FREE and Unlimited Text-To-Video AI is Here! ? Full Tutorials (Easy/Med/Hard)

Introduction

Text-to-video technology is finally evolving into a remarkable reality. The creative works produced by individuals using text-to-video tools are simply stunning, showcasing the potential of this groundbreaking technology. In this article, I’ll introduce you to two different products: one being a closed-source, advanced tool and the other an exciting new open-source project that you can run locally or on Google Colab. Let’s dive in!

Closed Source: Runway ML's Gen 2

Runway ML has launched its impressive Gen 2 product, which has been in beta for some time. Now available to the public, this tool allows users to generate short videos from text prompts for free (with certain limitations).

How to Use Gen 2

1. Visit Runway ML at Runway ML's website and click “Generate.”
2. You’ll notice a credit system where each second of video generation costs five credits, and you may start with 410 credits in total.

The results are usually around 4 to 5 seconds long. For example, a generated video of ducks on a lake showcases decent visual quality; however, some artifacts (like a duck appearing to have two heads) may be present.

Gen 2 is on the cutting edge of text-to-video technology, outperforming many alternatives. While the service is free initially, pricing details show a plan starting at $ 12 per month per editor, which provides higher resolution videos, watermark removal, and greater monthly video generation limits.

Open Source: Hugging Face's Project

Next, we look at an open-source project by Putat One hosted on Hugging Face. This project allows you to run text-to-video models on your local machine or via Google Colab.

Getting Started with Open Source

1. Visit the Hugging Face page for the project and navigate to its GitHub repository and configure settings such as number of steps and frames per second. Note that settings are usually capped to create shorter video clips for higher quality.
2. Start the video generation process and an output folder will appear in which you can download your video.

The limitation for this project is that the videos stay under one second without quality degradation. Users can also expand the duration of the videos, but they may face issues related to memory on Google Colab and quality inconsistency.

Running Locally

For those who prefer a local setup (especially if you have a powerful GPU), using Anaconda simplifies environment management. Here are the steps:

1. Install Anaconda and create a new folder for your project.
2. Set up a Python environment with version 3.10.1 (recommended for compatibility).
3. Use Anaconda commands to install necessary libraries like PyTorch and clone the relevant repositories from Hugging Face.
4. Check the CUDA setup and run the inference script while supplying the necessary arguments.

After executing the script, you will find your generated video in the outputs folder.

Conclusion

Both solutions—Runway ML's Gen 2 and the open-source models available on Hugging Face—offer impressive capabilities in text-to-video generation. While the closed-source option has premium features, the open-source alternative allows for deep customization and is accessible to anyone with local machine capabilities.

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Keywords:
Text-to-video, Runway ML, Gen 2, Hugging Face, Putat One, Google Colab, Anaconda, local setup, PyTorch, CUDA, open-source.

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FAQ:

1. What is text-to-video technology?
   Text-to-video technology allows users to generate videos based on textual prompts using advanced machine learning models.

2. What is Runway ML's Gen 2?
   Gen 2 is a closed-source tool that enables users to create short video clips from text prompts. It operates on a credit system and offers free usage with limitations.

3. How can I access the open-source text-to-video project?
   You can access it through Hugging Face and run it either locally or via Google Colab by following installation instructions from its GitHub page.

4. Can I run these text-to-video models on my computer?
   Yes, if you have an Nvidia GPU, using Anaconda to manage your Python environment can facilitate the installation and execution of these models locally.

5. Why do longer videos degrade in quality?
   Many of these models are trained on shorter clips, leading to a drop in quality when attempting to generate videos longer than 1-2 seconds.