okaris/controlnet – Run with an API on Replicate

okaris / controlnet

ControlNet implementation with custom SD1.5 fine tuned models

Cold

Public
188 runs
A100 (80GB)
GitHub
License

Run with an API

Playground API Examples README Versions

Input

controlnet_model

string

Type of ControlNet model to use

base_model

string

Shift + Return to add a new line

Type of base model to use

image

*file

Input image

prompt

*string

Shift + Return to add a new line

Prompt for the model

num_samples

string

Number of samples (higher values may OOM)

Default: "1"

image_resolution

string

Image resolution to be generated

Default: "512"

low_threshold

integer

(minimum: 1, maximum: 255)

Canny low threshold (only applicable when model type is 'canny')

Default: 100

high_threshold

integer

(minimum: 1, maximum: 255)

Canny high threshold (only applicable when model type is 'canny')

Default: 200

ddim_steps

integer

Steps

Default: 20

scale

number

(minimum: 0.1, maximum: 30)

Guidance Scale

Default: 9

seed

integer

Seed

eta

number

eta (DDIM)

Default: 0

a_prompt

string

Shift + Return to add a new line

Added Prompt

Default: "best quality, extremely detailed"

n_prompt

string

Shift + Return to add a new line

longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low qualitylongbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality

Negative Prompt

Default: "longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality"

detect_resolution

integer

(minimum: 128, maximum: 1024)

Resolution for detection)

Default: 512

bg_threshold

number

(minimum: 0, maximum: 1)

Background Threshold (only applicable when model type is 'normal')

Default: 0

value_threshold

number

(minimum: 0.01, maximum: 2)

Value Threshold (only applicable when model type is 'MLSD')

Default: 0.1

distance_threshold

number

(minimum: 0.01, maximum: 20)

Distance Threshold (only applicable when model type is 'MLSD')

Default: 0.1

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

npx create-replicate --model=okaris/controlnet

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

const output = await replicate.run(
  "okaris/controlnet:470b2bc02d891f11808b43d454261afa4772cee1fae3beea099756752035ce2f",
  {
    input: {
      eta: 0,
      scale: 9,
      a_prompt: "best quality, extremely detailed",
      n_prompt: "longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality",
      ddim_steps: 20,
      num_samples: "1",
      bg_threshold: 0,
      low_threshold: 100,
      high_threshold: 200,
      value_threshold: 0.1,
      controlnet_model: "Canny",
      image_resolution: "512",
      detect_resolution: 512,
      distance_threshold: 0.1
    }
  }
);

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

output = replicate.run(
    "okaris/controlnet:470b2bc02d891f11808b43d454261afa4772cee1fae3beea099756752035ce2f",
    input={
        "eta": 0,
        "scale": 9,
        "a_prompt": "best quality, extremely detailed",
        "n_prompt": "longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality",
        "ddim_steps": 20,
        "num_samples": "1",
        "bg_threshold": 0,
        "low_threshold": 100,
        "high_threshold": 200,
        "value_threshold": 0.1,
        "controlnet_model": "Canny",
        "image_resolution": "512",
        "detect_resolution": 512,
        "distance_threshold": 0.1
    }
)
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 okaris/controlnet 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": "470b2bc02d891f11808b43d454261afa4772cee1fae3beea099756752035ce2f",
    "input": {
      "eta": 0,
      "scale": 9,
      "a_prompt": "best quality, extremely detailed",
      "n_prompt": "longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality",
      "ddim_steps": 20,
      "num_samples": "1",
      "bg_threshold": 0,
      "low_threshold": 100,
      "high_threshold": 200,
      "value_threshold": 0.1,
      "controlnet_model": "Canny",
      "image_resolution": "512",
      "detect_resolution": 512,
      "distance_threshold": 0.1
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

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

Run time and cost

This model costs approximately $0.13 to run on Replicate, or 7 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 A100 (80GB) GPU hardware. Predictions typically complete within 90 seconds. The predict time for this model varies significantly based on the inputs.

Readme

You can pass your fine-tuned .ckpt as the base_model.