Prediction

fofr/juggernaut-xl-lightning:c9a24c32

Model

fofr/juggernaut-xl-lightning:c9a24c32

tw92mgyymhrgg0cfwg3t8xmy70

Status

Succeeded

Source

Web

Hardware

A40 (Large)

Total duration

3.4s

Created

10 months ago

Input

width: 1024
height: 1024
prompt: A portrait photo, neon red hair, lightning storm
output_format: webp
output_quality: 80
negative_prompt
number_of_images: 1

{
  "width": 1024,
  "height": 1024,
  "prompt": "A portrait photo, neon red hair, lightning storm",
  "output_format": "webp",
  "output_quality": 80,
  "negative_prompt": "",
  "number_of_images": 1
}

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";

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

Run fofr/juggernaut-xl-lightning using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "fofr/juggernaut-xl-lightning:c9a24c321ceb0b7843b872dcae82109dddadd1f82e94b115ee39289e0e182e40",
  {
    input: {
      width: 1024,
      height: 1024,
      prompt: "A portrait photo, neon red hair, lightning storm",
      output_format: "webp",
      output_quality: 80,
      negative_prompt: "",
      number_of_images: 1
    }
  }
);
console.log(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 fofr/juggernaut-xl-lightning using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "fofr/juggernaut-xl-lightning:c9a24c321ceb0b7843b872dcae82109dddadd1f82e94b115ee39289e0e182e40",
    input={
        "width": 1024,
        "height": 1024,
        "prompt": "A portrait photo, neon red hair, lightning storm",
        "output_format": "webp",
        "output_quality": 80,
        "negative_prompt": "",
        "number_of_images": 1
    }
)
print(output)

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 fofr/juggernaut-xl-lightning 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 $'{
    "version": "c9a24c321ceb0b7843b872dcae82109dddadd1f82e94b115ee39289e0e182e40",
    "input": {
      "width": 1024,
      "height": 1024,
      "prompt": "A portrait photo, neon red hair, lightning storm",
      "output_format": "webp",
      "output_quality": 80,
      "negative_prompt": "",
      "number_of_images": 1
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

Generated in

3.4 seconds

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