Replicate Node.js client

A Node.js client for Replicate. It lets you run models from your Node.js code, and everything else you can do with Replicate's HTTP API.

Important

This library can't interact with Replicate's API directly from a browser. For more information about how to build a web application check out our "Build a website with Next.js" guide.

Installation

This library requires Node.js >= 18.

Install it from npm:

npm install replicate

Usage

Import or require the package:

// CommonJS (default or using .cjs extension)
const Replicate = require("replicate");

// ESM (where `"module": true` in package.json or using .mjs extension)
import Replicate from "replicate";

Instantiate the client:

const replicate = new Replicate({
  // get your token from https://replicate.com/account/api-tokens
  auth: "my api token", // defaults to process.env.REPLICATE_API_TOKEN
});

Run a model and await the result:

const model = "stability-ai/stable-diffusion:27b93a2413e7f36cd83da926f3656280b2931564ff050bf9575f1fdf9bcd7478";
const input = {
  prompt: "a 19th century portrait of a raccoon gentleman wearing a suit",
};
const output = await replicate.run(model, { input });
// ['https://replicate.delivery/pbxt/GtQb3Sgve42ZZyVnt8xjquFk9EX5LP0fF68NTIWlgBMUpguQA/out-0.png']

You can also run a model in the background:

let prediction = await replicate.predictions.create({
  version: "27b93a2413e7f36cd83da926f3656280b2931564ff050bf9575f1fdf9bcd7478",
  input: {
    prompt: "painting of a cat by andy warhol",
  },
});

Then fetch the prediction result later:

prediction = await replicate.predictions.get(prediction.id);

Or wait for the prediction to finish:

prediction = await replicate.wait(prediction);
console.log(prediction.output);
// ['https://replicate.delivery/pbxt/RoaxeXqhL0xaYyLm6w3bpGwF5RaNBjADukfFnMbhOyeoWBdhA/out-0.png']

To run a model that takes a file input you can pass the data directly or pass a URL to a publicly accessible file.

const fs = require("node:fs/promises");

// Or when using ESM.
// import fs from "node:fs/promises";

const model = "nightmareai/real-esrgan:42fed1c4974146d4d2414e2be2c5277c7fcf05fcc3a73abf41610695738c1d7b";
const input = {
  image: await fs.readFile("path/to/image.png"),
};
const output = await replicate.run(model, { input });
// ['https://replicate.delivery/mgxm/e7b0e122-9daa-410e-8cde-006c7308ff4d/output.png']

Webhooks

Webhooks provide real-time updates about your prediction. Specify an endpoint when you create a prediction, and Replicate will send HTTP POST requests to that URL when the prediction is created, updated, and finished.

It is possible to provide a URL to the predictions.create() function that will be requested by Replicate when the prediction status changes. This is an alternative to polling.

To receive webhooks you’ll need a web server. The following example uses Hono, a web standards based server, but this pattern applies to most frameworks.

See example

import { serve } from '@hono/node-server';
import { Hono } from 'hono';

const app = new Hono();
app.get('/webhooks/replicate', async (c) => {
  // Get the prediction from the request.
  const prediction = await c.req.json();
  console.log(prediction);
  //=> {"id": "xyz", "status": "successful", ... }

  // Acknowledge the webhook.
  c.status(200);
  c.json({ok: true});
}));

serve(app, (info) => {
  console.log(`Listening on http://localhost:${info.port}`)
  //=> Listening on http://localhost:3000
});

Create the prediction passing in the webhook URL to webhook and specify which events you want to receive in webhook_events_filter out of "start", "output", ”logs” and "completed":

const Replicate = require("replicate");
const replicate = new Replicate();

const input = {
    image: "https://replicate.delivery/pbxt/KWDkejqLfER3jrroDTUsSvBWFaHtapPxfg4xxZIqYmfh3zXm/Screenshot%202024-02-28%20at%2022.14.00.png",
    denoising_strength: 0.5,
    instant_id_strength: 0.8
};

const callbackURL = `https://my.app/webhooks/replicate`;
await replicate.predictions.create({
  version: "19deaef633fd44776c82edf39fd60e95a7250b8ececf11a725229dc75a81f9ca",
  input: input,
  webhook: callbackURL,
  webhook_events_filter: ["completed"],
});

// The server will now handle the event and log:
// => {"id": "xyz", "status": "successful", ... }

Verifying webhooks

To prevent unauthorized requests, Replicate signs every webhook and its metadata with a unique key for each user or organization. You can use this signature to verify the webhook indeed comes from Replicate before you process it.

