width: 384
height: 512
prompt: 1woman, galaxy, glitter, dress, particle, wind, flower, upper body, dark simple background, looking at viewer, blonde
num_images: 1
guidance_scale: 0
num_inference_steps: 4

{
  "width": 384,
  "height": 512,
  "prompt": "1woman, galaxy, glitter, dress, particle, wind, flower, upper body, dark simple background, looking at viewer, blonde",
  "num_images": 1,
  "guidance_scale": 0,
  "num_inference_steps": 4
}

Install Replicate’s Node.js client library:

npm install replicate

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 espressotechie/lcm-anime using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "espressotechie/lcm-anime:4e5acb4caed161eda5778bd852276072612d7fc6461b3bdcb9154e5a38eb7af5",
  {
    input: {
      width: 384,
      height: 512,
      prompt: "1woman, galaxy, glitter, dress, particle, wind, flower, upper body, dark simple background, looking at viewer, blonde",
      num_images: 1,
      guidance_scale: 0,
      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

Import the client:

import replicate

Run espressotechie/lcm-anime using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "espressotechie/lcm-anime:4e5acb4caed161eda5778bd852276072612d7fc6461b3bdcb9154e5a38eb7af5",
    input={
        "width": 384,
        "height": 512,
        "prompt": "1woman, galaxy, glitter, dress, particle, wind, flower, upper body, dark simple background, looking at viewer, blonde",
        "num_images": 1,
        "guidance_scale": 0,
        "num_inference_steps": 4
    }
)

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

# To write the file to disk:
with open("my-image.png", "wb") as file:
    file.write(output[0].read())

To learn more, take a look at the guide on getting started with Python.

Run espressotechie/lcm-anime 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": "espressotechie/lcm-anime:4e5acb4caed161eda5778bd852276072612d7fc6461b3bdcb9154e5a38eb7af5",
    "input": {
      "width": 384,
      "height": 512,
      "prompt": "1woman, galaxy, glitter, dress, particle, wind, flower, upper body, dark simple background, looking at viewer, blonde",
      "num_images": 1,
      "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.

Output

Generated in

1.2 seconds

Tweak it Iterate in playground ShareReport

Want to make some of these yourself?

Run this model