
},
'95': {
inputs: {
text: '',
platform: 'alibaba',
source: 'auto',
target: 'en',
},
class_type: 'ZFTextTranslation',
_meta: {
title: '文本翻译',
},
},
'98': {
inputs: {
ckpt_name: 'majicmixRealistic_v7.safetensors',
vae_name: 'Baked VAE',
clip_skip: -2,
lora_name: 'None',
lora_model_strength: 1,
lora_clip_strength: 1,
positive: ['25', 0],
negative: ['26', 0],
token_normalization: 'none',
weight_interpretation: 'A1111',
empty_latent_width: 512,
empty_latent_height: 512,
batch_size: 1,
},
class_type: 'Efficient Loader',
_meta: {
title: '效率加载器',
},
},
'100': {
inputs: {
text: 'inpaiting_output_final_',
},
class_type: 'ttN text',
_meta: {
title: 'TTN文本',
},
},
};
================================================
FILE: src/draw/data/workflow_api_matting.json
================================================
{
"5": {
"inputs": {
"prompt": [
"67",
0
],
"threshold": 0.3,
"sam_model": [
"6",
0
],
"grounding_dino_model": [
"7",
0
],
"image": [
"62",
0
]
},
"class_type": "GroundingDinoSAMSegment (segment anything)",
"_meta": {
"title": "G-DinoSAM语义分割"
}
},
"6": {
"inputs": {
"model_name": "sam_vit_h_4b8939.pth",
"device_mode": "Prefer GPU"
},
"class_type": "SAMLoader",
"_meta": {
"title": "SAM加载器"
}
},
"7": {
"inputs": {
"model_name": "GroundingDINO_SwinB (938MB)"
},
"class_type": "GroundingDinoModelLoader (segment anything)",
"_meta": {
"title": "G-Dino模型加载器"
}
},
"44": {
"inputs": {
"mask": [
"5",
1
]
},
"class_type": "InvertMask",
"_meta": {
"title": "遮罩反转"
}
},
"59": {
"inputs": {
"image": [
"5",
0
],
"alpha": [
"44",
0
]
},
"class_type": "JoinImageWithAlpha",
"_meta": {
"title": "合并图像Alpha"
}
},
"62": {
"inputs": {
"image_path": "https://wangbo0808.oss-cn-shanghai.aliyuncs.com/aidraw/4c226c64d37f410c857f98ebb3ecb5ef.jpeg",
"RGBA": "false",
"filename_text_extension": "false"
},
"class_type": "Image Load",
"_meta": {
"title": "图像加载"
}
},
"63": {
"inputs": {
"output_path": "[time(%Y-%m-%d)]",
"filename_prefix": [
"69",
0
],
"filename_delimiter": "_",
"filename_number_padding": 4,
"filename_number_start": "false",
"extension": "png",
"quality": 100,
"lossless_webp": "false",
"overwrite_mode": "false",
"show_history": "false",
"show_history_by_prefix": "true",
"embed_workflow": "true",
"show_previews": "true",
"images": [
"59",
0
]
},
"class_type": "Image Save",
"_meta": {
"title": "图像保存"
}
},
"67": {
"inputs": {
"text": "西瓜",
"platform": "alibaba",
"source": "auto",
"target": "en"
},
"class_type": "ZFTextTranslation",
"_meta": {
"title": "文本翻译"
}
},
"69": {
"inputs": {
"delimiter": "_",
"clean_whitespace": "true",
"text_a": [
"62",
2
],
"text_b": [
"71",
0
]
},
"class_type": "Text Concatenate",
"_meta": {
"title": "文本连锁"
}
},
"71": {
"inputs": {
"text": "_segment_output_final_"
},
"class_type": "Text Multiline",
"_meta": {
"title": "多行文本"
}
}
}
================================================
FILE: src/draw/data/workflow_api_matting.ts
================================================
export const matting = {
'5': {
inputs: {
prompt: ['67', 0],
threshold: 0.3,
sam_model: ['6', 0],
grounding_dino_model: ['7', 0],
image: ['62', 0],
},
class_type: 'GroundingDinoSAMSegment (segment anything)',
_meta: {
title: 'G-DinoSAM语义分割',
},
},
'6': {
inputs: {
model_name: 'sam_vit_h_4b8939.pth',
device_mode: 'Prefer GPU',
},
class_type: 'SAMLoader',
_meta: {
title: 'SAM加载器',
},
},
'7': {
inputs: {
model_name: 'GroundingDINO_SwinB (938MB)',
},
class_type: 'GroundingDinoModelLoader (segment anything)',
_meta: {
title: 'G-Dino模型加载器',
},
},
'44': {
inputs: {
mask: ['5', 1],
},
class_type: 'InvertMask',
_meta: {
title: '遮罩反转',
},
},
'59': {
inputs: {
image: ['5', 0],
alpha: ['44', 0],
},
class_type: 'JoinImageWithAlpha',
_meta: {
title: '合并图像Alpha',
},
},
'62': {
inputs: {
image_path:
'https://wangbo0808.oss-cn-shanghai.aliyuncs.com/aidraw/4c226c64d37f410c857f98ebb3ecb5ef.jpeg',
RGBA: 'false',
filename_text_extension: 'false',
},
class_type: 'Image Load',
_meta: {
title: '图像加载',
},
},
'63': {
inputs: {
output_path: '[time(%Y-%m-%d)]',
filename_prefix: ['69', 0],
filename_delimiter: '_',
filename_number_padding: 4,
filename_number_start: 'false',
extension: 'png',
quality: 100,
lossless_webp: 'false',
overwrite_mode: 'false',
show_history: 'false',
show_history_by_prefix: 'true',
embed_workflow: 'true',
show_previews: 'true',
images: ['59', 0],
},
class_type: 'Image Save',
_meta: {
title: '图像保存',
},
},
'67': {
inputs: {
text: '西瓜',
platform: 'alibaba',
source: 'auto',
target: 'en',
},
class_type: 'ZFTextTranslation',
_meta: {
title: '文本翻译',
},
},
'69': {
inputs: {
delimiter: '_',
clean_whitespace: 'true',
text_a: ['62', 2],
text_b: ['71', 0],
},
class_type: 'Text Concatenate',
_meta: {
title: '文本连锁',
},
},
'71': {
inputs: {
text: '_segment_output_final_',
},
class_type: 'Text Multiline',
_meta: {
title: '多行文本',
},
},
};
================================================
FILE: src/draw/data/workflow_api_model.ts
================================================
export const workflowApiModel = {
'63': {
inputs: {
samples: ['64', 0],
mask: ['170', 1],
},
class_type: 'SetLatentNoiseMask',
_meta: {
title: '设置Latent噪波遮罩',
},
},
'64': {
inputs: {
pixels: ['177', 0],
vae: ['143', 4],
},
class_type: 'VAEEncode',
_meta: {
title: 'VAE编码',
},
},
'95': {
inputs: {
delimiter: ',',
clean_whitespace: 'true',
text_a: ['171', 0],
text_b: ['96', 0],
},
class_type: 'Text Concatenate',
_meta: {
title: '文本连锁',
},
},
'96': {
inputs: {
text: 'NSFW,lowrs,blurry,deformed, distorted, disfigured:1.3, stacked torsos:1.2, totem pole:1.1, poorly drawn, bad anatomy, wrong anatomy, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck,extra limb, missing limb, floating limbs, mutated hands and fingers:1.4, disconnected limbs, mutation, mutated, ugly, disgusting, blurry, amputation, extra fingers:1.2, worst quality, low quality:1.3out of frame, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, mutation, deformed, blurry, dehydrated, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs',
},
class_type: 'Text Multiline',
_meta: {
title: '多行文本',
},
},
'130': {
inputs: {
guide_size: 256,
guide_size_for: true,
max_size: 768,
seed: 63904235824370,
steps: 5,
cfg: 1.5,
sampler_name: 'lcm',
scheduler: 'sgm_uniform',
denoise: 0.5,
feather: 5,
noise_mask: true,
force_inpaint: true,
bbox_threshold: 0.5,
bbox_dilation: 10,
bbox_crop_factor: 3,
sam_detection_hint: 'center-1',
sam_dilation: 0,
sam_threshold: 0.93,
sam_bbox_expansion: 0,
sam_mask_hint_threshold: 0.7000000000000001,
sam_mask_hint_use_negative: 'False',
drop_size: 10,
wildcard: '',
cycle: 1,
inpaint_model: false,
noise_mask_feather: 20,
image: ['183', 5],
model: ['183', 0],
clip: ['143', 5],
vae: ['183', 4],
positive: ['183', 1],
negative: ['183', 2],
bbox_detector: ['138', 0],
},
class_type: 'FaceDetailer',
_meta: {
title: '面部细化',
},
},
'138': {
inputs: {
model_name: 'bbox/face_yolov8m.pt',
},
class_type: 'UltralyticsDetectorProvider',
_meta: {
title: '检测加载器',
},
},
'143': {
inputs: {
ckpt_name: '极氪写实MAX-极氪白系列模型_V6.safetensors',
vae_name: 'Baked VAE',
clip_skip: -2,
lora_name: 'None',
lora_model_strength: 1,
lora_clip_strength: 1,
positive: ['179', 0],
negative: ['95', 0],
token_normalization: 'none',
weight_interpretation: 'A1111',
empty_latent_width: 512,
empty_latent_height: 512,
batch_size: 1,
lora_stack: ['185', 0],
cnet_stack: ['153', 0],
},
class_type: 'Efficient Loader',
_meta: {
title: '效率加载器',
},
},
'153': {
inputs: {
switch_1: 'On',
controlnet_1: 'control_v11p_sd15_openpose.pth',
controlnet_strength_1: 1,
start_percent_1: 0,
end_percent_1: 1,
switch_2: 'Off',
controlnet_2: 'None',
controlnet_strength_2: 1,
start_percent_2: 0,
end_percent_2: 1,
switch_3: 'Off',
controlnet_3: 'None',
controlnet_strength_3: 1,
start_percent_3: 0,
end_percent_3: 1,
image_1: ['166', 0],
},
class_type: 'CR Multi-ControlNet Stack',
_meta: {
title: 'ControlNet堆',
},
},
'159': {
inputs: {
from_translate: 'auto',
to_translate: 'english',
add_proxies: 'disable',
proxies: '',
auth_data: '',
service: 'GoogleTranslator',
text: '一个女孩，正面视角，灰色背景，光着肩膀，光着腿，坐在凳子上,室内场景,露腰',
'Show proxy': 'proxy_hide',
'Show authorization': 'authorization_hide',
},
class_type: 'DeepTranslatorTextNode',
_meta: {
title: '翻译文本(高级)',
},
},
'166': {
inputs: {
detect_hand: 'enable',
detect_body: 'enable',
detect_face: 'enable',
resolution: 512,
bbox_detector: 'yolox_l.onnx',
pose_estimator: 'dw-ll_ucoco_384.onnx',
image: ['177', 0],
},
class_type: 'DWPreprocessor',
_meta: {
title: 'DW姿态预处理器',
},
},
'169': {
inputs: {
output_path: '[time(%Y-%m-%d)]',
filename_prefix: 'aimodel_output_final_',
filename_delimiter: '_',
filename_number_padding: 4,
filename_number_start: 'false',
extension: 'png',
quality: 100,
lossless_webp: 'false',
overwrite_mode: 'false',
show_history: 'false',
show_history_by_prefix: 'true',
embed_workflow: 'true',
show_previews: 'true',
images: ['130', 0],
},
class_type: 'Image Save',
_meta: {
title: '图像保存',
},
},
'170': {
inputs: {
image_path:
'https://wangbo0808.oss-cn-shanghai.aliyuncs.com/aidraw/AI%25E5%2581%2587%25E4%25BA%25BA%25E6%25A8%25A1%25E7%2589%25B9__segment_output_final__0001.png',
RGBA: 'false',
filename_text_extension: 'false',
},
class_type: 'Image Load',
_meta: {
title: '图像加载',
},
},
'171': {
inputs: {
text: '吊带',
platform: 'alibaba',
source: 'auto',
target: 'en',
},
class_type: 'ZFTextTranslation',
_meta: {
title: '文本翻译',
},
},
'177': {
inputs: {
image_path:
'https://wangbo0808.oss-cn-shanghai.aliyuncs.com/aidraw/AI%E5%81%87%E4%BA%BA%E6%A8%A1%E7%89%B9.png',
RGBA: 'false',
filename_text_extension: 'false',
},
class_type: 'Image Load',
_meta: {
title: '图像加载',
},
},
'179': {
inputs: {
text: '纯色背景，专业摄影',
platform: 'alibaba',
source: 'auto',
target: 'en',
},
class_type: 'ZFTextTranslation',
_meta: {
title: '文本翻译',
},
},
'183': {
inputs: {
seed: 598298253737174,
steps: 5,
cfg: 1.5,
sampler_name: 'lcm',
scheduler: 'sgm_uniform',
denoise: 1,
preview_method: 'none',
vae_decode: 'true',
model: ['143', 0],
positive: ['143', 1],
negative: ['143', 2],
latent_image: ['63', 0],
optional_vae: ['143', 4],
},
class_type: 'KSampler (Efficient)',
_meta: {
title: 'K采样器(效率)',
},
},
'185': {
inputs: {
input_mode: 'simple',
lora_count: 3,
lora_name_1: 'lcm\\lcm_sd1.5_pytorch_lora_weights.safetensors',
lora_wt_1: 1,
model_str_1: 1,
clip_str_1: 1,
lora_name_2: 'None',
lora_wt_2: 1,
model_str_2: 1,
clip_str_2: 1,
lora_name_3: 'None',
lora_wt_3: 1,
model_str_3: 1,
clip_str_3: 1,
lora_name_4: 'None',
lora_wt_4: 1,
model_str_4: 1,
clip_str_4: 1,
lora_name_5: 'None',
lora_wt_5: 1,
model_str_5: 1,
clip_str_5: 1,
lora_name_6: 'None',
lora_wt_6: 1,
model_str_6: 1,
clip_str_6: 1,
lora_name_7: 'None',
lora_wt_7: 1,
model_str_7: 1,
clip_str_7: 1,
lora_name_8: 'None',
lora_wt_8: 1,
model_str_8: 1,
clip_str_8: 1,
lora_name_9: 'None',
lora_wt_9: 1,
model_str_9: 1,
clip_str_9: 1,
lora_name_10: 'None',
lora_wt_10: 1,
model_str_10: 1,
clip_str_10: 1,
lora_name_11: 'None',
lora_wt_11: 1,
model_str_11: 1,
clip_str_11: 1,
lora_name_12: 'None',
lora_wt_12: 1,
model_str_12: 1,
clip_str_12: 1,
lora_name_13: 'None',
lora_wt_13: 1,
model_str_13: 1,
clip_str_13: 1,
lora_name_14: 'None',
lora_wt_14: 1,
model_str_14: 1,
clip_str_14: 1,
lora_name_15: 'None',
lora_wt_15: 1,
model_str_15: 1,
clip_str_15: 1,
lora_name_16: 'None',
lora_wt_16: 1,
model_str_16: 1,
clip_str_16: 1,
lora_name_17: 'None',
lora_wt_17: 1,
model_str_17: 1,
clip_str_17: 1,
lora_name_18: 'None',
lora_wt_18: 1,
model_str_18: 1,
clip_str_18: 1,
lora_name_19: 'None',
lora_wt_19: 1,
model_str_19: 1,
clip_str_19: 1,
lora_name_20: 'None',
lora_wt_20: 1,
model_str_20: 1,
clip_str_20: 1,
lora_name_21: 'None',
lora_wt_21: 1,
model_str_21: 1,
clip_str_21: 1,
lora_name_22: 'None',
lora_wt_22: 1,
model_str_22: 1,
clip_str_22: 1,
lora_name_23: 'None',
lora_wt_23: 1,
model_str_23: 1,
clip_str_23: 1,
lora_name_24: 'None',
lora_wt_24: 1,
model_str_24: 1,
clip_str_24: 1,
lora_name_25: 'None',
lora_wt_25: 1,
model_str_25: 1,
clip_str_25: 1,
lora_name_26: 'None',
lora_wt_26: 1,
model_str_26: 1,
clip_str_26: 1,
lora_name_27: 'None',
lora_wt_27: 1,
model_str_27: 1,
clip_str_27: 1,
lora_name_28: 'None',
lora_wt_28: 1,
model_str_28: 1,
clip_str_28: 1,
lora_name_29: 'None',
lora_wt_29: 1,
model_str_29: 1,
clip_str_29: 1,
lora_name_30: 'None',
lora_wt_30: 1,
model_str_30: 1,
clip_str_30: 1,
lora_name_31: 'None',
lora_wt_31: 1,
model_str_31: 1,
clip_str_31: 1,
lora_name_32: 'None',
lora_wt_32: 1,
model_str_32: 1,
clip_str_32: 1,
lora_name_33: 'None',
lora_wt_33: 1,
model_str_33: 1,
clip_str_33: 1,
lora_name_34: 'None',
lora_wt_34: 1,
model_str_34: 1,
clip_str_34: 1,
lora_name_35: 'None',
lora_wt_35: 1,
model_str_35: 1,
clip_str_35: 1,
lora_name_36: 'None',
lora_wt_36: 1,
model_str_36: 1,
clip_str_36: 1,
lora_name_37: 'None',
lora_wt_37: 1,
model_str_37: 1,
clip_str_37: 1,
lora_name_38: 'None',
lora_wt_38: 1,
model_str_38: 1,
clip_str_38: 1,
lora_name_39: 'None',
lora_wt_39: 1,
model_str_39: 1,
clip_str_39: 1,
lora_name_40: 'None',
lora_wt_40: 1,
model_str_40: 1,
clip_str_40: 1,
lora_name_41: 'None',
lora_wt_41: 1,
model_str_41: 1,
clip_str_41: 1,
lora_name_42: 'None',
lora_wt_42: 1,
model_str_42: 1,
clip_str_42: 1,
lora_name_43: 'None',
lora_wt_43: 1,
model_str_43: 1,
clip_str_43: 1,
lora_name_44: 'None',
lora_wt_44: 1,
model_str_44: 1,
clip_str_44: 1,
lora_name_45: 'None',
lora_wt_45: 1,
model_str_45: 1,
clip_str_45: 1,
lora_name_46: 'None',
lora_wt_46: 1,
model_str_46: 1,
clip_str_46: 1,
lora_name_47: 'None',
lora_wt_47: 1,
model_str_47: 1,
clip_str_47: 1,
lora_name_48: 'None',
lora_wt_48: 1,
model_str_48: 1,
clip_str_48: 1,
lora_name_49: 'None',
lora_wt_49: 1,
model_str_49: 1,
clip_str_49: 1,
},
class_type: 'LoRA Stacker',
_meta: {
title: 'LoRA堆',
},
},
};
================================================
FILE: src/draw/data/workflow_api_removebg.json
================================================
{
"62": {
"inputs": {
"image_path": "http://region-8.autodl.pro:23781/view?subfolder=2024-04-06&filename=R-C__segment_output_final__0001_inpaiting_output_final__0003.png&type=output",
"RGBA": "false",
"filename_text_extension": "true"
},
"class_type": "Image Load",
"_meta": {
"title": "图像加载"
}
},
"63": {
"inputs": {
"output_path": "[time(%Y-%m-%d)]",
"filename_prefix": [
"66",
0
],
"filename_delimiter": "_",
"filename_number_padding": 4,
"filename_number_start": "false",
"extension": "png",
"quality": 100,
"lossless_webp": "false",
"overwrite_mode": "false",
"show_history": "false",
"show_history_by_prefix": "true",
"embed_workflow": "true",
"show_previews": "true",
"images": [
"72",
0
]
},
"class_type": "Image Save",
"_meta": {
"title": "图像保存"
}
},
"66": {
"inputs": {