This client includes a validateWebhook convenience function that you can use to validate webhooks.

To validate webhooks:

1. Check out the webhooks guide to get started.
2. Retrieve your webhook signing secret and store it in your enviroment.
3. Update your webhook handler to call validateWebhook(request, secret), where request is an instance of a [web-standard Request object](https://developer.mozilla.org/en-US/docs/Web/API/object, and secret is the signing secret for your environment.

Here's an example of how to validate webhooks using Next.js:

import { NextResponse } from 'next/server';
import { validateWebhook } from 'replicate';

export async function POST(request) {
  const secret = process.env.REPLICATE_WEBHOOK_SIGNING_SECRET;

  if (!secret) {
    console.log("Skipping webhook validation. To validate webhooks, set REPLICATE_WEBHOOK_SIGNING_SECRET")
    const body = await request.json();
    console.log(body);
    return NextResponse.json({ detail: "Webhook received (but not validated)" }, { status: 200 });
  }
  
  const webhookIsValid = await validateWebhook(request.clone(), secret);

  if (!webhookIsValid) {
    return NextResponse.json({ detail: "Webhook is invalid" }, { status: 401 });
  }

  // process validated webhook here...
  console.log("Webhook is valid!");
  const body = await request.json();
  console.log(body);

  return NextResponse.json({ detail: "Webhook is valid" }, { status: 200 });
}

TypeScript

The Replicate constructor and all replicate.* methods are fully typed.

Currently in order to support the module format used by replicate you'll need to set esModuleInterop to true in your tsconfig.json.

API

Constructor

const replicate = new Replicate(options);

name	type	description
options.auth	string	Required. API access token
options.userAgent	string	Identifier of your app. Defaults to replicate-javascript/${packageJSON.version}
options.baseUrl	string	Defaults to https://api.replicate.com/v1
options.fetch	function	Fetch function to use. Defaults to globalThis.fetch

The client makes requests to Replicate's API using fetch. By default, the globalThis.fetch function is used, which is available on Node.js 18 and later, as well as Cloudflare Workers, Vercel Edge Functions, and other environments.

On earlier versions of Node.js and other environments where global fetch isn't available, you can install a fetch function from an external package like cross-fetch and pass it to the fetch option in the constructor.

const Replicate = require("replicate");
const fetch = require("fetch");

// Using ESM:
// import Replicate from "replicate";
// import fetch from "cross-fetch";

const replicate = new Replicate({ fetch });

You can also use the fetch option to add custom behavior to client requests, such as injecting headers or adding log statements.

const customFetch = (url, options) => {
  const headers = options && options.headers ? { ...options.headers } : {};
  headers["X-Custom-Header"] = "some value";

  console.log("fetch", { url, ...options, headers });

  return fetch(url, { ...options, headers });
};

const replicate = new Replicate({ fetch: customFetch });

replicate.run

Run a model and await the result. Unlike replicate.prediction.create, this method returns only the prediction output rather than the entire prediction object.

const output = await replicate.run(identifier, options, progress);

name	type	description
identifier	string	Required. The model version identifier in the format {owner}/{name}:{version}, for example stability-ai/sdxl:8beff3369e81422112d93b89ca01426147de542cd4684c244b673b105188fe5f
options.input	object	Required. An object with the model inputs.
options.wait	object	Options for waiting for the prediction to finish
options.wait.interval	number	Polling interval in milliseconds. Defaults to 500
options.webhook	string	An HTTPS URL for receiving a webhook when the prediction has new output
options.webhook_events_filter	string[]	An array of events which should trigger webhooks. Allowable values are start, output, logs, and completed
options.signal	object	An AbortSignal to cancel the prediction
progress	function	Callback function that receives the prediction object as it's updated. The function is called when the prediction is created, each time it's updated while polling for completion, and when it's completed.

