c-barron/grounded-sam | Run with an API on Replicate

Input

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

npx create-replicate --model=c-barron/grounded-sam

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 c-barron/grounded-sam using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "c-barron/grounded-sam:81831427a4ab1d447830592975b4c74b79f018744fcabf8db449a3a9c3ba4b2a",
  {
    input: {
      image: "https://st.mngbcn.com/rcs/pics/static/T5/fotos/outfit/S20/57034757_56-99999999_01.jpg",
      prompts: ["face."],
      threshold: 0.3
    }
  }
);

// 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 c-barron/grounded-sam using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "c-barron/grounded-sam:81831427a4ab1d447830592975b4c74b79f018744fcabf8db449a3a9c3ba4b2a",
    input={
        "image": "https://st.mngbcn.com/rcs/pics/static/T5/fotos/outfit/S20/57034757_56-99999999_01.jpg",
        "prompts": ["face."],
        "threshold": 0.3
    }
)

# The c-barron/grounded-sam 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/c-barron/grounded-sam/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 c-barron/grounded-sam 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": "c-barron/grounded-sam:81831427a4ab1d447830592975b4c74b79f018744fcabf8db449a3a9c3ba4b2a",
    "input": {
      "image": "https://st.mngbcn.com/rcs/pics/static/T5/fotos/outfit/S20/57034757_56-99999999_01.jpg",
      "prompts": ["face."],
      "threshold": 0.3
    }
  }' \
  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/c-barron/grounded-sam@sha256:81831427a4ab1d447830592975b4c74b79f018744fcabf8db449a3a9c3ba4b2a \
  -i 'image="https://st.mngbcn.com/rcs/pics/static/T5/fotos/outfit/S20/57034757_56-99999999_01.jpg"' \
  -i 'prompts=["face."]' \
  -i 'threshold=0.3'

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/c-barron/grounded-sam@sha256:81831427a4ab1d447830592975b4c74b79f018744fcabf8db449a3a9c3ba4b2a
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "image": "https://st.mngbcn.com/rcs/pics/static/T5/fotos/outfit/S20/57034757_56-99999999_01.jpg",
      "prompts": ["face."],
      "threshold": 0.3
    }
  }' \
  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 L40S GPU hardware. We don't yet have enough runs of this model to provide performance information.

Readme

This model doesn't have a readme.