"delimiter": "-",
"clean_whitespace": "true",
"text_a": [
"62",
2
],
"text_b": [
"73",
0
]
},
"class_type": "Text Concatenate",
"_meta": {
"title": "文本连锁"
}
},
"72": {
"inputs": {
"transparency": true,
"model": "u2net",
"post_processing": false,
"only_mask": false,
"alpha_matting": false,
"alpha_matting_foreground_threshold": 240,
"alpha_matting_background_threshold": 10,
"alpha_matting_erode_size": 10,
"background_color": "none",
"images": [
"62",
0
]
},
"class_type": "Image Rembg (Remove Background)",
"_meta": {
"title": "移除背景"
}
},
"73": {
"inputs": {
"text": "removebg_output_final_"
},
"class_type": "Text Multiline",
"_meta": {
"title": "多行文本"
}
}
}
================================================
FILE: src/draw/data/workflow_api_removebg.ts
================================================
export const removebg = {
'62': {
inputs: {
image_path:
'http://region-8.autodl.pro:23781/view?subfolder=2024-04-06&filename=R-C__segment_output_final__0001_inpaiting_output_final__0003.png&type=output',
RGBA: 'false',
filename_text_extension: 'true',
},
class_type: 'Image Load',
_meta: {
title: '图像加载',
},
},
'63': {
inputs: {
output_path: '[time(%Y-%m-%d)]',
filename_prefix: ['66', 0],
filename_delimiter: '_',
filename_number_padding: 4,
filename_number_start: 'false',
extension: 'png',
quality: 100,
lossless_webp: 'false',
overwrite_mode: 'false',
show_history: 'false',
show_history_by_prefix: 'true',
embed_workflow: 'true',
show_previews: 'true',
images: ['72', 0],
},
class_type: 'Image Save',
_meta: {
title: '图像保存',
},
},
'66': {
inputs: {
delimiter: '-',
clean_whitespace: 'true',
text_a: ['62', 2],
text_b: ['73', 0],
},
class_type: 'Text Concatenate',
_meta: {
title: '文本连锁',
},
},
'72': {
inputs: {
transparency: true,
model: 'u2net',
post_processing: false,
only_mask: false,
alpha_matting: false,
alpha_matting_foreground_threshold: 240,
alpha_matting_background_threshold: 10,
alpha_matting_erode_size: 10,
background_color: 'none',
images: ['62', 0],
},
class_type: 'Image Rembg (Remove Background)',
_meta: {
title: '移除背景',
},
},
'73': {
inputs: {
text: 'removebg_output_final_',
},
class_type: 'Text Multiline',
_meta: {
title: '多行文本',
},
},
};
================================================
FILE: src/draw/data/workflow_api_tagger.ts
================================================
export const workflowApiTagger = {
'10': {
inputs: {
image_path: './ComfyUI/input/example.png',
RGBA: 'false',
filename_text_extension: 'true',
},
class_type: 'Image Load',
_meta: {
title: '图像加载',
},
},
'11': {
inputs: {
model: 'wd-v1-4-moat-tagger-v2',
threshold: 0.35,
character_threshold: 0.85,
replace_underscore: false,
trailing_comma: false,
exclude_tags: '',
tags: 'simple_background, monochrome, comic, greyscale, no_humans, black_background, negative_space',
image: ['10', 0],
},
class_type: 'WD14Tagger|pysssss',
_meta: {
title: 'WD14反推提示词',
},
},
'14': {
inputs: {
text: ['11', 0],
label: 'Text Output',
},
class_type: 'Text to Console',
_meta: {
title: '输出文本到控制台',
},
},
};
================================================
FILE: src/draw/data/workflow_api_text2img.json
================================================
{
"18": {
"inputs": {
"text": "paintings, sketches, (worst quality:2), (low quality:2), (normal quality:2), lowres, normal quality, ((monochrome)), ((grayscale)), skin spots, acnes, skin blemishes, age spot, manboobs,(ugly:1.331), (duplicate:1.331), (morbid:1.21), (mutilated:1.21), (tranny:1.331), mutated hands, (poorly drawn hands:1.331), blurry, (bad anatomy:1.21), (bad proportions:1.331), extra limbs, (disfigured:1.331), (more than 2 nipples:1.331), (missing arms:1.331), (extra legs:1.331), (fused fingers:1.61051), (too many fingers:1.61051), (unclear eyes:1.331), bad hands, missing fingers, extra digit, (futa:1.1), bad body, NG_DeepNegative_V1_75T,"
},
"class_type": "ttN text",
"_meta": {
"title": "TTN文本"
}
},
"19": {
"inputs": {
"text": "(8k, best quality, masterpiece:1.2)"
},
"class_type": "ttN text",
"_meta": {
"title": "TTN文本"
}
},
"25": {
"inputs": {
"delimiter": ",",
"clean_whitespace": "true",
"text_a": [
"55",
0
],
"text_b": [
"19",
0
]
},
"class_type": "Text Concatenate",
"_meta": {
"title": "文本连锁"
}
},
"26": {
"inputs": {
"delimiter": "",
"clean_whitespace": "true",
"text_a": [
"58",
0
],
"text_b": [
"18",
0
]
},
"class_type": "Text Concatenate",
"_meta": {
"title": "文本连锁"
}
},
"44": {
"inputs": {
"upscale_type": "latent",
"hires_ckpt_name": "(use same)",
"latent_upscaler": "nearest-exact",
"pixel_upscaler": "4x-UltraSharp.pth",
"upscale_by": 1.5,
"use_same_seed": true,
"seed": -1,
"hires_steps": 12,
"denoise": 0.3,
"iterations": 1,
"use_controlnet": false,
"control_net_name": "control_sd15_random_color.pth",
"strength": 1,
"preprocessor": "CannyEdgePreprocessor",
"preprocessor_imgs": false
},
"class_type": "HighRes-Fix Script",
"_meta": {
"title": "高清修复"
}
},
"45": {
"inputs": {
"input_mode": "simple",
"lora_count": 5,
"lora_name_1": "lcm\\lcm_sd1.5_pytorch_lora_weights.safetensors",
"lora_wt_1": 1,
"model_str_1": 1,
"clip_str_1": 1,
"lora_name_2": "None",
"lora_wt_2": 0.5,
"model_str_2": 1,
"clip_str_2": 1,
"lora_name_3": "None",
"lora_wt_3": 0.5,
"model_str_3": 1,
"clip_str_3": 1,
"lora_name_4": "None",
"lora_wt_4": 1,
"model_str_4": 1,
"clip_str_4": 1,
"lora_name_5": "None",
"lora_wt_5": 1,
"model_str_5": 1,
"clip_str_5": 1,
"lora_name_6": "None",
"lora_wt_6": 1,
"model_str_6": 1,
"clip_str_6": 1,
"lora_name_7": "None",
"lora_wt_7": 1,
"model_str_7": 1,
"clip_str_7": 1,
"lora_name_8": "None",
"lora_wt_8": 1,
"model_str_8": 1,
"clip_str_8": 1,
"lora_name_9": "None",
"lora_wt_9": 1,
"model_str_9": 1,
"clip_str_9": 1,
"lora_name_10": "None",
"lora_wt_10": 1,
"model_str_10": 1,
"clip_str_10": 1,
"lora_name_11": "None",
"lora_wt_11": 1,
"model_str_11": 1,
"clip_str_11": 1,
"lora_name_12": "None",
"lora_wt_12": 1,
"model_str_12": 1,
"clip_str_12": 1,
"lora_name_13": "None",
"lora_wt_13": 1,
"model_str_13": 1,
"clip_str_13": 1,
"lora_name_14": "None",
"lora_wt_14": 1,
"model_str_14": 1,
"clip_str_14": 1,
"lora_name_15": "None",
"lora_wt_15": 1,
"model_str_15": 1,
"clip_str_15": 1,
"lora_name_16": "None",
"lora_wt_16": 1,
"model_str_16": 1,
"clip_str_16": 1,
"lora_name_17": "None",
"lora_wt_17": 1,
"model_str_17": 1,
"clip_str_17": 1,
"lora_name_18": "None",
"lora_wt_18": 1,
"model_str_18": 1,
"clip_str_18": 1,
"lora_name_19": "None",
"lora_wt_19": 1,
"model_str_19": 1,
"clip_str_19": 1,
"lora_name_20": "None",
"lora_wt_20": 1,
"model_str_20": 1,
"clip_str_20": 1,
"lora_name_21": "None",
"lora_wt_21": 1,
"model_str_21": 1,
"clip_str_21": 1,
"lora_name_22": "None",
"lora_wt_22": 1,
"model_str_22": 1,
"clip_str_22": 1,
"lora_name_23": "None",
"lora_wt_23": 1,
"model_str_23": 1,
"clip_str_23": 1,
"lora_name_24": "None",
"lora_wt_24": 1,
"model_str_24": 1,
"clip_str_24": 1,
"lora_name_25": "None",
"lora_wt_25": 1,
"model_str_25": 1,
"clip_str_25": 1,
"lora_name_26": "None",
"lora_wt_26": 1,
"model_str_26": 1,
"clip_str_26": 1,
"lora_name_27": "None",
"lora_wt_27": 1,
"model_str_27": 1,
"clip_str_27": 1,
"lora_name_28": "None",
"lora_wt_28": 1,
"model_str_28": 1,
"clip_str_28": 1,
"lora_name_29": "None",
"lora_wt_29": 1,
"model_str_29": 1,
"clip_str_29": 1,
"lora_name_30": "None",
"lora_wt_30": 1,
"model_str_30": 1,
"clip_str_30": 1,
"lora_name_31": "None",
"lora_wt_31": 1,
"model_str_31": 1,
"clip_str_31": 1,
"lora_name_32": "None",
"lora_wt_32": 1,
"model_str_32": 1,
"clip_str_32": 1,
"lora_name_33": "None",
"lora_wt_33": 1,
"model_str_33": 1,
"clip_str_33": 1,
"lora_name_34": "None",
"lora_wt_34": 1,
"model_str_34": 1,
"clip_str_34": 1,
"lora_name_35": "None",
"lora_wt_35": 1,
"model_str_35": 1,
"clip_str_35": 1,
"lora_name_36": "None",
"lora_wt_36": 1,
"model_str_36": 1,
"clip_str_36": 1,
"lora_name_37": "None",
"lora_wt_37": 1,
"model_str_37": 1,
"clip_str_37": 1,
"lora_name_38": "None",
"lora_wt_38": 1,
"model_str_38": 1,
"clip_str_38": 1,
"lora_name_39": "None",
"lora_wt_39": 1,
"model_str_39": 1,
"clip_str_39": 1,
"lora_name_40": "None",
"lora_wt_40": 1,
"model_str_40": 1,
"clip_str_40": 1,
"lora_name_41": "None",
"lora_wt_41": 1,
"model_str_41": 1,
"clip_str_41": 1,
"lora_name_42": "None",
"lora_wt_42": 1,
"model_str_42": 1,
"clip_str_42": 1,
"lora_name_43": "None",
"lora_wt_43": 1,
"model_str_43": 1,
"clip_str_43": 1,
"lora_name_44": "None",
"lora_wt_44": 1,
"model_str_44": 1,
"clip_str_44": 1,
"lora_name_45": "None",
"lora_wt_45": 1,
"model_str_45": 1,
"clip_str_45": 1,
"lora_name_46": "None",
"lora_wt_46": 1,
"model_str_46": 1,
"clip_str_46": 1,
"lora_name_47": "None",
"lora_wt_47": 1,
"model_str_47": 1,
"clip_str_47": 1,
"lora_name_48": "None",
"lora_wt_48": 1,
"model_str_48": 1,
"clip_str_48": 1,
"lora_name_49": "None",
"lora_wt_49": 1,
"model_str_49": 1,
"clip_str_49": 1
},
"class_type": "LoRA Stacker",
"_meta": {
"title": "LoRA堆"
}
},
"51": {
"inputs": {
"output_path": "[time(%Y-%m-%d)]",
"filename_prefix": "text2img_output_final_",
"filename_delimiter": "_",
"filename_number_padding": 4,
"filename_number_start": "false",
"extension": "png",
"quality": 100,
"lossless_webp": "false",
"overwrite_mode": "false",
"show_history": "false",
"show_history_by_prefix": "true",
"embed_workflow": "true",
"show_previews": "true",
"images": [
"54",
5
]
},
"class_type": "Image Save",
"_meta": {
"title": "图像保存"
}
},
"52": {
"inputs": {
"ckpt_name": "majicmixRealistic_v7.safetensors",
"vae_name": "Baked VAE",
"clip_skip": -2,
"lora_name": "None",
"lora_model_strength": 1,
"lora_clip_strength": 1,
"positive": [
"25",
0
],
"negative": [
"26",
0
],
"token_normalization": "none",
"weight_interpretation": "A1111",
"empty_latent_width": 512,
"empty_latent_height": 512,
"batch_size": 1
},
"class_type": "Efficient Loader",
"_meta": {
"title": "效率加载器"
}
},
"54": {
"inputs": {
"seed": 127132725962822,
"steps": 20,
"cfg": 7,
"sampler_name": "dpmpp_2m",
"scheduler": "karras",
"denoise": 1,
"preview_method": "none",
"vae_decode": "true",
"model": [
"52",
0
],
"positive": [
"52",
1
],
"negative": [
"52",
2
],
"latent_image": [
"52",
3
],
"optional_vae": [
"52",
4
]
},
"class_type": "KSampler (Efficient)",
"_meta": {
"title": "K采样器(效率)"
}
},
"55": {
"inputs": {
"text": "一个女孩",
"platform": "alibaba",
"source": "auto",
"target": "en"
},
"class_type": "ZFTextTranslation",
"_meta": {
"title": "文本翻译"
}
},
"58": {
"inputs": {
"text": "",
"platform": "alibaba",
"source": "auto",
"target": "en"
},
"class_type": "ZFTextTranslation",
"_meta": {
"title": "文本翻译"
}
}
}
================================================
FILE: src/draw/data/workflow_api_text2img.ts
================================================
export const text2img = {
'18': {
inputs: {
text: 'paintings, sketches, (worst quality:2), (low quality:2), (normal quality:2), lowres, normal quality, ((monochrome)), ((grayscale)), skin spots, acnes, skin blemishes, age spot, manboobs,(ugly:1.331), (duplicate:1.331), (morbid:1.21), (mutilated:1.21), (tranny:1.331), mutated hands, (poorly drawn hands:1.331), blurry, (bad anatomy:1.21), (bad proportions:1.331), extra limbs, (disfigured:1.331), (more than 2 nipples:1.331), (missing arms:1.331), (extra legs:1.331), (fused fingers:1.61051), (too many fingers:1.61051), (unclear eyes:1.331), bad hands, missing fingers, extra digit, (futa:1.1), bad body, NG_DeepNegative_V1_75T,',
},
class_type: 'ttN text',
_meta: {
title: 'TTN文本',
},
},
'19': {
inputs: {
text: '(8k, best quality, masterpiece:1.2)',
},
class_type: 'ttN text',
_meta: {
title: 'TTN文本',
},
},
'25': {
inputs: {
delimiter: ',',
clean_whitespace: 'true',
text_a: ['55', 0],
text_b: ['19', 0],
},
class_type: 'Text Concatenate',
_meta: {
title: '文本连锁',
},
},
'26': {
inputs: {
delimiter: '',
clean_whitespace: 'true',
text_a: ['58', 0],
text_b: ['18', 0],
},
class_type: 'Text Concatenate',
_meta: {
title: '文本连锁',
},
},
'44': {
inputs: {
upscale_type: 'latent',
hires_ckpt_name: '(use same)',
latent_upscaler: 'nearest-exact',
pixel_upscaler: '4x-UltraSharp.pth',
upscale_by: 1.5,
use_same_seed: true,
seed: -1,
hires_steps: 12,
denoise: 0.3,
iterations: 1,
use_controlnet: false,
control_net_name: 'control_sd15_random_color.pth',
strength: 1,
preprocessor: 'CannyEdgePreprocessor',
preprocessor_imgs: false,
},
class_type: 'HighRes-Fix Script',
_meta: {
title: '高清修复',
},
},
'45': {
inputs: {
input_mode: 'simple',
lora_count: 5,
lora_name_1: 'lcm\\lcm_sd1.5_pytorch_lora_weights.safetensors',
lora_wt_1: 1,
model_str_1: 1,
clip_str_1: 1,
lora_name_2: 'None',
lora_wt_2: 0.5,
model_str_2: 1,
clip_str_2: 1,
lora_name_3: 'None',
lora_wt_3: 0.5,
model_str_3: 1,
clip_str_3: 1,
lora_name_4: 'None',
lora_wt_4: 1,
model_str_4: 1,
clip_str_4: 1,
lora_name_5: 'None',
lora_wt_5: 1,
model_str_5: 1,
clip_str_5: 1,
lora_name_6: 'None',
lora_wt_6: 1,
model_str_6: 1,
clip_str_6: 1,
lora_name_7: 'None',
lora_wt_7: 1,
model_str_7: 1,
clip_str_7: 1,
lora_name_8: 'None',
lora_wt_8: 1,
model_str_8: 1,
clip_str_8: 1,
lora_name_9: 'None',
lora_wt_9: 1,
model_str_9: 1,
clip_str_9: 1,
lora_name_10: 'None',
lora_wt_10: 1,
model_str_10: 1,
clip_str_10: 1,
lora_name_11: 'None',
lora_wt_11: 1,
model_str_11: 1,
clip_str_11: 1,
lora_name_12: 'None',
lora_wt_12: 1,
model_str_12: 1,
clip_str_12: 1,
lora_name_13: 'None',
lora_wt_13: 1,
model_str_13: 1,
clip_str_13: 1,
lora_name_14: 'None',
lora_wt_14: 1,
model_str_14: 1,
clip_str_14: 1,
lora_name_15: 'None',
lora_wt_15: 1,
model_str_15: 1,
clip_str_15: 1,
lora_name_16: 'None',
lora_wt_16: 1,
model_str_16: 1,
clip_str_16: 1,
lora_name_17: 'None',
lora_wt_17: 1,
model_str_17: 1,
clip_str_17: 1,
lora_name_18: 'None',
lora_wt_18: 1,
model_str_18: 1,
clip_str_18: 1,
lora_name_19: 'None',
lora_wt_19: 1,
model_str_19: 1,
clip_str_19: 1,
lora_name_20: 'None',
lora_wt_20: 1,
model_str_20: 1,
clip_str_20: 1,
lora_name_21: 'None',
lora_wt_21: 1,
model_str_21: 1,
clip_str_21: 1,
lora_name_22: 'None',
lora_wt_22: 1,
model_str_22: 1,
clip_str_22: 1,
lora_name_23: 'None',
lora_wt_23: 1,
model_str_23: 1,
clip_str_23: 1,
lora_name_24: 'None',
lora_wt_24: 1,
model_str_24: 1,
clip_str_24: 1,
lora_name_25: 'None',
lora_wt_25: 1,
model_str_25: 1,
clip_str_25: 1,
lora_name_26: 'None',
lora_wt_26: 1,
model_str_26: 1,
clip_str_26: 1,
lora_name_27: 'None',
lora_wt_27: 1,
model_str_27: 1,
clip_str_27: 1,
lora_name_28: 'None',
lora_wt_28: 1,
model_str_28: 1,
clip_str_28: 1,
lora_name_29: 'None',
lora_wt_29: 1,
model_str_29: 1,
clip_str_29: 1,
lora_name_30: 'None',
lora_wt_30: 1,
model_str_30: 1,
clip_str_30: 1,
lora_name_31: 'None',
lora_wt_31: 1,
model_str_31: 1,
clip_str_31: 1,
lora_name_32: 'None',
lora_wt_32: 1,
model_str_32: 1,
