espressotechie/lcm-anime – Run with an API on Replicate

Input

prompt

string

Shift + Return to add a new line

1woman, galaxy, glitter, dress, particle, wind, flower, upper body, dark simple background, looking at viewer, blonde1woman, galaxy, glitter, dress, particle, wind, flower, upper body, dark simple background, looking at viewer, blonde

Input prompt

Default: "Self-portrait oil painting, a beautiful cyborg with golden hair, 8k"

negative_prompt

string

Shift + Return to add a new line

Negative prompt

width

integer

Width of output image. Lower if out of memory

Default: 512

height

integer

Height of output image. Lower if out of memory

Default: 512

num_images

integer

(minimum: 1, maximum: 50)

Number of images to output

Default: 1

num_inference_steps

integer

(minimum: 1, maximum: 20)

Number of denoising steps. Recommend 1 to 8 steps.

Default: 4

guidance_scale

number

(minimum: 0, maximum: 20)

Scale for classifier-free guidance

Default: 0

seed

integer

Random seed. Leave blank to randomize the seed

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

npx create-replicate --model=espressotechie/lcm-anime

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 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
    }
  }
);
console.log(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 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
    }
)
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 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": "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 itReport

Run time and cost

This model costs approximately $0.0026 to run on Replicate, or 384 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 L40S GPU hardware. Predictions typically complete within 3 seconds. The predict time for this model varies significantly based on the inputs.