ControlNet - Openpose face [TensorArt]
TLDRThis tutorial explores the use of OpenPose for facial pose analysis within the TensorArt platform. The video demonstrates how to add ControlNet and select OpenPose to analyze facial expressions and poses. It showcases the process of uploading close-up images of faces, adjusting pre-processor settings, and using models to render images in a cartoon style. The tutorial also explains how to use facial OpenPose to control character generation, optimize comic-like effects, and achieve desired poses. It further illustrates creating ensemble images with multiple characters by manipulating facial maps and adjusting settings in TensorArt. The video concludes with an upcoming project teaser on creating group images using a portrait puzzle approach with Photopia and TensorArt, promising new creative possibilities and astonishing results.
Takeaways
- 📈 **TensorArt ControlNet**: The tutorial focuses on using TensorArt's ControlNet to analyze facial poses with OpenPose technology.
- 🎭 **Facial Pose Analysis**: OpenPose is used to understand and capture facial expressions and poses, enhancing the potential for character creation.
- 🖼️ **Image Upload and Processing**: Close-up images of faces are uploaded and processed to generate images with a black background and facial landmarks.
- 📸 **OpenPose Face Command**: A new command, 'OpenPose Face', is introduced to capture facial expressions and character poses.
- 🎨 **Model Selection**: Different models are chosen to render images in various styles, such as cartoon style, to achieve desired effects.
- 🔍 **Control Over Generation**: ControlNet allows for greater control over character poses and expressions, reducing the need for repeated image generations.
- 👥 **Multiple Character Images**: The technology is tested for creating ensemble images featuring two or more characters, going beyond single-portrait images.
- 🖌️ **Photo Editing**: Photo editing tools like Photo Pier are used to manipulate and combine facial maps for creating images with multiple aligned faces.
- 🧩 **Portrait Puzzle**: A creative approach is introduced to create a 'portrait puzzle' by arranging individual portraits to form a unique composition.
- 🔗 **Integration with TensorArt**: The facial pose maps from the portrait puzzle are used in TensorArt to generate final images that capture the group's individuality.
- 📚 **Learning Journey Continuation**: The video is part of an ongoing learning journey, inviting viewers to explore more captivating topics through the playlist.
Q & A
What is the main topic of this tutorial?
-The main topic of this tutorial is using OpenPose to analyze and understand facial poses within the context of TensorArt's ControlNet.
How does the ControlNet button in TensorArt workspace allow the user to proceed?
-The ControlNet button allows the user to add ControlNet to their workspace, which is then followed by selecting OpenPose from a subsequent screen.
What type of image is imported into the ControlNet for facial pose analysis?
-A close-up image of a face, preferably captured in an iconic moment, is imported into the ControlNet for facial pose analysis.
What is the purpose of changing the pre-processor setting to 'Open Pose Face Only'?
-Changing the pre-processor setting to 'Open Pose Face Only' allows the system to capture not only the facial expression but also a portion of the character's pose.
How does the selection of a model affect the final output when rendering an image in a cartoon style?
-The choice of the model can significantly vary the final effect, allowing for different levels of cartoon-like rendering of the subject's image.
What is the significance of using facial OpenPose in generating characters?
-Facial OpenPose provides greater control over the generation of characters, allowing for the communication of both the desired pose and facial expression to the artificial intelligence, which can save time by reducing the need for repeated image generations.
How does the aspect ratio setting in the ControlNet affect the depiction of a singer in the generated images?
-Selecting the portrait format for the aspect ratio ensures a perfect depiction of a singer by framing the subject in a way that is traditionally associated with singers.
What is the role of the VAE model in enhancing the realism of the generated images?
-The VAE (Variational Autoencoder) model contributes to the realism of the generated images by providing a more detailed and accurate representation of the subject.
How does using the ControlNet with a downloaded and reloaded image help in achieving a specific pose?
-By using the ControlNet with a downloaded and reloaded image as a pose reference, the facial pose map generated by TensorArt can be used to start the generation of new images with the same desired pose.
What is the goal when modifying the pre-processor to 'Open Pose Face Only' in the context of creating ensemble images?
-The goal is to obtain only the facial map on a black background, which can then be manipulated and combined to create ensemble images featuring multiple characters.
How does the use of Photo Pier (or similar tools) contribute to the creation of a quartet of singers in the final map?