Throws Error if the prediction failed.

Returns Promise<object> which resolves with the output of running the model.

Example:

const model = "stability-ai/sdxl:8beff3369e81422112d93b89ca01426147de542cd4684c244b673b105188fe5f";
const input = { prompt: "a 19th century portrait of a raccoon gentleman wearing a suit" };
const output = await replicate.run(model, { input });

Example that logs progress as the model is running:

const model = "stability-ai/sdxl:8beff3369e81422112d93b89ca01426147de542cd4684c244b673b105188fe5f";
const input = { prompt: "a 19th century portrait of a raccoon gentleman wearing a suit" };
const onProgress = (prediction) => {
   const last_log_line = prediction.logs.split("\n").pop()
   console.log({id: prediction.id, log: last_log_line})
}
const output = await replicate.run(model, { input }, onProgress)

replicate.stream

Run a model and stream its output. Unlike replicate.prediction.create, this method returns only the prediction output rather than the entire prediction object.

for await (const event of replicate.stream(identifier, options)) { /* ... */ }

name	type	description
identifier	string	Required. The model version identifier in the format {owner}/{name} or {owner}/{name}:{version}, for example meta/llama-2-70b-chat
options.input	object	Required. An object with the model inputs.
options.webhook	string	An HTTPS URL for receiving a webhook when the prediction has new output
options.webhook_events_filter	string[]	An array of events which should trigger webhooks. Allowable values are start, output, logs, and completed
options.signal	object	An AbortSignal to cancel the prediction

Throws Error if the prediction failed.

Returns AsyncGenerator<ServerSentEvent> which yields the events of running the model.

Example:

const model = "meta/llama-2-70b-chat";
const options = {
  input: {
    prompt: "Write a poem about machine learning in the style of Mary Oliver.",
  },
  // webhook: "https://smee.io/dMUlmOMkzeyRGjW" // optional
};
const output = [];

for await (const { event, data } of replicate.stream(model, options)) {
  if (event === "output") {
    output.push(data);
  }
}

console.log(output.join("").trim());

Server-sent events

A stream generates server-sent events with the following properties:

name	type	description
event	string	The type of event. Possible values are output, logs, error, and done
data	string	The event data
id	string	The event id
retry	number	The number of milliseconds to wait before reconnecting to the server

As the prediction runs, the generator yields output and logs events. If an error occurs, the generator yields an error event with a JSON object containing the error message set to the data property. When the prediction is done, the generator yields a done event with an empty JSON object set to the data property.

Events with the output event type have their toString() method overridden to return the event data as a string. Other event types return an empty string.

replicate.models.get

Get metadata for a public model or a private model that you own.

const response = await replicate.models.get(model_owner, model_name);

name	type	description
model_owner	string	Required. The name of the user or organization that owns the model.
model_name	string	Required. The name of the model.

{
  "url": "https://replicate.com/replicate/hello-world",
  "owner": "replicate",
  "name": "hello-world",
  "description": "A tiny model that says hello",
  "visibility": "public",
  "github_url": "https://github.com/replicate/cog-examples",
  "paper_url": null,
  "license_url": null,
  "latest_version": {
    /* ... */
  }
}

replicate.models.list

Get a paginated list of all public models.

const response = await replicate.models.list();

{
  "next": null,
  "previous": null,
  "results": [
    {
      "url": "https://replicate.com/replicate/hello-world",
      "owner": "replicate",
      "name": "hello-world",
      "description": "A tiny model that says hello",
      "visibility": "public",
      "github_url": "https://github.com/replicate/cog-examples",
      "paper_url": null,
      "license_url": null,
      "run_count": 5681081,
      "cover_image_url": "...",
      "default_example": {
        /* ... */
      },
      "latest_version": {
        /* ... */
      }
    }
  ]
}

replicate.models.create

Create a new public or private model.

