lucataco/thinkdiffusionxl:c41c1275 | Run with an API on Replicate

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lucataco /thinkdiffusionxl:c41c1275

Playground API

Input

prompt

string

Shift + Return to add a new line

Input prompt

Default: "An astronaut riding a rainbow unicorn"

negative_prompt

string

Shift + Return to add a new line

Input Negative Prompt

Default: ""

width

integer

Width of output image

Default: 1024

height

integer

Height of output image

Default: 1024

num_outputs

integer

(minimum: 1, maximum: 4)

Number of images to output.

Default: 1

scheduler

string

scheduler

Default: "DPMSolverMultistep"

num_inference_steps

integer

(minimum: 1, maximum: 500)

Number of denoising steps

Default: 30

guidance_scale

number

(minimum: 1, maximum: 50)

Scale for classifier-free guidance

Default: 7

seed

integer

Random seed. Leave blank to randomize the seed

nsfw_checker

boolean

Select to filter for NSFW content

Default: true

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

npx create-replicate --model=lucataco/thinkdiffusionxl

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 lucataco/thinkdiffusionxl using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "lucataco/thinkdiffusionxl:c41c12756b561bc81047a9307c9143589d158ef084655dbb3073b4f08ff54f32",
  {
    input: {
      width: 1024,
      height: 1024,
      prompt: "An astronaut riding a rainbow unicorn",
      scheduler: "DPMSolverMultistep",
      num_outputs: 1,
      nsfw_checker: true,
      guidance_scale: 7,
      negative_prompt: "",
      num_inference_steps: 30
    }
  }
);

// 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 lucataco/thinkdiffusionxl using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "lucataco/thinkdiffusionxl:c41c12756b561bc81047a9307c9143589d158ef084655dbb3073b4f08ff54f32",
    input={
        "width": 1024,
        "height": 1024,
        "prompt": "An astronaut riding a rainbow unicorn",
        "scheduler": "DPMSolverMultistep",
        "num_outputs": 1,
        "nsfw_checker": True,
        "guidance_scale": 7,
        "negative_prompt": "",
        "num_inference_steps": 30
    }
)
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 lucataco/thinkdiffusionxl 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": "lucataco/thinkdiffusionxl:c41c12756b561bc81047a9307c9143589d158ef084655dbb3073b4f08ff54f32",
    "input": {
      "width": 1024,
      "height": 1024,
      "prompt": "An astronaut riding a rainbow unicorn",
      "scheduler": "DPMSolverMultistep",
      "num_outputs": 1,
      "nsfw_checker": true,
      "guidance_scale": 7,
      "negative_prompt": "",
      "num_inference_steps": 30
    }
  }' \
  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.