piyushk52/controlnet1.1_openpose_template – Run with an API on Replicate

piyushk52 / controlnet1.1_openpose_template

Cold

Public
1 run
T4

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=piyushk52/controlnet1.1_openpose_template

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 piyushk52/controlnet1.1_openpose_template using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "piyushk52/controlnet1.1_openpose_template:dce2f91c06f0a705b706be48e65eb1f794a30ab00abb7c24a8829aa7695a22fe",
  {
    input: {
      width: 512,
      height: 512,
      prompt: "photo of cjw person",
      scheduler: "DPMSolverMultistep",
      num_outputs: 1,
      guidance_scale: 7.5,
      prompt_strength: 0.8,
      num_inference_steps: 50,
      disable_safety_check: false
    }
  }
);

// To access the file URL:
console.log(output[0].url()); //=> "http://example.com"

// To write the file to disk:
fs.writeFile("my-image.png", output[0]);

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 piyushk52/controlnet1.1_openpose_template using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "piyushk52/controlnet1.1_openpose_template:dce2f91c06f0a705b706be48e65eb1f794a30ab00abb7c24a8829aa7695a22fe",
    input={
        "width": 512,
        "height": 512,
        "prompt": "photo of cjw person",
        "scheduler": "DPMSolverMultistep",
        "num_outputs": 1,
        "guidance_scale": 7.5,
        "prompt_strength": 0.8,
        "num_inference_steps": 50,
        "disable_safety_check": False
    }
)

# The piyushk52/controlnet1.1_openpose_template model can stream output as it's running.
# The predict method returns an iterator, and you can iterate over that output.
for item in output:
    # https://replicate.com/piyushk52/controlnet1.1_openpose_template/api#output-schema
    print(item)

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 piyushk52/controlnet1.1_openpose_template 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": "dce2f91c06f0a705b706be48e65eb1f794a30ab00abb7c24a8829aa7695a22fe",
    "input": {
      "width": 512,
      "height": 512,
      "prompt": "photo of cjw person",
      "scheduler": "DPMSolverMultistep",
      "num_outputs": 1,
      "guidance_scale": 7.5,
      "prompt_strength": 0.8,
      "num_inference_steps": 50,
      "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/piyushk52/controlnet1.1_openpose_template@sha256:dce2f91c06f0a705b706be48e65eb1f794a30ab00abb7c24a8829aa7695a22fe \
  -i 'width=512' \
  -i 'height=512' \
  -i 'prompt="photo of cjw person"' \
  -i 'scheduler="DPMSolverMultistep"' \
  -i 'num_outputs=1' \
  -i 'guidance_scale=7.5' \
  -i 'prompt_strength=0.8' \
  -i 'num_inference_steps=50' \
  -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/piyushk52/controlnet1.1_openpose_template@sha256:dce2f91c06f0a705b706be48e65eb1f794a30ab00abb7c24a8829aa7695a22fe
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "width": 512,
      "height": 512,
      "prompt": "photo of cjw person",
      "scheduler": "DPMSolverMultistep",
      "num_outputs": 1,
      "guidance_scale": 7.5,
      "prompt_strength": 0.8,
      "num_inference_steps": 50,
      "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.