const response = await replicate.models.create(model_owner, model_name, options);

name	type	description
model_owner	string	Required. The name of the user or organization that will own the model. This must be the same as the user or organization that is making the API request. In other words, the API token used in the request must belong to this user or organization.
model_name	string	Required. The name of the model. This must be unique among all models owned by the user or organization.
options.visibility	string	Required. Whether the model should be public or private. A public model can be viewed and run by anyone, whereas a private model can be viewed and run only by the user or organization members that own the model.
options.hardware	string	Required. The SKU for the hardware used to run the model. Possible values can be found by calling replicate.hardware.list().
options.description	string	A description of the model.
options.github_url	string	A URL for the model's source code on GitHub.
options.paper_url	string	A URL for the model's paper.
options.license_url	string	A URL for the model's license.
options.cover_image_url	string	A URL for the model's cover image. This should be an image file.

replicate.hardware.list

List available hardware for running models on Replicate.

const response = await replicate.hardware.list()

[
  {"name": "CPU", "sku": "cpu" },
  {"name": "Nvidia T4 GPU", "sku": "gpu-t4" },
  {"name": "Nvidia A40 GPU", "sku": "gpu-a40-small" },
  {"name": "Nvidia A40 (Large) GPU", "sku": "gpu-a40-large" },
]

replicate.models.versions.list

Get a list of all published versions of a model, including input and output schemas for each version.

const response = await replicate.models.versions.list(model_owner, model_name);

name	type	description
model_owner	string	Required. The name of the user or organization that owns the model.
model_name	string	Required. The name of the model.

{
  "previous": null,
  "next": null,
  "results": [
    {
      "id": "5c7d5dc6dd8bf75c1acaa8565735e7986bc5b66206b55cca93cb72c9bf15ccaa",
      "created_at": "2022-04-26T19:29:04.418669Z",
      "cog_version": "0.3.0",
      "openapi_schema": {
        /* ... */
      }
    },
    {
      "id": "e2e8c39e0f77177381177ba8c4025421ec2d7e7d3c389a9b3d364f8de560024f",
      "created_at": "2022-03-21T13:01:04.418669Z",
      "cog_version": "0.3.0",
      "openapi_schema": {
        /* ... */
      }
    }
  ]
}

replicate.models.versions.get

Get metatadata for a specific version of a model.

const response = await replicate.models.versions.get(model_owner, model_name, version_id);

name	type	description
model_owner	string	Required. The name of the user or organization that owns the model.
model_name	string	Required. The name of the model.
version_id	string	Required. The model version

{
  "id": "5c7d5dc6dd8bf75c1acaa8565735e7986bc5b66206b55cca93cb72c9bf15ccaa",
  "created_at": "2022-04-26T19:29:04.418669Z",
  "cog_version": "0.3.0",
  "openapi_schema": {
    /* ... */
  }
}

replicate.collections.get

Get a list of curated model collections. See replicate.com/collections.

const response = await replicate.collections.get(collection_slug);

name	type	description
collection_slug	string	Required. The slug of the collection. See http://replicate.com/collections

replicate.predictions.create

Run a model with inputs you provide.

const response = await replicate.predictions.create(options);

name	type	description
options.version	string	Required. The model version
options.input	object	Required. An object with the model's inputs
options.stream	boolean	Requests a URL for streaming output output
options.webhook	string	An HTTPS URL for receiving a webhook when the prediction has new output
options.webhook_events_filter	string[]	You can change which events trigger webhook requests by specifying webhook events (start | output | logs | completed)

{
  "id": "ufawqhfynnddngldkgtslldrkq",
  "version": "5c7d5dc6dd8bf75c1acaa8565735e7986bc5b66206b55cca93cb72c9bf15ccaa",
  "status": "succeeded",
  "input": {
    "text": "Alice"
  },
  "output": null,
  "error": null,
  "logs": null,
  "metrics": {},
  "created_at": "2022-04-26T22:13:06.224088Z",
  "started_at": null,
  "completed_at": null,
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/ufawqhfynnddngldkgtslldrkq",
    "cancel": "https://api.replicate.com/v1/predictions/ufawqhfynnddngldkgtslldrkq/cancel",
    "stream": "https://streaming.api.replicate.com/v1/predictions/ufawqhfynnddngldkgtslldrkq" // Present only if `options.stream` is `true`
  }
}

Streaming

Specify the stream option when creating a prediction to request a URL to receive streaming output using server-sent events (SSE).

