tmappdev/lang-segment-anything – Run with an API on Replicate

Input

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

npx create-replicate --model=tmappdev/lang-segment-anything

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 tmappdev/lang-segment-anything using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "tmappdev/lang-segment-anything:891411c38a6ed2d44c004b7b9e44217df7a5b07848f29ddefd2e28bc7cbf93bc",
  {
    input: {
      image: "https://replicate.delivery/pbxt/M2tUXKe06UEAExSwWbcYvMOoGVXGtEbvsD52HaSgC3vulSfR/a2ed4.jpg",
      text_prompt: "text,watermark"
    }
  }
);

// 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 tmappdev/lang-segment-anything using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "tmappdev/lang-segment-anything:891411c38a6ed2d44c004b7b9e44217df7a5b07848f29ddefd2e28bc7cbf93bc",
    input={
        "image": "https://replicate.delivery/pbxt/M2tUXKe06UEAExSwWbcYvMOoGVXGtEbvsD52HaSgC3vulSfR/a2ed4.jpg",
        "text_prompt": "text,watermark"
    }
)
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 tmappdev/lang-segment-anything 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": "891411c38a6ed2d44c004b7b9e44217df7a5b07848f29ddefd2e28bc7cbf93bc",
    "input": {
      "image": "https://replicate.delivery/pbxt/M2tUXKe06UEAExSwWbcYvMOoGVXGtEbvsD52HaSgC3vulSfR/a2ed4.jpg",
      "text_prompt": "text,watermark"
    }
  }' \
  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/tmappdev/lang-segment-anything@sha256:891411c38a6ed2d44c004b7b9e44217df7a5b07848f29ddefd2e28bc7cbf93bc \
  -i 'image="https://replicate.delivery/pbxt/M2tUXKe06UEAExSwWbcYvMOoGVXGtEbvsD52HaSgC3vulSfR/a2ed4.jpg"' \
  -i 'text_prompt="text,watermark"'

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/tmappdev/lang-segment-anything@sha256:891411c38a6ed2d44c004b7b9e44217df7a5b07848f29ddefd2e28bc7cbf93bc
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "image": "https://replicate.delivery/pbxt/M2tUXKe06UEAExSwWbcYvMOoGVXGtEbvsD52HaSgC3vulSfR/a2ed4.jpg",
      "text_prompt": "text,watermark"
    }
  }' \
  http://localhost:5000/predictions

To learn more, take a look at the Cog documentation.

Output

Generated in

2.2 seconds

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Run time and cost

This model costs approximately $0.0014 to run on Replicate, or 714 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 1 seconds.