replicategithubwc/sdxl-turbo-controlnet:b8a45ead

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replicategithubwc /sdxl-turbo-controlnet:b8a45ead

Playground API

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"

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: 1

guidance_scale

number

(minimum: 0, maximum: 20)

Scale for classifier-free guidance

Default: 0

scheduler

string

scheduler

Default: "DPM++_SDE_Karras"

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

const output = await replicate.run(
  "replicategithubwc/sdxl-turbo-controlnet:b8a45eadcb916f48ca95618ce71104b45e38f56043a3195f31dea9b36c099730",
  {
    input: {
      seed: 0,
      width: 1024,
      height: 1024,
      prompt: "aerial view, a futuristic research complex in a bright foggy jungle, hard lighting",
      scheduler: "DPM++_SDE_Karras",
      model_type: "canny",
      num_outputs: 1,
      low_threshold: 100,
      guidance_scale: 0,
      high_threshold: 200,
      condition_scale: 0.5,
      num_inference_steps: 1,
      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/sdxl-turbo-controlnet using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "replicategithubwc/sdxl-turbo-controlnet:b8a45eadcb916f48ca95618ce71104b45e38f56043a3195f31dea9b36c099730",
    input={
        "seed": 0,
        "width": 1024,
        "height": 1024,
        "prompt": "aerial view, a futuristic research complex in a bright foggy jungle, hard lighting",
        "scheduler": "DPM++_SDE_Karras",
        "model_type": "canny",
        "num_outputs": 1,
        "low_threshold": 100,
        "guidance_scale": 0,
        "high_threshold": 200,
        "condition_scale": 0.5,
        "num_inference_steps": 1,
        "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/sdxl-turbo-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": "b8a45eadcb916f48ca95618ce71104b45e38f56043a3195f31dea9b36c099730",
    "input": {
      "seed": 0,
      "width": 1024,
      "height": 1024,
      "prompt": "aerial view, a futuristic research complex in a bright foggy jungle, hard lighting",
      "scheduler": "DPM++_SDE_Karras",
      "model_type": "canny",
      "num_outputs": 1,
      "low_threshold": 100,
      "guidance_scale": 0,
      "high_threshold": 200,
      "condition_scale": 0.5,
      "num_inference_steps": 1,
      "adapter_conditioning_factor": 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/replicategithubwc/sdxl-turbo-controlnet@sha256:b8a45eadcb916f48ca95618ce71104b45e38f56043a3195f31dea9b36c099730 \
  -i 'seed=0' \
  -i 'width=1024' \
  -i 'height=1024' \
  -i 'prompt="aerial view, a futuristic research complex in a bright foggy jungle, hard lighting"' \
  -i 'scheduler="DPM++_SDE_Karras"' \
  -i 'model_type="canny"' \
  -i 'num_outputs=1' \
  -i 'low_threshold=100' \
  -i 'guidance_scale=0' \
  -i 'high_threshold=200' \
  -i 'condition_scale=0.5' \
  -i 'num_inference_steps=1' \
  -i 'adapter_conditioning_factor=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/replicategithubwc/sdxl-turbo-controlnet@sha256:b8a45eadcb916f48ca95618ce71104b45e38f56043a3195f31dea9b36c099730
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "seed": 0,
      "width": 1024,
      "height": 1024,
      "prompt": "aerial view, a futuristic research complex in a bright foggy jungle, hard lighting",
      "scheduler": "DPM++_SDE_Karras",
      "model_type": "canny",
      "num_outputs": 1,
      "low_threshold": 100,
      "guidance_scale": 0,
      "high_threshold": 200,
      "condition_scale": 0.5,
      "num_inference_steps": 1,
      "adapter_conditioning_factor": 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.