clip_str_32: 1,
lora_name_33: 'None',
lora_wt_33: 1,
model_str_33: 1,
clip_str_33: 1,
lora_name_34: 'None',
lora_wt_34: 1,
model_str_34: 1,
clip_str_34: 1,
lora_name_35: 'None',
lora_wt_35: 1,
model_str_35: 1,
clip_str_35: 1,
lora_name_36: 'None',
lora_wt_36: 1,
model_str_36: 1,
clip_str_36: 1,
lora_name_37: 'None',
lora_wt_37: 1,
model_str_37: 1,
clip_str_37: 1,
lora_name_38: 'None',
lora_wt_38: 1,
model_str_38: 1,
clip_str_38: 1,
lora_name_39: 'None',
lora_wt_39: 1,
model_str_39: 1,
clip_str_39: 1,
lora_name_40: 'None',
lora_wt_40: 1,
model_str_40: 1,
clip_str_40: 1,
lora_name_41: 'None',
lora_wt_41: 1,
model_str_41: 1,
clip_str_41: 1,
lora_name_42: 'None',
lora_wt_42: 1,
model_str_42: 1,
clip_str_42: 1,
lora_name_43: 'None',
lora_wt_43: 1,
model_str_43: 1,
clip_str_43: 1,
lora_name_44: 'None',
lora_wt_44: 1,
model_str_44: 1,
clip_str_44: 1,
lora_name_45: 'None',
lora_wt_45: 1,
model_str_45: 1,
clip_str_45: 1,
lora_name_46: 'None',
lora_wt_46: 1,
model_str_46: 1,
clip_str_46: 1,
lora_name_47: 'None',
lora_wt_47: 1,
model_str_47: 1,
clip_str_47: 1,
lora_name_48: 'None',
lora_wt_48: 1,
model_str_48: 1,
clip_str_48: 1,
lora_name_49: 'None',
lora_wt_49: 1,
model_str_49: 1,
clip_str_49: 1,
},
class_type: 'LoRA Stacker',
_meta: {
title: 'LoRA堆',
},
},
'51': {
inputs: {
output_path: '[time(%Y-%m-%d)]',
filename_prefix: 'text2img_output_final_',
filename_delimiter: '_',
filename_number_padding: 4,
filename_number_start: 'false',
extension: 'png',
quality: 100,
lossless_webp: 'false',
overwrite_mode: 'false',
show_history: 'false',
show_history_by_prefix: 'true',
embed_workflow: 'true',
show_previews: 'true',
images: ['54', 5],
},
class_type: 'Image Save',
_meta: {
title: '图像保存',
},
},
'52': {
inputs: {
ckpt_name: 'majicmixRealistic_v7.safetensors',
vae_name: 'Baked VAE',
clip_skip: -2,
lora_name: 'None',
lora_model_strength: 1,
lora_clip_strength: 1,
positive: ['25', 0],
negative: ['26', 0],
token_normalization: 'none',
weight_interpretation: 'A1111',
empty_latent_width: 512,
empty_latent_height: 512,
batch_size: 1,
},
class_type: 'Efficient Loader',
_meta: {
title: '效率加载器',
},
},
'54': {
inputs: {
seed: 127132725962822,
steps: 20,
cfg: 7,
sampler_name: 'dpmpp_2m',
scheduler: 'karras',
denoise: 1,
preview_method: 'none',
vae_decode: 'true',
model: ['52', 0],
positive: ['52', 1],
negative: ['52', 2],
latent_image: ['52', 3],
optional_vae: ['52', 4],
},
class_type: 'KSampler (Efficient)',
_meta: {
title: 'K采样器(效率)',
},
},
'55': {
inputs: {
text: '一个女孩',
platform: 'alibaba',
source: 'auto',
target: 'en',
},
class_type: 'ZFTextTranslation',
_meta: {
title: '文本翻译',
},
},
'58': {
inputs: {
text: '',
platform: 'alibaba',
source: 'auto',
target: 'en',
},
class_type: 'ZFTextTranslation',
_meta: {
title: '文本翻译',
},
},
};
================================================
FILE: src/draw/draw.controller.ts
================================================
import { Get, Query, Body, Controller, Post } from '@nestjs/common';
import { DrawService } from './draw.service';
import { ApiOperation, ApiTags } from '@nestjs/swagger';
import { DrawTask } from './data/DrawConfig';
@ApiTags('AI绘画')
@Controller('draw')
export class DrawController {
constructor(private readonly drawService: DrawService) {}
@ApiOperation({
summary: '获取comfyui节点信息',
description: '获取comfyui节点信息',
})
@Get('getObjectinfo')
async getObjectinfo() {
return await this.drawService.getObject_info();
}
@ApiOperation({
summary: '通用接口，',
description:
'AI绘画接口，可提交任意绘图的comfyui工作流API任务，直接返回绘图成功的结果：图片或者视频的url',
operationId: 'submitDrawTask',
tags: ['AI绘画'],
requestBody: {
content: {
'application/json': {
schema: {
type: 'object',
properties: {
source: {
type: 'string',
description:
'web or wechat,来源识别，区分web端任务和微信端任务，默认web',
example: 'web',
},
prompt: {
type: 'object',
description: 'comfyui绘画API',
example: { prompt: 'your comfyui prompt' },
},
client_id: {
type: 'string',
description: 'client_id，客户端唯一标识',
example: 'your client id',
},
socket_id: {
type: 'string',
description:
'socket_id，websocket唯一标识,web端调用的时候必须，否则无法接受到websocket实时消息',
example: 'your socket id',
},
api: {
type: 'string',
description: 'api名称具体，文生图，图生图，决定任务的超时时间',
example: '文生图',
},
lifo: {
type: 'boolean',
description: '是否使用lifo队列，默认false',
},
},
},
},
},
},
})
@Post('submitTask')
async submitDrawTask(@Body() data: DrawTask) {
const { source, socket_id, client_id, prompt, api } = data;
return await this.drawService.submitDrawTask(data);
}
@ApiOperation({
summary: '文生图',
description: '文生图:图片或者视频的url',
operationId: 'text2img',
tags: ['AI绘画'],
requestBody: {
content: {
'application/json': {
schema: {
type: 'object',
properties: {
client_id: {
type: 'string',
description: 'client_id，客户端唯一标识',
example: 'your client id',
},
socket_id: {
type: 'string',
description:
'socket_id，websocket唯一标识,web端调用的时候必须，否则无法接受到websocket实时消息',
example: 'your socket id',
},
params: {
type: 'object',
description: 'comfyui绘画API关键参数',
example: {
positive: 'a asia girl',
negative: '丑陋的',
seed: 12345678912345,
width: 512,
height: 768,
ckpt_name_id: 0,
filename_prefix: 'iamgename', //文件名前缀
upscale_by: 1,
},
},
options: {
type: 'object',
description: '其他可选控制任务分发和队列参数等',
example: {
source: 'web', //web or wechat,来源识别，区分web端任务和微信端任务，默认web
apisource: 'default',
lifo: false, //是否使用lifo队列，默认false
},
},
},
},
},
},
},
})
@Post('text2img')
async text2img(
@Body('params') params: any,
@Body('client_id') client_id: string,
@Body('socket_id') socket_id: string,
@Body('options') options: any,
) {
console.log('收到的绘画参数', params);
return await this.drawService.text2img(
client_id,
socket_id,
params,
options,
);
}
/**
* 图生图
* @param params
* @param client_id
* @param socket_id
* @param options
*/
@ApiOperation({
summary: '图生图',
description: '图生图，直接返回绘图成功的结果:图片或者视频的url',
operationId: 'image2img',
tags: ['AI绘画'],
requestBody: {
content: {
'application/json': {
schema: {
type: 'object',
properties: {
client_id: {
type: 'string',
description: 'client_id，客户端唯一标识',
example: 'your client id',
},
socket_id: {
type: 'string',
description:
'socket_id，websocket唯一标识,web端调用的时候必须，否则无法接受到websocket实时消息',
example: 'your socket id',
},
params: {
type: 'object',
description: 'comfyui绘画API关键参数',
example: {
image_path:
'https://wangbo0808.oss-cn-shanghai.aliyuncs.com/aidraw/t2i_1.jpg',
positive: '',
nagative: '',
denoise: 0.5,
noise_seed: 1212121212121212,
ckpt_name_id: 0,
filename_prefix: 'your imagename here', //文件名前缀
upscale_by: 1,
sd3_style_preset: '', //SD3风格
sd3_aspect_ratio: '', //SD3比例
sd3_model: 'sd3', //SD3模型
sd3_strength: 0.5, //SD3强度
},
},
options: {
type: 'object',
description: '其他可选控制任务分发和队列参数等',
example: {
source: 'web', //web or wechat,来源识别，区分web端任务和微信端任务，默认web
apisource: 'default',
lifo: false, //是否使用lifo队列，默认false
},
},
},
},
},
},
},
})
@Post('img2img')
async img2img(
@Body('params') params: any,
@Body('client_id') client_id: string,
@Body('socket_id') socket_id: string,
@Body('options') options: any,
) {
console.log(socket_id);
return await this.drawService.image2img(
client_id,
socket_id,
params,
options,
);
}
@ApiOperation({
summary: '图生视频',
description: '图生视频，直接返回绘图成功的结果:图片或者视频的url',
operationId: 'image2video',
tags: ['AI绘画'],
requestBody: {
content: {
'application/json': {
schema: {
type: 'object',
properties: {
client_id: {
type: 'string',
description: 'client_id，客户端唯一标识',
example: 'your client id',
},
socket_id: {
type: 'string',
description:
'socket_id，websocket唯一标识,web端调用的时候必须，否则无法接受到websocket实时消息',
example: 'your socket id',
},
params: {
type: 'object',
description: 'comfyui绘画API关键参数',
example: {
image_path:
'https://wangbo0808.oss-cn-shanghai.aliyuncs.com/aidraw/u%3D1090338134%2C2696420997%26fm%3D253%26fmt%3Dauto%26app%3D138%26f%3DJPEG.webp',
video_frames: 25,
fps: 8,
motion_bucket_id: 127, //运动幅度，默认127
augmentation_level: 0, //增强，默认为0
filename_prefix: '文生视频',
cfg: 3,
steps: 20,
min_cfg: 1,
},
},
options: {
type: 'object',
description: '其他可选控制任务分发和队列参数等',
example: {
source: 'web', //web or wechat,来源识别，区分web端任务和微信端任务，默认web
lifo: false, //是否使用lifo队列，默认false
},
},
},
},
},
},
},
})
@Post('img2video')
async img2video(
@Body('params') params: any,
@Body('client_id') client_id: string,
@Body('socket_id') socket_id: string,
) {
console.log(socket_id);
return await this.drawService.image2video(client_id, socket_id, params);
}
@ApiOperation({
summary: '抠图',
description:
'抠图，万物皆可扣，目前最强大的抠图，任意文本抠图，不需要任何复杂操作，直接返回绘图成功的结果:图片或者视频的url',
operationId: 'segmentAnything',
tags: ['AI绘画'],
requestBody: {
content: {
'application/json': {
schema: {
type: 'object',
properties: {
client_id: {
type: 'string',
description: 'client_id，客户端唯一标识',
example: 'your client id',
},
socket_id: {
type: 'string',
description:
'socket_id，websocket唯一标识,web端调用的时候必须，否则无法接受到websocket实时消息',
example: 'your socket id',
},
params: {
type: 'object',
description: 'comfyui绘画API关键参数',
example: {
image_path:
'https://wangbo0808.oss-cn-shanghai.aliyuncs.com/aidraw/4c226c64d37f410c857f98ebb3ecb5ef.jpeg',
segmentparts: '西瓜',
},
},
options: {
type: 'object',
description: '其他可选控制任务分发和队列参数等',
example: {
source: 'web', //web or wechat,来源识别，区分web端任务和微信端任务，默认web
lifo: false, //是否使用lifo队列，默认false
},
},
},
},
},
},
},
})
@Post('segmentAnything')
async segmentAnything(
@Body('params') params: any,
@Body('client_id') client_id: string,
@Body('socket_id') socket_id: string,
) {
// console.log(socket_id, params);
return await this.drawService.segmentAnything(client_id, params, socket_id);
}
@ApiOperation({
summary: '局部重绘',
description:
'局部重绘，上传原图及遮罩，将遮罩部分进行重绘，直接返回绘图成功的结果图片',
operationId: 'inpainting',
tags: ['AI绘画'],
requestBody: {
content: {
'application/json': {
schema: {
type: 'object',
properties: {
client_id: {
type: 'string',
description: 'client_id，客户端唯一标识',
example: 'your client id',
},
socket_id: {
type: 'string',
description:
'socket_id，websocket唯一标识,web端调用的时候必须，否则无法接受到websocket实时消息',
example: 'your socket id',
},
params: {
type: 'object',
description: 'comfyui绘画API关键参数',
example: {
image_path:
'https://wangbo0808.oss-cn-shanghai.aliyuncs.com/aidraw/R-C.jpg',
image_path_mask:
'https://wangbo0808.oss-cn-shanghai.aliyuncs.com/aidraw/R-C__segment_output_final__0001.png',
ckpt_name_id: 0,
denoise: 0.5,
positive: '一个女孩',
nagative: '',
},
},
options: {
type: 'object',
description: '其他可选控制任务分发和队列参数等',
example: {
source: 'web', //web or wechat,来源识别，区分web端任务和微信端任务，默认web
lifo: false, //是否使用lifo队列，默认false
},
},
},
},
},
},
},
})
@Post('inpainting')
async inpainting(
@Body('params') params: any,
@Body('client_id') client_id: string,
@Body('socket_id') socket_id: string,
) {
return await this.drawService.inpainting(client_id, params, socket_id);
}
@ApiOperation({
summary: '背景擦除',
description:
'背景擦除，仅仅需要去除背景的时候，建议使用这个接口，速度比抠图segmentanything快，直接返回去除背景之后的透明背景图片',
operationId: 'removebg',
tags: ['AI绘画'],
requestBody: {
content: {
'application/json': {
schema: {
type: 'object',
properties: {
client_id: {
type: 'string',
description: 'client_id，客户端唯一标识',
example: 'your client id',
},
socket_id: {
type: 'string',
description:
'socket_id，websocket唯一标识,web端调用的时候必须，否则无法接受到websocket实时消息',
example: 'your socket id',
},
params: {
type: 'object',
description: 'comfyui绘画API关键参数',
example: {
image_path:
'https://wangbo0808.oss-cn-shanghai.aliyuncs.com/aidraw/R-C.jpg',
},
},
options: {
type: 'object',
description: '其他可选控制任务分发和队列参数等',
example: {
source: 'web', //web or wechat,来源识别，区分web端任务和微信端任务，默认web
lifo: false, //是否使用lifo队列，默认false
},
},
},
},
},
},
},
})
@Post('removebg')
async removebg(
@Body('params') params: any,
@Body('client_id') client_id: string,
@Body('socket_id') socket_id: string,
) {
return await this.drawService.removebg(client_id, params, socket_id);
}
@ApiOperation({
summary: 'HD修复',
description:
'HD修复，上传原图，将原图进行HD修复，直接返回绘图成功的结果图片',
operationId: 'hdfix',
tags: ['AI绘画'],
requestBody: {
content: {
'application/json': {
schema: {
type: 'object',
properties: {
client_id: {
type: 'string',
description: 'client_id，客户端唯一标识',
example: 'your client id',
},
socket_id: {
type: 'string',
description:
'socket_id，websocket唯一标识,web端调用的时候必须，否则无法接受到websocket实时消息',
example: 'your socket id',
},
params: {
type: 'object',
description: 'comfyui绘画API关键参数',
example: {
image_path:
'https://wangbo0808.oss-cn-shanghai.aliyuncs.com/aidraw/ComfyUI_00001_.png',
},
},
options: {
type: 'object',
description: '其他可选控制任务分发和队列参数等',
example: {
source: 'web', //web or wechat,来源识别，区分web端任务和微信端任务，默认web
lifo: false, //是否使用lifo队列，默认false
},
},
},
},
},
},
},
})
@Post('hdfix')
async hdfix(
@Body('params') params: any,
@Body('client_id') client_id: string,
@Body('socket_id') socket_id: string,
@Body('options') options: any,
) {
return await this.drawService.workflowApiHdfix4(
client_id,
params,
socket_id,
options,
);
}
@ApiOperation({
summary: '人脸融合-换脸',
description: '人脸融合（换脸），将人物脸部转移参考图，实现AI艺术照的效果',
operationId: 'faceswap',
tags: ['AI绘画'],
requestBody: {
content: {
'application/json': {
schema: {
type: 'object',
properties: {
client_id: {
type: 'string',
description: 'client_id，客户端唯一标识',
example: 'your client id',
},
socket_id: {
type: 'string',
description:
'socket_id，websocket唯一标识,web端调用的时候必须，否则无法接受到websocket实时消息',
example: 'your socket id',
},
params: {
type: 'object',
description: 'comfyui绘画API关键参数',
example: {
image_path_face:
'https://wangbo0808.oss-cn-shanghai.aliyuncs.com/aidraw/R-C.jpg',
image_path_refer:
'https://wangbo0808.oss-cn-shanghai.aliyuncs.com/aidraw/t2i_1.jpg',
},
},
options: {
type: 'object',
description: '其他可选控制任务分发和队列参数等',
example: {
source: 'web', //web or wechat,来源识别，区分web端任务和微信端任务，默认web
lifo: false, //是否使用lifo队列，默认false
},
},
},
},
},
},
},
})
@Post('faceswap')
async faceswap(
@Body('params') params: any,
@Body('client_id') client_id: string,
@Body('socket_id') socket_id: string,
@Body('options') options: any,
) {
return await this.drawService.faceSwap(
client_id,
params,
socket_id,
options,
);
}
@ApiOperation({
summary: 'AI模特-电商换装',
description:
'讲人台模特照或者商拍中指定部分保留，其余地方重绘，可以拍摄商品，服装，鞋子，包包均可',
operationId: 'aimodel',
tags: ['AI绘画'],
requestBody: {
content: {
'application/json': {
schema: {
type: 'object',
properties: {
client_id: {
type: 'string',
description: 'client_id，客户端唯一标识',
example: 'your client id',
},
socket_id: {
type: 'string',
description:
'socket_id，websocket唯一标识,web端调用的时候必须，否则无法接受到websocket实时消息',
example: 'your socket id',
},
params: {
type: 'object',
description: 'comfyui绘画API关键参数',
example: {
parts: '裙子',
image_path_model:
'https://wangbo0808.oss-cn-shanghai.aliyuncs.com/aidraw/AI%E5%81%87%E4%BA%BA%E6%A8%A1%E7%89%B9.png',
image_path_mask:
'https://wangbo0808.oss-cn-shanghai.aliyuncs.com/aidraw/AI%25E5%2581%2587%25E4%25BA%25BA%25E6%25A8%25A1%25E7%2589%25B9__segment_output_final__0001.png',
