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

Matthew Berman
12 Jun 202308:09

TLDRThe video introduces two innovative text-to-video AI tools: RunwayML's Gen 2 and an open-source project by potat1. RunwayML's Gen 2 is a free service with limitations on video length, offering high-quality results and a monthly credit system. The open-source project, utilizing Google Colab and various libraries, allows users to generate short, customizable videos on their local machines. Despite the current limitations in video duration and potential quality degradation with longer videos, both tools demonstrate the cutting-edge capabilities of text-to-video AI technology. The video also provides a detailed tutorial on setting up the open-source project locally, emphasizing the use of Anaconda for Python version management and the importance of a powerful GPU for better performance. The creator encourages viewers to experiment with different models and offers support through Discord.

Takeaways

  • 🌟 The video discusses two text-to-video AI products: RunwayML's Gen 2 and an open-source project by potat1.
  • 🆓 RunwayML's Gen 2 is free to use but has limitations on the number of seconds of video that can be generated.
  • 📈 Gen 2 is described as being on the cutting edge of text-to-video technology and outperforms other solutions.
  • 🎥 An example prompt 'ducks on a lake' was used to generate a video, resulting in a short but accurate clip.
  • 💳 To access more features on RunwayML, such as higher resolution and no watermarks, a subscription costing $12 per editor per month is required.
  • 🔍 The open-source project uses Google Colab and various text-to-video libraries, with a focus on xeroscope V 1.1.
  • 🚀 The open-source project is easy to start with by simply clicking a play button to install necessary libraries and clone repositories.
  • 🚨 A limitation of the open-source project is that longer videos can lead to memory issues on Google Colab and quick quality degradation.
  • 💻 For local running, the video demonstrates using Anaconda for Python version management and installing necessary libraries like PyTorch.
  • 🔗 The video provides a step-by-step guide to set up and run the open-source text-to-video project on a local Windows machine with an Nvidia GPU.
  • 📉 The quality of generated videos degrades as the length increases, with the models being trained primarily on one to two-second videos.
  • 🔄 The community is actively working on improving video quality for longer durations, and the video offers guidance on how to engage with them for support.

Q & A

  • What is the name of the text-to-video AI product mentioned in the transcript?

    -The text-to-video AI product mentioned is called runwayml's Gen 2.

  • Is the Gen 2 product free to use?

    -Yes, Gen 2 is free to use, but users are limited in the number of seconds of video they can generate.

  • What is the cost for the premium version of Gen 2?

    -The premium version of Gen 2 costs twelve dollars per editor per month.

  • What are the benefits of the premium version of Gen 2?

    -The premium version offers upscale resolution, removal of watermarks, shorter wait times, and 125 seconds of generated video every month.

  • What is the limitation of the open source text-to-video project by potat1?

    -The limitation is that it can only generate short videos, and increasing the video length too much can cause memory issues and rapid quality degradation.

  • How can one run the open source text-to-video project locally?

    -To run it locally, one needs to install Anaconda, set up a conda environment with Python 3.10.11, install necessary torch libraries, clone the required repositories, and then run the inference script with the correct paths.

  • What is the role of Anaconda in running the open source text-to-video project?

    -Anaconda is used for Python version management, which helps avoid issues with Python version and module version mismatches.

  • What is the name of the text-to-video library used in the open source project?

    -The text-to-video library used is xeroscope V 1.1.

  • What is the issue with increasing the number of frames in the open source text-to-video project?

    -Increasing the number of frames beyond a certain limit can lead to memory issues on Google Colab and a rapid degradation in video quality.

  • How can one get help if they encounter issues while setting up the open source text-to-video project?

    -One can get help by joining the Discord communities of the project creator or cam enduru.

  • What is the current limitation in terms of video length when using the open source text-to-video project?

    -The current limitation is that the models are trained on one to two second videos, and increasing the video length beyond this range can lead to a decrease in quality.

  • What does the future hold for the open source text-to-video project?

    -The project is actively working on improving the quality of longer videos, and there are plans to introduce new models that can handle longer durations.

Outlines

00:00

🚀 Introduction to Text-to-Video Products

The video begins by expressing excitement about the advancements in text-to-video technology. Two products are highlighted: RunwayML's Gen 2, which is a closed-source product that has recently become available to the public, and an open-source project that can be run locally or on Google Colab. Gen 2 is praised for its cutting-edge technology and impressive video generation capabilities, albeit with a limit on the number of seconds that can be generated for free. The video then demonstrates the process of generating a short video using the phrase 'ducks on a lake' and discusses the limitations and costs associated with using Gen 2 for longer videos.

