Control-Netの導入と基本的な使い方解説！自由自在にポージングしたり塗りだけAIでやってみよう！【Stable Diffusion】

Control-Net, a revolutionary technology released in February 2023 by Iliasviel, has made it easier to generate images with specific poses. The video introduces Mikubill's 'SD-WebUI-ControlNet', an extension that allows users to utilize Control Net on the web UI. The installation process is explained in detail, including downloading the necessary files from Hugging Face and installing the models. The video demonstrates how to use Control Net to reproduce poses from stick-figures or images, and how to use functions like Open Pose and CannyEdge for line extraction. It also discusses the pre-processor's role in preparing images for pose reproduction. The script showcases the power of Control Net in generating images with specific poses and outlines, and how it can be used for character design, game development, and even in creating samples for VTubers. The video concludes by highlighting the ease and efficiency Control Net brings to image generation and design.

Takeaways

• 🚀 **Revolutionary Technology**: Control-Net, released by Iliasviel in February 2023, has made it easier to pose characters in images.
• 🌐 **Web UI Integration**: Mikubill's 'SD-WebUI-ControlNet' allows running Control Net directly on the web UI, enhancing user experience.
• 📚 **Installation Process**: The process involves downloading and installing extensions from GitHub, and adding model files to the Web UI Install folder.
• 🔄 **Restart & Apply**: After installation, a restart is required and the Control-Net should appear in the script for successful setup.
• 📈 **Model Installation**: Download and install specific model files from Hugging Face to utilize the full functionality of Control-Net.
• 🎨 **Posing with Open Pose**: The Open Pose function can reproduce a pose from an image, useful for generating images from stick-figures.
• 🖌️ **Line Art with CannyEdge**: CannyEdge is a line extraction function that can generate line art from an image, aiding in creating illustrations.
• 🔍 **Pre-processor Role**: The pre-processor extracts necessary elements from an image for the model to reproduce the desired outcome.
• 📂 **Saving Detected Maps**: Users can save the 'detected map' for future use, which is the extracted outline from the target image.
• 👗 **Efficient Character Design**: Control-Net streamlines character design by allowing for easy pose adjustments and line art creation.
• 🌟 **Additional Functions**: Control-Net offers various functions like MLSD, Normal Map, Depth, and others, suitable for different aspects of image generation and design.
• 🔧 **Practical Applications**: Ideal for game development, character design, material design, and even for VTubers looking to create clothing samples efficiently.

Q & A

What is Control-Net and how does it revolutionize the way we interact with image generative AI?

Control-Net is a revolutionary technology released by Iliasviel in February 2023 that allows for easier manipulation of poses in generated images. It is considered a breakthrough because it enables users to direct the AI to produce specific poses or outcomes without the need for complex 'spells' or multiple iterations, thus streamlining the process of image generation.

How can one install and use the Control-Net on a web UI?

To install Control-Net on a web UI, you first need to access Mikubill's GitHub page and follow the URL to install the extension via the web UI's extension tab. After installing the extension, you must install the model for Control Net by downloading the necessary files from Hugging Face and placing them in the appropriate folder within the Web UI Install directory.

What is the role of the 'pre-processor' in the Control-Net system?

The pre-processor in Control-Net is responsible for pre-processing the input, such as extracting the stick-figure or line art from an image before it is used to influence the AI's generation. It works in tandem with the model to ensure that the desired features from the input image are reflected in the generated output.

How does the Open Pose function of Control-Net assist in pose reproduction?

The Open Pose function is a representative feature of Control-Net that helps in reproducing a specific pose from an image. It can take a stick-figure or an image and generate an image with the same pose, even when the input prompt is vague, like 'One Girl'.

What is the CannyEdge function in Control-Net and how is it used?

CannyEdge is a line extraction function within Control-Net that can be used to generate images with a strong sense of line art. It is particularly useful for creating detailed line art from a simple prompt, which can then be used as a basis for painting or further illustration.

How does the detected map feature of Control-Net help in the image generation process?

The detected map is the outcome of using the Control-Net's line extraction functions, such as Open Pose or CannyEdge. It is an intermediate image that represents the extracted features from the input image. By saving the detected map, users can use it as a basis for further image generation, allowing for more control and precision in the final output.

What is the significance of the 'invert input color' option when using line art with Control-Net?

The 'invert input color' option is used when the line art is drawn with black on a white background, which is a common practice for human artists. Since AI might interpret the black areas as the background, this option helps correct this by reversing the colors, ensuring that the line art is properly recognized and used in the generation process.

