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.

Supported platforms

• Node.js >= 18
• Bun >= 1.0
• Deno >= 1.28

You can also use this client library on most serverless platforms, including Cloudflare Workers, Vercel functions, and AWS Lambda.

Installation

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 either a URL to a publicly accessible file on the Internet or a handle to a file on your local device.

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']

> [!NOTE] > File handle inputs are automatically uploaded to Replicate. > See replicate.files.create for more information. > The maximum size for uploaded files is 100MiB. > To run a model with a larger file as an input, > upload the file to your own storage provider > and pass a publicly accessible URL.

TypeScript usage

This library exports TypeScript definitions. You can import them like this:

import Replicate, { type Prediction } from 'replicate';

Here's an example that uses the Prediction type with a custom onProgress callback:

import Replicate, { type Prediction } from 'replicate';

const replicate = new Replicate();
const model = "black-forest-labs/flux-schnell";
const prompt = "a 19th century portrait of a raccoon gentleman wearing a suit";
function onProgress(prediction: Prediction) {
  console.log({ prediction });
}

const output = await replicate.run(model, { input: { prompt } }, onProgress)
console.log({ output })

See the full list of exported types in index.d.ts.

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.

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 https://localhost:${info.port}`)
  //=> Listening on https://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 });
}

If your environment doesn't support Request objects, you can pass the required information to validateWebhook directly:

const requestData = {
  id: "123",            // the `Webhook-Id` header
  timestamp: "0123456", // the `Webhook-Timestamp` header
  signature: "xyz",     // the `Webhook-Signature` header
  body: "{...}",        // the request body as a string, ArrayBuffer or ReadableStream
  secret: "shhh",       // the webhook secret, obtained from the `replicate.webhooks.defaul.secret` endpoint
};
const webhookIsValid = await validateWebhook(requestData);

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);

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 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);

Throws Error if the prediction failed.

Returns Promise 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)) { /* ... */ }

Throws Error if the prediction failed.

Returns AsyncGenerator 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:

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);

{
  "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.search

Search for public models on Replicate.

const response = await replicate.models.search(query);

replicate.models.create

Create a new public or private model.

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

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);

{
  "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);

{
  "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);

replicate.predictions.create

Run a model with inputs you provide.

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

{
  "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:

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);

{
  "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);

{
  "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);

{
  "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);

{
  "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);

{
  "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);

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);

{
  "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);

{
  "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.files.create

Upload a file to Replicate.

> [!TIP] > The client library calls this endpoint automatically to upload the contents of > file handles provided as prediction and training inputs. > You don't need to call this method directly unless you want more control. > For example, you might want to reuse a file across multiple predictions > without re-uploading it each time, > or you may want to set custom metadata on the file resource. > > You can configure how a client handles file handle inputs > by setting the fileEncodingStrategy option in the > client constructor.

const response = await replicate.files.create(file, metadata);

{
    "id": "MTQzODcyMDct0YjZkLWE1ZGYtMmRjZTViNWIwOGEyNjNhNS0",
    "name": "photo.webp",
    "content_type": "image/webp",
    "size": 96936,
    "etag": "f211779ff7502705bbf42e9874a17ab3",
    "checksums": {
        "sha256": "7282eb6991fa4f38d80c312dc207d938c156d714c94681623aedac846488e7d3",
        "md5": "f211779ff7502705bbf42e9874a17ab3"
    },
    "metadata": {
        "customer_reference_id": "123"
    },
    "created_at": "2024-06-28T10:16:04.062Z",
    "expires_at": "2024-06-29T10:16:04.062Z",
    "urls": {
        "get": "https://api.replicate.com/v1/files/MTQzODcyMDct0YjZkLWE1ZGYtMmRjZTViNWIwOGEyNjNhNS0"
    }
}

Files uploaded to Replicate using this endpoint expire after 24 hours.

Pass the urls.get property of a file resource to use it as an input when running a model on Replicate. The value of urls.get is opaque, and shouldn't be inferred from other attributes.

The contents of a file are only made accessible to a model running on Replicate, and only when passed as a prediction or training input by the user or organization who created the file.

replicate.files.list

List all files you've uploaded.

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

replicate.files.get

Get metadata for a specific file.

const response = await replicate.files.get(file_id);

replicate.files.delete

Delete a file.

Files uploaded using the replicate.files.create method expire after 24 hours. You can use this method to delete them sooner.

const response = await replicate.files.delete(file_id);

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);

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.