zsxkib/step-video-t2v:418b87f2 – Run with an API on Replicate

Version

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zsxkib /step-video-t2v:418b87f2

Playground API

Input

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

npx create-replicate --model=zsxkib/step-video-t2v

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 zsxkib/step-video-t2v using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "zsxkib/step-video-t2v:418b87f260d311e63b1ea5da1b1a84f09f79d2d0d8f163845f9c361a4ff8da79",
  {
    input: {
      fps: 25,
      prompt: "An astronaut on the moon",
      quality: 5,
      cfg_scale: 9,
      num_frames: 51,
      negative_prompt: "low resolution, text",
      num_inference_steps: 30
    }
  }
);

// 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 zsxkib/step-video-t2v using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "zsxkib/step-video-t2v:418b87f260d311e63b1ea5da1b1a84f09f79d2d0d8f163845f9c361a4ff8da79",
    input={
        "fps": 25,
        "prompt": "An astronaut on the moon",
        "quality": 5,
        "cfg_scale": 9,
        "num_frames": 51,
        "negative_prompt": "low resolution, text",
        "num_inference_steps": 30
    }
)
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 zsxkib/step-video-t2v 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": "418b87f260d311e63b1ea5da1b1a84f09f79d2d0d8f163845f9c361a4ff8da79",
    "input": {
      "fps": 25,
      "prompt": "An astronaut on the moon",
      "quality": 5,
      "cfg_scale": 9,
      "num_frames": 51,
      "negative_prompt": "low resolution, text",
      "num_inference_steps": 30
    }
  }' \
  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/zsxkib/step-video-t2v@sha256:418b87f260d311e63b1ea5da1b1a84f09f79d2d0d8f163845f9c361a4ff8da79 \
  -i 'fps=25' \
  -i 'prompt="An astronaut on the moon"' \
  -i 'quality=5' \
  -i 'cfg_scale=9' \
  -i 'num_frames=51' \
  -i 'negative_prompt="low resolution, text"' \
  -i 'num_inference_steps=30'

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/zsxkib/step-video-t2v@sha256:418b87f260d311e63b1ea5da1b1a84f09f79d2d0d8f163845f9c361a4ff8da79
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "fps": 25,
      "prompt": "An astronaut on the moon",
      "quality": 5,
      "cfg_scale": 9,
      "num_frames": 51,
      "negative_prompt": "low resolution, text",
      "num_inference_steps": 30
    }
  }' \
  http://localhost:5000/predictions

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

Output

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