Input

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

npx create-replicate --model=cjwbw/segmind-vegart

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 cjwbw/segmind-vegart using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "cjwbw/segmind-vegart:95be94f58749a793ad91388597f17882bb1bbc734daa892fc20fde789f9b1c01",
  {
    input: {
      prompt: "Self-portrait oil painting, a beautiful cyborg with golden hair, 8k",
      negative_prompt: "(worst quality, low quality, illustration, 3d, 2d, painting, cartoons, sketch)",
      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 cjwbw/segmind-vegart using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "cjwbw/segmind-vegart:95be94f58749a793ad91388597f17882bb1bbc734daa892fc20fde789f9b1c01",
    input={
        "prompt": "Self-portrait oil painting, a beautiful cyborg with golden hair, 8k",
        "negative_prompt": "(worst quality, low quality, illustration, 3d, 2d, painting, cartoons, sketch)",
        "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 cjwbw/segmind-vegart 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": "cjwbw/segmind-vegart:95be94f58749a793ad91388597f17882bb1bbc734daa892fc20fde789f9b1c01",
    "input": {
      "prompt": "Self-portrait oil painting, a beautiful cyborg with golden hair, 8k",
      "negative_prompt": "(worst quality, low quality, illustration, 3d, 2d, painting, cartoons, sketch)",
      "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

2.0 seconds

Tweak itReport

Examples

View more examples

Run time and cost

This model costs approximately $0.0022 to run on Replicate, or 454 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.

Readme

Segmind-VegaRT - Latent Consistency Model (LCM) LoRA of Segmind-Vega

Segmind-VegaRT a distilled consistency adapter for Segmind-Vega that allows to reduce the number of inference steps to only between 2 - 8 steps.

You can try the Segmind-Vega demo here: https://replicate.com/cjwbw/segmind-vega

Latent Consistency Model (LCM) LoRA was proposed in LCM-LoRA: A universal Stable-Diffusion Acceleration Module by Simian Luo, Yiqin Tan, Suraj Patil, Daniel Gu et al.