lightweight-ai/controlnet_test:5ad6a0b3 | Run with an API on Replicate

Version

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lightweight-ai /controlnet_test:5ad6a0b3

Input

prompt

string

Shift + Return to add a new line

A bohemian-style female travel blogger with sun-kissed skin and messy beach wavesA bohemian-style female travel blogger with sun-kissed skin and messy beach waves

Prompt for generated image

Default: "A bohemian-style female travel blogger with sun-kissed skin and messy beach waves"

image

file

Upload an image for inpainting. This will be the base image that will be partially modified.

inpaint

boolean

Default: false

controlnet

boolean

Default: false

mask

file

Upload a mask image for inpainting. White areas (255) indicate regions to be inpainted, while black areas (0) will be preserved from the original image.

width

integer

Width of output image

Default: 1024

height

integer

Height of output image

Default: 1024

prompt_strength

number

(minimum: 0, maximum: 1)

Prompt strength (or denoising strength) when using image to image. 1.0 corresponds to full destruction of information in image.

Default: 0.8

num_outputs

integer

(minimum: 1, maximum: 4)

Number of images to output.

Default: 1

num_inference_steps

integer

(minimum: 1, maximum: 50)

Number of inference steps

Default: 28

loras

string[]

Lora list (Realism, Karina, IU)

Default: []

lora_scales

number[]

Enter a string of numbers separated by commas

Lora scales

Default: []

seed

integer

Random seed. Set for reproducible generation

output_format

string

Format of the output images

Default: "png"

output_quality

integer

(minimum: 0, maximum: 100)

Quality when saving the output images, from 0 to 100. 100 is best quality, 0 is lowest quality. Not relevant for .png outputs

Default: 100

control_type

string

Control type

Default: "canny"

control_strength

number

(minimum: 0, maximum: 1)

ControlNet strength, depth works best at 0.2, canny works best at 0.4. Recommended range is 0.3-0.8

Default: 0.2

control_image

file

Control image

Default: ""

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

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

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

const output = await replicate.run(
  "lightweight-ai/controlnet_test:5ad6a0b371b0e0da4f834ff4bdcc27513b1a01777b2656a0974687a96f1740dc",
  {
    input: {
      loras: [],
      width: 1024,
      height: 1024,
      prompt: "A bohemian-style female travel blogger with sun-kissed skin and messy beach waves",
      inpaint: false,
      controlnet: false,
      lora_scales: [],
      num_outputs: 1,
      control_type: "canny",
      control_image: "",
      output_format: "png",
      output_quality: 100,
      prompt_strength: 0.8,
      control_strength: 0.2,
      num_inference_steps: 28
    }
  }
);

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

output = replicate.run(
    "lightweight-ai/controlnet_test:5ad6a0b371b0e0da4f834ff4bdcc27513b1a01777b2656a0974687a96f1740dc",
    input={
        "loras": [],
        "width": 1024,
        "height": 1024,
        "prompt": "A bohemian-style female travel blogger with sun-kissed skin and messy beach waves",
        "inpaint": False,
        "controlnet": False,
        "lora_scales": [],
        "num_outputs": 1,
        "control_type": "canny",
        "control_image": "",
        "output_format": "png",
        "output_quality": 100,
        "prompt_strength": 0.8,
        "control_strength": 0.2,
        "num_inference_steps": 28
    }
)

# 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.

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/controlnet_test 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/controlnet_test:5ad6a0b371b0e0da4f834ff4bdcc27513b1a01777b2656a0974687a96f1740dc",
    "input": {
      "loras": [],
      "width": 1024,
      "height": 1024,
      "prompt": "A bohemian-style female travel blogger with sun-kissed skin and messy beach waves",
      "inpaint": false,
      "controlnet": false,
      "lora_scales": [],
      "num_outputs": 1,
      "control_type": "canny",
      "control_image": "",
      "output_format": "png",
      "output_quality": 100,
      "prompt_strength": 0.8,
      "control_strength": 0.2,
      "num_inference_steps": 28
    }
  }' \
  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/controlnet_test@sha256:5ad6a0b371b0e0da4f834ff4bdcc27513b1a01777b2656a0974687a96f1740dc \
  -i 'loras=[]' \
  -i 'width=1024' \
  -i 'height=1024' \
  -i 'prompt="A bohemian-style female travel blogger with sun-kissed skin and messy beach waves"' \
  -i 'inpaint=false' \
  -i 'controlnet=false' \
  -i 'lora_scales=[]' \
  -i 'num_outputs=1' \
  -i 'control_type="canny"' \
  -i 'control_image=""' \
  -i 'output_format="png"' \
  -i 'output_quality=100' \
  -i 'prompt_strength=0.8' \
  -i 'control_strength=0.2' \
  -i 'num_inference_steps=28'

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/controlnet_test@sha256:5ad6a0b371b0e0da4f834ff4bdcc27513b1a01777b2656a0974687a96f1740dc
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "loras": [],
      "width": 1024,
      "height": 1024,
      "prompt": "A bohemian-style female travel blogger with sun-kissed skin and messy beach waves",
      "inpaint": false,
      "controlnet": false,
      "lora_scales": [],
      "num_outputs": 1,
      "control_type": "canny",
      "control_image": "",
      "output_format": "png",
      "output_quality": 100,
      "prompt_strength": 0.8,
      "control_strength": 0.2,
      "num_inference_steps": 28
    }
  }' \
  http://localhost:5000/predictions

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

Output

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