05:00

🌐 Exploring Open Source Text-to-Video with Hugging Face

The video then shifts focus to an open-source text-to-video project by potat1, which is hosted on Hugging Face's GitHub page. The presenter guides viewers through the process of using Google Colab to generate a video, starting with the installation of necessary libraries and cloning of repositories. The video generation process is demonstrated with a prompt of 'ducks on a lake', and the presenter discusses the limitations of frame count and the resulting video length. It's noted that increasing the frame count too much can lead to memory issues and a decrease in video quality. The presenter also shares their experience running the project locally on a Windows machine with an Nvidia GPU, emphasizing the benefits of using Anaconda for Python version management and the need for a powerful GPU to handle the processing demands of longer video generation. The video concludes with a call to action for viewers to try out the technology and offers support through Discord.

Mindmap

Keywords

💡Text-To-Video AI

Text-To-Video AI refers to artificial intelligence technology that converts text descriptions into video content. In the video, this technology is showcased through two different products, demonstrating how users can generate videos from textual prompts like 'ducks on a lake.' It's a cutting-edge application of AI that transforms the way content is created.

💡RunwayML's Gen 2

RunwayML's Gen 2 is a proprietary text-to-video AI product mentioned in the video. It has been in development and has undergone a private beta phase. The product is described as impressive and on the cutting edge of text-to-video technology. It allows users to generate a limited number of video seconds for free, with the option to pay for additional features and credits.

💡Open Source Project

An open source project refers to a collaborative effort where the source code or the underlying designs of a software or system are accessible to the public. In the context of the video, an open source text-to-video project by 'potat1' is discussed, which allows users to run the AI model on their local computers or Google Colab, offering a free and customizable alternative to proprietary solutions.

💡Google Colab

Google Colab is a cloud-based platform provided by Google that allows users to write and execute Python code in a simple and collaborative environment. It is mentioned in the video as a platform where one can run the open source text-to-video AI project, highlighting its utility for machine learning and AI-related tasks.

💡Hugging Face

Hugging Face is an open-source organization that provides tools and libraries for natural language processing (NLP). In the video, it is mentioned as the source of the text-to-video AI model that can be used with Google Colab. It represents the community-driven aspect of AI development.

💡Cuda

CUDA (Compute Unified Device Architecture) is a parallel computing platform and programming model developed by Nvidia that allows software to utilize the power of Nvidia GPUs for computation. In the video, it is essential for running the text-to-video AI model locally, as it helps in handling the intensive processing required for video generation.

💡Anaconda

Anaconda is a distribution of Python programming language that simplifies the process of managing different versions of Python and packages. It is recommended in the video for setting up the environment to run the open source text-to-video AI project, emphasizing its role in streamlining development workflows.

💡PyTorch

PyTorch is an open-source machine learning library based on the Torch library, used for applications such as computer vision and natural language processing. It is mentioned as a necessary library for running the text-to-video AI model, showcasing its importance in the field of AI and deep learning.

💡Video Generation Credits

In the context of the video, video generation credits refer to the units of currency within the RunwayML's Gen 2 platform that determine how much video a user can generate. Each second of video generation consumes a certain number of credits, which highlights the resource-intensive nature of video creation using AI.

💡Model Degradation

Model degradation in the video refers to the decline in the quality of the generated video as the length of the video increases beyond the model's training limits. The video discusses this challenge when attempting to generate longer videos, noting that the models are currently trained to produce one to two-second clips effectively.

💡Discord

Discord is a communication platform that allows for text, voice, and video conversations. In the video, it is mentioned as a community hub where users can seek help and engage with the developers or others interested in the text-to-video AI projects. It represents the social and collaborative aspect of open-source development.

Highlights

Text-to-video AI technology is becoming a reality with impressive results.

Two different text-to-video products are showcased: one closed source and one open source.

RunwayML's Gen 2 product is free but has limitations on the number of seconds of video that can be generated.

Gen 2 is on the cutting edge of text-to-video technology and outperforms other solutions.

The video generated by Gen 2 is accurate but may have minor imperfections like the two-headed duck example.

To use Gen 2, visit runwayml.com where credits are provided monthly, with additional costs for more features.

An open-source text-to-video project by potat1 is introduced, which can be run on a local computer or Google Colab.

The open-source project uses different text-to-video libraries and is demonstrated using xeroscope V 1.1 on Google Colab.

The process for running the open-source text-to-video project on Google Colab is straightforward, with a play button to install necessary libraries.

Increasing the number of frames in the open-source project can lead to memory issues on Google Colab and a drop in video quality.

Local machine with a powerful GPU can create longer videos, but the method to maintain quality for longer videos is still being developed.

To run the open-source project locally, Anaconda is recommended for Python version management to avoid version mismatch issues.

The process of setting up the local environment includes creating a conda environment, installing necessary libraries, and cloning repositories.

A checker script is used to ensure the correct version of torch and Cuda are installed and available for the project.

Running the inference file generates a video, with the quality degrading as the video length increases beyond two seconds.

The project's Discord community offers support and the opportunity to try different models for better text-to-video results.

The video demonstrates the process of increasing the frame count and the resulting changes in video quality.

The open-source text-to-video project is a significant step forward, offering local and customizable solutions despite current limitations.

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