camenduru/visual-style-prompting-controlnet – Run with an API on Replicate

camenduru / visual-style-prompting-controlnet

Visual Style Prompting with Swapping Self-Attention

Cold

Public
914 runs
A100 (80GB)
GitHub
Paper
License

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=camenduru/visual-style-prompting-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 camenduru/visual-style-prompting-controlnet using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "camenduru/visual-style-prompting-controlnet:5c67279640102e520d0e5cbc612cf4d56cc6d3bf55e69a9fca6d11b1d341a768",
  {
    input: {
      prompt: "",
      style_name: "line-art",
      depth_image: "https://replicate.delivery/pbxt/KZccXvxIrAPKRoK2ptD9etXWNi7MBqEJwiq0Q8vq6J5lpei4/gundam.png",
      style_image: "https://replicate.delivery/pbxt/KZd1EawcS3C0isEIigAUcejhWAsDviZWBqWE3ReA5kT39azZ/ref_line-art_an%20owl.png",
      diffusion_steps: 50,
      controlnet_scale: 0.5
    }
  }
);

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

// To write the file to disk:
fs.writeFile("my-image.png", 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 camenduru/visual-style-prompting-controlnet using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "camenduru/visual-style-prompting-controlnet:5c67279640102e520d0e5cbc612cf4d56cc6d3bf55e69a9fca6d11b1d341a768",
    input={
        "prompt": "",
        "style_name": "line-art",
        "depth_image": "https://replicate.delivery/pbxt/KZccXvxIrAPKRoK2ptD9etXWNi7MBqEJwiq0Q8vq6J5lpei4/gundam.png",
        "style_image": "https://replicate.delivery/pbxt/KZd1EawcS3C0isEIigAUcejhWAsDviZWBqWE3ReA5kT39azZ/ref_line-art_an%20owl.png",
        "diffusion_steps": 50,
        "controlnet_scale": 0.5
    }
)
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 camenduru/visual-style-prompting-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": "5c67279640102e520d0e5cbc612cf4d56cc6d3bf55e69a9fca6d11b1d341a768",
    "input": {
      "prompt": "",
      "style_name": "line-art",
      "depth_image": "https://replicate.delivery/pbxt/KZccXvxIrAPKRoK2ptD9etXWNi7MBqEJwiq0Q8vq6J5lpei4/gundam.png",
      "style_image": "https://replicate.delivery/pbxt/KZd1EawcS3C0isEIigAUcejhWAsDviZWBqWE3ReA5kT39azZ/ref_line-art_an%20owl.png",
      "diffusion_steps": 50,
      "controlnet_scale": 0.5
    }
  }' \
  https://api.replicate.com/v1/predictions

To learn more, take a look at Replicate’s HTTP API reference docs.

Output

{
  "completed_at": "2024-03-15T11:18:45.256023Z",
  "created_at": "2024-03-15T11:13:55.977489Z",
  "data_removed": false,
  "error": null,
  "id": "4dasfijbgznreqqouv2exptpna",
  "input": {
    "prompt": "",
    "style_name": "line-art",
    "depth_image": "https://replicate.delivery/pbxt/KZccXvxIrAPKRoK2ptD9etXWNi7MBqEJwiq0Q8vq6J5lpei4/gundam.png",
    "style_image": "https://replicate.delivery/pbxt/KZd1EawcS3C0isEIigAUcejhWAsDviZWBqWE3ReA5kT39azZ/ref_line-art_an%20owl.png",
    "diffusion_steps": 50,
    "controlnet_scale": 0.5
  },
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  "metrics": {
    "predict_time": 27.068939,
    "total_time": 289.278534
  },
  "output": "https://replicate.delivery/pbxt/6X2gqgKjSyqJGNNxRPpumm9EjTiFiMhUsIX5U70VpRVlgHoE/output_image.png",
  "started_at": "2024-03-15T11:18:18.187084Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/4dasfijbgznreqqouv2exptpna",
    "cancel": "https://api.replicate.com/v1/predictions/4dasfijbgznreqqouv2exptpna/cancel"
  },
  "version": "bfd1dedbb46545a191b3a42b5f3778d6c97fa6b6c0283de7d099403c05560b65"
}

Generated in

27.1 seconds

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This output was created using a different version of the model, camenduru/visual-style-prompting-controlnet:bfd1dedb.

Examples

View more examples

Run time and cost

This model costs approximately $0.075 to run on Replicate, or 13 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 A100 (80GB) GPU hardware. Predictions typically complete within 54 seconds. The predict time for this model varies significantly based on the inputs.

Readme

🐣 Please follow me for new updates https://twitter.com/camenduru
🔥 Please join our discord server https://discord.gg/k5BwmmvJJU
🥳 Please join my patreon community https://patreon.com/camenduru

Input

Output

Examples

Run time and cost

Readme

🕸 Replicate

🧬 Code

📄 Paper

🌐 Page

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