Incredible face swap with AI in Stable Diffusion
TLDRThis video tutorial showcases an innovative method for face swapping using AI in Stable Diffusion, a technique that merges faces from different models and poses to create stunning new images. The process begins with installing necessary extensions and adjusting settings for optimal results. The tutorial demonstrates how to use the RPG V4 checkpoint model with DPM++ SDE Karras sampling, and how to fine-tune the settings for the best outcome. The face swapping is simplified with the new group extension, allowing for easy swapping and restoring of faces. The video also explores the impact of different settings, such as the restore phase and control net, on the final image quality. The host encourages viewers to experiment with various options to achieve their desired results and provides resources for further learning and installation of the required tools.
Takeaways
- 🔍 **Install Necessary Extensions**: The video demonstrates how to install and enable extensions for face swapping using Stable Diffusion.
- 🛠️ **ControlNet Manipulation**: ControlNet is used for manipulating poses and is installed as part of the process.
- ⚙️ **Settings Configuration**: It's important to adjust settings such as unchecking 'do not append map' and setting the maximum models to three for optimal results.
- 📈 **Sampling Method**: The video recommends using DPM++ with 55 sampling steps for the RPG V4 checkpoint model.
- 🖼️ **Photorealistic Output**: A photorealistic Rembrandt painting portrait is used as a base for the face swap.
- 🔄 **Face Swapping Process**: The AI can swap faces with a new, easier method by using the 'group' extension and enabling 'restore face'.
- 📉 **CFG and Scale Adjustments**: The video suggests tweaking the CFG value and scale to fine-tune the face restoration process.
- 🎭 **Pose and Face Combination**: By combining face swapping with different poses from ControlNet, more complex and varied images can be created.
- 🔗 **Pixel Perfect Feature**: Using the 'Pixel Perfect' feature ensures the control net sampling matches the size of the images for precise adjustments.
- 🧩 **Random Seed Variation**: Experimenting with different seeds and restore phases can produce a range of unique outcomes.
- 🚀 **Batch Processing**: The video shows how to generate multiple images at once, adjusting settings like CFG for more flexibility in the output.
Q & A
What is the main topic of the video?
-The main topic of the video is about an incredible way to perform face swaps using new extensions in Stable Diffusion, which can produce stunning results by not only swapping faces but also changing poses and combining features from different models.
What are the necessary extensions needed to perform face swapping as described in the video?
-The necessary extensions for face swapping as described in the video are 'group' or 'r' for face swapping and 'controlnet' for pose manipulation.
How can one ensure that all libraries and models are downloaded for the extensions?
-To ensure that all libraries and models are downloaded for the extensions, one should go to the 'install' tab and click on 'apply and restart' after installing the required extensions.
What is the recommended setting for the RPG V4 checkpoint model?
-The recommended settings for the RPG V4 checkpoint model include using DPM++ with M-diffusion sampling methods, a height of 768, and 55 sampling steps.
How can one achieve the best results with the RPG V4 model?
-To achieve the best results with the RPG V4 model, one should use a sampling step range of 35 to 75.
What is the purpose of disabling extensions that are not actively being used?
-Disabling extensions that are not actively being used can prevent conflicts between extensions and reduce the chance of encountering errors that could make the system unusable.
How does the 'restore face' feature work in the face swapping process?
-The 'restore face' feature allows the system to maintain the facial features and structure of the original face while swapping it with another, ensuring a more natural and accurate swap.
What is the 'Pixel Perfect' feature used for in the control net?
-The 'Pixel Perfect' feature is used to analyze the size of the images and resize the sampling for the control net, ensuring that the control net is specifically tailored to the size of the images being used.
How can one generate different variations of the face swapped image?
-To generate different variations of the face swapped image, one can use the 'random seed' feature or adjust the control net settings to change the pose and facial features.
What is the effect of the 'restore phase' on the final image?
-The 'restore phase' adds sharpness to the face in the final image, improving the clarity and reducing blurriness compared to an image without the restore phase applied.
