replicategithubwc/controlnet-sdxl – Run with an API on Replicate

replicategithubwc / controlnet-sdxl

Cold

Public
1.6K runs
A100 (80GB)

Run with an API

Playground API Examples README Versions

Input

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

npx create-replicate --model=replicategithubwc/controlnet-sdxl

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

const output = await replicate.run(
  "replicategithubwc/controlnet-sdxl:73cd44907a04eca8931003dbcd092021553fd813df32a1b4a3a238bed47cf1e4",
  {
    input: {
      seed: 0,
      width: 1024,
      height: 1024,
      prompt: "aerial view, a futuristic research complex in a bright foggy jungle, hard lighting",
      scheduler: "K_EULER",
      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/controlnet-sdxl using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "replicategithubwc/controlnet-sdxl:73cd44907a04eca8931003dbcd092021553fd813df32a1b4a3a238bed47cf1e4",
    input={
        "seed": 0,
        "width": 1024,
        "height": 1024,
        "prompt": "aerial view, a futuristic research complex in a bright foggy jungle, hard lighting",
        "scheduler": "K_EULER",
        "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/controlnet-sdxl 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": "73cd44907a04eca8931003dbcd092021553fd813df32a1b4a3a238bed47cf1e4",
    "input": {
      "seed": 0,
      "width": 1024,
      "height": 1024,
      "prompt": "aerial view, a futuristic research complex in a bright foggy jungle, hard lighting",
      "scheduler": "K_EULER",
      "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.

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/controlnet-sdxl@sha256:73cd44907a04eca8931003dbcd092021553fd813df32a1b4a3a238bed47cf1e4 \
  -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="K_EULER"' \
  -i 'model_type="canny"' \
  -i 'num_outputs=1' \
  -i 'low_threshold=100' \
  -i 'guidance_scale=7.5' \
  -i 'high_threshold=200' \
  -i 'condition_scale=0.5' \
  -i 'negative_prompt="low quality, bad quality, sketches"' \
  -i 'num_inference_steps=50' \
  -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/controlnet-sdxl@sha256:73cd44907a04eca8931003dbcd092021553fd813df32a1b4a3a238bed47cf1e4
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": "K_EULER",
      "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
    }
  }' \
  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 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.