pipeline-examples/in-context-lora | Run with an API on Replicate

pipeline-examples / in-context-lora

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CPU (Small)

Playground API Examples README

Input

Run this model in Node.js with one line of code:

npx create-replicate --model=pipeline-examples/in-context-lora

or set up a project from scratch

Install Replicate’s Node.js client library:

npm install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Import and set up the client:

import Replicate from "replicate";
import fs from "node:fs";

const replicate = new Replicate({
  auth: process.env.REPLICATE_API_TOKEN,
});

Run pipeline-examples/in-context-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const input = {
  prompt: "this logo is applied as a trademark on the shoulder of a chrome robot, the robot is standing on a wet city street, against a distant sunset"
};

const output = await replicate.run("pipeline-examples/in-context-lora", { input });

// To access the file URL:
console.log(output.url()); //=> "http://example.com"

// To write the file to disk:
fs.writeFile("my-image.png", output);

To learn more, take a look at the guide on getting started with Node.js.

Install Replicate’s Python client library:

pip install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Import the client:

import replicate

Run pipeline-examples/in-context-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "pipeline-examples/in-context-lora",
    input={
        "prompt": "this logo is applied as a trademark on the shoulder of a chrome robot, the robot is standing on a wet city street, against a distant sunset"
    }
)

# To access the file URL:
print(output.url())
#=> "http://example.com"

# To write the file to disk:
with open("my-image.png", "wb") as file:
    file.write(output.read())

To learn more, take a look at the guide on getting started with Python.

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Run pipeline-examples/in-context-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

curl -s -X POST \
  -H "Authorization: Bearer $REPLICATE_API_TOKEN" \
  -H "Content-Type: application/json" \
  -H "Prefer: wait" \
  -d $'{
    "input": {
      "prompt": "this logo is applied as a trademark on the shoulder of a chrome robot, the robot is standing on a wet city street, against a distant sunset"
    }
  }' \
  https://api.replicate.com/v1/models/pipeline-examples/in-context-lora/predictions

To learn more, take a look at Replicate’s HTTP API reference docs.

Output

No output yet! Press "Submit" to start a prediction.

Readme

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