Incredible face swap with AI in Stable Diffusion

Vladimir Chopine [GeekatPlay]
30 Jun 202312:37

TLDRIn this video, the host demonstrates an innovative method of face swapping using AI with the Stable Diffusion tool. The process involves installing necessary extensions, configuring settings for optimal results, and leveraging models like RPG V4 with DPM++ SDE Karras sampling. The tutorial covers changing faces with different poses, using control net for fine adjustments, and experimenting with various seeds for diverse outcomes. The host emphasizes the ease and speed of face swapping, showcasing the final images with perfect lighting and facial features, and encourages viewers to explore and experiment with the tool. Links to resources and further instructions on installing and using Control Net and Stable Diffusion are provided for those interested in trying it out themselves.

Takeaways

  • 🤖 Install necessary extensions for face swapping in Stable Diffusion by going to the Extensions tab and enabling 'face swapping' and 'control net' features.
  • 🔄 Disable extensions not in use to prevent conflicts and errors that may arise from their interaction.
  • ⚙️ Adjust settings for Control Net, ensuring 'Do not append map' is unchecked to view the created map and setting the maximum models to three.
  • 🖼️ Use the RPG V4 checkpoint model with DPM++ SDE Karras sampling method for optimal results.
  • 🔢 Set the height to 768 and use 55 sampling steps, which is recommended for the RPG4 model.
  • 🎨 Generate an initial image using a photorealistic Rembrandt painting portrait as a positive example and 'nude' as a negative NSFW filter.
  • 🔄 Swap the generated face with another image by using the 'group' extension and enabling 'restore face' with a CFG scale of 5.5.
  • 📐 Utilize 'Pixel Perfect' in Control Net to match the size and sampling of the image for precise face swapping.
  • 🧩 Experiment with different poses and faces by adjusting Control Net settings and using various seeds for diverse outcomes.
  • 🆕 Generate multiple images in a batch using the same settings to create a series of face-swapped images with consistent style.
  • 🌟 The final result should showcase a perfect face swap with accurate positioning, highlights, and shadows, maintaining the artistic style of the original image.
  • 🔗 Additional resources and tutorials are provided for further learning on how to install and use Control Net and Stable Diffusion effectively.

Q & A

  • What is the main topic of the video?

    -The video is about an incredible way to perform face swaps using new extensions within Stable Diffusion, which produces stunning results.

  • What are the necessary extensions that need to be installed for face swapping?

    -The necessary extensions for face swapping are 'group' or 'r' for face swapping and 'controlnet' for pose manipulation.

  • Why is it recommended to disable extensions not actively used in the project?

    -Disabling unused extensions can prevent conflicts between them and avoid errors that might make the project unusable.

  • What settings are recommended for the RPG V4 checkpoint model?

    -The recommended settings for the RPG V4 checkpoint model are a height of 768, 55 sampling steps, and using DPM++ with M-cars sampling method.

  • How can one find a style they like in the generated images?

    -One can generate multiple images until they find a style they like, and then use the 'reuse seed' option to recreate similar results.

  • What is the purpose of the 'restore face' feature in the face swapping process?

    -The 'restore face' feature ensures that the swapped face maintains a high level of detail and clarity, avoiding blurriness.

  • How does the 'Pixel Perfect' option in Control Net affect the face swapping process?

    -'Pixel Perfect' analyzes the size of the images and resizes the sampling for the Control Net, ensuring a better match between the swapped face and the original image.

  • What is the significance of the 'CFG' scale in the face swapping process?

    -The 'CFG' scale adjusts the creativity level of the model. A lower value results in a more conservative swap, while a higher value allows for more creative and flexible results.

  • How can one create different variations of the swapped face image?

    -One can create different variations by using a random seed, changing the pose in Control Net, or adjusting the 'CFG' scale and other settings.

  • What is the effect of the 'restore phase' on the final image?

    -The 'restore phase' adds sharpness to the face in the final image, making it look more defined and less blurry compared to an image without the restore phase.

  • How can one ensure that the swapped face matches the pose and angle of the original image?

    -By using Control Net and selecting the appropriate pose, one can ensure that the swapped face aligns with the pose and angle of the original image.

  • What additional resources does the video provide for those interested in learning more about Control Net and Stable Diffusion?

    -The video provides a link to resources for further learning, including how to install and use Control Net, as well as additional information on Stable Diffusion.

Outlines

00:00

😀 Installing Extensions for Face Swapping

In this paragraph, the speaker explains the first step of the process, which is to install necessary extensions for face swapping. They guide the viewer to go to the extension tab, load the 'group or r' extension for face swapping, and also install the 'control net' extension. The speaker also advises disabling other extensions that are not being used to avoid conflicts and errors. They then show how to adjust the settings for control net, including unchecking 'do not append map' and setting the maximum models and sketch size. Finally, they mention using the RPG V4 checkpoint model with DPM++ SDE Karras sampling method and adjusting the height and sampling steps for optimal results.

