ji4chenli/t2v-turbo – Run with an API on Replicate

ji4chenli / t2v-turbo

Fast and High-Quality Text-to-video Generation

Cold

Public
4.5K runs
L40S
GitHub
Paper

Run with an API

Playground API Examples README Versions

Input

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

npx create-replicate --model=ji4chenli/t2v-turbo

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 ji4chenli/t2v-turbo using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "ji4chenli/t2v-turbo:027457b88cbf5a1914038a87910d00cc4f766db4b4ef2377de7882d80e7c0ddd",
  {
    input: {
      fps: 8,
      prompt: "With the style of low-poly game art, A majestic, white horse gallops gracefully across a moonlit beach",
      num_frames: 16,
      guidance_scale: 7.5,
      num_inference_steps: 4
    }
  }
);

// 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 ji4chenli/t2v-turbo using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "ji4chenli/t2v-turbo:027457b88cbf5a1914038a87910d00cc4f766db4b4ef2377de7882d80e7c0ddd",
    input={
        "fps": 8,
        "prompt": "With the style of low-poly game art, A majestic, white horse gallops gracefully across a moonlit beach",
        "num_frames": 16,
        "guidance_scale": 7.5,
        "num_inference_steps": 4
    }
)
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 ji4chenli/t2v-turbo 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": "027457b88cbf5a1914038a87910d00cc4f766db4b4ef2377de7882d80e7c0ddd",
    "input": {
      "fps": 8,
      "prompt": "With the style of low-poly game art, A majestic, white horse gallops gracefully across a moonlit beach",
      "num_frames": 16,
      "guidance_scale": 7.5,
      "num_inference_steps": 4
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

Generated in

5.6 seconds

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Examples

View more examples

Run time and cost

This model costs approximately $0.021 to run on Replicate, or 47 runs per $1, but this varies depending on your inputs. It is also open source and you can run it on your own computer with Docker.

This model runs on Nvidia L40S GPU hardware. Predictions typically complete within 23 seconds. The predict time for this model varies significantly based on the inputs.

Readme

T2V-Turbo: Breaking the Quality Bottleneck of Video Consistency Model with Mixed Reward Feedback

Fast and High-Quality Text-to-video Generation 🚀

This demo uses the T2V-Turbo (VC2) model, with resolution of 320x512

Citation

@misc{li2024t2vturbo,
      title={T2V-Turbo: Breaking the Quality Bottleneck of Video Consistency Model with Mixed Reward Feedback}, 
      author={Jiachen Li and Weixi Feng and Tsu-Jui Fu and Xinyi Wang and Sugato Basu and Wenhu Chen and William Yang Wang},
      year={2024},
      eprint={2405.18750},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}