positives: '一个女孩，淡蓝色背景，摄影效果，超精细，氛围',
negatives: '丑陋的,吊带，遮挡皮肤',
ckpt_name_id: 0,
},
},
options: {
type: 'object',
description: '其他可选控制任务分发和队列参数等',
example: {
source: 'web', //web or wechat,来源识别，区分web端任务和微信端任务，默认web
lifo: false, //是否使用lifo队列，默认false
},
},
},
},
},
},
},
})
@Post('model')
async model(
@Body('params') params: any,
@Body('client_id') client_id: string,
@Body('socket_id') socket_id: string,
@Body('options') options: any,
) {
console.log(params);
return await this.drawService.model(client_id, params, socket_id, options);
}
@ApiOperation({
summary: '提示词反推',
description: 'AI标签-图片标签，将图片进行标签识别，返回标签结果',
operationId: 'image2tagger',
tags: ['AI绘画'],
requestBody: {
content: {
'application/json': {
schema: {
type: 'object',
properties: {
client_id: {
type: 'string',
description: 'client_id，客户端唯一标识',
example: 'your client id',
},
socket_id: {
type: 'string',
description:
'socket_id，websocket唯一标识,web端调用的时候必须，否则无法接受到websocket实时消息',
example: 'your socket id',
},
params: {
type: 'object',
description: 'comfyui绘画API关键参数',
example: {
image_path:
'https://wangbo0808.oss-cn-shanghai.aliyuncs.com/aidraw/R-C.jpg',
},
},
options: {
type: 'object',
description: '其他可选控制任务分发和队列参数等',
example: {
source: 'web', //web or wechat,来源识别，区分web端任务和微信端任务，默认web
lifo: false, //是否使用lifo队列，默认false
},
},
},
},
},
},
},
})
@Post('image2tagger')
async image2tagger(
@Body('params') params: any,
@Body('client_id') client_id: string,
@Body('socket_id') socket_id: string,
@Body('options') options: any,
) {
return await this.drawService.image2tagger(
client_id,
params,
socket_id,
options,
);
}
@ApiOperation({
summary: '加入绘画黑名单',
description: '加入绘画黑名单',
operationId: 'addBlackList',
tags: ['用户管理'],
parameters: [
{
name: 'client_id',
in: 'query',
description: 'client_id，客户端唯一标识',
required: true,
schema: {
type: 'string',
example: 'your client id',
},
},
],
})
@Get('addBlackList')
async addBlackList(@Query('client_id') client_id: string) {
console.log(client_id);
return await this.drawService.addBlackList(client_id);
}
@ApiOperation({
summary: '获取绘画黑名单',
description: '获取绘画黑名单',
})
@Get('getBlackList')
async getBlackList() {
return await this.drawService.getBlackList();
}
@ApiOperation({
summary: '移除绘画黑名单',
description: '移除绘画黑名单',
parameters: [
{
name: 'client_id',
in: 'query',
description: 'client_id，客户端唯一标识',
required: true,
schema: {
type: 'string',
example: 'your client id',
},
},
],
})
@Get('removeBlackList')
async removeBlackList(@Query('client_id') uid: string) {
return await this.drawService.removeBlackList(uid);
}
}
================================================
FILE: src/draw/draw.module.ts
================================================
import { Module } from '@nestjs/common';
import { DrawService } from './draw.service';
import { DrawController } from './draw.controller';
import { BullModule } from '@nestjs/bull';
import { ConfigService } from '@nestjs/config/dist';
import { CacheModule } from '../cache/cache.module';
import { WsGateway } from '../ws/ws.gateway';
import { FileModule } from '../file/file.module';
import * as process from 'node:process';
@Module({
controllers: [DrawController],
exports: [DrawService, BullModule],
imports: [
BullModule.forRootAsync({
useFactory: (config: ConfigService) => ({
redis: {
host: config.get('CONFIG_COMFYUI_QUENE_REDIS_HOST'),
port: config.get('CONFIG_COMFYUI_QUENE_REDIS_PORT'),
password: config.get('CONFIG_COMFYUI_QUENE_REDIS_PASSWORD'),
},
}),
inject: [ConfigService],
}),
BullModule.registerQueue({
name: 'draw',
}),
CacheModule,
FileModule,
],
providers: [DrawService],
})
export class DrawModule {}
================================================
FILE: src/draw/draw.service.ts
================================================
import { Injectable, Logger } from '@nestjs/common';
import { InjectQueue } from '@nestjs/bull';
import { Queue } from 'bull';
import { ConfigService } from '@nestjs/config';
import axios from 'axios';
// eslint-disable-next-line @typescript-eslint/no-var-requires
const FormData = require('form-data');
// 普通文生图
import { text2img } from './data/workflow_api_text2img';
// 图生图
import { image2img } from './data/workflow_api_image2img';
// 图生视频
import { img2video } from './data/workflow_api_img2video';
// 抠图
import { matting } from './data/workflow_api_matting';
// 局部重绘
import { inpainting } from './data/workflow_api_inpainting';
// 移除背景
import { removebg } from './data/workflow_api_removebg';
import { CacheService } from '../cache/cache.service';
// 高清修复放大4倍
import { workflowApiHdfix4 } from './data/workflow_api_hdfix_4';
// 换脸
import { FaceSwap } from './data/workflow_api_faceswap';
// AI模特，AI电商换装
import { workflowApiModel } from './data/workflow_api_model';
import { ApiTimeOut, drawConfig, DrawTask } from './data/DrawConfig';
import { FileService } from '../file/file.service';
//图片反推提示词API
import { workflowApiTagger } from './data/workflow_api_tagger';
import * as fs from 'node:fs';
// 微信绘画模式
export type WeChatDrawModel = 'text2img' | 'image2img' | 'img2video';
type SD3AspectRatio =
| '1:1'
| '16:9'
| '21:9'
| '2:3'
| '3:2'
| '3:4'
| '4:3'
| '9:16';
type SD3StylePreset =
| '3d-model'
| 'analog-film'
| 'anime'
| 'cinematic'
| 'comic-book'
| 'digital-art'
| 'enhance'
| 'fantasy-art'
| 'isometric'
| 'line-art'
| 'low-poly'
| 'modeling-compound'
| 'neon-punk'
| 'origami'
| 'photographic'
| 'pixel-art'
| 'tile-texture';
@Injectable()
export class DrawService {
private readonly logger = new Logger(DrawService.name);
private Object_info = null as any;
private ckpt_names = [] as any[]; //模型集合
public local_comfyui = this.configService.get('CONFIG_COMFYUI_SERVER_URL');
public remote_comfyui = this.configService.get(
'CONFIG_COMFYUI_SERVER_REMOTE_URL',
);
private oss_enable = this.configService.get('OSS_ACCESSKEYSECRET');
private accesstoken = ''; //访问远程服务器必须的token
constructor(
@InjectQueue('draw') private drawQueue: Queue,
private readonly cacheService: CacheService,
private readonly configService: ConfigService,
private readonly fileService: FileService,
) {
// 添加异常处理
this.comfyuiAxios.interceptors.response.use(
(response) => {
return response;
},
async (error) => {
const originalRequest = error.config;
if (error?.response?.status === 401 && !originalRequest._retry) {
setTimeout(() => {
originalRequest._retry = true;
}, 2000);
}
this.logger.error('响应超时');
return Promise.reject(error);
},
);
this.Initalize();
}
private readonly comfyuiAxios = axios.create({
// baseURL: "/sdApi",
timeout: 100000,
headers: {
'Access-Control-Allow-Origin': '*',
Accept: '*/*',
Authorization: 'Bearer ' + this.configService.get('CONFIG_COMFYUI_TOKEN'),
},
});
/**
* 讲绘画任务加入到任务队列
* @param data
*/
async sendToQueue(data: DrawTask) {
// 黑名单管理
if (await this.isInBlackList(data.client_id)) {
this.logger.log('黑名单用户', data.client_id);
return;
}
return await this.drawQueue.add('drawtask', data, {
timeout:
ApiTimeOut.find((item) => item.type === data.api)?.timeout * 1000 ||
drawConfig.defaultTimeOut * 1000,
lifo: data.lifo || false,
});
}
/**
* 提交绘画任务，返回绘画结果
* @param data
*/
async submitDrawTask(data: DrawTask): Promise {
return new Promise(async (resolve, reject) => {
const job = await this.sendToQueue(data);
const intervalId = setInterval(async () => {
// 查询任务状态
this.logger.debug(`定时器查询绘画进度中………………${intervalId}`);
const jobTemp = await this.drawQueue.getJob(job.id);
if (await jobTemp.isCompleted()) {
this.logger.log(
'任务完成',
jobTemp.returnvalue,
`任务耗时:${(jobTemp.finishedOn - jobTemp.processedOn) / 1000}`,
);
clearInterval(intervalId);
resolve(jobTemp.returnvalue);
}
if (await jobTemp.isFailed()) {
// 任务失败,尝试去按照prompt_id去远程服务器获取结果
const { prompt_id } = jobTemp.data;
const server = this.remote_comfyui || this.local_comfyui;
if (server) {
const { data } = await this.comfyuiAxios.get(
`${server}/history/${prompt_id}`,
);
this.logger.log('远程服务器获取结果成功', data);
if (data[prompt_id]) {
//从data中尝试解构出来结果
const { outputs } = data[prompt_id];
Object.keys(outputs).forEach((key) => {
console.log(key, outputs[key]);
if (
outputs[key]['images'] &&
outputs[key]['images'].length > 0
) {
let imageUrl = '';
const { filename, subfolder, type } =
outputs[key]['images'][0];
if (subfolder) {
imageUrl = `${server}/view?subfolder=${subfolder}&filename=${filename}&type=${type}`;
} else {
imageUrl = `${server}/view?filename=${filename}&type=${type}`;
}
clearInterval(intervalId);
resolve(imageUrl);
return;
}
});
}
}
clearInterval(intervalId);
reject({ staus: 'error', message: '任务失败' });
}
}, 500);
});
}
async getQueueLength() {
return await this.drawQueue.getJobCounts();
}
/**
* 判断uid是否已经有任务在队列中
* @param uid
*/
async isInQueue(uid: string) {
const jobs = await this.drawQueue.getJobs(['waiting']);
for (let i = 0; i < jobs.length; i++) {
const { client_id } = jobs[i].data;
if (client_id && client_id === uid) {
return true;
}
}
return false;
}
/**
* 获取ComfyUI的节点信息
*/
async getObject_info() {
try {
let url = `${this.local_comfyui}/object_info`;
if (this.remote_comfyui) {
url = `${this.remote_comfyui}/draw/getObjectinfo`;
}
const { data } = await this.comfyuiAxios.get(url);
// this.logger.log('获取ComfyUI的节点信息成功', data);
this.Object_info = data;
//后去模型清单
this.ckpt_names =
this.Object_info['CheckpointLoaderSimple'].input.required.ckpt_name[0];
return data;
} catch (error) {
// this.logger.error('初始化节点信息发生错误');
}
}
/**
* 返送任务到ComfyUI服务器
* @param comfyuihttpUrl
* @param data
*/
async sendTackprompt(comfyuihttpUrl: string, data: any): Promise {
try {
const {
data: { prompt_id },
} = await this.comfyuiAxios.post(comfyuihttpUrl + '/prompt', data);
return prompt_id;
// console.log('res', res);
} catch (error) {
return '';
}
}
/**
* 获取远程服务调用的token，暂时未启用
*/
async getAccessToken() {
const username = this.configService.get(
'CONFIG_COMFUI_SERVER_REMOTE_AUTH_USERNAME',
);
const password = this.configService.get(
'CONFIG_COMFUI_SERVER_REMOTE_AUTH_PASSWORD',
);
if (!this.accesstoken) {
const { data } = await this.comfyuiAxios.get(
`${this.remote_comfyui}/api/auth/signin?username=${username}&password=${password}`,
);
this.accesstoken = data.access_token;
}
this.logger.log('成功获取远程绘画服务器的token');
return this.accesstoken;
}
/**
* 远程服务执行微信绘图任务
*
*/
async wechatDrawFromRemoteServer(
type: WeChatDrawModel,
params: {
positive?: string;
image_path?: string;
ckpt_name_id?: number;
},
) {
this.logger.log(`微信绘画参数为${JSON.stringify(params)}`);
if (type === 'text2img') {
const url = `${this.remote_comfyui}/draw/text2img`;
const { data } = await this.comfyuiAxios.post(url, {
clinet_id: '123',
params,
});
return data;
}
if (type === 'image2img') {
const url = `${this.remote_comfyui}/draw/img2img`;
const { data } = await this.comfyuiAxios.post(url, {
clinet_id: '123',
params,
});
return data;
}
if (type === 'img2video') {
const url = `${this.remote_comfyui}/draw/img2video`;
const { data } = await this.comfyuiAxios.post(url, {
clinet_id: '123',
params,
});
return data;
}
}
/**
* 生成随机数
* @param length
*/
getSeed = (length: number) => {
const min = Math.pow(10, length - 1);
const max = Math.pow(10, length) - 1;
return Math.floor(Math.random() * (max - min + 1) + min);
};
/**
* 普通文生图
* @param client_id
* @param socket_id
* @param params
* @param options
*/
async text2img(
client_id: string,
socket_id?: string,
params?: {
positive?: string;
negative?: string;
seed?: number;
width?: number;
height?: number;
ckpt_name_id?: number;
filename_prefix?: string | number; //文件名前缀
upscale_by?: number;
sd3_style_preset?: SD3StylePreset; //SD3风格
sd3_aspect_ratio?: SD3AspectRatio; //SD3比例
sd3_model?: 'sd3' | 'sd3-turbo'; //SD3模型
},
options?: {
source: 'web' | 'wechat';
apisource?: 'default' | 'sd3';
lifo?: boolean;
},
) {
//如果apisource是sd3则调用sd3文生图
if (options?.apisource === 'sd3') {
return await this.text2imgSD3({
prompt: params.positive || '',
style_preset: params.sd3_style_preset,
aspect_ratio: params.sd3_aspect_ratio,
negative_prompt: params.negative || '',
model: params.sd3_model || 'sd3',
});
}
await this.Initalize(); //初始化获取ComfyUI的节点信息
//正向提示词
text2img[55].inputs.text = params.positive || '一个女孩';
//负向提示词
text2img[58].inputs.text = params.negative || '';
//随机种子
text2img[54].inputs.seed = params.seed || this.getSeed(15);
// console.log("seed:" + sdStore.txt2imgParams.seed)
//图片尺寸
text2img[52].inputs.empty_latent_width = params.width || 512;
text2img[52].inputs.empty_latent_height = params.height || 768;
text2img[52].inputs.ckpt_name = this.ckpt_names[params.ckpt_name_id || 0];
text2img[51].inputs.filename_prefix =
params.filename_prefix + '_text2img_output_final_';
// 放大倍数
text2img[44].inputs.upscale_by = params.upscale_by || 1;
const data = {
source: options?.source || 'web',
prompt: text2img,
api: '文生图',
client_id,
socket_id,
lifo: options?.lifo || false,
} as DrawTask;
const _v = await this.submitDrawTask(data);
if (this.oss_enable) {
// 启用OSS
return await this.fileService.uploadFileToOSS(_v, `image`);
} else {
return _v;
}
}
/**
*图生图
*
*/
async image2img(
client_id: string,
socket_id?: string,
params?: {
image_path: string;
positive?: string;
nagative?: string;
denoise?: number;
noise_seed?: number;
ckpt_name_id?: number;
filename_prefix?: string | number; //文件名前缀
upscale_by?: number;
sd3_style_preset?: SD3StylePreset; //SD3风格
sd3_aspect_ratio?: SD3AspectRatio; //SD3比例
sd3_model?: 'sd3' | 'sd3-turbo'; //SD3模型
sd3_strength?: number; //SD3强度
},
options?: {
source: 'web' | 'wechat';
apisource?: 'default' | 'sd3';
lifo?: boolean;
},
) {
//如果apisource是sd3则调用sd3文生图
const AB = await this.fileService.urlToArrayBuffer(params.image_path);
const bf = Buffer.from(AB);
//将file保存到服务上
fs.writeFileSync('temp.png', bf);
if (options?.apisource === 'sd3') {
return await this.text2imgSD3({
prompt: params.positive || '',
seed: params.noise_seed,
mode: 'image-to-image',
image: fs.createReadStream('temp.png'),
style_preset: params.sd3_style_preset,
negative_prompt: params.nagative || '',
strength: params.sd3_strength || 0.5,
model: params.sd3_model || 'sd3',
});
}
await this.Initalize(); //初始化获取ComfyUI的节点信息
//图片路径
image2img[70].inputs.image_path = params.image_path;
image2img[85].inputs.denoise = params.denoise || 0.5;
image2img[85].inputs.seed = params.noise_seed || this.getSeed(15);
// if (params.upscale_by) {
// image2img[74].inputs.scale_by = params.upscale_by;
// }
image2img[101].inputs.ckpt_name = this.ckpt_names[params.ckpt_name_id || 0];
image2img[51].inputs.filename_prefix =
params.filename_prefix + '_image2img_output_final_';
const data = {
source: options?.source || 'web',
prompt: image2img,
api: '图生图',
client_id,
socket_id,
lifo: options?.lifo || false,
} as DrawTask;
