myaiteam2/lighting-video | Run with an API on Replicate

Input

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

npx create-replicate --model=myaiteam2/lighting-video

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 myaiteam2/lighting-video using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "myaiteam2/lighting-video:0365d6a84c1b16eb4826f9e22c81235e38ffe744c7ec7293f78e04ad39639f42",
  {
    input: {}
  }
);

// 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 myaiteam2/lighting-video using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "myaiteam2/lighting-video:0365d6a84c1b16eb4826f9e22c81235e38ffe744c7ec7293f78e04ad39639f42",
    input={}
)

# To access the file URL:
print(output.url())
#=> "http://example.com"

# To write the file to disk:
with open("my-image.png", "wb") as file:
    file.write(output.read())

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 myaiteam2/lighting-video 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": "myaiteam2/lighting-video:0365d6a84c1b16eb4826f9e22c81235e38ffe744c7ec7293f78e04ad39639f42",
    "input": {}
  }' \
  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/myaiteam2/lighting-video@sha256:0365d6a84c1b16eb4826f9e22c81235e38ffe744c7ec7293f78e04ad39639f42 \

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 r8.im/myaiteam2/lighting-video@sha256:0365d6a84c1b16eb4826f9e22c81235e38ffe744c7ec7293f78e04ad39639f42
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {}
  }' \
  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.