How can the Control-Net technology be beneficial for game developers or character designers?

Control-Net can be highly beneficial for game developers and character designers as it allows for the efficient creation of character designs and poses without the need for extensive manual drawing. It can also be used to quickly generate multiple design variations or to finalize character poses for animations or game mechanics.

What are some other functions of Control-Net apart from Open Pose and CannyEdge?

Apart from Open Pose and CannyEdge, Control-Net has several other functions including MLSD (Multi-Scale Line Segment Detector) for straight line extraction, Normal Map for surface unevenness detection, Depth for extracting image depth, Holistically Nested Edge Detection for outline detection, Pixel Difference Network for clear line drawing, Fake Scribble for creating images from graffiti, and Segmentation for indoor design.

How does the Control-Net technology differ from traditional image-to-image generation methods?

Unlike traditional image-to-image generation methods that may only produce similar atmospheres or styles, Control-Net allows for more accurate tracing and manipulation of specific elements like poses, line art, and depth. This level of control is what makes Control-Net a significant advancement in image generative AI.

What are some practical applications of Control-Net for users who are not professional designers or artists?

Control-Net can be used by non-professionals for a variety of purposes, such as creating personalized artwork, generating social media content, or even for educational purposes to understand the principles of pose and composition in images. It can also be a tool for brainstorming and visualizing ideas quickly and efficiently.

How does the installation process of Control-Net ensure that users have successfully installed the necessary components?

The installation process involves several steps, including downloading and installing the extension and model files. Users can confirm successful installation by checking for the presence of 'ControlNets' in the list of installed extensions and by seeing the model names they downloaded in the Models pull-down menu within the web UI.

Outlines

00:00

🚀 Introduction to Control-Net Technology

This paragraph introduces the revolutionary Control-Net technology released by Iliasviel in February 2023, which simplifies the process of making a character take a preferred pose. Previously, users had to resort to complex 'spells' or use 3D drawing software to achieve desired poses. The paragraph explains the installation process of Mikubill's 'SD-WebUI-ControlNet' extension, which allows users to run Control Net on the web UI. It also covers the steps to install the necessary model files from Hugging Face and how to use the Control Net effectively.

05:01

🎨 Utilizing Control-Net for Image Generation

The second paragraph delves into the practical application of Control-Net, focusing on its ability to reproduce poses from images and generate images from stick-figures. It discusses the use of the Open Pose function and the pre-processor's role in preparing images for pose reproduction. The paragraph also highlights the CannyEdge function, which extracts line art from images, and how it can be used to generate line art for further artistic or design work. Additionally, it touches on the concept of a 'detected map' and the settings required to save and utilize these maps for image generation.

10:03

🛠️ Exploring Additional Functions of Control-Net

The third paragraph provides an overview of the various functions available within the Control-Net, emphasizing that while there are many, the open-pose and cannyedge functions are sufficient for most tasks. It briefly describes other models such as MLSD for straight line extraction, Normal Map for surface unevenness detection, Depth for maintaining composition and body shape, and Holistically Nested Edge Detection for outlining. The paragraph also mentions Pixel Difference Network, Fake Scribble for creating images from graffiti, and Segmentation for indoor design. It concludes by emphasizing the utility of Control-Net for character illustrations, background creation, material design, and its potential to streamline the design process for professionals and hobbyists alike.

Keywords

💡Control-Net

Control-Net is a revolutionary technology introduced in February 2023 by Iliasviel that allows for easier manipulation of poses in character generation. It is a significant advancement in image generative AI, enabling users to specify poses and features more precisely, which was previously more challenging and required the use of 'spells' or 3D drawing software. In the context of the video, Control-Net is used to generate images from stick-figures and to guide the AI in creating specific poses and character designs.

💡SD-WebUI-ControlNet

SD-WebUI-ControlNet is an extension developed by Mikubill that allows the use of Control-Net technology directly within a web UI environment. It is a tool that has been expanded to facilitate the operation of Control-Net, making it more accessible for users who want to generate images with specific poses without the need for extensive technical knowledge or alternative software. In the video, the hosts demonstrate how to download, install, and use SD-WebUI-ControlNet to apply Control-Net to their image generation process.

💡Pose Generation

Pose generation refers to the process of creating a character or object in a specific position or posture. Traditionally, this required writing descriptive 'spells' or using 3D drawing software to achieve the desired pose. With the advent of Control-Net, pose generation becomes more straightforward, allowing users to input a pose from a stick-figure or an image and have the AI replicate it in the generated image. The video showcases this by demonstrating how to use Control-Net to reproduce poses from sample images.