const _v = await this.submitDrawTask(data);
if (this.oss_enable) {
// 启用OSS
return await this.fileService.uploadFileToOSS(_v, `image`);
} else {
return _v;
}
}
/**
* 图生视频
* @param client_id
* @param socket_id
* @param params
* @param options
*/
async image2video(
client_id: string,
socket_id?: string,
params?: {
image_path: string;
video_frames?: number;
fps?: number;
motion_bucket_id?: number; //运动幅度，默认127
augmentation_level?: number; //增强，默认为0
filename_prefix?: string;
cfg?: number;
steps?: number;
min_cfg?: number;
},
options?: {
source: 'web' | 'wechat';
lifo?: boolean;
},
) {
await this.Initalize(); //初始化获取ComfyUI的节点信息
//图片路径
img2video[37].inputs.image_path = params.image_path;
img2video[8].inputs.video_frames = params.video_frames || 14;
img2video[8].inputs.fps = params.fps || 8;
img2video[8].inputs.motion_bucket_id = params.motion_bucket_id || 127;
img2video[8].inputs.augmentation_level = params.augmentation_level || 0;
img2video[19].inputs.cfg = params.cfg || 3;
img2video[19].inputs.steps = params.steps || 20;
img2video[23].inputs.filename_prefix =
params.filename_prefix + '_image2video_output_final_';
img2video[23].inputs.frame_rate = params.fps || 8;
img2video[18].inputs.min_cfg = params.min_cfg || 1;
const data = {
source: options?.source || 'web',
prompt: img2video,
api: '图生视频',
client_id,
socket_id,
lifo: options?.lifo || false,
} as DrawTask;
const _v = await this.submitDrawTask(data);
if (this.oss_enable) {
// 启用OSS
return await this.fileService.uploadFileToOSS(_v, `video`);
} else {
return _v;
}
}
/**
* 抠图
* @param client_id
* @param params
* @param socket_id
* @param options
*/
async segmentAnything(
client_id: string,
params: {
image_path: string;
segmentparts: string;
},
socket_id?: string,
options?: {
source: 'web' | 'wechat';
lifo?: boolean;
},
) {
await this.Initalize(); //初始化获取ComfyUI的节点信息
matting[67].inputs.text = params.segmentparts;
matting[62].inputs.image_path = params.image_path;
const data = {
source: options?.source || 'web',
prompt: matting,
api: '抠图',
client_id,
socket_id,
lifo: options?.lifo || false,
} as DrawTask;
const _v = await this.submitDrawTask(data);
if (this.oss_enable) {
// 启用OSS
return await this.fileService.uploadFileToOSS(_v, `image`);
} else {
return _v;
}
}
/**
*局部重绘
* @param client_id
* @param params
* @param socket_id
* @param options
*/
async inpainting(
client_id: string,
params: {
image_path: string;
image_path_mask: string;
ckpt_name_id?: number;
denoise?: number;
positive?: string;
nagative?: string;
},
socket_id?: string,
options?: {
source: 'web' | 'wechat';
lifo?: boolean;
},
) {
await this.Initalize(); //初始化获取ComfyUI的节点信息
inpainting[70].inputs.image_path = params.image_path;
inpainting[87].inputs.image_path = params.image_path_mask;
inpainting[98].inputs.ckpt_name = this.ckpt_names[params.ckpt_name_id];
inpainting[85].inputs.denoise = params.denoise || 0.5;
inpainting[95].inputs.text = params.positive || '';
inpainting[95].inputs.text = params.nagative || '';
const data = {
source: options?.source || 'web',
prompt: inpainting,
api: '局部重绘',
client_id,
socket_id,
lifo: options?.lifo || false,
} as DrawTask;
const _v = await this.submitDrawTask(data);
if (this.oss_enable) {
// 启用OSS
return await this.fileService.uploadFileToOSS(_v, `image`);
} else {
return _v;
}
}
/**
* 移除背景
* @param client_id
* @param params
* @param socket_id
* @param options
*/
async removebg(
client_id: string,
params: {
image_path: string;
},
socket_id?: string,
options?: {
source: 'web' | 'wechat';
lifo?: boolean;
},
) {
await this.Initalize(); //初始化获取ComfyUI的节点信息
removebg[62].inputs.image_path = params.image_path;
const data = {
source: options?.source || 'web',
prompt: removebg,
api: '移除背景',
client_id,
socket_id,
lifo: options?.lifo || false,
} as DrawTask;
const _v = await this.submitDrawTask(data);
if (this.oss_enable) {
// 启用OSS
return await this.fileService.uploadFileToOSS(_v, `image`);
} else {
return _v;
}
}
/**
* 高清修复
* @param client_id
* @param params
* @param socket_id
* @param options
*/
async workflowApiHdfix4(
client_id: string,
params: {
image_path: string; //原图路径
},
socket_id?: string,
options?: {
source: 'web' | 'wechat';
lifo?: boolean;
},
) {
await this.Initalize(); //初始化获取ComfyUI的节点信息
workflowApiHdfix4[15].inputs.image_path = params.image_path;
const data = {
source: options?.source || 'web',
prompt: workflowApiHdfix4,
api: '高清修复x4',
client_id,
socket_id,
lifo: options?.lifo || false,
} as DrawTask;
return await this.submitDrawTask(data); //统一通过这个方法提交绘画API
}
/**
* 图片反推提示词
*/
async image2tagger(
client_id: string,
params: {
image_path: string; //原图路径
},
socket_id?: string,
options?: {
source: 'web' | 'wechat';
lifo?: boolean;
},
) {
await this.Initalize(); //初始化获取ComfyUI的节点信息
workflowApiTagger[10].inputs.image_path = params.image_path;
const data = {
source: options?.source || 'web',
prompt: workflowApiTagger,
api: '图片反推提示词',
client_id,
socket_id,
lifo: options?.lifo || false,
} as DrawTask;
return await this.submitDrawTask(data); //统一通过这个方法提交绘画API
}
/**
* 换脸
*/
async faceSwap(
client_id: string,
params: {
image_path_face: string; //人脸图片路径
image_path_refer: string; //参考图片路径
},
socket_id?: string,
options?: {
source: 'web' | 'wechat';
lifo?: boolean;
},
) {
await this.Initalize(); //初始化获取ComfyUI的节点信息
FaceSwap[91].inputs.image_path = params.image_path_refer;
FaceSwap[92].inputs.image_path = params.image_path_face;
const data = {
source: options?.source || 'web',
prompt: FaceSwap,
api: '换脸',
client_id,
socket_id,
lifo: options?.lifo || false,
} as DrawTask;
const _v = await this.submitDrawTask(data);
if (this.oss_enable) {
// 启用OSS
return await this.fileService.uploadFileToOSS(_v, `image`);
} else {
return _v;
}
}
/**
* AI模特，AI电商换装
*/
async model(
client_id: string,
params: {
image_path_model: string; //人台模特照片
parts?: string; //需要给模特试穿的部分
image_path_mask?: string; //自定义遮罩
positive?: string;
nagative?: string;
ckpt_name_id?: number;
},
socket_id?: string,
options?: {
source: 'web' | 'wechat';
lifo?: boolean;
},
) {
await this.Initalize(); //初始化获取ComfyUI的节点信息
const image_path_mask =
params.image_path_mask ||
(await this.segmentAnything(client_id, {
image_path: params.image_path_model,
segmentparts: params.parts,
}));
workflowApiModel[177].inputs.image_path = params.image_path_model;
workflowApiModel[170].inputs.image_path = image_path_mask;
workflowApiModel[179].inputs.text = params.positive || '';
workflowApiModel[171].inputs.text = params.nagative || '';
workflowApiModel[143].inputs.ckpt_name =
this.ckpt_names[params.ckpt_name_id || 0];
workflowApiModel[183].inputs.seed = this.getSeed(15);
const data = {
source: options?.source || 'web',
prompt: workflowApiModel,
api: 'AI模特',
client_id,
socket_id,
lifo: options?.lifo || false,
} as DrawTask;
const _v = await this.submitDrawTask(data);
if (this.oss_enable) {
// 启用OSS
return await this.fileService.uploadFileToOSS(_v, `image`);
} else {
return _v;
}
}
/**
* SD3 官方官方文生图
*/
async text2imgSD3(params: {
prompt: string;
mode?: 'text-to-image' | 'image-to-image';
aspect_ratio?: SD3AspectRatio;
negative_prompt?: string;
model?: 'sd3' | 'sd3-turbo';
image?: any; //image-to-image 必须
strength?: number; //image-to-image 必须
seed?: number;
style_preset?: SD3StylePreset;
}) {
const sd3url = 'https://api.stability.ai/v2beta/stable-image/generate/sd3';
// const formData = new FormData();
// formData.append('prompt', params.prompt);
// formData.append('mode', params.mode || 'text-to-image');
// formData.append('negative_prompt', params.negative_prompt || '');
// formData.append('model', params.model || 'sd3');
// formData.append('image', params.image);
// formData.append('strength', params.strength || 0.5);
// formData.append('seed', this.getSeed(9));
// formData.append('style_preset', params.style_preset || '');
// formData.append('output_format', 'png');
// if (params.mode === 'text-to-image') {
// formData.append('aspect_ratio', params.aspect_ratio || '1:1');
// }
const formData = {
prompt: params.prompt,
mode: params.mode || 'text-to-image',
negative_prompt: params.negative_prompt || '',
model: params.model || 'sd3',
seed: this.getSeed(9),
style_preset: params.style_preset || '',
output_format: 'png',
};
if (!params.mode || params.mode === 'text-to-image') {
Object.assign(formData, {
aspect_ratio: params.aspect_ratio || '1:1',
});
}
if (params.mode === 'image-to-image') {
Object.assign(formData, {
strength: params.strength || 0.5,
image: params.image,
});
}
this.logger.log('提交SD的绘画数据', formData);
const response = await this.comfyuiAxios.postForm(
sd3url,
axios.toFormData(formData, new FormData()),
{
validateStatus: undefined,
responseType: 'arraybuffer',
headers: {
Authorization: `Bearer ${this.configService.get('CONFIG_SD3_APIKEY')}`,
Accept: 'image/*',
ContentType: 'multipart/form-data',
},
},
);
//将获取的Buffer转换为图片文件
if (response.status === 200) {
// console.log('data', response.data);
const buffer = Buffer.from(response.data);
//将Buffer上传到阿里云
return await this.fileService.uploadBufferToOSS(buffer, `image`);
} else {
console.log(response.request);
throw new Error(`${response.status}: ${response.data.toString()}`);
}
}
/**
* 初始化节点数据
*
*/
async Initalize() {
// 初始化获取ComfyUI的节点信息
this.logger.log(
'ComfyUI服务器地址',
this.configService.get('CONFIG_COMFYUI_SERVER_URL'),
);
if (!this.Object_info) {
await this.getObject_info();
if (!this.Object_info) {
this.logger.error('获取ComfyUI的节点信息失败');
return;
} else {
this.ckpt_names =
this.Object_info[
'CheckpointLoaderSimple'
].input.required.ckpt_name[0];
}
}
//如果启用远程绘画服务
if (this.remote_comfyui) {
// await this.getAccessToken();
}
}
/**
* 加入黑名单
*/
async addBlackList(uid: string) {
const blacklist = (await this.cacheService.get('blacklist')) || [];
if (blacklist.includes(uid)) {
return;
}
blacklist.push(uid);
await this.cacheService.set('blacklist', blacklist);
}
/**
* 判断是否再黑名单
*/
async isInBlackList(uid: string) {
const blacklist = (await this.cacheService.get('blacklist')) || [];
return blacklist.includes(uid);
}
/**
* 移除黑名单
*/
async removeBlackList(uid: string) {
let blacklist = await this.cacheService.get('blacklist');
if (!blacklist) return;
blacklist = blacklist.filter((item) => item !== uid);
await this.cacheService.set('blacklist', blacklist);
}
/**
* 获取黑名单
*/
async getBlackList() {
return await this.cacheService.get('blacklist');
}
}
================================================
FILE: src/file/file.controller.ts
================================================
import { Controller, Get } from '@nestjs/common';
import { FileService } from './file.service';
@Controller('file')
export class FileController {
constructor(private readonly fileService: FileService) {}
@Get('test')
async upload() {
const testurl= "https://wangbo0808.oss-cn-shanghai.aliyuncs.com/aidraw/%E6%9D%8E%E4%BA%91%E6%80%9D1130-01.mp4";
// const testurl= "https://wangbo0808.oss-cn-shanghai.aliyuncs.com/aidraw/audio/temps/tts_undefined_%0A%0A%E4%B8%8D%E8%A6%81%E5%BF%98%E8%AE%B0%EF%BC%8C%E9%A3%8E%E9%9B%A8%E8%BF%87%E5%90%8E%EF%BC%8C%E5%BD%A9%E8%99%B9%E4%BC%9A%E5%8D%87%E8%B5%B7.wav";
const _file = await this.fileService.urlToFile(
testurl,
'test.wav',
'audio/wav',
);
return await this.fileService.uploadFileToOSS(testurl);
}
}
================================================
FILE: src/file/file.module.ts
================================================
import { Module } from '@nestjs/common';
import { FileService } from './file.service';
import { FileController } from './file.controller';
@Module({
controllers: [FileController],
providers: [FileService],
exports: [FileService],
})
export class FileModule {}
================================================
FILE: src/file/file.service.ts
================================================
import { Injectable } from '@nestjs/common';
import { ConfigService } from '@nestjs/config';
import { nanoid } from 'nanoid';
// eslint-disable-next-line @typescript-eslint/no-var-requires
const OSS = require('ali-oss');
// const fs = require('fs');
@Injectable()
export class FileService {
constructor(
private readonly configService: ConfigService,
) {
}
/**
* 将url转换成file
* @param url 图片链接
* @param fileName 文件名
* @param mimeType 文件类型
* @private
*/
async urlToFile(url, fileName, mimeType): Promise {
return fetch(url)
.then((res) => res.arrayBuffer())
.then((buffer) => {
return new File([buffer], fileName, { type: mimeType });
});
}
/**
* 将url转换成ArrayBuffer
*
* @param url
*/
async urlToArrayBuffer(url: string): Promise {
return fetch(url)
.then((res) => res.arrayBuffer())
.then((buffer) => {
return buffer;
});
}
/**
* 上传文件到OSS,返回上传成功的文件名
*
* @param file
* @param type
*/
async uploadFileToOSS(
file: string,
type?: 'image' | 'audio' | 'video',
): Promise {
// 创建OSS客户端实例
const client = new OSS({
region: this.configService.get('OSS_REGION'), // 替换为你的OSS区域
accessKeyId: this.configService.get('OSS_ACCESSKEYID'), // 替换为你的AccessKeyId
accessKeySecret: this.configService.get('OSS_ACCESSKEYSECRET'), // 替换为你的AccessKeySecret
bucket: this.configService.get('OSS_BUCKET'), // 替换为你的Bucket名称
});
let _f = '';
let _r = '';
const filename = nanoid(15);
// 根据文件类型转换成对应的文件
switch (type) {
case 'image':
_f = filename + '.png';
_r = 'aidraw/image/temps/';
break;
case 'audio':
_f = filename + '.wav';
_r = 'aidraw/audio/temps/';
break;
case 'video':
_f = filename + '.mp4';
_r = 'aidraw/video/temps/';
break;
default:
_f = filename + '.png';
_r = 'aidraw/others/temps/';
break;
}
//将文件保存到服务器
const targetPath = `${_r}${_f}`;
try {
const _f = await this.urlToArrayBuffer(file);
//ArrayBuffer转换成Buffer
const buffer = Buffer.from(_f);
const { url } = await client.put(targetPath, buffer);
return url;
} catch (e) {
console.error('Error uploading file to OSS:', e);
throw e;
}
}
//将Buffer上传到OSS
async uploadBufferToOSS(
buffer: Buffer,
type?: 'image' | 'audio' | 'video',
): Promise {
// 创建OSS客户端实例
const client = new OSS({
region: this.configService.get('OSS_REGION'), // 替换为你的OSS区域
accessKeyId: this.configService.get('OSS_ACCESSKEYID'), // 替换为你的AccessKeyId
accessKeySecret: this.configService.get('OSS_ACCESSKEYSECRET'), // 替换为你的AccessKeySecret
bucket: this.configService.get('OSS_BUCKET'), // 替换为你的Bucket名称
});
let _f = '';
let _r = '';
const filename = nanoid(15);
// 根据文件类型转换成对应的文件
switch (type) {
case 'image':
_f = filename + '.png';
_r = 'aidraw/image/temps/';
break;
case 'audio':
_f = filename + '.wav';
_r = 'aidraw/audio/temps/';
break;
case 'video':
_f = filename + '.mp4';
_r = 'aidraw/video/temps/';
break;
default:
_f = filename + '.png';
_r = 'aidraw/others/temps/';
break;
}
//将文件保存到服务器
const targetPath = `${_r}${_f}`;
try {
const { url } = await client.put(targetPath, buffer);
