chigozienri/densepose – Run with an API on Replicate

Input

input

*file

Input image or video

model

string

DensePose model. Read about the available models: https://github.com/facebookresearch/detectron2/blob/main/projects/DensePose/doc/DENSEPOSE_IUV.md

Default: "R_50_FPN_s1x"

overlay

boolean

Overlay the segmentation on the input image

Default: false

visualizer

string

Visualizer to use

Default: "FineSegmentation"

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

npx create-replicate --model=chigozienri/densepose

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

const output = await replicate.run(
  "chigozienri/densepose:e2466793b9a60a3891202f1b93527c5db7bbcd7dbb181c11958b50e25f2d55c3",
  {
    input: {
      model: "R_50_FPN_s1x",
      overlay: false,
      visualizer: "FineSegmentation"
    }
  }
);

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

output = replicate.run(
    "chigozienri/densepose:e2466793b9a60a3891202f1b93527c5db7bbcd7dbb181c11958b50e25f2d55c3",
    input={
        "model": "R_50_FPN_s1x",
        "overlay": False,
        "visualizer": "FineSegmentation"
    }
)
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 chigozienri/densepose 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": "chigozienri/densepose:e2466793b9a60a3891202f1b93527c5db7bbcd7dbb181c11958b50e25f2d55c3",
    "input": {
      "model": "R_50_FPN_s1x",
      "overlay": false,
      "visualizer": "FineSegmentation"
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

Generated in

13.3 seconds

Tweak itReport

This output was created using a different version of the model, chigozienri/densepose:31f04460.

Examples

View more examples

Run time and cost

This model costs approximately $0.0037 to run on Replicate, or 270 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 4 seconds. The predict time for this model varies significantly based on the inputs.