If the requested model version supports streaming, then the returned prediction will have a stream entry in its urls property with a URL that you can use to construct an EventSource.

if (prediction && prediction.urls && prediction.urls.stream) {
  const source = new EventSource(prediction.urls.stream, { withCredentials: true });

  source.addEventListener("output", (e) => {
    console.log("output", e.data);
  });

  source.addEventListener("error", (e) => {
    console.error("error", JSON.parse(e.data));
  });

  source.addEventListener("done", (e) => {
    source.close();
    console.log("done", JSON.parse(e.data));
  });
}

A prediction's event stream consists of the following event types:

event	format	description
output	plain text	Emitted when the prediction returns new output
error	JSON	Emitted when the prediction returns an error
done	JSON	Emitted when the prediction finishes

A done event is emitted when a prediction finishes successfully, is cancelled, or produces an error.

replicate.predictions.get

const response = await replicate.predictions.get(prediction_id);

name	type	description
prediction_id	number	Required. The prediction id

{
  "id": "ufawqhfynnddngldkgtslldrkq",
  "version": "5c7d5dc6dd8bf75c1acaa8565735e7986bc5b66206b55cca93cb72c9bf15ccaa",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/ufawqhfynnddngldkgtslldrkq",
    "cancel": "https://api.replicate.com/v1/predictions/ufawqhfynnddngldkgtslldrkq/cancel"
  },
  "status": "starting",
  "input": {
    "text": "Alice"
  },
  "output": null,
  "error": null,
  "logs": null,
  "metrics": {},
  "created_at": "2022-04-26T22:13:06.224088Z",
  "started_at": null,
  "completed_at": null
}

replicate.predictions.cancel

Stop a running prediction before it finishes.

const response = await replicate.predictions.cancel(prediction_id);

name	type	description
prediction_id	number	Required. The prediction id

{
  "id": "ufawqhfynnddngldkgtslldrkq",
  "version": "5c7d5dc6dd8bf75c1acaa8565735e7986bc5b66206b55cca93cb72c9bf15ccaa",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/ufawqhfynnddngldkgtslldrkq",
    "cancel": "https://api.replicate.com/v1/predictions/ufawqhfynnddngldkgtslldrkq/cancel"
  },
  "status": "canceled",
  "input": {
    "text": "Alice"
  },
  "output": null,
  "error": null,
  "logs": null,
  "metrics": {},
  "created_at": "2022-04-26T22:13:06.224088Z",
  "started_at": "2022-04-26T22:13:06.224088Z",
  "completed_at": "2022-04-26T22:13:06.224088Z"
}

replicate.predictions.list

Get a paginated list of all the predictions you've created.

const response = await replicate.predictions.list();

replicate.predictions.list() takes no arguments.

{
  "previous": null,
  "next": "https://api.replicate.com/v1/predictions?cursor=cD0yMDIyLTAxLTIxKzIzJTNBMTglM0EyNC41MzAzNTclMkIwMCUzQTAw",
  "results": [
    {
      "id": "jpzd7hm5gfcapbfyt4mqytarku",
      "version": "b21cbe271e65c1718f2999b038c18b45e21e4fba961181fbfae9342fc53b9e05",
      "urls": {
        "get": "https://api.replicate.com/v1/predictions/jpzd7hm5gfcapbfyt4mqytarku",
        "cancel": "https://api.replicate.com/v1/predictions/jpzd7hm5gfcapbfyt4mqytarku/cancel"
      },
      "source": "web",
      "status": "succeeded",
      "created_at": "2022-04-26T20:00:40.658234Z",
      "started_at": "2022-04-26T20:00:84.583803Z",
      "completed_at": "2022-04-26T20:02:27.648305Z"
    }
    /* ... */
  ]
}

replicate.trainings.create

Use the training API to fine-tune language models to make them better at a particular task. To see what language models currently support fine-tuning, check out Replicate's collection of trainable language models.