-Photo Pier allows for the manipulation of the facial map, such as cropping, expanding, and aligning multiple faces side by side, to create a composite image that represents a group of singers.
What is the final step in TensorArt after editing the facial map with Photo Pier?
-The final step is to adjust the control net parameter in TensorArt to fit the newly created map into the allowed dimensions, and then initiate the generation of images to obtain the desired quartet of singers.
Outlines
😀 Introduction to Tensor Arts Control Net and Open Pose
The video script begins with a warm welcome to the channel and an introduction to the topic of using Tensor Arts Control Net for analyzing facial poses with Open Pose. The presenter outlines the learning journey and encourages viewers to catch up on previous videos if they have missed any. The tutorial focuses on importing a close-up image of a face and using Open Pose to analyze facial expressions and character poses. The process involves changing the command to 'open pose face only' and uploading an image to observe the facial landmarks on a black background. The presenter also demonstrates how to use different models to render the image in a cartoon style and discusses the potential for optimizing the comic-like effect.
🖼️ Control Net for Precise Character Poses
This paragraph delves into using the Control Net to achieve specific poses for character images. The presenter describes how to use the first image as a pose reference and how Open Pose Face can help in achieving greater control over character generation. The process includes generating images of a singing girl, selecting an appropriate model, and using the prompt and negative prompt to guide the AI. The presenter then uses the Control Net to refine the pose, camera framing, and face direction, demonstrating the potential of Control Net for precise and customized results.
🎭 Creating Ensemble Images with Open Pose
The third paragraph explores the possibility of creating ensemble images featuring multiple characters using facial Open Pose. The presenter aims to test the technology's ability to move beyond a single portrait and create images with two or more characters. The process involves configuring settings, accessing Open Pose settings, and modifying the pre-processor to obtain only the facial map on a black background. Using Photo Pier (or similar software), the presenter manipulates the facial map to create multiple aligned faces, intending to create a quartet of singers. Adjustments are made in Tensor Art to fit the new map into allowed dimensions, and the presenter demonstrates how to enhance the resolution and initiate the generation of images.
🌟 Conclusion and Future Project Tease
In the concluding paragraph, the presenter emphasizes the importance of utilizing Tensor Arts functionalities like facial Open Pose for customization and precise results. The presenter shares the successful outcome of generating a quartet of singers with the same pose, showcasing the technology's potential. The video ends with a teaser for an upcoming project that involves creating group images using Photopia and Tensor Art, hinting at a creative approach to generating a portrait puzzle. The presenter invites viewers to subscribe to the YouTube channel for more exciting techniques and projects, thanking them for their attention and participation in the community.
Mindmap
Keywords
💡TensorArt
💡OpenPose
💡Facial Poses
💡ControlNet
💡Pre-processor
💡Cartoon Style
💡Control Net Parameters
💡Photo Pier
💡Portrait Puzzle
💡Artificial Intelligence (AI)
💡Image Generation
Highlights
The tutorial focuses on using OpenPose to analyze and understand facial poses within the TensorArt platform.
ControlNet is added to the TensorArt workspace to select OpenPose for facial analysis.
Pre-processor settings are modified to use 'OpenPose face only' for a more focused facial expression capture.
Close-up images of soccer players celebrating goals are used to demonstrate the facial pose analysis.
The video shows how changing pre-processor settings can capture additional character poses.
A cartoon style model is chosen to render the soccer player's image, with the final effect varying by model choice.
The generation of three images is initiated to showcase the potential of OpenPose for facial poses.
Further optimization of the comic-like effect is discussed in subsequent examples.
TensorArt is used to generate images of a singing girl, with specific models and prompts for customization.
ControlNet is employed to achieve a user-defined pose, enhancing the control over character generation.
The facial pose map generated by TensorArt is used to create images with the same pose for all characters.
Photo Pier is utilized to modify the facial map for creating ensemble images with multiple characters.
The facial map is adjusted in dimensions to fit within TensorArt's allowed dimensions for image generation.
Higher resolution settings are activated to enhance the final image quality.
A quartet of singers is generated using the modified facial map, demonstrating the potential for group images.
An upcoming project is teased, involving creating group images using Photopia and TensorArt to form a 'portrait puzzle'.
The 'portrait puzzle' approach allows for a unique composition representing the individuality of each group member.
The tutorial concludes by inviting viewers to subscribe for more artistic adventures and techniques.
