This library provides an easy way to interact with the Sogni AI Supernet - a DePIN protocol for creative AI. It is written in TypeScript and can be used in both TypeScript and JavaScript projects such as backend Node.js and browser environments.
Behind the scenes this SDK uses WebSocket connection for communication between clients, server, and workers. It harnesses an event-based API to interact with Supernet to make things super efficient.
Add library to your project using npm or yarn:
npm install @sogni-ai/sogni-client
or
yarn add @sogni-ai/sogni-client
In order to use Sogni Supernet you need an active Sogni account (in the form of a username and password) with a positive $tSOGNI balance. You can create a free account in our Web App or Mac App which will give you tokens just for signing up and confirming your email. You can get daily bonus tokens by claiming them (under rewards) each 24-hours.
Your account is tied to a Base Wallet that is created during signup. The current network is Base Sepolia and will be Base Mainnet after mainnet launch.
The Library will use a default provider if none is provided, but note that it is not guaranteed to be always available. It is recommended to use your own Node endpoint such as alchemy.com (free and paid plans). You can specify your own node in jsonRpcUrl settings below.
There are 2 worker network types available:
fast - this network runs on high-end GPUs and is optimized for speed. It is more expensive than relaxed network at roughly 1 $tSOGNI token per render.relaxed - this network runs on Apple Mac devices and is optimized for cost. It is cheaper than fast network at roughly 0.5 $tSOGNI token per render.In both options the more complex your query is (the more steps) the higher the cost in tokens.
One request for image generation is called a Project. Project can generate one or more images. Each image is represented by a Job.
When you send a project to Supernet, it will be processed by one or more workers. The resulting images will be encrypted and uploaded to Sogni servers where it will be stored for 24 hours. After this period images will be auto-deleted.
To initialize client you need to provide appId, and account credentials.
import { SogniClient } from '@sogni-ai/sogni-client';
const USERNAME = 'your-username';
const PASSWORD = 'your-password';
// If using default provider
const options = {
appId: 'your-app-id', // Required, must be unique string, UUID is recommended
network: 'fast', // Network to use, 'fast' or 'relaxed'
}
// If using custom provider
const options ={
appId: 'your-app-id', // Required, must be unique string, UUID is recommended
jsonRpcUrl: 'https://sepolia.base.org', // Put your custom JSON-RPC URL here
network: 'fast', // Network to use, 'fast' or 'relaxed'
}
const client = await SogniClient.createInstance(options);
// Login to Sogni account and establish WebSocket connection to Supernet
await client.account.login(USERNAME, PASSWORD);
// Now wait until list of available models is received.
// This step is only needed if you want to create project immediately.
const models = await client.projects.waitForModels();
// You can get list of available models any time from `client.projects.availableModels`
Important Note:
await on the top level, if you are CommomJS you will need to wrap await calls in an async function.appId must be unique string, UUID is recommended. It is used to identify your application.appId is allowed. If you try to connect with the same appId multiple times, the previous connection will be closed.After calling login method, the client will establish a WebSocket connection to Sogni Supernet. Within a short period of time the
client will receive current balance and list of available models. After this you can start using the client to generate images.
// Find model that has the most workers
const mostPopularModel = client.projects.availableModels.reduce((a, b) =>
a.workerCount > b.workerCount ? a : b
);
// Create a project using the most popular model
const project = await client.projects.create({
modelId: mostPopularModel.id,
positivePrompt: 'A cat wearing a hat',
negativePrompt:
'malformation, bad anatomy, bad hands, missing fingers, cropped, low quality, bad quality, jpeg artifacts, watermark',
stylePrompt: 'anime',
steps: 20,
guidance: 7.5,
numberOfImages: 1
});
Note: Full project parameter list can be found in ProjectParams docs.
In general there are 2 ways to work with API:
async/await syntax.Project and Job class instances.const project = await client.projects.create({
modelId: mostPopularModel.id,
steps: 20,
guidance: 7.5,
positivePrompt: 'A cat wearing a hat',
negativePrompt:
'malformation, bad anatomy, bad hands, missing fingers, cropped, low quality, bad quality, jpeg artifacts, watermark',
stylePrompt: 'anime',
numberOfImages: 4
});
project.on('progress', (progress) => {
console.log('Project progress:', progress);
});
const imageUrls = await project.waitForCompletion();
// Now you can use image URLs to download images.
// Note that images will be available for 24 hours only!
console.log('Image URLs:', imageUrls);
const project = await client.projects.create({
modelId: mostPopularModel.id,
steps: 20,
guidance: 7.5,
positivePrompt: 'A cat wearing a hat',
negativePrompt:
'malformation, bad anatomy, bad hands, missing fingers, cropped, low quality, bad quality, jpeg artifacts, watermark',
stylePrompt: 'anime',
numberOfImages: 4
});
// Fired when one of project jobs completed, you can get the resultUrl from the job
// without waiting for the entire project to complete
project.on('jobCompleted', (job) => {
console.log('Job completed:', job.id, job.resultUrl);
});
// Fired when one of project jobs failed
project.on('jobFailed', (job) => {
console.log('Job failed:', job.id, job.error);
});
// Receive project completion percentage in real-time
project.on('progress', (progress) => {
// console.log('Project progress:', progress);
});
// Fired when the project is fully completed
project.on('completed', async (images) => {
console.log('Project completed:', images);
});
// Fired when the project failed
project.on('failed', async (errorData) => {
console.log('Project failed:', errorData);
});
You can find more code examples in the examples directory.