juergengunz/sdxl-openpose-lora | Run with an API on Replicate

Input

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

npx create-replicate --model=juergengunz/sdxl-openpose-lora

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";
import fs from "node:fs";

const replicate = new Replicate({
  auth: process.env.REPLICATE_API_TOKEN,
});

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

const output = await replicate.run(
  "juergengunz/sdxl-openpose-lora:c20d10c1aa5cd40f0327fc863a06342bbf30a7897fe366a4dad494a92d77a0f4",
  {
    input: {
      prompt: "An portrait of an astronaut standing on the moon",
      refine: "no_refiner",
      scheduler: "K_EULER",
      lora_scale: 0.8,
      num_outputs: 1,
      refine_steps: 10,
      guidance_scale: 5,
      apply_watermark: false,
      condition_scale: 0.9,
      negative_prompt: "",
      num_inference_steps: 50
    }
  }
);

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

output = replicate.run(
    "juergengunz/sdxl-openpose-lora:c20d10c1aa5cd40f0327fc863a06342bbf30a7897fe366a4dad494a92d77a0f4",
    input={
        "prompt": "An portrait of an astronaut standing on the moon",
        "refine": "no_refiner",
        "scheduler": "K_EULER",
        "lora_scale": 0.8,
        "num_outputs": 1,
        "refine_steps": 10,
        "guidance_scale": 5,
        "apply_watermark": False,
        "condition_scale": 0.9,
        "negative_prompt": "",
        "num_inference_steps": 50
    }
)
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 juergengunz/sdxl-openpose-lora 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": "juergengunz/sdxl-openpose-lora:c20d10c1aa5cd40f0327fc863a06342bbf30a7897fe366a4dad494a92d77a0f4",
    "input": {
      "prompt": "An portrait of an astronaut standing on the moon",
      "refine": "no_refiner",
      "scheduler": "K_EULER",
      "lora_scale": 0.8,
      "num_outputs": 1,
      "refine_steps": 10,
      "guidance_scale": 5,
      "apply_watermark": false,
      "condition_scale": 0.9,
      "negative_prompt": "",
      "num_inference_steps": 50
    }
  }' \
  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/juergengunz/sdxl-openpose-lora@sha256:c20d10c1aa5cd40f0327fc863a06342bbf30a7897fe366a4dad494a92d77a0f4 \
  -i 'prompt="An portrait of an astronaut standing on the moon"' \
  -i 'refine="no_refiner"' \
  -i 'scheduler="K_EULER"' \
  -i 'lora_scale=0.8' \
  -i 'num_outputs=1' \
  -i 'refine_steps=10' \
  -i 'guidance_scale=5' \
  -i 'apply_watermark=false' \
  -i 'condition_scale=0.9' \
  -i 'negative_prompt=""' \
  -i 'num_inference_steps=50'

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/juergengunz/sdxl-openpose-lora@sha256:c20d10c1aa5cd40f0327fc863a06342bbf30a7897fe366a4dad494a92d77a0f4
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "prompt": "An portrait of an astronaut standing on the moon",
      "refine": "no_refiner",
      "scheduler": "K_EULER",
      "lora_scale": 0.8,
      "num_outputs": 1,
      "refine_steps": 10,
      "guidance_scale": 5,
      "apply_watermark": false,
      "condition_scale": 0.9,
      "negative_prompt": "",
      "num_inference_steps": 50
    }
  }' \
  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 costs approximately $0.025 to run on Replicate, or 40 runs per $1, but this varies depending on your inputs. It is also open source and you can run it on your own computer with Docker.

This model runs on Nvidia L40S GPU hardware. Predictions typically complete within 27 seconds.

Readme

This model doesn't have a readme.