💡Pre-processor

A pre-processor in the context of the video is a tool used to prepare or manipulate the input data before it is used by the main model to generate an output. Specifically, when using Control-Net, the pre-processor extracts the essential features, such as the pose or line art, from the input image. This extracted information is then used by the model to generate an image that reflects the desired characteristics. The video explains that when using a stick-figure image, no pre-processor is needed, but when using a regular image, the pre-processor uses Open Pose to extract the pose.

💡Model

In the context of AI and machine learning, a model refers to the algorithmic framework that is trained to perform specific tasks, such as image generation. The video discusses the use of different models within the Control-Net framework, such as Open Pose and CannyEdge, which are used to generate images based on the extracted features from the pre-processor. The choice of model determines how the input data, such as a pose or line art, is translated into the final generated image.

💡CannyEdge

CannyEdge is a feature within the Control-Net technology that is used for line extraction from an image. It is a pre-processor function that identifies and extracts the line art from a reference image, which is then used to guide the AI in generating an image with a strong sense of line art. In the video, CannyEdge is demonstrated as a way to generate images with a distinct line art style, which can be particularly useful for artists who want to create line art and then paint over it.

💡Line Art

Line art refers to the artwork that primarily uses lines to define the shapes and forms of the subject. It is a fundamental part of many artistic disciplines, including illustration and design. In the video, the hosts discuss how Control-Net can be used to extract line art from an image using the CannyEdge function, allowing artists to generate line art that can then be painted over by the AI or used as a basis for further artistic development.

💡Illustration

An illustration is a type of artwork that is used to decorate, explain, or tell a story. In the context of the video, the term is used to describe the final output generated by the AI, which is guided by the Control-Net technology to create images that are not only visually appealing but also adhere to specific artistic styles or requirements. The video demonstrates how Control-Net can be used to create illustrations with specific poses, line art styles, and even color treatments.

💡Invert Input Color

The 'invert input color' function is a tool within the Control-Net framework that reverses the colors of the input image, swapping black and white. This is particularly useful when working with line art created by humans, where black lines are typically drawn on a white canvas. The video explains that AI tends to interpret the black areas as the background or coloring part, so inverting the colors helps to correct this recognition error and ensures that the line art is accurately represented in the generated image.

💡Character Design

Character design is the process of creating the visual appearance of a character for a story, game, or other media. The video discusses how Control-Net can be used to enhance the character design process by allowing for the easy generation of poses and the extraction of line art from images. This can lead to more efficient and creative character design workflows, as designers can quickly iterate on ideas and see how characters would look in various poses and styles.

💡Live2D

Live2D is a software that allows for the creation of 2D animations with a three-dimensional feel. It is often used in the design of virtual avatars for VTubers and other digital media. The video mentions that Control-Net can be particularly useful for VTubers using Live2D, as it enables them to easily create different clothing samples for their avatars. By using Control-Net, they can generate various poses and outfits, making it simpler to customize their avatars for different content distributions.

Highlights

Control-Net is a revolutionary technology that simplifies the process of posing characters in images.

SD-WebUI-ControlNet allows users to run Control Net on the web UI, making it more accessible.

The installation process for SD-WebUI-ControlNet involves downloading from GitHub and installing via the web UI's extension tab.

Automatic1111, a prerequisite for installing SD-WebUI-ControlNet, is already installed in the local version.

The Control Net model requires downloading approximately 6GB of files from Hugging Face.

Open Pose is a key function of Control Net that reproduces poses from images.

CannyEdge is a line extraction function within Control Net that enhances line art in generated images.

The pre-processor and model in Control Net work as a set, depending on the type of image used.

Control Net can generate images based on stick-figures, greatly simplifying the creation process.

The detected map, created by Control Net, is a stick-figure representation used for generating images.

Users can save the detected map for future use, enhancing workflow efficiency.

Inverting input color is a useful feature for generating images from black line art on a white canvas.

Control Net's ability to accurately trace and reproduce images is a significant advancement in image generative AI.

MLSD, Normal Map, Depth, and Segmentation are additional functions of Control Net for specialized tasks.

Control Net is particularly useful for character illustrations, background creation, and material design.

The technology aids in the design field by streamlining the process of generating poses and ideas.

VTuber Live2D users can utilize Control Net to easily create clothing change samples for their characters.

Control Net represents a breakthrough in the capabilities of image generative AI, offering new possibilities for creators.