anotherjesse/control-paul – Run with an API on Replicate

anotherjesse / control-paul

this should be same as multi-control but testing different inputs

Cold

Public
26 runs
T4

Run with an API

Playground API Examples README Versions

Input

prompt

*string

Shift + Return to add a new line

Prompt for the model

negative_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"

controlnet_1

string

Structure of controlnet

controlnet_1_image

file

Control image for controlnet

controlnet_1_conditioning_scale

number

override scale for controlnet

controlnet_2

string

Structure of controlnet

controlnet_2_image

file

Control image for controlnet

controlnet_2_conditioning_scale

number

override scale for controlnet

controlnet_3

string

Structure of controlnet

controlnet_3_image

file

Control image for controlnet

controlnet_3_conditioning_scale

number

override scale for controlnet

controlnet_4

string

Structure of controlnet

controlnet_4_image

file

Control image for controlnet

controlnet_4_conditioning_scale

number

override scale for controlnet

num_outputs

integer

(minimum: 1, maximum: 10)

Number of images to generate

Default: 1

image_resolution

integer

Resolution of image (smallest dimension)

Default: 512

scheduler

string

Choose a scheduler.

Default: "KerrasDPM"

num_inference_steps

integer

Steps to run denoising

Default: 20

guidance_scale

number

(minimum: 0.1, maximum: 30)

Scale for classifier-free guidance

Default: 9

seed

integer

Seed

eta

number

Controls the amount of noise that is added to the input data during the denoising diffusion process. Higher value -> more noise

Default: 0

low_threshold

integer

(minimum: 1, maximum: 255)

[canny only] Line detection low threshold`

Default: 100

high_threshold

integer

(minimum: 1, maximum: 255)

[canny only] Line detection high threshold

Default: 200

guess_mode

boolean

In this mode, the ControlNet encoder will try best to recognize the content of the input image even if you remove all prompts. The `guidance_scale` between 3.0 and 5.0 is recommended.

Default: false

disable_safety_check

boolean

Disable safety check. Use at your own risk!

Default: false

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

npx create-replicate --model=anotherjesse/control-paul

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

const output = await replicate.run(
  "anotherjesse/control-paul:15def3a053e4f2fae9cbb629c21dc4e9087fbbc9b5eb4ee5f566156fdba2d75e",
  {
    input: {
      eta: 0,
      scheduler: "KerrasDPM",
      guess_mode: false,
      num_outputs: 1,
      controlnet_1: "canny",
      controlnet_2: "canny",
      controlnet_3: "canny",
      controlnet_4: "canny",
      low_threshold: 100,
      guidance_scale: 9,
      high_threshold: 200,
      negative_prompt: "Longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality",
      image_resolution: 512,
      num_inference_steps: 20,
      disable_safety_check: false
    }
  }
);
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 anotherjesse/control-paul using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "anotherjesse/control-paul:15def3a053e4f2fae9cbb629c21dc4e9087fbbc9b5eb4ee5f566156fdba2d75e",
    input={
        "eta": 0,
        "scheduler": "KerrasDPM",
        "guess_mode": False,
        "num_outputs": 1,
        "controlnet_1": "canny",
        "controlnet_2": "canny",
        "controlnet_3": "canny",
        "controlnet_4": "canny",
        "low_threshold": 100,
        "guidance_scale": 9,
        "high_threshold": 200,
        "negative_prompt": "Longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality",
        "image_resolution": 512,
        "num_inference_steps": 20,
        "disable_safety_check": False
    }
)
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 anotherjesse/control-paul 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": "15def3a053e4f2fae9cbb629c21dc4e9087fbbc9b5eb4ee5f566156fdba2d75e",
    "input": {
      "eta": 0,
      "scheduler": "KerrasDPM",
      "guess_mode": false,
      "num_outputs": 1,
      "controlnet_1": "canny",
      "controlnet_2": "canny",
      "controlnet_3": "canny",
      "controlnet_4": "canny",
      "low_threshold": 100,
      "guidance_scale": 9,
      "high_threshold": 200,
      "negative_prompt": "Longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality",
      "image_resolution": 512,
      "num_inference_steps": 20,
      "disable_safety_check": false
    }
  }' \
  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/anotherjesse/control-paul@sha256:15def3a053e4f2fae9cbb629c21dc4e9087fbbc9b5eb4ee5f566156fdba2d75e \
  -i 'eta=0' \
  -i 'scheduler="KerrasDPM"' \
  -i 'guess_mode=false' \
  -i 'num_outputs=1' \
  -i 'controlnet_1="canny"' \
  -i 'controlnet_2="canny"' \
  -i 'controlnet_3="canny"' \
  -i 'controlnet_4="canny"' \
  -i 'low_threshold=100' \
  -i 'guidance_scale=9' \
  -i 'high_threshold=200' \
  -i 'negative_prompt="Longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality"' \
  -i 'image_resolution=512' \
  -i 'num_inference_steps=20' \
  -i 'disable_safety_check=false'

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/anotherjesse/control-paul@sha256:15def3a053e4f2fae9cbb629c21dc4e9087fbbc9b5eb4ee5f566156fdba2d75e
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "eta": 0,
      "scheduler": "KerrasDPM",
      "guess_mode": false,
      "num_outputs": 1,
      "controlnet_1": "canny",
      "controlnet_2": "canny",
      "controlnet_3": "canny",
      "controlnet_4": "canny",
      "low_threshold": 100,
      "guidance_scale": 9,
      "high_threshold": 200,
      "negative_prompt": "Longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality",
      "image_resolution": 512,
      "num_inference_steps": 20,
      "disable_safety_check": false
    }
  }' \
  http://localhost:5000/predictions

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

Output

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

Run time and cost

This model runs on Nvidia T4 GPU hardware. We don't yet have enough runs of this model to provide performance information.

Readme

This model doesn't have a readme.