05:02

😲 Swapping Faces with Control Net

In this paragraph, the speaker demonstrates how to swap faces using the installed extensions. They show how to load an image into the 'group' extension and enable it to swap the AI-generated face with a different face from a photo or another person. The speaker also explains how to use the 'restore face' option and adjust the CFG scale and GFP gain for better results. They then demonstrate swapping faces using control net to match the pose and features of the original image. The speaker also shows how to generate multiple variations by changing the seed and using different restore phases. They emphasize the ease and speed of face swapping with the new extensions.

10:02

🎨 Experimenting with Different Styles and Control Net Settings

In the final paragraph, the speaker experiments with different styles and control net settings to achieve various effects. They reuse a seed and switch the restore face to compare the results with and without the restore phase. The speaker also shows how to generate a batch of 8 different images with different poses and styles using control net. They adjust the CFG scale and enable the 'Pixel Perfect' option to match the sampling size to the image. The speaker also discusses whether to use more prompt or control net for different effects. Finally, they render the images and showcase the results, highlighting the perfect face swapping, lighting, and pose matching achieved with control net. The speaker encourages viewers to try out different settings and have fun experimenting.

Mindmap

Keywords

💡Face Swap

Face Swap refers to the process of digitally replacing one person's face with another's in a photograph or video. In the context of the video, it is a key technique used to create stunning results by swapping faces from different models and poses, contributing to the main theme of exploring new ways to manipulate images with AI.

💡Stable Diffusion

Stable Diffusion is a term that likely refers to a specific AI model or software used for generating images. It is central to the video's theme as it is the platform where the face swapping and image manipulation are performed, showcasing the capabilities of AI in creating complex and detailed images.

💡Extensions

In the context of the video, extensions are additional software components that enhance the functionality of the base AI model. They are crucial for enabling advanced features like face swapping, and the video provides a step-by-step guide on how to install and use these extensions to achieve the desired effects.

💡ControlNet

ControlNet is a specific extension mentioned in the video that allows for more nuanced control over the image generation process. It is used to manipulate poses and facial features, which is a significant part of the video's demonstration on how to achieve complex image manipulations.

💡DPM++

DPM++ stands for Deep Pyramid Deformation with a Plus Plus variant, which is a sampling method used in the context of the video to enhance the quality of the generated images. It is part of the recommended settings for achieving the best results with the RPG V4 checkpoint model.

💡RPG V4 Checkpoint Model

The RPG V4 Checkpoint Model is a specific version of a model used within the AI system for generating images. It is mentioned as a preferred choice for the video's demonstrations, indicating that it is optimized for creating high-quality, detailed images.

💡Sampling Steps

Sampling steps refer to the number of iterations or stages in the image generation process. The video suggests using 55 sampling steps with the RPG4 model to achieve the best results, highlighting the importance of this parameter in the overall quality of the output.

💡Restore Face

Restore Face is a feature within the AI system that allows for the reconstruction of facial features in the generated image. It is used in the video to maintain the quality and sharpness of the swapped faces, which is essential for creating realistic and high-quality results.

💡Pixel Perfect

Pixel Perfect is a term used to describe an image or visual element that has been precisely aligned or sized to fit perfectly within a given space. In the video, it is used to analyze and resize the sampling for the control net to match the size of the images, ensuring a high-quality and well-aligned output.

💡CFG

CFG, which stands for Control Flow Graph, is a concept used in the context of the video to refer to a setting that affects the flexibility and detail of the generated image. Adjusting the CFG value allows for more or less control over the final output, which is demonstrated in the video through various examples.

💡Batch Count

Batch Count refers to the number of images that are processed or generated at one time. In the video, the creator sets the batch count to eight, which means that eight different images are generated simultaneously, showcasing the efficiency of the AI system in handling multiple tasks.

Highlights

The video showcases a new incredible way to swap faces using AI in Stable Diffusion.

It introduces the use of extensions to produce stunning results in face swapping.

The process allows for adding complexity by swapping faces from one model and poses from another.

Viewers are guided on how to install necessary extensions for face swapping.

ControlNet and SD web control net manipulation are key extensions used in the process.

The presenter shares general settings adjustments for optimal extension performance.

RPG V4 checkpoint model is utilized with DPM++ and M-Diffusion sampling methods.

A recommended setting of 768 height and 55 sampling steps is suggested for the best results.

The video demonstrates rendering a photorealistic Rembrandt painting portrait with face swapping.

The presenter explains how to change the face of the generated person with an image from a photo.

The group extension simplifies the process of face swapping compared to previous methods.

Restoring faces is achieved using a CFG scale and gfp gain for better results.

ControlNet is used to change the pose and swap faces with precision.

Pixel Perfect feature analyzes and resizes sampling for control net to match image size.

The presenter discusses the impact of different seeds on the variation of the generated images.

The video illustrates the difference between using restore phase and not using it in the output quality.

Batch processing is demonstrated to generate multiple images with different styles and poses.

The presenter emphasizes the ease and speed of face swapping with the new AI technology.

The video concludes with a call to action for viewers to experiment with the technology and provides resources for further learning.

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