lightweight-ai/test_sk2ig_f:1a7f1828 | Run with an API on Replicate

Version

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lightweight-ai /test_sk2ig_f:1a7f1828

Playground API

Input

prompt

string

Shift + Return to add a new line

Prompt for the generated image

Default: "High quality, 4K, UHD"

style_name

string

Select a visual style

Default: "Photographic"

negative_prompt

string

Shift + Return to add a new line

Negative prompt to avoid undesired results

Default: "ugly, blurry, deformed"

image

file

Upload an input image

hed_enabled

boolean

Enable HED preprocessing

Default: true

canny_enabled

boolean

Enable Canny preprocessing

Default: false

num_inference_steps

integer

Number of inference steps

Default: 25

guidance_scale

number

Classifier-free guidance scale

Default: 3.5

controlnet_conditioning_scale

number

ControlNet conditioning scale

Default: 0.7

seed

integer

Random seed for reproducibility

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

npx create-replicate --model=lightweight-ai/test_sk2ig_f

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 lightweight-ai/test_sk2ig_f using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "lightweight-ai/test_sk2ig_f:1a7f18286236d403c350cd6c51fcc5fd435a9ce5bcad95b8d43cbe6d554d9ae5",
  {
    input: {
      prompt: "High quality, 4K, UHD",
      style_name: "Photographic",
      hed_enabled: true,
      canny_enabled: false,
      guidance_scale: 3.5,
      negative_prompt: "ugly, blurry, deformed",
      num_inference_steps: 25,
      controlnet_conditioning_scale: 0.7
    }
  }
);

// 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 lightweight-ai/test_sk2ig_f using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "lightweight-ai/test_sk2ig_f:1a7f18286236d403c350cd6c51fcc5fd435a9ce5bcad95b8d43cbe6d554d9ae5",
    input={
        "prompt": "High quality, 4K, UHD",
        "style_name": "Photographic",
        "hed_enabled": True,
        "canny_enabled": False,
        "guidance_scale": 3.5,
        "negative_prompt": "ugly, blurry, deformed",
        "num_inference_steps": 25,
        "controlnet_conditioning_scale": 0.7
    }
)
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 lightweight-ai/test_sk2ig_f 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": "lightweight-ai/test_sk2ig_f:1a7f18286236d403c350cd6c51fcc5fd435a9ce5bcad95b8d43cbe6d554d9ae5",
    "input": {
      "prompt": "High quality, 4K, UHD",
      "style_name": "Photographic",
      "hed_enabled": true,
      "canny_enabled": false,
      "guidance_scale": 3.5,
      "negative_prompt": "ugly, blurry, deformed",
      "num_inference_steps": 25,
      "controlnet_conditioning_scale": 0.7
    }
  }' \
  https://api.replicate.com/v1/predictions

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

You can run this model locally using Cog. First, install Cog:

brew install cog

If you don’t have Homebrew, there are other installation options available.

Run this to download the model and run it in your local environment:

cog predict r8.im/lightweight-ai/test_sk2ig_f@sha256:1a7f18286236d403c350cd6c51fcc5fd435a9ce5bcad95b8d43cbe6d554d9ae5 \
  -i 'prompt="High quality, 4K, UHD"' \
  -i 'style_name="Photographic"' \
  -i 'hed_enabled=true' \
  -i 'canny_enabled=false' \
  -i 'guidance_scale=3.5' \
  -i 'negative_prompt="ugly, blurry, deformed"' \
  -i 'num_inference_steps=25' \
  -i 'controlnet_conditioning_scale=0.7'

To learn more, take a look at the Cog documentation.

Run this to download the model and run it in your local environment:

docker run -d -p 5000:5000 --gpus=all r8.im/lightweight-ai/test_sk2ig_f@sha256:1a7f18286236d403c350cd6c51fcc5fd435a9ce5bcad95b8d43cbe6d554d9ae5
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "prompt": "High quality, 4K, UHD",
      "style_name": "Photographic",
      "hed_enabled": true,
      "canny_enabled": false,
      "guidance_scale": 3.5,
      "negative_prompt": "ugly, blurry, deformed",
      "num_inference_steps": 25,
      "controlnet_conditioning_scale": 0.7
    }
  }' \
  http://localhost:5000/predictions

To learn more, take a look at the Cog documentation.

Output

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