FREE and Unlimited Text-To-Video AI is Here! 🙏 Full Tutorials (Easy/Med/Hard)
TLDRDiscover the groundbreaking text-to-video AI technology with this tutorial showcasing both Runway ML's Gen 2 and an open-source project. Runway ML offers free video generation with limitations on length, while the open-source alternative allows local video creation on platforms like Google Colab. Learn how to set up and generate short, impressive videos using these tools, and explore the potential of this emerging tech.
Takeaways
- 🆓 Text-to-video AI technology is now available for free and unlimited use.
- 🔥 Two products are showcased: Runway ML's Gen 2 (closed source) and an open-source project.
- 🚀 Runway ML's Gen 2 is impressive but has a limit on the number of seconds of video you can generate.
- 🦆 An example of 'ducks on a lake' generates a 4-5 second video with decent accuracy.
- 💸 Runway ML offers a free tier with monthly credits, but requires payment for more extensive use.
- 💻 The open-source project can be run locally or on Google Colab, offering flexibility.
- 🔗 Links to the Hugging Face page and GitHub for the open-source project are provided.
- 📈 The open-source project has limitations, such as memory issues and quality degradation with longer videos.
- 💾 Setting up a local environment with Anaconda and Python is recommended for smoother operation.
- 🛠️ Cloning the necessary repositories and installing the correct libraries are key steps for local setup.
- 🎥 The quality of generated videos can degrade with increased video length, especially beyond 2 seconds.
Q & A
What is the main topic of the video?
-The main topic of the video is the introduction and tutorial of two text-to-video AI tools: Runway ML's Gen 2 product and an open-source text-to-video project by Hugging Face.
What are the limitations of Runway ML's Gen 2 product?
-Runway ML's Gen 2 product is free but has a limitation on the number of seconds of video that can be generated. Each second of video generation uses five credits, and users have a certain number of credits per month.
How much does Runway ML's Gen 2 product cost after the free credits are used up?
-After the free credits are used up, the cost is $12 per editor per month, which includes upscaling resolution, removal of watermarks, shorter wait times, and 1225 seconds of generated video every month.
What is the open-source text-to-video project mentioned in the video?
-The open-source text-to-video project mentioned is available on Hugging Face's GitHub page and uses different text-to-video libraries, including zeroscope v1.1.
What are the system requirements for running the open-source text-to-video project locally?
-Running the open-source text-to-video project locally requires a system with Python, Anaconda for version management, and an Nvidia GPU for processing.
What is the main challenge when generating longer videos with the open-source project?
-The main challenge when generating longer videos with the open-source project is that the quality degrades quickly and there's a risk of running out of memory on Google Colab.
How can users get more credits for Runway ML's Gen 2 product?
-Users can get new credits every month for free, but after that, they have to pay for additional credits.
What is the process of generating a video using the open-source text-to-video project on Google Colab?
-The process involves installing necessary libraries, cloning the required repositories, entering the prompt for the video, and running the script to generate the video.
What is Anaconda and why is it recommended for running the open-source project?
-Anaconda is a platform for Python and R languages that provides version management for Python and alleviates issues related to Python and module version mismatches. It is recommended because it simplifies the setup process and avoids common installation problems.
How can users ensure they have the correct version of Torch and CUDA for the open-source project?
-Users can run a checker script provided in the project to ensure they have the correct version of Torch and CUDA, and that CUDA is available for use.
What is the current limitation in terms of video length when using the open-source text-to-video project?
-The current limitation is that increasing the video length beyond 2 seconds results in a severe degradation of video quality due to the models being trained on 1 to 2 second videos.
Outlines
🚀 Introduction to Text-to-Video Technologies
The script introduces the emerging technology of text-to-video conversion, highlighting the impressive creations made possible by this technology. It discusses two products: one is a closed-source product called Runway ML's Gen 2, which is now publicly available for free with limitations on video length. The other is an open-source project that can be run locally or on Google Colab. The video demonstrates the process of generating a short video using Runway ML's Gen 2 by inputting the prompt 'ducks on a lake'. It also mentions the cost structure and the computational power required for video generation. The script then transitions to discussing an open-source text-to-video project by Hugging Face, which is accessible via Google Colab and GitHub.
💻 Setting Up Open Source Text-to-Video Locally
The script provides a step-by-step guide on setting up an open-source text-to-video project locally on a Windows machine with an Nvidia GPU. It starts with the installation of Anaconda for Python version management to avoid conflicts. The guide then proceeds to cloning necessary repositories, installing required libraries with pip, and setting up the environment to ensure compatibility with CUDA. The script also includes a check for CUDA installation and availability. Finally, it demonstrates how to run the inference script to generate a video, discusses the limitations on video length due to model training, and suggests trying different models for better results. The video concludes with an invitation for viewers to join Discord communities for help and to show support by liking and subscribing.
Mindmap
Keywords
💡Text-to-Video AI
💡Runway ML's Gen 2
💡Credits
💡Open Source Project
💡Google Colab
💡Prompt
💡Resolution
💡Watermarks
💡Anaconda
💡GPU
💡Cuda
Highlights
Text-to-video AI is now a reality, creating impressive results.
Two different text-to-video products are showcased: one closed source and one open source.
Runway ML's Gen 2 product is free but has a limit on video length.
Each second of video generation on Runway ML uses five credits.
Gen 2 is on the cutting edge of text-to-video technology.
Runway ML offers a paid plan at $12 per editor per month for additional features.
An open-source text-to-video project by Hugging Face is introduced.
The project offers Google Colab versions using different text-to-video libraries.
Zeroscope v1.1 text-to-video Colab is used for demonstration.
The process to run the open-source project on Google Colab is explained.
There's a limitation on video length when using the open-source project on Google Colab.
Increasing the video length can cause memory issues and quality degradation.
The video quality of the open-source project is comparable to Gen 2.
Instructions on how to run the open-source project locally on a Windows machine are provided.
Anaconda is recommended for Python version management.
The process of setting up the environment and installing necessary libraries is detailed.
Cuda is checked to ensure compatibility with the project.
The inference script is run to generate a video, showcasing the process.
The video quality degrades when the length is increased beyond 2 seconds.
The community is working on improving video quality for longer durations.
The video concludes with an invitation to join Discord for help and updates.