replicategithubwc/juggernaut-xl-controlnet – Run with an API on Replicate

replicategithubwc / juggernaut-xl-controlnet

Cold

Public
297 runs
A100 (80GB)

Run with an API

Playground API Examples README Versions

Input

image

file

Input image for img2img or inpaint mode

prompt

string

Shift + Return to add a new line

aerial view, a futuristic research complex in a bright foggy jungle, hard lightingaerial view, a futuristic research complex in a bright foggy jungle, hard lighting

Input prompt

Default: "aerial view, a futuristic research complex in a bright foggy jungle, hard lighting"

negative_prompt

string

Shift + Return to add a new line

Input Negative Prompt

Default: "low quality, bad quality, sketches"

model_type

string

ControlNet model type to use

Default: "canny"

width

integer

Width of output image

Default: 1024

height

integer

Height of output image

Default: 1024

num_outputs

integer

(minimum: 1, maximum: 4)

Number of images to output.

Default: 1

num_inference_steps

integer

(minimum: 1, maximum: 500)

Number of denoising steps

Default: 50

guidance_scale

number

(minimum: 1, maximum: 20)

Scale for classifier-free guidance

Default: 7.5

scheduler

string

scheduler

Default: "DPMSolverMultistep"

condition_scale

number

(minimum: 0, maximum: 1)

controlnet conditioning scale for generalization

Default: 0.5

adapter_conditioning_factor

number

(minimum: 0, maximum: 1)

Factor to scale image by

Default: 1

low_threshold

integer

(minimum: 1, maximum: 255)

Canny line detection low threshold

Default: 100

high_threshold

integer

(minimum: 1, maximum: 255)

Canny line detection high threshold

Default: 200

seed

integer

Random seed. Set to 0 to randomize the seed

Default: 0

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

npx create-replicate --model=replicategithubwc/juggernaut-xl-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 replicategithubwc/juggernaut-xl-controlnet using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "replicategithubwc/juggernaut-xl-controlnet:2635814b5f3d2fd8d395bdea53444c401c6a5278c99b85da08b53a075402fd2a",
  {
    input: {
      seed: 0,
      width: 1024,
      height: 1024,
      prompt: "aerial view, a futuristic research complex in a bright foggy jungle, hard lighting",
      scheduler: "DPMSolverMultistep",
      model_type: "canny",
      num_outputs: 1,
      low_threshold: 100,
      guidance_scale: 7.5,
      high_threshold: 200,
      condition_scale: 0.5,
      negative_prompt: "low quality, bad quality, sketches",
      num_inference_steps: 50,
      adapter_conditioning_factor: 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 replicategithubwc/juggernaut-xl-controlnet using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "replicategithubwc/juggernaut-xl-controlnet:2635814b5f3d2fd8d395bdea53444c401c6a5278c99b85da08b53a075402fd2a",
    input={
        "seed": 0,
        "width": 1024,
        "height": 1024,
        "prompt": "aerial view, a futuristic research complex in a bright foggy jungle, hard lighting",
        "scheduler": "DPMSolverMultistep",
        "model_type": "canny",
        "num_outputs": 1,
        "low_threshold": 100,
        "guidance_scale": 7.5,
        "high_threshold": 200,
        "condition_scale": 0.5,
        "negative_prompt": "low quality, bad quality, sketches",
        "num_inference_steps": 50,
        "adapter_conditioning_factor": 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 replicategithubwc/juggernaut-xl-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": "2635814b5f3d2fd8d395bdea53444c401c6a5278c99b85da08b53a075402fd2a",
    "input": {
      "seed": 0,
      "width": 1024,
      "height": 1024,
      "prompt": "aerial view, a futuristic research complex in a bright foggy jungle, hard lighting",
      "scheduler": "DPMSolverMultistep",
      "model_type": "canny",
      "num_outputs": 1,
      "low_threshold": 100,
      "guidance_scale": 7.5,
      "high_threshold": 200,
      "condition_scale": 0.5,
      "negative_prompt": "low quality, bad quality, sketches",
      "num_inference_steps": 50,
      "adapter_conditioning_factor": 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 runs on Nvidia A100 (80GB) GPU hardware. We don't yet have enough runs of this model to provide performance information.

Readme

This model doesn't have a readme.