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:db21e45d3f7023abc2a46ee38a23973f6dce16bb082a930b0c49861f96d1e5bf",
["prompt": "a 19th century portrait of a wombat gentleman"]
)
print(output)
// ["https://replicate.com/api/models/stability-ai/stable-diffusion/files/50fcac81-865d-499e-81ac-49de0cb79264/out-0.png"]Or fetch a model by name and create a prediction against its latest version:
let model = try await replicate.getModel("stability-ai/stable-diffusion")
if let latestVersion = model.latestVersion {
let prompt = "a 19th century portrait of a wombat gentleman"
let prediction = try await replicate.createPrediction(version: latestVersion.id,
input: ["prompt": "\(prompt)"],
wait: true)
print(prediction.output)
// ["https://replicate.com/api/models/stability-ai/stable-diffusion/files/50fcac81-865d-499e-81ac-49de0cb79264/out-0.png"]
}Some models,
like tencentarc/gfpgan,
receive images as inputs.
To pass a file as an input,
read the contents of the file into a Data object,
and use the `uriEncoded(mimeType:) helper method to create a URI-encoded string.
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.com/api/models/tencentarc/gfpgan/files/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 != nilTo 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: "0.21.0"),
],
targets: [
.target(name: "<target>", dependencies: [
// other dependencies
.product(name: "Replicate", package: "replicate-swift"),
]),
// other targets
]
)