return url;
} catch (e) {
console.error('Error uploading file to OSS:', e);
throw e;
}
}
}
================================================
FILE: src/main.ts
================================================
import { NestFactory } from '@nestjs/core';
import { AppModule } from './app.module';
import { SwaggerModule, DocumentBuilder } from '@nestjs/swagger';
import { ConfigService } from '@nestjs/config';
async function bootstrap() {
const app = await NestFactory.create(AppModule,{
cors: true,
});
app.enableCors({
origin: '*',
methods: '*',
allowedHeaders: '*',
credentials: true,
});
const config = new DocumentBuilder()
.setTitle('ComfyUI server API文档')
.setDescription(
'使用ComfyUI的工作流就像使用API一样简单！comfy-server是ComfyUI原生接口的服务端增强版本，可以将comfyUI强大的工作流转换为API供前端调用',
)
.setVersion('1.0')
.addBearerAuth({
type: 'http',
scheme: 'bearer',
bearerFormat: 'JWT',
name: 'JWT',
description: 'Enter JWT token',
in: 'header',
})
.build();
const document = SwaggerModule.createDocument(app, config);
SwaggerModule.setup('api-docs', app, document);
await app.listen(new ConfigService().get('CONFIG_BASE_POST'));
}
bootstrap();
================================================
FILE: src/middleware/XML.middleware.ts
================================================
import { Injectable, NestMiddleware } from '@nestjs/common';
import { NextFunction, Request, Response } from 'express';
import { parseString } from 'xml2js';
/**
* XML 中间件,微信消息前置处理为json
*
*/
@Injectable()
export class XMLMiddleware implements NestMiddleware {
async use(req: Request, res: Response, next: NextFunction) {
console.log('XML middlewarem excting');
const buffer: any[] = []; // 创建一个空数组，用于存储请求的数据
// 监听 req 的 data 事件，每当有数据到达时，就将数据推入 buffer 数组中
req.on('data', (chunk) => {
buffer.push(chunk);
});
// 监听 req 的 end 事件，表示请求数据已经接收完毕时，执行以下操作：
req.on('end', () => {
// 将 buffer 数组中的数据拼接成一个字符串，并以 utf-8 编码转换为 msgXml 变量
const msgXml = Buffer.concat(buffer).toString('utf-8');
// 调用 parseString 函数，将 msgXml 变量中的 xml 格式的数据解析为 JavaScript 对象，并赋值给 result 变量。
// 如果解析过程出现错误，则抛出异常并拒绝 promise。
parseString(msgXml, (err, result) => {
if (err) {
throw err;
}
// 将 result 变量赋值给 req.body 属性，表示将请求体转换为 JavaScript 对象。
req.body = result;
// 调用 next 函数，表示继续执行下一个中间件函数。
next();
});
});
}
}
================================================
FILE: src/oneapi/tokens/dto/create-token.dto.ts
================================================
import { Token } from '../entities/token.entity';
export class CreateTokenDto extends Token {}
================================================
FILE: src/oneapi/tokens/dto/update-token.dto.ts
================================================
import { PartialType } from '@nestjs/mapped-types';
import { CreateTokenDto } from './create-token.dto';
export class UpdateTokenDto extends PartialType(CreateTokenDto) {}
================================================
FILE: src/oneapi/tokens/entities/token.entity.ts
================================================
import { Column, Entity, PrimaryGeneratedColumn } from 'typeorm';
@Entity({ name: 'tokens' })
export class Token {
@PrimaryGeneratedColumn()
id: number;
@Column({ nullable: true })
user_id: number;
@Column({ nullable: true })
key: string;
@Column({ nullable: true })
status: number;
@Column({ nullable: true })
name: string;
@Column({ nullable: true })
created_time: number;
@Column({ nullable: true })
accessed_time: number;
@Column({ nullable: true })
expired_time: number;
@Column({ nullable: true })
remain_quota: number;
@Column({ nullable: true })
unlimited_quota: boolean;
@Column({ nullable: true })
used_quota: number;
}
================================================
FILE: src/oneapi/tokens/tokens.controller.ts
================================================
import { Controller, Get, Post, Body, Patch, Param, Delete } from '@nestjs/common';
import { TokensService } from './tokens.service';
import { CreateTokenDto } from './dto/create-token.dto';
import { UpdateTokenDto } from './dto/update-token.dto';
import { ApiTags } from '@nestjs/swagger';
@ApiTags('OneAPI')
@Controller('oneapi/tokens')
export class TokensController {
constructor(private readonly tokensService: TokensService) {}
@Post()
create(@Body() createTokenDto: CreateTokenDto) {
return this.tokensService.create(createTokenDto);
}
@Get()
findAll() {
return this.tokensService.findAll();
}
@Get(':id')
findOne(@Param('id') id: string) {
return this.tokensService.findOne(+id);
}
@Patch(':id')
update(@Param('id') id: string, @Body() updateTokenDto: UpdateTokenDto) {
return this.tokensService.update(+id, updateTokenDto);
}
@Delete(':id')
remove(@Param('id') id: string) {
return this.tokensService.remove(+id);
}
}
================================================
FILE: src/oneapi/tokens/tokens.module.ts
================================================
import { Module } from '@nestjs/common';
import { TokensService } from './tokens.service';
import { TokensController } from './tokens.controller';
import { Token } from './entities/token.entity';
import { TypeOrmModule } from '@nestjs/typeorm';
@Module({
imports: [TypeOrmModule.forFeature([Token])],
controllers: [TokensController],
providers: [TokensService],
})
export class TokensModule {}
================================================
FILE: src/oneapi/tokens/tokens.service.ts
================================================
import { Injectable } from '@nestjs/common';
import { CreateTokenDto } from './dto/create-token.dto';
import { UpdateTokenDto } from './dto/update-token.dto';
import { InjectRepository } from '@nestjs/typeorm';
import { Token } from './entities/token.entity';
import { Repository } from 'typeorm';
@Injectable()
export class TokensService {
constructor(
@InjectRepository(Token)
private readonly usersRepository: Repository,
) {}
async create(createTokenDto: CreateTokenDto) {
const res = await this.findOne(createTokenDto.user_id);
console.log(res);
if (res) {
return res;
} else {
return await this.usersRepository.save(createTokenDto);
}
// return 'This action adds a new token';
}
findAll() {
return `This action returns all tokens`;
}
async findOne(id: number) {
return await this.usersRepository.findOneBy({ user_id: id });
}
update(id: number, updateTokenDto: UpdateTokenDto) {
return `This action updates a #${id} token`;
}
remove(id: number) {
return `This action removes a #${id} token`;
}
}
================================================
FILE: src/oneapi/users/dto/create-user.dto.ts
================================================
import { OneAPIUser } from '../entities/user.entity';
export class CreateUserDto extends OneAPIUser {}
================================================
FILE: src/oneapi/users/dto/update-user.dto.ts
================================================
import { PartialType } from '@nestjs/swagger';
import { CreateUserDto } from './create-user.dto';
export class UpdateUserDto extends PartialType(CreateUserDto) {}
================================================
FILE: src/oneapi/users/entities/user.entity.ts
================================================
import {
Entity,
Column,
PrimaryGeneratedColumn,
BaseEntity,
} from 'typeorm';
import { ApiProperty } from '@nestjs/swagger';
@Entity({ name: 'users' })
export class OneAPIUser extends BaseEntity {
@PrimaryGeneratedColumn()
id: number;
@ApiProperty({
description: 'username',
example: 'zhangsan',
})
@Column()
username: string;
@ApiProperty({
description: 'password',
example: '123456',
})
@Column()
password: string;
@ApiProperty({
description: '余额',
example: '123456',
})
@Column()
quota: number;
@ApiProperty({
description: '显示名称',
example: 'zhangsan',
})
@Column({ nullable: true })
display_name: string;
@ApiProperty({
description: '角色编号',
example: '1',
})
@Column({ nullable: true })
role: number;
@ApiProperty({
description: '状态',
example: '1',
})
@Column({ nullable: true })
status: number;
@ApiProperty({
description: '邮箱',
example: 'nnheo@example.com',
})
@Column({ nullable: true })
email: string;
@ApiProperty({
description: 'unionid',
example: 'wxc1234567890',
})
@Column({ nullable: true })
github_id: string; //用户唯一标识,微信uniid
@ApiProperty({
description: 'openid',
example: 'wxopenidc1234567890',
})
@Column({ nullable: true })
wechat_id: string;
@ApiProperty({
description: 'access_token',
example: 'toen1234567890',
})
@Column({ nullable: true })
access_token: string;
@ApiProperty({
description: '已使用流量',
example: 100,
})
@Column({ nullable: true })
used_quota: number;
@ApiProperty({
description: '请求次数',
example: 100,
})
@Column({ nullable: true })
request_count: number;
@ApiProperty({
description: '分组',
example: '100',
})
@Column({ nullable: true })
group: string;
@Column({ nullable: true })
aff_code: string;
@Column({ nullable: true })
inviter_id: number;
}
================================================
FILE: src/oneapi/users/users.controller.ts
================================================
import {
Controller,
Get,
Post,
Body,
Patch,
Param,
Delete,
} from '@nestjs/common';
import { OneAPIUsersService } from './users.service';
import { ApiOperation, ApiTags } from '@nestjs/swagger';
import { OneAPIUser } from './entities/user.entity';
@ApiTags('OneAPI')
@Controller('oneapi/users')
export class UsersController {
constructor(private readonly oneAPIUsersService: OneAPIUsersService) {}
@Post()
create(@Body() user: OneAPIUser) {
return this.oneAPIUsersService.create(user);
}
@ApiOperation({
summary: '创建ONEAPI用户',
description: '根据微信uniId传建用户，如果存在则直接返回，没有就重新创建',
})
@Post('creatbyuniId')
async CreateByUniId(@Body() user: OneAPIUser) {
return await this.oneAPIUsersService.CreateByUniId(user);
}
@ApiOperation({
summary: '获取所有用户',
description: 'Get all users',
})
@Get()
findAll() {
return this.oneAPIUsersService.findAll();
}
@ApiOperation({
summary: '根据id获取用户',
description: 'Get user by id',
})
@Get(':id')
findOne(@Param('id') id: string) {
return this.oneAPIUsersService.findOne(+id);
}
@ApiOperation({
summary: '更新用户',
description: 'Update user by id',
})
@Patch(':id')
update(@Param('id') id: string, @Body() user: OneAPIUser) {
return this.oneAPIUsersService.update(+id, user);
}
@ApiOperation({
summary: '删除用户',
description: 'Delete user by id',
})
@Delete(':id')
remove(@Param('id') id: string) {
return this.oneAPIUsersService.remove(+id);
}
}
================================================
FILE: src/oneapi/users/users.module.ts
================================================
import { Module } from '@nestjs/common';
import { OneAPIUsersService } from './users.service';
import { UsersController } from './users.controller';
import { TypeOrmModule } from '@nestjs/typeorm';
import { OneAPIUser } from './entities/user.entity';
@Module({
imports: [TypeOrmModule.forFeature([OneAPIUser])],
controllers: [UsersController],
providers: [OneAPIUsersService],
exports: [OneAPIUsersService, TypeOrmModule],
})
export class OneAPIUsersModule {}
================================================
FILE: src/oneapi/users/users.service.ts
================================================
import { Injectable, Logger } from '@nestjs/common';
import { OneAPIUser } from './entities/user.entity';
import { InjectRepository } from '@nestjs/typeorm';
import { Repository } from 'typeorm';
@Injectable()
export class OneAPIUsersService {
private readonly logger = new Logger(OneAPIUsersService.name);
constructor(
@InjectRepository(OneAPIUser)
private readonly usersRepository: Repository,
) {}
async create(user: OneAPIUser) {
return await this.usersRepository.save(user);
}
findAll() {
return this.usersRepository.find();
}
findOne(id: number) {
return this.usersRepository.findOneBy({ id });
}
async findOneByUniId(github_id: string) {
return await this.usersRepository.findOneBy({ github_id });
}
/**
* 根据uniid创建用户,如果存在存在返回，没有就创建
* @param user
* @constructor
*/
async CreateByUniId(user: OneAPIUser) {
//根据id或者用户名查找
const res =
(await this.findOneByUniId(user.github_id)) ||
(await this.usersRepository.findOneBy({
username: user.username,
}));
console.log(res);
if (res) {
this.logger.log('用户已存在');
return res;
} else {
this.logger.log('用户不存在，创建用户');
return await this.create(user);
}
// return this.usersRepository.findOneBy({ id });
}
update(id: number, user: OneAPIUser) {
this.usersRepository.update(id, user);
return `This action updates a #${id} user`;
}
remove(id: number) {
this.usersRepository.delete(id);
return `This action removes a #${id} user`;
}
}
================================================
FILE: src/tweet/dto/create-tweet.dto.ts
================================================
import { Tweet } from '../entities/tweet.schema';
export class CreateTweetDto extends Tweet{}
================================================
FILE: src/tweet/dto/update-tweet.dto.ts
================================================
import { PartialType } from '@nestjs/swagger';
import { CreateTweetDto } from './create-tweet.dto';
export class UpdateTweetDto extends PartialType(CreateTweetDto) {}
================================================
FILE: src/tweet/entities/tweet.schema.ts
================================================
import { Prop, Schema, SchemaFactory } from '@nestjs/mongoose';
import { HydratedDocument } from 'mongoose';
import { ApiProperty } from '@nestjs/swagger';
export type UserDocument = HydratedDocument;
@Schema()
export class Tweet {
@Prop()
@ApiProperty({
description: 'user_id',
example: '1234567890',
})
user_id: string;
@Prop()
models: object[];
@Prop()
transitions: object[];
@Prop()
effects: object[];
@Prop()
animations: object[];
@Prop()
@ApiProperty({
description: '推文项目',
example: [
{
projectName: 'projectName',
Resolution_id: 0,
cover_url: 'cover_url',
full_contents: 'full_contents',
useVideo: true,
backgroundMusic_id: 1,
contents: [
{
id: 1,
content: 'content',
prompts: 'prompts',
images: ['images'],
choose_image_index: 1,
audio: ['audio'],
isTalk: true,
role: 'role',
scene_id: 1,
video: 'video',
transition: 'transition',
video_effect: 'video_effect',
animation: 'animation',
},
],
},
],
})
projects: TweetProject[];
@Prop()
roles: object[];
@Prop()
scenes: object[];
}
export interface TweetProject {
projectName: string;
Resolution_id: number;
cover_url: string;
full_contents: string;
useVideo: boolean;
backgroundMusic_id: number;
contents: TweetData[];
}
// 工作空间的数据接口
export interface TweetData {
id?: number;
content?: string;
prompts?: string;
images: string[];
choose_image_index?: number;
audio: string[];
isTalk?: boolean; //是否是对话
role?: string; //人物名称
scene_id?: number; //场景id
video: string;
transition?: string;
video_effect?: string;
animation?: string;
}
export const TweetSchema = SchemaFactory.createForClass(Tweet);
