This is a Swift client for Replicate. It lets you run models from your Swift code, and do various other things on Replicate.
To learn how to use it, take a look at our guide to building a SwiftUI app with Replicate.
Grab your API token from replicate.com/account
and pass it to Client(token:)
:
import Foundation
import Replicate
let replicate = Replicate.Client(token: <#token#>)
[!WARNING] Don't store secrets in code or any other resources bundled with your app. Instead, fetch them from CloudKit or another server and store them in the keychain.
You can run a model and get its output:
let output = try await replicate.run(
"stability-ai/stable-diffusion-3",
["prompt": "a 19th century portrait of a gentleman otter"]
) { prediction in
// Print the prediction status after each update
print(prediction.status)
}
print(output)
// ["https://replicate.delivery/yhqm/bh9SsjWXY3pGKJyQzYjQlsZPzcNZ4EYOeEsPjFytc5TjYeNTA/R8_SD3_00001_.webp"]
Or fetch a model by name and create a prediction against its latest version:
let model = try await replicate.getModel("stability-ai/stable-diffusion-3")
if let latestVersion = model.latestVersion {
let prompt = "a 19th century portrait of a gentleman otter"
let prediction = try await replicate.createPrediction(version: latestVersion.id,
input: ["prompt": "\(prompt)"],
wait: true)
print(prediction.id)
// "s654jhww3hrm60ch11v8t3zpkg"
print(prediction.output)
// ["https://replicate.delivery/yhqm/bh9SsjWXY3pGKJyQzYjQlsZPzcNZ4EYOeEsPjFytc5TjYeNTA/R8_SD3_00001_.webp"]
}
Some models, like tencentarc/gfpgan, receive images as inputs. To run a model that takes a file input you can pass either a URL to a publicly accessible file on the Internet or use the `uriEncoded(mimeType:) helper method to create a base64-encoded data URL from the contents of a local file.
let model = try await replicate.getModel("tencentarc/gfpgan")
if let latestVersion = model.latestVersion {
let data = try! Data(contentsOf: URL(fileURLWithPath: "/path/to/image.jpg"))
let mimeType = "image/jpeg"
let prediction = try await replicate.createPrediction(version: latestVersion.id,
input: ["img": "\(data.uriEncoded(mimeType: mimeType))"])
print(prediction.output)
// ["https://replicate.delivery/mgxm/85f53415-0dc7-4703-891f-1e6f912119ad/output.png"]
}
You can start a model and run it in the background:
let model = replicate.getModel("kvfrans/clipdraw")
let prompt = "watercolor painting of an underwater submarine"
var prediction = replicate.createPrediction(version: model.latestVersion!.id,
input: ["prompt": "\(prompt)"])
print(prediction.status)
// "starting"
try await prediction.wait(with: replicate)
print(prediction.status)
// "succeeded"
You can cancel a running prediction:
let model = replicate.getModel("kvfrans/clipdraw")
let prompt = "watercolor painting of an underwater submarine"
var prediction = replicate.createPrediction(version: model.latestVersion!.id,
input: ["prompt": "\(prompt)"])
print(prediction.status)
// "starting"
try await prediction.cancel(with: replicate)
print(prediction.status)
// "canceled"
You can list all the predictions you've run:
var predictions: [Prediction] = []
var cursor: Replicate.Client.Pagination<Prediction>.Cursor?
let limit = 100
repeat {
let page = try await replicate.getPredictions(cursor: cursor)
predictions.append(contentsOf: page.results)
cursor = page.next
} while predictions.count < limit && cursor != nil
Replicate
as a DependencyTo use the Replicate
library in a Swift project,
add it to the dependencies for your package and your target:
let package = Package(
// name, platforms, products, etc.
dependencies: [
// other dependencies
.package(url: "https://github.com/replicate/replicate-swift", from: "0.24.0"),
],
targets: [
.target(name: "<target>", dependencies: [
// other dependencies
.product(name: "Replicate", package: "replicate-swift"),
]),
// other targets
]
)