petelouise/sunny-side-up | Run with an API on Replicate

Input

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

npx create-replicate --model=petelouise/sunny-side-up

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 petelouise/sunny-side-up using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "petelouise/sunny-side-up:375aff5a989c8b291298d0f42907cb9b384292c7054257d3d0d5198768afb67d",
  {
    input: {
      top_k: 250,
      top_p: 0,
      duration: 8,
      temperature: 1,
      continuation: false,
      output_format: "wav",
      continuation_start: 0,
      multi_band_diffusion: false,
      normalization_strategy: "loudness",
      classifier_free_guidance: 3
    }
  }
);

// 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 petelouise/sunny-side-up using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "petelouise/sunny-side-up:375aff5a989c8b291298d0f42907cb9b384292c7054257d3d0d5198768afb67d",
    input={
        "top_k": 250,
        "top_p": 0,
        "duration": 8,
        "temperature": 1,
        "continuation": False,
        "output_format": "wav",
        "continuation_start": 0,
        "multi_band_diffusion": False,
        "normalization_strategy": "loudness",
        "classifier_free_guidance": 3
    }
)
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 petelouise/sunny-side-up 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": "petelouise/sunny-side-up:375aff5a989c8b291298d0f42907cb9b384292c7054257d3d0d5198768afb67d",
    "input": {
      "top_k": 250,
      "top_p": 0,
      "duration": 8,
      "temperature": 1,
      "continuation": false,
      "output_format": "wav",
      "continuation_start": 0,
      "multi_band_diffusion": false,
      "normalization_strategy": "loudness",
      "classifier_free_guidance": 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/petelouise/sunny-side-up@sha256:375aff5a989c8b291298d0f42907cb9b384292c7054257d3d0d5198768afb67d \
  -i 'top_k=250' \
  -i 'top_p=0' \
  -i 'duration=8' \
  -i 'temperature=1' \
  -i 'continuation=false' \
  -i 'output_format="wav"' \
  -i 'continuation_start=0' \
  -i 'multi_band_diffusion=false' \
  -i 'normalization_strategy="loudness"' \
  -i 'classifier_free_guidance=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/petelouise/sunny-side-up@sha256:375aff5a989c8b291298d0f42907cb9b384292c7054257d3d0d5198768afb67d
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "top_k": 250,
      "top_p": 0,
      "duration": 8,
      "temperature": 1,
      "continuation": false,
      "output_format": "wav",
      "continuation_start": 0,
      "multi_band_diffusion": false,
      "normalization_strategy": "loudness",
      "classifier_free_guidance": 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.