prunaai/sdxl-lightning-cheetah – Run with an API on Replicate

prunaai / sdxl-lightning-cheetah

An optimized version of sdxl-lightning from Bytedance that is more than 2x faster and 2x cheaper

Cold

Public
29 runs
A100 (80GB)

Iterate in playground

Run with an API

Playground API Examples README Versions

Input

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

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

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";

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

Run prunaai/sdxl-lightning-cheetah 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-cheetah:034df980d2ce71732b9cd901d257449caad473c29e69e45b78e9831be4e4159e",
  {
    input: {
      seed: 42,
      prompt: "a smiling girl",
      num_images: 1,
      image_width: 1024,
      image_height: 1024,
      guidance_scale: 0,
      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-cheetah using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "prunaai/sdxl-lightning-cheetah:034df980d2ce71732b9cd901d257449caad473c29e69e45b78e9831be4e4159e",
    input={
        "seed": 42,
        "prompt": "a smiling girl",
        "num_images": 1,
        "image_width": 1024,
        "image_height": 1024,
        "guidance_scale": 0,
        "num_inference_steps": 4
    }
)
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 prunaai/sdxl-lightning-cheetah 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-cheetah:034df980d2ce71732b9cd901d257449caad473c29e69e45b78e9831be4e4159e",
    "input": {
      "seed": 42,
      "prompt": "a smiling girl",
      "num_images": 1,
      "image_width": 1024,
      "image_height": 1024,
      "guidance_scale": 0,
      "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-cheetah@sha256:034df980d2ce71732b9cd901d257449caad473c29e69e45b78e9831be4e4159e \
  -i 'seed=42' \
  -i 'prompt="a smiling girl"' \
  -i 'num_images=1' \
  -i 'image_width=1024' \
  -i 'image_height=1024' \
  -i 'guidance_scale=0' \
  -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-cheetah@sha256:034df980d2ce71732b9cd901d257449caad473c29e69e45b78e9831be4e4159e
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "seed": 42,
      "prompt": "a smiling girl",
      "num_images": 1,
      "image_width": 1024,
      "image_height": 1024,
      "guidance_scale": 0,
      "num_inference_steps": 4
    }
  }' \
  http://localhost:5000/predictions

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