harmonai / dance-diffusion

Tools to train a generative model on arbitrary audio samples

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5.2K runs
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Run with an API

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Prediction

harmonai/dance-diffusion:7f27a989b169da7e984136b1185b294b744f4c38090f4a91c13de5b34a09ee7e

Model

harmonai/dance-diffusion:7f27a989

k4s5kds5freore4udcxupmnnfq

Status

Succeeded

Source

Web

Hardware

–

Total duration

7m 55s

Created

almost 3 years ago by @voodoohop

Input

steps: 100
length: 16
batch_size: 1
model_name: maestro-150k

{
  "steps": 100,
  "length": 16,
  "batch_size": 1,
  "model_name": "maestro-150k"
}

Install Replicate’s Node.js client library:

npm install replicate

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 harmonai/dance-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "harmonai/dance-diffusion:7f27a989b169da7e984136b1185b294b744f4c38090f4a91c13de5b34a09ee7e",
  {
    input: {
      steps: 100,
      length: 16,
      batch_size: 1,
      model_name: "maestro-150k"
    }
  }
);

// 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

Import the client:

import replicate

Run harmonai/dance-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "harmonai/dance-diffusion:7f27a989b169da7e984136b1185b294b744f4c38090f4a91c13de5b34a09ee7e",
    input={
        "steps": 100,
        "length": 16,
        "batch_size": 1,
        "model_name": "maestro-150k"
    }
)

# 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.

Run harmonai/dance-diffusion 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": "harmonai/dance-diffusion:7f27a989b169da7e984136b1185b294b744f4c38090f4a91c13de5b34a09ee7e",
    "input": {
      "steps": 100,
      "length": 16,
      "batch_size": 1,
      "model_name": "maestro-150k"
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

Video Player is loading.

Current Time 00:00:000

Duration 00:00:000

Loaded: 0%

Stream Type LIVE

Remaining Time 00:00:000

Generated in

2 minutes 17 seconds

Tweak it Share Report

Prediction

harmonai/dance-diffusion:7f27a989b169da7e984136b1185b294b744f4c38090f4a91c13de5b34a09ee7e

Model

harmonai/dance-diffusion:7f27a989

3m3y5cyb2zctbism6gje5wpin4

Status

Succeeded

Source

Web

Hardware

–

Total duration

2m 43s

Created

almost 3 years ago by @voodoohop

Input

steps: 100
length: 8
batch_size: 1
model_name: maestro-150k

{
  "steps": 100,
  "length": 8,
  "batch_size": 1,
  "model_name": "maestro-150k"
}

Install Replicate’s Node.js client library:

npm install replicate

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 harmonai/dance-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "harmonai/dance-diffusion:7f27a989b169da7e984136b1185b294b744f4c38090f4a91c13de5b34a09ee7e",
  {
    input: {
      steps: 100,
      length: 8,
      batch_size: 1,
      model_name: "maestro-150k"
    }
  }
);

// 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

Import the client:

import replicate

Run harmonai/dance-diffusion using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "harmonai/dance-diffusion:7f27a989b169da7e984136b1185b294b744f4c38090f4a91c13de5b34a09ee7e",
    input={
        "steps": 100,
        "length": 8,
        "batch_size": 1,
        "model_name": "maestro-150k"
    }
)

# 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.

Run harmonai/dance-diffusion 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": "harmonai/dance-diffusion:7f27a989b169da7e984136b1185b294b744f4c38090f4a91c13de5b34a09ee7e",
    "input": {
      "steps": 100,
      "length": 8,
      "batch_size": 1,
      "model_name": "maestro-150k"
    }
  }' \
  https://api.replicate.com/v1/predictions

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