lucataco / modelscope-facefusion

Auto fuse a user's face onto the template image, with a similar appearance to the user

Public
12K runs
L40S
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=lucataco/modelscope-facefusion

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

const output = await replicate.run(
  "lucataco/modelscope-facefusion:52edbb2b42beb4e19242f0c9ad5717211a96c63ff1f0b0320caa518b2745f4f7",
  {
    input: {
      user_image: "https://replicate.delivery/pbxt/Jspfs5W4cTCdvdSOkLlOafXtfEnnAe744K1S72SMqtFHBdir/facefusion_user.jpg",
      template_image: "https://replicate.delivery/pbxt/JspfsVBSEz9zwoL8NmSkieVHP4JI23cdqS1OG7nfilyWfBYt/facefusion_template.jpg"
    }
  }
);

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

output = replicate.run(
    "lucataco/modelscope-facefusion:52edbb2b42beb4e19242f0c9ad5717211a96c63ff1f0b0320caa518b2745f4f7",
    input={
        "user_image": "https://replicate.delivery/pbxt/Jspfs5W4cTCdvdSOkLlOafXtfEnnAe744K1S72SMqtFHBdir/facefusion_user.jpg",
        "template_image": "https://replicate.delivery/pbxt/JspfsVBSEz9zwoL8NmSkieVHP4JI23cdqS1OG7nfilyWfBYt/facefusion_template.jpg"
    }
)
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 lucataco/modelscope-facefusion 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": "52edbb2b42beb4e19242f0c9ad5717211a96c63ff1f0b0320caa518b2745f4f7",
    "input": {
      "user_image": "https://replicate.delivery/pbxt/Jspfs5W4cTCdvdSOkLlOafXtfEnnAe744K1S72SMqtFHBdir/facefusion_user.jpg",
      "template_image": "https://replicate.delivery/pbxt/JspfsVBSEz9zwoL8NmSkieVHP4JI23cdqS1OG7nfilyWfBYt/facefusion_template.jpg"
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

output

Generated in

3.4 seconds

Examples

View more examples

Run time and cost

This model costs approximately $0.00098 to run on Replicate, or 1020 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 1 seconds.

Readme

Implementation of face fusion model

Image and face fusion

Given a template image and a target user image, the image-face fusion model can automatically fuse the faces in the user image into the template face image to generate a face image that is similar to the target face and has the appearance of the template image. Featured new images.

Model limitations and possible biases

It is recommended that the contours of the facial features in the image be complete without obvious occlusion, otherwise it may affect the face detection results and lead to poor fusion results.

The algorithm supports a certain angle of face lateralization, and can achieve better results when the lateralization angle does not exceed 30 degrees.

The face size in the image is recommended to be larger than 64×64 pixels, and the face area is recommended to be no larger than 2/3 of the entire image area. Otherwise, it will affect the face detection results and make the fusion operation impossible.

If the face shapes of the two faces are too different, it may affect the fusion effect of the edge areas of the face.