cneural/sdxl-lightning-t4 | Run with an API on Replicate

Input

prompt

string

Shift + Return to add a new line

Input prompt

Default: "A superhero smiling"

negative_prompt

string

Shift + Return to add a new line

Negative Input prompt

Default: "worst quality, low quality"

width

integer

Width of output image. Recommended 1024 or 1280

Default: 1024

height

integer

Height of output image. Recommended 1024 or 1280

Default: 1024

num_outputs

integer

(minimum: 1, maximum: 4)

Number of images to output.

Default: 1

scheduler

string

scheduler

Default: "K_EULER"

num_inference_steps

integer

(minimum: 1, maximum: 10)

Number of denoising steps. 4 for best results

Default: 4

guidance_scale

number

(minimum: 0, maximum: 50)

Scale for classifier-free guidance. Recommended 7-8

Default: 0

seed

integer

Random seed. Leave blank to randomize the seed

disable_safety_checker

boolean

Disable safety checker for generated images

Default: false

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

npx create-replicate --model=cneural/sdxl-lightning-t4

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 cneural/sdxl-lightning-t4 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "cneural/sdxl-lightning-t4:2d4b463281188a9382f8ea690d9d93249de3ab053ce426d666eb2f0e6b8fbdfb",
  {
    input: {
      width: 1024,
      height: 1024,
      prompt: "A superhero smiling",
      scheduler: "K_EULER",
      num_outputs: 1,
      guidance_scale: 0,
      negative_prompt: "worst quality, low quality",
      num_inference_steps: 4
    }
  }
);

// To access the file URL:
console.log(output[0].url()); //=> "http://example.com"

// To write the file to disk:
fs.writeFile("my-image.png", output[0]);

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 cneural/sdxl-lightning-t4 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "cneural/sdxl-lightning-t4:2d4b463281188a9382f8ea690d9d93249de3ab053ce426d666eb2f0e6b8fbdfb",
    input={
        "width": 1024,
        "height": 1024,
        "prompt": "A superhero smiling",
        "scheduler": "K_EULER",
        "num_outputs": 1,
        "guidance_scale": 0,
        "negative_prompt": "worst quality, low quality",
        "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 cneural/sdxl-lightning-t4 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": "cneural/sdxl-lightning-t4:2d4b463281188a9382f8ea690d9d93249de3ab053ce426d666eb2f0e6b8fbdfb",
    "input": {
      "width": 1024,
      "height": 1024,
      "prompt": "A superhero smiling",
      "scheduler": "K_EULER",
      "num_outputs": 1,
      "guidance_scale": 0,
      "negative_prompt": "worst quality, low quality",
      "num_inference_steps": 4
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

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

Run time and cost

This model costs approximately $0.00092 to run on Replicate, or 1086 runs per $1, but this varies depending on your inputs. It is also open source and you can run it on your own computer with Docker.

This model runs on Nvidia T4 GPU hardware. Predictions typically complete within 5 seconds.

Readme

This model doesn't have a readme.