If you're looking to fine-tune image models, check out Replicate's guide to fine-tuning image models.

const response = await replicate.trainings.create(model_owner, model_name, version_id, options);

name	type	description
model_owner	string	Required. The name of the user or organization that owns the model.
model_name	string	Required. The name of the model.
version	string	Required. The model version
options.destination	string	Required. The destination for the trained version in the form {username}/{model_name}
options.input	object	Required. An object with the model's inputs
options.webhook	string	An HTTPS URL for receiving a webhook when the training has new output
options.webhook_events_filter	string[]	You can change which events trigger webhook requests by specifying webhook events (start | output | logs | completed)

{
  "id": "zz4ibbonubfz7carwiefibzgga",
  "version": "3ae0799123a1fe11f8c89fd99632f843fc5f7a761630160521c4253149754523",
  "status": "starting",
  "input": {
    "text": "..."
  },
  "output": null,
  "error": null,
  "logs": null,
  "started_at": null,
  "created_at": "2023-03-28T21:47:58.566434Z",
  "completed_at": null
}

Warning If you try to fine-tune a model that doesn't support training, you'll get a 400 Bad Request response from the server.

replicate.trainings.get

Get metadata and status of a training.

const response = await replicate.trainings.get(training_id);

name	type	description
training_id	number	Required. The training id

{
  "id": "zz4ibbonubfz7carwiefibzgga",
  "version": "3ae0799123a1fe11f8c89fd99632f843fc5f7a761630160521c4253149754523",
  "status": "succeeded",
  "input": {
    "data": "..."
    "param1": "..."
  },
  "output": {
    "version": "..."
  },
  "error": null,
  "logs": null,
  "webhook_completed": null,
  "started_at": "2023-03-28T21:48:02.402755Z",
  "created_at": "2023-03-28T21:47:58.566434Z",
  "completed_at": "2023-03-28T02:49:48.492023Z"
}

replicate.trainings.cancel

Stop a running training job before it finishes.

const response = await replicate.trainings.cancel(training_id);

name	type	description
training_id	number	Required. The training id

{
  "id": "zz4ibbonubfz7carwiefibzgga",
  "version": "3ae0799123a1fe11f8c89fd99632f843fc5f7a761630160521c4253149754523",
  "status": "canceled",
  "input": {
    "data": "..."
    "param1": "..."
  },
  "output": {
    "version": "..."
  },
  "error": null,
  "logs": null,
  "webhook_completed": null,
  "started_at": "2023-03-28T21:48:02.402755Z",
  "created_at": "2023-03-28T21:47:58.566434Z",
  "completed_at": "2023-03-28T02:49:48.492023Z"
}

replicate.trainings.list

Get a paginated list of all the trainings you've run.

const response = await replicate.trainings.list();

replicate.trainings.list() takes no arguments.

{
  "previous": null,
  "next": "https://api.replicate.com/v1/trainings?cursor=cD0yMDIyLTAxLTIxKzIzJTNBMTglM0EyNC41MzAzNTclMkIwMCUzQTAw",
  "results": [
    {
      "id": "jpzd7hm5gfcapbfyt4mqytarku",
      "version": "b21cbe271e65c1718f2999b038c18b45e21e4fba961181fbfae9342fc53b9e05",
      "urls": {
        "get": "https://api.replicate.com/v1/trainings/jpzd7hm5gfcapbfyt4mqytarku",
        "cancel": "https://api.replicate.com/v1/trainings/jpzd7hm5gfcapbfyt4mqytarku/cancel"
      },
      "source": "web",
      "status": "succeeded",
      "created_at": "2022-04-26T20:00:40.658234Z",
      "started_at": "2022-04-26T20:00:84.583803Z",
      "completed_at": "2022-04-26T20:02:27.648305Z"
    }
    /* ... */
  ]
}

replicate.deployments.predictions.create

Run a model using your own custom deployment.

