prunaai/sdxl-lightning | Run with an API on Replicate

Input

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

npx create-replicate --model=prunaai/sdxl-lightning

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 prunaai/sdxl-lightning using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "prunaai/sdxl-lightning:123d4264aec4d1c81d8bf142e0d90d6fd4c346f1a869406019f6d1653882d7c3",
  {
    input: {
      seed: 42,
      prompt: "self-portrait of a woman, lightning in the background",
      num_images: 1,
      image_width: 1024,
      image_height: 1024,
      output_format: "jpg",
      guidance_scale: 0,
      output_quality: 80,
      num_inference_steps: 4
    }
  }
);

// 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 prunaai/sdxl-lightning using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "prunaai/sdxl-lightning:123d4264aec4d1c81d8bf142e0d90d6fd4c346f1a869406019f6d1653882d7c3",
    input={
        "seed": 42,
        "prompt": "self-portrait of a woman, lightning in the background",
        "num_images": 1,
        "image_width": 1024,
        "image_height": 1024,
        "output_format": "jpg",
        "guidance_scale": 0,
        "output_quality": 80,
        "num_inference_steps": 4
    }
)

# 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 prunaai/sdxl-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": "prunaai/sdxl-lightning:123d4264aec4d1c81d8bf142e0d90d6fd4c346f1a869406019f6d1653882d7c3",
    "input": {
      "seed": 42,
      "prompt": "self-portrait of a woman, lightning in the background",
      "num_images": 1,
      "image_width": 1024,
      "image_height": 1024,
      "output_format": "jpg",
      "guidance_scale": 0,
      "output_quality": 80,
      "num_inference_steps": 4
    }
  }' \
  https://api.replicate.com/v1/predictions

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

You can run this model locally using Cog. First, install Cog:

brew install cog

If you don’t have Homebrew, there are other installation options available.

Run this to download the model and run it in your local environment:

cog predict r8.im/prunaai/sdxl-lightning@sha256:123d4264aec4d1c81d8bf142e0d90d6fd4c346f1a869406019f6d1653882d7c3 \
  -i 'seed=42' \
  -i 'prompt="self-portrait of a woman, lightning in the background"' \
  -i 'num_images=1' \
  -i 'image_width=1024' \
  -i 'image_height=1024' \
  -i 'output_format="jpg"' \
  -i 'guidance_scale=0' \
  -i 'output_quality=80' \
  -i 'num_inference_steps=4'

To learn more, take a look at the Cog documentation.

Run this to download the model and run it in your local environment:

docker run -d -p 5000:5000 --gpus=all r8.im/prunaai/sdxl-lightning@sha256:123d4264aec4d1c81d8bf142e0d90d6fd4c346f1a869406019f6d1653882d7c3
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "seed": 42,
      "prompt": "self-portrait of a woman, lightning in the background",
      "num_images": 1,
      "image_width": 1024,
      "image_height": 1024,
      "output_format": "jpg",
      "guidance_scale": 0,
      "output_quality": 80,
      "num_inference_steps": 4
    }
  }' \
  http://localhost:5000/predictions

To learn more, take a look at the Cog documentation.

Output

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Examples

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Run time and cost

This model runs on Nvidia A100 (80GB) GPU hardware. We don't yet have enough runs of this model to provide performance information.

Readme

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