pschaldenbrand/text2video:0730c97d – Run with an API on Replicate

Version

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pschaldenbrand /text2video:0730c97d

Playground API

Input

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

npx create-replicate --model=pschaldenbrand/text2video

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 pschaldenbrand/text2video using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "pschaldenbrand/text2video:0730c97d436b20ef02435b18905bc6bddb17ece40c8b59b2b1a0679574cd1686",
  {
    input: {
      fast: true,
      width: 640,
      height: 360,
      frame_rate: 8,
      temperature: 30,
      frames_per_promt: 20
    }
  }
);
console.log(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 pschaldenbrand/text2video using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "pschaldenbrand/text2video:0730c97d436b20ef02435b18905bc6bddb17ece40c8b59b2b1a0679574cd1686",
    input={
        "fast": True,
        "width": 640,
        "height": 360,
        "frame_rate": 8,
        "temperature": 30,
        "frames_per_promt": 20
    }
)

# The pschaldenbrand/text2video 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/pschaldenbrand/text2video/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 pschaldenbrand/text2video 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": "0730c97d436b20ef02435b18905bc6bddb17ece40c8b59b2b1a0679574cd1686",
    "input": {
      "fast": true,
      "width": 640,
      "height": 360,
      "frame_rate": 8,
      "temperature": 30,
      "frames_per_promt": 20
    }
  }' \
  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/pschaldenbrand/text2video@sha256:0730c97d436b20ef02435b18905bc6bddb17ece40c8b59b2b1a0679574cd1686 \
  -i 'fast=true' \
  -i 'width=640' \
  -i 'height=360' \
  -i 'frame_rate=8' \
  -i 'temperature=30' \
  -i 'frames_per_promt=20'

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/pschaldenbrand/text2video@sha256:0730c97d436b20ef02435b18905bc6bddb17ece40c8b59b2b1a0679574cd1686
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "fast": true,
      "width": 640,
      "height": 360,
      "frame_rate": 8,
      "temperature": 30,
      "frames_per_promt": 20
    }
  }' \
  http://localhost:5000/predictions

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

Output

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