================================================
FILE: src/tweet/tweet.controller.ts
================================================
import { Controller, Get, Post, Body, Patch, Param, Delete } from '@nestjs/common';
import { TweetService } from './tweet.service';
import { CreateTweetDto } from './dto/create-tweet.dto';
import { UpdateTweetDto } from './dto/update-tweet.dto';
import { TweetProject } from './entities/tweet.schema';
import { ApiOperation } from '@nestjs/swagger';
@Controller('tweet')
export class TweetController {
constructor(private readonly tweetService: TweetService) {}
@ApiOperation({
summary: '保存项目',
description: '保存项目',
})
@Post('savePrject')
savePrject(@Body() createTweetDto: CreateTweetDto) {
return this.tweetService.savePrject(
createTweetDto.user_id,
createTweetDto.projects[0],
);
}
@Get()
findAll() {
return this.tweetService.findAll();
}
@Get(':id')
findOne(@Param('id') id: string) {
return this.tweetService.findOne(id);
}
@Patch(':id')
update(@Param('id') id: string, @Body() updateTweetDto: UpdateTweetDto) {
return this.tweetService.update(+id, updateTweetDto);
}
@Delete(':id')
remove(@Param('id') id: string) {
return this.tweetService.remove(+id);
}
}
================================================
FILE: src/tweet/tweet.module.ts
================================================
import { Module } from '@nestjs/common';
import { TweetService } from './tweet.service';
import { TweetController } from './tweet.controller';
import { MongooseModule } from '@nestjs/mongoose';
import { Tweet, TweetSchema } from './entities/tweet.schema';
@Module({
imports: [
MongooseModule.forFeature([{ name: Tweet.name, schema: TweetSchema }]),
],
controllers: [TweetController],
providers: [TweetService],
})
export class TweetModule {}
================================================
FILE: src/tweet/tweet.service.ts
================================================
import { Injectable } from '@nestjs/common';
import { CreateTweetDto } from './dto/create-tweet.dto';
import { UpdateTweetDto } from './dto/update-tweet.dto';
import { InjectModel } from '@nestjs/mongoose';
import { Model } from 'mongoose';
import { Tweet, TweetProject } from './entities/tweet.schema';
@Injectable()
export class TweetService {
constructor(@InjectModel(Tweet.name) private tweetModel: Model) {}
create(createTweetDto: CreateTweetDto) {
const newTweet = new this.tweetModel(createTweetDto);
return newTweet.save();
}
/**
* 保存项目
* @param user_id
* @param project
*/
async savePrject(user_id: string, project: TweetProject) {
//首先看是否有记录存在
const _tweet = await this.findOne(user_id);
if (_tweet) {
//有记录存在，则更新
//根据项目名称判断，当前项目是否存在
const _project = _tweet.projects.find(
(item) => item.projectName === project.projectName,
);
if (_project) {
//存在，则更新
return this.tweetModel.updateOne(
{ user_id, 'projects.projectName': _project.projectName },
{
$set: {
'projects.$': project,
},
},
);
} else {
//如果项目名称不存在，则添加
return this.tweetModel.updateOne(
{ user_id },
{
$push: {
projects: project,
},
},
);
}
}
return this.tweetModel.create({ user_id, projects: [project] });
}
/**
* 根据user_id获取项目所有的历史项目
*/
async getData(user_id: string) {
const _tweet = await this.findOne(user_id);
if (_tweet) {
return _tweet;
}
return null;
}
findAll() {
return this.tweetModel.find();
}
async findOne(id: string) {
return await this.tweetModel.findOne({ user_id: id }).exec();
}
update(id: number, updateTweetDto: UpdateTweetDto) {
return `This action updates a #${id} tweet`;
}
remove(id: number) {
return `This action removes a #${id} tweet`;
}
}
================================================
FILE: src/users/dto/create-user.dto.ts
================================================
import { User } from '../schema/user.schema';
export class CreateUserDto extends User{}
================================================
FILE: src/users/dto/update-user.dto.ts
================================================
import { PartialType } from '@nestjs/swagger';
import { CreateUserDto } from './create-user.dto';
export class UpdateUserDto extends PartialType(CreateUserDto) {}
================================================
FILE: src/users/schema/user.schema.ts
================================================
import { Prop, Schema, SchemaFactory } from '@nestjs/mongoose';
import { HydratedDocument } from 'mongoose';
import { ApiProperty } from '@nestjs/swagger';
export type UserDocument = HydratedDocument;
@Schema()
export class User {
@Prop()
@ApiProperty({
description: '昵称',
example: '张三',
})
nickname: string;
@ApiProperty({
description: '密码',
example: '123456',
})
@Prop()
password: string;
@ApiProperty({
description: '用户名',
example: 'wechat_abdfkdfjdkfdkfjjkdjf',
})
@Prop()
username: string;
@ApiProperty({
description: '微信openid',
example: 'abdfkdfjdkfdkfjjkdjf',
})
@Prop()
wx_openid: string;
@ApiProperty({
description: '微信unionid',
example: 'abdfkdfjdkfdkfjjkdjf',
})
@Prop()
wx_unionid: string;
@ApiProperty({
description: '微信头像',
example: 'abdfkdfjdkfdkfjjkdjf',
})
@Prop()
avatar_url: string; //头像
@Prop()
@ApiProperty({
description: '邮箱',
example: 'efpyi@example.com',
})
email: string;
@Prop()
@ApiProperty({
description: '邀请人id',
example: 'UVGTBDS',
})
inviter_uid: string;
@Prop()
@ApiProperty({
description: '最后登录时间',
example: '121212133900',
})
last_login_date: number;
@Prop()
@ApiProperty({
description: '注册时间',
example: '121212133900',
})
register_date: number;
@Prop()
@ApiProperty({
description: '最后登录ip',
example: '127.0.0.1',
})
last_login_ip: string;
@Prop()
@ApiProperty({
description: '手机号',
example: '13888888888',
})
mobile: string;
@Prop()
@ApiProperty({
description: '邀请码',
example: '123456',
})
my_invite_code: string;
@Prop()
@ApiProperty({
description: '角色',
example: ['admin', 'user'],
})
role: Array;
@Prop()
@ApiProperty({
description: 'token',
example: '2121213424gfgdfdfdfsdfsdfdffg',
})
token: string;
}
export const UserSchema = SchemaFactory.createForClass(User);
================================================
FILE: src/users/users.controller.ts
================================================
import {
Query,
Controller,
Get,
Post,
Body,
Patch,
Param,
Delete,
} from '@nestjs/common';
import { UsersService } from './users.service';
import { UpdateUserDto } from './dto/update-user.dto';
import { CreateUserDto } from './dto/create-user.dto';
import { ApiTags, ApiOperation } from '@nestjs/swagger';
import { AuthGuard } from '../auth/auth.guard';
import { ApiBearerAuth } from '@nestjs/swagger';
@ApiTags('用户管理')
@Controller('users')
export class UsersController {
constructor(
private readonly usersService: UsersService,
) {
}
@Post()
createUser(@Body() createUserDto: CreateUserDto) {
return this.usersService.create(createUserDto);
}
@ApiOperation({
summary: '微信登录',
description: '微信登录',
})
@Post('loginBywechat')
loginBywechat(@Body() createUserDto: CreateUserDto) {
return this.usersService.loginBywechat(createUserDto);
}
@ApiOperation({
summary: '用户名注册',
description: '根据用户名注册',
})
@Post('registerByUsername')
registerByUsername(@Body() createUserDto: CreateUserDto) {
return this.usersService.registerByUsername(createUserDto);
}
@ApiOperation({
summary: '用户名登录',
description: '根据用户名登录',
})
@Post('loginByUsername')
loginByUsername(@Body() createUserDto: CreateUserDto) {
return this.usersService.loginByUsername(createUserDto);
}
@Get()
findAll() {
return this.usersService.findAll();
}
@Get(':id')
findOne(@Param('id') id: string) {
return this.usersService.findOne(id);
}
@Patch(':id')
update(@Param('id') id: string, @Body() updateUserDto: UpdateUserDto) {
return this.usersService.update(id, updateUserDto);
}
@Delete(':id')
remove(@Param('id') id: string) {
return this.usersService.remove(id);
}
}
================================================
FILE: src/users/users.module.ts
================================================
import { Module } from '@nestjs/common';
import { UsersService } from './users.service';
import { UsersController } from './users.controller';
import { MongooseModule } from '@nestjs/mongoose';
import { User, UserSchema } from './schema/user.schema';
import { CacheModule } from '../cache/cache.module';
@Module({
imports: [
MongooseModule.forFeature([{ name: User.name, schema: UserSchema }]),
],
controllers: [UsersController],
providers: [UsersService],
exports: [UsersService, MongooseModule],
})
export class UsersModule {}
================================================
FILE: src/users/users.service.ts
================================================
import { Injectable } from '@nestjs/common';
import { CreateUserDto } from './dto/create-user.dto';
import { InjectModel } from '@nestjs/mongoose';
import { Model } from 'mongoose';
import { User } from './schema/user.schema';
import { UpdateUserDto } from './dto/update-user.dto';
import * as bcrypt from 'bcrypt';
import { JwtService } from '@nestjs/jwt';
/**
* 注册结果消息接口
*/
export type RegistRes = {
status: number;
msg: string;
data: User;
};
@Injectable()
export class UsersService {
constructor(
@InjectModel(User.name) private userModel: Model,
private readonly jwtService: JwtService,
) {}
/**
* 注意，账号创建不做用户验证
* @param createUserDto
*/
async create(createUserDto: CreateUserDto) {
//根据username查找用户是否存在
// let user;
// // 用户存在
// if (createUserDto.username) {
// user = await this.findByUsername(createUserDto.username);
// }
// // 微信ID存在
// if (createUserDto.wx_unionid) {
// user = await this.findByWxUnionid(createUserDto.wx_unionid);
// }
// // 邮箱存在
// if (createUserDto.email) {
// user = await this.findByEmail(createUserDto.email);
// }
// if (user) {
// console.log('user:', user);
// return user;
// }
//根据email查找用户是否存在
// const emailUser = await this.findByEmail(createUserDto.email);
// if (emailUser) {
// console.log('emailUser:', emailUser);
// return emailUser;
// }
createUserDto.password = await this.hashPassword(createUserDto.password);
const createdUser = new this.userModel(createUserDto);
return createdUser.save();
}
/**
* 注册
*/
async registerByUsername(createUserDto: CreateUserDto) {
const { username } = createUserDto;
console.log(username);
if (username) {
const user = await this.findByUsername(username);
console.log(user);
if (user) {
return {
status: 1,
msg: '用户名已存在',
data: null,
} as RegistRes;
}
const createdUser = await this.create(createUserDto);
//更新token，并返回User
await this.updateToken(createdUser._id, createdUser.username);
return {
status: 0,
msg: '注册成功',
data: await this.findOne(createdUser._id + ''),
} as RegistRes;
}
}
/**
* 根据unionid实现微信登录，如果不存在就创建账号
* @param createUserDto
*/
async loginBywechat(createUserDto: CreateUserDto) {
const res = await this.findByWxUnionid(createUserDto.wx_unionid);
return res ? res : await this.create(createUserDto);
}
/**
* login,通过账号密码登录
*/
async loginByUsername(createUserDto: CreateUserDto) {
const { username, password } = createUserDto;
const user = await this.findByUsername(username);
if (!user) {
return {
status: 1,
msg: '用户名不存在',
data: null,
} as RegistRes;
}
const isMatch = await this.comparePassword(password, user.password);
if (!isMatch) {
return {
status: 1,
msg: '密码错误',
data: null,
} as RegistRes;
}
return {
status: 0,
msg: '登录成功',
data: await this.updateToken(user._id, user.username),
} as RegistRes;
}
/**
* 根据加密的密码和实现自动登录
*/
async autoLogin(user: User) {
const { password } = user;
}
findAll() {
return this.userModel.find().exec();
}
findOne(id: string) {
return this.userModel.findById(id).exec();
}
async findByUsername(username: string) {
return await this.userModel.findOne({ username }).exec();
}
async findByEmail(email: string) {
return await this.userModel.findOne({ email }).exec();
}
async findByWxUnionid(wx_unionid: string) {
return await this.userModel.findOne({ wx_unionid }).exec();
}
async update(id: string, updateUserDto: UpdateUserDto) {
// const updateUser = new this.userModel(updateUserDto);
return await this.userModel.findByIdAndUpdate(id, updateUserDto);
}
async remove(id: string) {
return await this.userModel.findByIdAndDelete(id);
}
private iv = null as Buffer;
private key = null as Buffer;
async hashPassword(password: string) {
const saltOrRounds = 10;
return await bcrypt.hash(password, saltOrRounds);
}
async comparePassword(password: string, hash: string) {
try {
return await bcrypt.compare(password, hash);
} catch (e) {
throw new Error(e);
}
}
/**
* 用户id,用户名生成token
* @param _id
* @param username
*/
async createToken(_id: string, username: string) {
const payload = {
sub: _id,
username,
};
return await this.jwtService.signAsync(payload);
}
/**
* 更新token
* @param _id
* @param username
*/
async updateToken(_id, username) {
return this.userModel.findByIdAndUpdate(_id, {
token: await this.createToken(_id, username),
});
}
/**
* 根据token获取用户信息,可以用validateToken替代这个函数功能，也能返回最终的User
* @param token
*/
private async getUserInfoByToken(token: string) {
const payload = await this.jwtService.decode(token);
return await this.userModel.findById(payload['sub']);
}
/**
* 验证token有效性
*/
async validateToken(token: string): Promise {
const { sub, exp } = await this.jwtService.decode(token);
if (exp < Math.floor(Date.now() / 1000)) {
return false;
}
if (!sub) {
return false;
}
return await this.userModel.findById(sub);
}
}
================================================
FILE: src/wechat-auth/wechat-auth.controller.ts
================================================
import {
Body,
Controller,
Get,
HttpCode,
Logger,
Post,
Query,
Req,
Res,
} from '@nestjs/common';
import e, { Request, Response } from 'express';
import { WechatAuthService } from './wechat-auth.service';
import { ApiOperation, ApiTags } from '@nestjs/swagger';
import { create } from 'xmlbuilder2';
import { DrawService, WeChatDrawModel } from '../draw/draw.service';
import { UsersService } from '../users/users.service';
@ApiTags('微信相关')
@Controller('wechatauth')
export class WechatAuthController {
constructor(
private readonly wechatAuthService: WechatAuthService,
private readonly drawService: DrawService,
private readonly usersService: UsersService,
) {}
private readonly logger = new Logger(WechatAuthController.name);
@ApiOperation({
summary: '获取微信用户信息',
description: '根据服务返回的code码，获取微信用户的信息',
operationId: 'getUserInfo',
tags: ['wechat'],
externalDocs: {
description: 'wechat',
url: 'https://developers.weixin.qq.com/doc/offiaccount/OA_Web_Apps/Wechat_webpage_authorization.html',
},
})
@Get('getuserinfo')
async getUserInfo(@Query('code') code: string) {
this.logger.log(code);
return await this.wechatAuthService.getUserinfo(code);
}
@ApiOperation({
summary: '微信登录',
description: '根据微信的openid unionid headimgurl nickname自动登录',