How can one create a batch of different images with the same face swap?
-To create a batch of different images with the same face swap, one can adjust the batch count and use the 'Pixel Perfect' feature to ensure the control net settings are properly applied to each image in the batch.
Outlines
😀 Installing Extensions for Face Swapping
The video begins with an introduction to a new method of face swapping using stable diffusion with extensions. The first step involves installing necessary extensions, which can be done by navigating to the extension tab, selecting the 'available' tab, and searching for 'group' or 'r' to find the face swapping extension. The user is instructed to install 'control net' and 'SD web control net manipulation' if they are not already installed. The presenter also shares a tip on disabling unused extensions to avoid conflicts and errors. Settings adjustments are made, including ensuring that the map is not appended and setting the maximum models and sketch size to three. The video then proceeds to use the RPG V4 checkpoint model with DPM++ SDE Karras sampling methods, recommending a height of 768 and 55 sampling steps for optimal results.
🖼️ Face Swapping and Pose Adjustment
The video continues with a demonstration of face swapping using a photorealistic Rembrandt painting of a beautiful woman as a base image. The presenter guides the viewer on how to change the face of the AI-generated person with a photo or another person's face. This process has been simplified with the new group extension, which allows for easier swapping without the need for multiple control nets or masking. The presenter also explains how to use the 'restore face' feature and adjust the CFG scale to achieve the desired result. The video shows the face swapping process in action, highlighting the ability to generate multiple variations and the impact of the restore phase on the final image's sharpness.
🎨 Experimenting with Different Styles and Control Net
The video concludes with further experiments, including reusing a seed for a new image and switching different faces while using the control net to maintain similar facial features. The presenter discusses the use of 'Pixel Perfect' to analyze and resize the sampling for the control net to match the image size. The video also explores the effects of the restore phase on the image's sharpness and blurriness. The presenter runs an experiment with a random seed to generate eight different images, adjusting settings such as CFG and control net to achieve a desired style. The video ends with a reminder to check out the provided resources for more information on installing and using control net and stable diffusion, thanking the viewers for their support.
Mindmap
Keywords
💡Stable Diffusion
💡Face Swapping
💡Extensions
💡ControlNet
💡DPM++ and M-CARS
💡Sampling Steps
💡Photorealistic
💡Restore Face
💡Pixel Perfect
💡RPG V4 Checkpoint Model
💡CFG (Control Flow Guide)
Highlights
The video introduces a new incredible way to perform face swaps using extensions in Stable Diffusion.
The process can add complexity by swapping faces from one model and poses from another.
To begin, install necessary extensions such as 'group' or 'r' for face swapping.
ControlNet and SD web ControlNet manipulation are also required extensions.
After installing extensions, apply changes and restart to ensure all libraries and models are downloaded.
Disable extensions not in use to avoid conflicts and errors.
Settings should be adjusted in ControlNet options, such as unchecking 'do not append map'.
The RPG V4 checkpoint model is used with DPM++ SDE Karras as the sampling method.
A recommended setting is to switch to 768 height and use 55 sampling steps for the best results.
Photorealistic Rembrandt painting portrait is used as a positive example, with 'nude' as a negative.
The face of the generated person can be easily swapped with an image from another source.
The 'restore face' feature ensures the swapped face maintains its original features.
CFG scale can be adjusted for better face restoration.
Pixel Perfect feature analyzes and resizes sampling for the ControlNet to match the image size.
Random seeds can be used to generate different variations of the swapped face.
The 'restore phase' adds sharpness to the face, enhancing the final image quality.
Batch count can be set to generate multiple images at once.
Different styles and time periods, such as Barocco or Rococo, can influence the coloring and style of the final image.
ControlNet allows for different poses and facial expressions to be swapped in, creating unique and dynamic images.
The video concludes with a demonstration of the ease and speed of face swapping with Stable Diffusion extensions.
Links to resources and further instructions on installing and using ControlNet and Stable Diffusion are provided.