Deployments allow you to run a model with a private, fixed API endpoint. You can configure the version of the model, the hardware it runs on, and how it scales. See the deployments guide to learn more and get started.

const response = await replicate.deployments.predictions.create(deployment_owner, deployment_name, options);

name	type	description
deployment_owner	string	Required. The name of the user or organization that owns the deployment
deployment_name	string	Required. The name of the deployment
options.input	object	Required. An object with the model's inputs
options.webhook	string	An HTTPS URL for receiving a webhook when the prediction has new output
options.webhook_events_filter	string[]	You can change which events trigger webhook requests by specifying webhook events (start | output | logs | completed)

Use replicate.wait to wait for a prediction to finish, or replicate.predictions.cancel to cancel a prediction before it finishes.

replicate.deployments.list

List your deployments.

const response = await replicate.deployments.list();

{
  "next": null,
  "previous": null,
  "results": [
    {
      "owner": "acme",
      "name": "my-app-image-generator",
      "current_release": { /* ... */ }
    }
    /* ... */
  ]
}

replicate.deployments.create

Create a new deployment.

const response = await replicate.deployments.create(options);

name	type	description
options.name	string	Required. Name of the new deployment
options.model	string	Required. Name of the model in the format {username}/{model_name}
options.version	string	Required. ID of the model version
options.hardware	string	Required. SKU of the hardware to run the deployment on (cpu, gpu-a100, etc.)
options.min_instances	number	Minimum number of instances to run. Defaults to 0
options.max_instances	number	Maximum number of instances to scale up to based on traffic. Defaults to 1

{
  "owner": "acme",
  "name": "my-app-image-generator",
  "current_release": {
    "number": 1,
    "model": "stability-ai/sdxl",
    "version": "da77bc59ee60423279fd632efb4795ab731d9e3ca9705ef3341091fb989b7eaf",
    "created_at": "2024-03-14T11:43:32.049157Z",
    "created_by": {
       "type": "organization",
       "username": "acme",
       "name": "Acme, Inc.",
       "github_url": "https://github.com/replicate"
    },
    "configuration": {
      "hardware": "gpu-a100",
      "min_instances": 1,
      "max_instances": 0
    }
  }
}

replicate.deployments.update

Update an existing deployment.

const response = await replicate.deployments.update(deploymentOwner, deploymentName, options);

name	type	description
deploymentOwner	string	Required. Owner of the deployment
deploymentName	string	Required. Name of the deployment to update
options.model	string	Name of the model in the format {username}/{model_name}
options.version	string	ID of the model version
options.hardware	string	Required. SKU of the hardware to run the deployment on (cpu, gpu-a100, etc.)
options.min_instances	number	Minimum number of instances to run
options.max_instances	number	Maximum number of instances to scale up to

{
  "owner": "acme",
  "name": "my-app-image-generator",
  "current_release": {
    "number": 2,
    "model": "stability-ai/sdxl",
    "version": "39ed52f2a78e934b3ba6e2a89f5b1c712de7dfea535525255b1aa35c5565e08b",
    "created_at": "2024-03-14T11:43:32.049157Z",
    "created_by": {
       "type": "organization",
       "username": "acme",
       "name": "Acme, Inc.",
       "github_url": "https://github.com/replicate"
    },
    "configuration": {
      "hardware": "gpu-a100",
      "min_instances": 1,
      "max_instances": 0
    }
  }
}

replicate.paginate

Pass another method as an argument to iterate over results that are spread across multiple pages.

This method is implemented as an async generator function, which you can use in a for loop or iterate over manually.

// iterate over paginated results in a for loop
for await (const page of replicate.paginate(replicate.predictions.list)) {
  /* do something with page of results */
}

// iterate over paginated results one at a time
let paginator = replicate.paginate(replicate.predictions.list);
const page1 = await paginator.next();
const page2 = await paginator.next();
// etc.

replicate.request

Low-level method used by the Replicate client to interact with API endpoints.

const response = await replicate.request(route, parameters);

name	type	description
options.route	string	Required. REST API endpoint path.
options.parameters	object	URL, query, and request body parameters for the given route.

The replicate.request() method is used by the other methods to interact with the Replicate API. You can call this method directly to make other requests to the API.

Troubleshooting

Predictions hanging in Next.js

Next.js App Router adds some extensions to fetch to make it cache responses. To disable this behavior, set the cache option to "no-store" on the Replicate client's fetch object:

replicate = new Replicate({/*...*/})
replicate.fetch = (url, options) => {
  return fetch(url, { ...options, cache: "no-store" });
};

Alternatively you can use Next.js noStore to opt out of caching for your component.