operationId: 'loginByOpenid',
tags: ['wechat'],
externalDocs: {
description: 'wechat',
url: 'https://developers.weixin.qq.com/doc/offiaccount/OA_Web_Apps/Wechat_webpage_authorization.html',
},
requestBody: {
description: 'openid unionid headimgurl nickname',
content: {
'application/json': {
schema: {
type: 'object',
properties: {
openid: {
type: 'string',
description: 'openid',
example: 'o6_bmjrPTlm6_2sgVt7hMZOPfL2M',
},
unionid: {
type: 'string',
description: 'unionid',
example: 'o6_bjrPTlm6_2sgVt7hMZOPfL2M',
},
headimgurl: {
type: 'string',
description: '头像',
example:
'http://thirdwx.qlogo.cn/mmopen/g3MonUZtNHkdmzicIlibx6iaFqAc56vxLSUfpb6n5WKSYVY0ChQKkiaJSgQ1dZuTOgvLLrhJbERQQ4eMsv84eavHiaiceqxibJxCfHe/46',
},
nickname: {
type: 'string',
description: '昵称',
example: 'Band',
},
},
},
},
},
},
})
@Post('loginByOpenid')
async loginByOpenid(
@Body('openid') openid: string,
@Body('unionid') unionid: string,
@Body('headimgurl') headimgurl: string,
@Body('nickname') nickname: string,
) {
console.log('openid unionid' + openid + unionid);
const user = await this.usersService.loginBywechat({
wx_openid: openid,
wx_unionid: unionid,
nickname: nickname,
avatar_url: headimgurl,
email: '',
inviter_uid: '',
last_login_date: Date.now(),
last_login_ip: '',
mobile: '',
my_invite_code: '',
password: await this.usersService.hashPassword('123456'),
register_date: 0,
role: undefined,
token: '',
username: `wechat_${openid}`,
});
const { _id, username } = user;
//更新toekn
await this.usersService.updateToken(_id, username);
return await this.usersService.findOne(_id + '');
}
/**
* 处理微信公众号号消息
* @param xml
* @param req
* @param res
*/
@ApiOperation({
summary: '处理微信公众号号消息',
description: 'xml',
operationId: 'hangleMessage',
tags: ['wechat'],
externalDocs: {
description: 'wechat',
url: 'https://developers.weixin.qq.com/doc/offiaccount/Message_Management/Receiving_standard_messages.html',
},
requestBody: {
description: 'xml',
content: {
'application/xml': {
schema: {
type: 'object',
properties: {
xml: {
type: 'string',
description: 'xml',
example:
'15824045432250878104354478081',
},
},
},
},
},
},
})
@HttpCode(200)
@Post('handleMessage')
async hangleMessage(
@Body() xml: any,
@Req() req: Request,
@Res() res: Response,
) {
this.logger.log('收到微信服务器转发的消息')
const {
xml: {
ToUserName: [to],
FromUserName: [from],
MsgType: [type],
},
} = xml;
//处理消息参数
let drawmodel = 'text2img' as WeChatDrawModel;
const params = {
positive: 'a girl',
ckpt_name_id: 0, //使用的大模型id,调用参数参考API文档
image_path: '',
// 其他参数可以加载后面
};
// 根据消息类型，调用不同的绘画接口
if (type === 'text') {
//文生图
const {
xml: {
// ToUserName: [to],
FromUserName: [from],
// MsgType: [type],
Content: [content],
},
} = xml;
if (content.includes('图生图') || content.includes('图生视频')) {
//将用户的指令先暂存起来,保存到redis
await this.wechatAuthService.saveCommand(content, from);
// 回复客户端响应消息
const resResult = create({
xml: {
ToUserName: from, // 接收方帐号（收到的OpenID）
FromUserName: to, // 开发者微信号
CreateTime: new Date().getTime(), // 消息创建时间 （整型）
MsgType: 'text',
Content:
'已收到主人的绘画指令，请传入一张图片，我可以帮你完成' + content,
},
}).end({ prettyPrint: true });
res.type('application/xml');
res.send(resResult);
return;
} else {
// 回复客户端响应消息
drawmodel = 'text2img';
const resResult = create({
xml: {
ToUserName: from, // 接收方帐号（收到的OpenID）
FromUserName: to, // 开发者微信号
CreateTime: new Date().getTime(), // 消息创建时间 （整型）
MsgType: 'text',
Content: 'AI绘图中，请稍后',
},
}).end({ prettyPrint: true });
res.type('application/xml');
res.send(resResult);
}
params.positive = content;
}
if (type === 'image') {
// 读取用户的指令
const {
xml: {
// ToUserName: [to],
FromUserName: [from],
PicUrl: [imageurl],
},
} = xml;
// 回复客户端响应消息
const resResult = create({
xml: {
ToUserName: from, // 接收方帐号（收到的OpenID）
FromUserName: to, // 开发者微信号
CreateTime: new Date().getTime(), // 消息创建时间 （整型）
MsgType: 'text',
Content: 'AI绘图中，请稍后',
},
}).end({ prettyPrint: true });
res.type('application/xml');
res.send(resResult);
params.image_path = imageurl;
const command = await this.wechatAuthService.getCommand(from);
if (command.includes('图生视频')) {
drawmodel = 'img2video';
} else {
drawmodel = 'image2img';
params.positive = command.replace('图生图', '');
}
}
// 启用了远程绘画服务器
if (this.drawService.remote_comfyui) {
this.logger.log(`启动远程服务器绘画功能，绘画模式是${drawmodel}`);
const result = await this.drawService.wechatDrawFromRemoteServer(
drawmodel,
params,
);
// 调用客服接口回复消息
const mediaId = await this.wechatAuthService.getMediaId(
result,
drawmodel.includes('video') ? 'video' : 'image',
);
await this.wechatAuthService.sendServiceImageMessge(
mediaId,
from,
drawmodel.includes('video') ? 'video' : 'image',
);
} else {
// 本地绘画
this.logger.log('启动本地绘画功能');
if (drawmodel === 'text2img') {
//文生图
const result = await this.drawService.text2img(from, undefined, params);
// 调用客服接口回复消息
const mediaId = await this.wechatAuthService.getMediaId(result);
await this.wechatAuthService.sendServiceImageMessge(mediaId, from);
}
if (drawmodel === 'image2img') {
// 图生图
const result = await this.drawService.image2img(
from,
undefined,
params,
);
// 调用客服接口回复消息
const mediaId = await this.wechatAuthService.getMediaId(result);
await this.wechatAuthService.sendServiceImageMessge(mediaId, from);
}
if (drawmodel === 'img2video') {
// 图生视频
const result = await this.drawService.image2video(
from,
undefined,
params,
);
// 调用客服接口回复消息
const mediaId = await this.wechatAuthService.getMediaId(
result,
'video',
);
await this.wechatAuthService.sendServiceImageMessge(
mediaId,
from,
'video',
);
}
}
}
/**
* 对接服务器验证
* @param signature
* @param timestamp
* @param nonce
* @param echostr
*/
@ApiOperation({
summary: '对接服务器验证',
description: '自动对接微信服务器设置，微信服务器配置时，请选择明文模式',
operationId: 'handleMessage',
tags: ['wechat'],
externalDocs: {
description: 'wechat',
url: 'https://developers.weixin.qq.com/doc/offiaccount/Basic_Information/Access_Overview.html',
},
})
@Get('handleMessage')
receiveMessage(
@Query('signature') signature: string,
@Query('timestamp') timestamp: string,
@Query('nonce') nonce: string,
@Query('echostr') echostr: string,
) {
// this.logger.log(echostr);
return echostr || 'error';
}
}
================================================
FILE: src/wechat-auth/wechat-auth.module.ts
================================================
import { Module } from '@nestjs/common';
import { WechatAuthService } from './wechat-auth.service';
import { WechatAuthController } from './wechat-auth.controller';
import { DrawModule } from '../draw/draw.module';
// import { DrawService } from '../draw/DrawService';
import { UsersModule } from '../users/users.module';
import { FileModule } from '../file/file.module';
@Module({
imports: [DrawModule, UsersModule, FileModule],
controllers: [WechatAuthController],
providers: [WechatAuthService],
exports: [WechatAuthService],
})
export class WechatAuthModule {}
================================================
FILE: src/wechat-auth/wechat-auth.service.ts
================================================
import { Injectable, Logger } from '@nestjs/common';
import axios from 'axios';
import { ConfigService } from '@nestjs/config/dist';
import { DrawService } from '../draw/draw.service';
import { CacheService } from '../cache/cache.service';
import { FileService } from '../file/file.service';
@Injectable()
export class WechatAuthService {
constructor(
// @InjectQueue('draw') private drawQueue: Queue,
private readonly configService: ConfigService,
private readonly drawService: DrawService,
private cacheService: CacheService,
private readonly fileService: FileService,
) {}
private readonly logger = new Logger(WechatAuthService.name);
private APPID = this.configService.get('CONFIG_AUTH_WECHAT_APPID');
private SECRET = this.configService.get('CONFIG_AUTH_WECHAT_SECRET');
private wx_baseurl = 'https://api.weixin.qq.com';
// private uni_baseurl = 'https://unicloudapi.gptpro.ink'; //小程序账号登录链接
/**
*获取Access_token
* @param code 用户扫码后获取到的code
* @returns 返回相应结果，{"access_token":"ACCESS_TOKEN","expires_in":7200,"refresh_token":"REFRESH_TOKEN","openid":"OPENID","scope":"SCOPE","unionid": "UNIONID"}
*/
private async getAccess_token(code) {
const access_url =
'https://api.weixin.qq.com/sns/oauth2/access_token?appid=' +
this.APPID +
'&secret=' +
this.SECRET +
'&code=' +
code +
'&grant_type=authorization_code';
const access_res = await axios.get(access_url);
return access_res.data;
}
/**
*根据code获取用户信息
* @param code 微信返回的code,页面参数
* @returns
*/
async getUserinfo(code: string) {
this.logger.debug('传入的code', code);
const Access_res = await this.getAccess_token(code);
this.logger.debug('获取token', Access_res);
const { access_token, openid } = Access_res;
if (access_token) {
const userinfo_url =
'https://api.weixin.qq.com/sns/userinfo?access_token=' +
access_token +
'&openid=' +
openid;
const { data } = await axios.get(userinfo_url);
return data;
} else {
return null;
}
}
/**
* 上传图片文件作为临时素材，获取微信服务器的media_id
* @param imageUrl
* @param type
*/
async getMediaId(imageUrl: string, type?: string): Promise {
const access_token = await this.getAccessToken();
const upload_url =
this.wx_baseurl +
`/cgi-bin/media/upload?access_token=${access_token}&type=${type || 'image'}`;
const file = await this.fileService.urlToFile(
imageUrl,
type === 'video' ? 'video.mp4' : 'image.png',
type === 'video' ? 'video/mp4' : 'image/png',
);
const formdata = new FormData();
console.log('上传的文件', file);
formdata.append(type || 'image', file);
const res = await axios.post(upload_url, formdata, {
headers: {
'Content-Type': 'multipart/form-data',
},
});
console.log('上传微信服务返回的数据', res.data);
const { media_id } = res.data;
return media_id;
}
private draw_access_token = {
token: '',
expires_in: Date.now(),
};
/**
* 获取微信公众号的token，如果token没有过期，直接使用，过期重新获取
* @private
*/
private async getAccessToken(): Promise {
const APPID = this.configService.get('CONFIG_OFFICIAL_WECHAT_APPID');
const AppSecret = this.configService.get('CONFIG_OFFICIAL_WECHAT_SECRET');
const token_url = `https://api.weixin.qq.com/cgi-bin/token?grant_type=client_credential&appid=${APPID}&secret=${AppSecret}`;
if (
!this.draw_access_token.token ||
this.draw_access_token.expires_in <= Date.now()
) {
// 获取access_token
const resResult = await axios.get(token_url);
console.log(resResult);
const { access_token } = resResult.data;
this.draw_access_token.token = access_token;
this.draw_access_token.expires_in = Date.now() + 7000 * 1000;
return access_token;
}
if (
this.draw_access_token.token &&
this.draw_access_token.expires_in > Date.now()
) {
return this.draw_access_token.token;
}
}
/**
* 发送客服图片消息
* @param media_id
* @param touser
* @param msgtype
*/
async sendServiceImageMessge(
media_id: string,
touser: string,
msgtype?: string,
) {
const access_token = await this.getAccessToken();
const url = `https://api.weixin.qq.com/cgi-bin/message/custom/send?access_token=${access_token}`;
let message;
if (msgtype === 'video') {
message = {
touser: touser,
msgtype: 'video',
video: {
media_id: media_id,
title: 'AI视频',
description: '视频由AI生成',
},
};
} else {
message = {
touser: touser,
msgtype: 'image',
image: {
media_id: media_id,
},
};
}
const res = await axios.post(url, message);
console.log('发送结果：', res.data);
}
/**
* 保存用户的指令
*/
async saveCommand(command: string, openid: string) {
const key = `command:${openid}`;
await this.cacheService.set(key, command);
}
/**
* 获取用户的指令
*/
async getCommand(openid: string) {
const key = `command:${openid}`;
return await this.cacheService.get(key);
}
}
================================================
FILE: src/ws/ws.gateway.ts
================================================
import {
SubscribeMessage,
WebSocketGateway,
OnGatewayInit,
OnGatewayConnection,
} from '@nestjs/websockets';
import { Logger } from '@nestjs/common';
import { Socket } from 'socket.io';
import { DrawService } from '../draw/draw.service';
import { DrawTask } from '../draw/data/DrawConfig';
@WebSocketGateway(3002, {
// 解决跨域
allowEIO3: true,
cors: {
origin: '*',
},
})
export class WsGateway implements OnGatewayInit, OnGatewayConnection {
constructor(
private readonly drawService: DrawService, //绘画任务队列
) {}
private readonly logger = new Logger(WsGateway.name);
afterInit(server: any) {
this.logger.log(`WebSocket server started on port ${server}`);
WsGateway.server = server;
}
public static server;
@SubscribeMessage('connection')
handleConnection(client: Socket) {
this.logger.log(client.id, '@连接到了服务器');
}
@SubscribeMessage('message')
handleMessage(client: any, payload: any): string {
this.logger.log('来自客户端：' + client.id + '@@@@@@@的消息：' + payload);
return 'Hello world1!';
}
@SubscribeMessage('draw')
async handleDrawMessage(client: any, payload: any): Promise {
this.logger.log('来自客户端' + client.id + '发来绘画指令');
const { client_id, prompt, api } = payload;
//黑名单，如果满足条件发送系统消息给客户端
const userList = ['65dab231189f86e370b2967a'];
if (client_id && userList.includes(client_id)) {
//返送警告信息
this.sendSystemMessage(
client.id,
'您已被加入黑名单，请勿再次发送绘画指令',
);
return;
}
// AI推文不限制加入队列
if ((await this.drawService.isInQueue(client_id)) && api != 'AI推文') {
const message = {
type: 'reject',
queue_remaining: await this.drawService.getQueueLength(),
};
client.emit('message', JSON.stringify(message));
} else {
//加入队列
const data = {
source: 'web',
client_id,
prompt,
api,
socket_id: client.id,
} as DrawTask;
await this.drawService.sendToQueue(data);
//回复消息给客户端
const message = {
type: 'receive',
queue_remaining: await this.drawService.getQueueLength(),
};
client.emit('message', JSON.stringify(message));
}
return 'Hello world1!';
}
/**
* 发送系统消息
*/
sendSystemMessage(socketid: string, message: string) {
const sysinfo = {
message: message,
type: 'warning',
timeout: -1,
};
const client = WsGateway.server.sockets.connected[socketid];
if (client) {
client.emit('sysinfo', sysinfo);
}
}
}
================================================
FILE: tsconfig.build.json
================================================
{
"extends": "./tsconfig.json",
"exclude": ["node_modules", "test", "dist", "**/*spec.ts"]
}
================================================
FILE: tsconfig.json
================================================
{
"compilerOptions": {
"module": "commonjs",
"declaration": true,
"removeComments": true,
"emitDecoratorMetadata": true,
"experimentalDecorators": true,
"allowSyntheticDefaultImports": true,
"target": "ES2021",
"sourceMap": true,
"outDir": "./dist",
"baseUrl": "./",
"incremental": true,
"skipLibCheck": true,
"strictNullChecks": false,
"noImplicitAny": false,
"strictBindCallApply": false,
"forceConsistentCasingInFileNames": false,
"noFallthroughCasesInSwitch": false,
"resolveJsonModule":true,
}
}