FREE AI Deepfake: Control Expressions & Emotion | Image to Video with Live Portrait in Google Colab
TLDRDiscover Live Portrait, an advanced deepfake tool that transforms images into lifelike videos. By inputting a photo and a source video, the tool can map complex expressions and emotions onto the image, creating a realistic talking or singing portrait without distortion. This tutorial showcases three online methods to use Live Portrait for free: Hugging Face for quick uploads, Replicate for more control, and Google Colab for longer, high-quality videos. The process is user-friendly, requiring only basic setup and file uploads. The technology's potential is immense, offering a glimpse into the future of AI-generated content.
Takeaways
- 😀 Live Portrait is an advanced deepfake tool that can map video expressions onto a photo.
- 🔍 You can use Live Portrait by inputting an image and a source video to create a talking or singing photo.
- 💻 The tool requires a graphics card for installation, but there are online methods to use it for free.
- 🤗 The first method involves using Hugging Face, where you can upload your source image and driving video.
- 🖼️ Live Portrait can handle various image styles, including black and white pictures, realistic photos, oil paintings, and fictional statues.
- 🔄 The second method is Replicate, which offers more control but limits video length to 5 seconds.
- 🔗 For Replicate, you can change the driving video URL and adjust settings like frame load cap and size scale ratio.
- 🌐 The third method is using Google Colab, where you run two cells in sequence after selecting the T4 GPU.
- 📂 In Google Colab, upload your video and image, copy their paths, and adjust them in the cells to generate the animation.
- 💾 Download the generated video from the 'live portrait' folder in Google Colab.
- 🚀 This technology showcases the incredible potential of AI in creating realistic and expressive video animations.
Q & A
What is Live Portrait and how does it work?
-Live Portrait is an advanced deepfake tool that allows you to input an image and a source video. It maps the video's expressions onto the photo, enabling the image to mimic complex facial expressions and movements without distortion.
Where can I find the Live Portrait GitHub repo?
-The Live Portrait GitHub repo can be found through the link provided in the description of the video script.
What are the system requirements for installing Live Portrait?
-To install Live Portrait, you will need a computer with a graphics card.
How many online methods are mentioned in the script to use Live Portrait for free?
-The script mentions three easy online methods to use Live Portrait for free.
What is the first method mentioned for using Live Portrait online?
-The first method involves using Hugging Face, where you can upload your source image and driving video through an interface provided on their website.
What should be the aspect ratio of the video when using the Hugging Face method?
-The aspect ratio of the video should be 1:1 when using the Hugging Face method.
Who developed Live Portrait?
-Live Portrait is developed by Quow, the same company behind Clling AI, which is known for being one of the best AI video generators.
What is the second method mentioned for using Live Portrait online?
-The second method is called 'replicate', where you can upload an image and a driving video URL, and adjust settings like video frame load cap, size scale ratio, lip and eye retargeting.
What is the limitation of the 'replicate' method mentioned in the script?
-The 'replicate' method cannot create videos longer than 5 seconds.
How can you use Live Portrait through Google Colab?
-To use Live Portrait through Google Colab, you open the provided page, run the two segments or cells one after the other, and ensure that the T4 GPU is selected. You then upload your video footage and image, adjust their paths in the second cell, and run it to generate the video.
What is the result of using Live Portrait through Google Colab?
-After running the process in Google Colab, you can download the generated video from the 'live portrait' folder in the 'animations' folder.
Outlines
🎨 Introduction to Live Portraits Deepfake Tool
This paragraph introduces an advanced open-source deepfake tool called Live Portraits. It allows users to input an image and a source video, which the tool then uses to map the video's expressions onto the photo, enabling it to mimic complex facial expressions without distortion. The speaker provides a link to the GitHub repository for installation and mentions the requirement of a graphics card. Additionally, three online methods to use the tool for free are briefly mentioned, with the first method involving the use of Hugging Face, an interface for uploading images and videos to create animated results.
🔍 Using Hugging Face for Live Portraits
The speaker elaborates on the first online method, using Hugging Face, to utilize Live Portraits. Users can upload their source image and driving video on the provided interface, ensuring the video aspect ratio is 1:1. The tool is developed by Quow, the company behind Clling AI, which is recognized as one of the best AI video generators. The process involves selecting example images and videos, uploading custom ones, and then animating them to produce videos that replicate expressions flawlessly. The paragraph showcases various image styles that can be used, including black and white pictures, realistic photos, oil paintings, and fictional statues, highlighting the impressive outcomes of the technology.
🔄 Exploring the Replicate Method for Video Generation
The second method discussed is Replicate, which offers more control over the video generation process but limits the output to videos no longer than 5 seconds. Users can change the example image in the video by uploading their own and adjust settings such as video frame load cap, size scale ratio, lip, and eye retargeting. The speaker provides a link to Replicate and describes the process of running the tool with default settings to produce a short video output.
🖥️ Using Google Colab for Live Portraits Animation
The final method introduced is using Google Colab, which involves running two segments or cells in sequence after selecting the T4 GPU and connecting to it. Users upload their video footage and image, copy their paths, and paste them into the respective cells. The process is initiated by running the cells, which results in a green check mark upon completion. The video can be downloaded from the 'animations' folder within the 'live portrait' directory. The paragraph emphasizes the ease of creating new videos by simply uploading new files and adjusting their paths in the second cell before running it again.
Mindmap
Keywords
💡Deepfake
💡Live Portrait
💡Expression Mapping
💡Aspect Ratio
💡Hugging Face
💡Replicate
💡Google Colab
💡T4 GPU
💡Runtime Type
💡Animation Folder
💡Quow
Highlights
Live Portrait is an advanced open-source deepfake tool that can map video expressions onto a photo.
The tool allows for the creation of talking, singing, and complex facial expressions without distortion.
To get started, the Live Portrait GitHub repo can be accessed from the provided link.
A graphics card is required to install Live Portrait on your computer.
Three easy online methods to use Live Portrait for free are demonstrated.
The first method uses Hugging Face for an interface to upload source images and driving videos.
Ensure the aspect ratio of the video is 1:1 when using Hugging Face.
Live Portrait is developed by Quow, the company behind Clling AI, a top AI video generator.
Hugging Face provides example images and videos for users to select and animate.
The output from Hugging Face can be downloaded or played back after a few seconds.
Replicate is the second method, offering more control with advanced settings like frame load cap and size scale ratio.
Replicate can create videos but is limited to a maximum length of 5 seconds.
Google Colab is the third method, requiring the selection of a T4 GPU and connecting to it.
Users must upload video footage and an image in Google Colab and adjust file paths accordingly.
Running the cells in Google Colab will process the video, resulting in a green check mark upon completion.
The final video can be downloaded from the 'animations' folder in Google Colab.
The technology's potential is showcased through the results of different image styles and video generations.
For new videos, only the second cell in Google Colab needs to be run with updated file paths.
The video concludes by encouraging viewers to like and subscribe for more content.