anotherjesse/multi-control:e785fdfe – Run with an API on Replicate

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anotherjesse /multi-control:e785fdfe

Playground API

Input

prompt

*string

Shift + Return to add a new line

Prompt for the model

canny_image

file

Control image for canny controlnet

canny_conditioning_scale

number

Conditioning scale for canny controlnet

Default: 1

depth_image

file

Control image for depth controlnet

depth_conditioning_scale

number

Conditioning scale for depth controlnet

Default: 1

hed_image

file

Control image for hed controlnet

hed_conditioning_scale

number

Conditioning scale for hed controlnet

Default: 1

hough_image

file

Control image for hough controlnet

hough_conditioning_scale

number

Conditioning scale for hough controlnet

Default: 1

normal_image

file

Control image for normal controlnet

normal_conditioning_scale

number

Conditioning scale for normal controlnet

Default: 1

pose_image

file

Control image for pose controlnet

pose_conditioning_scale

number

Conditioning scale for pose controlnet

Default: 1

scribble_image

file

Control image for scribble controlnet

scribble_conditioning_scale

number

Conditioning scale for scribble controlnet

Default: 1

seg_image

file

Control image for seg controlnet

seg_conditioning_scale

number

Conditioning scale for seg controlnet

Default: 1

qr_image

file

Control image for qr controlnet

qr_conditioning_scale

number

Conditioning scale for qr controlnet

Default: 1

num_samples

integer

(minimum: 1, maximum: 4)

Number of samples (higher values may OOM)

Default: 1

image_resolution

integer

Resolution of image (smallest dimension)

Default: 512

scheduler

string

Choose a scheduler.

Default: "DDIM"

steps

integer

Steps

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

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"

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

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

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

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

const output = await replicate.run(
  "anotherjesse/multi-control:e785fdfe4b636f62e95835cad6ddd53505687ef4c10571d10fb6b2d0185d46aa",
  {
    input: {
      eta: 0,
      steps: 20,
      scheduler: "DDIM",
      guess_mode: false,
      num_samples: 1,
      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,
      qr_conditioning_scale: 1,
      hed_conditioning_scale: 1,
      seg_conditioning_scale: 1,
      pose_conditioning_scale: 1,
      canny_conditioning_scale: 1,
      depth_conditioning_scale: 1,
      hough_conditioning_scale: 1,
      normal_conditioning_scale: 1,
      scribble_conditioning_scale: 1
    }
  }
);
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/multi-control using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "anotherjesse/multi-control:e785fdfe4b636f62e95835cad6ddd53505687ef4c10571d10fb6b2d0185d46aa",
    input={
        "eta": 0,
        "steps": 20,
        "scheduler": "DDIM",
        "guess_mode": False,
        "num_samples": 1,
        "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,
        "qr_conditioning_scale": 1,
        "hed_conditioning_scale": 1,
        "seg_conditioning_scale": 1,
        "pose_conditioning_scale": 1,
        "canny_conditioning_scale": 1,
        "depth_conditioning_scale": 1,
        "hough_conditioning_scale": 1,
        "normal_conditioning_scale": 1,
        "scribble_conditioning_scale": 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 anotherjesse/multi-control 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": "e785fdfe4b636f62e95835cad6ddd53505687ef4c10571d10fb6b2d0185d46aa",
    "input": {
      "eta": 0,
      "steps": 20,
      "scheduler": "DDIM",
      "guess_mode": false,
      "num_samples": 1,
      "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,
      "qr_conditioning_scale": 1,
      "hed_conditioning_scale": 1,
      "seg_conditioning_scale": 1,
      "pose_conditioning_scale": 1,
      "canny_conditioning_scale": 1,
      "depth_conditioning_scale": 1,
      "hough_conditioning_scale": 1,
      "normal_conditioning_scale": 1,
      "scribble_conditioning_scale": 1
    }
  }' \
  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/multi-control@sha256:e785fdfe4b636f62e95835cad6ddd53505687ef4c10571d10fb6b2d0185d46aa \
  -i 'eta=0' \
  -i 'steps=20' \
  -i 'scheduler="DDIM"' \
  -i 'guess_mode=false' \
  -i 'num_samples=1' \
  -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 'qr_conditioning_scale=1' \
  -i 'hed_conditioning_scale=1' \
  -i 'seg_conditioning_scale=1' \
  -i 'pose_conditioning_scale=1' \
  -i 'canny_conditioning_scale=1' \
  -i 'depth_conditioning_scale=1' \
  -i 'hough_conditioning_scale=1' \
  -i 'normal_conditioning_scale=1' \
  -i 'scribble_conditioning_scale=1'

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/multi-control@sha256:e785fdfe4b636f62e95835cad6ddd53505687ef4c10571d10fb6b2d0185d46aa
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "eta": 0,
      "steps": 20,
      "scheduler": "DDIM",
      "guess_mode": false,
      "num_samples": 1,
      "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,
      "qr_conditioning_scale": 1,
      "hed_conditioning_scale": 1,
      "seg_conditioning_scale": 1,
      "pose_conditioning_scale": 1,
      "canny_conditioning_scale": 1,
      "depth_conditioning_scale": 1,
      "hough_conditioning_scale": 1,
      "normal_conditioning_scale": 1,
      "scribble_conditioning_scale": 1
    }
  }' \
  http://localhost:5000/predictions

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

Output

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