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】

テルルとロビン【てるろび】旧やすらぼ
9 Mar 202313:58

TLDRThe video script introduces a revolutionary AI technology called Control-Net by Iliasviel, which simplifies the process of generating images with specific poses. It explains the installation and use of Mikubill's "SD-WebUI-ControlNet" for web UI, allowing users to generate images with desired poses through various functions like Open Pose and CannyEdge. The script demonstrates how Control-Net can accurately reproduce poses from stick figures or line art, and how it can be utilized in character design and game development, offering a significant advancement in image generative AI.

Takeaways

  • 🚀 Introduction of Control-Net by Iliasviel in February 2023 revolutionized the process of posing characters in web UI.
  • 🤖 Mikubill's 'SD-WebUI-ControlNet' expansion simplifies the use of Control-Net on web UI for users.
  • 🔧 Installation of the local version, Automatic 1111, is a prerequisite for using Control-Net on the web UI.
  • 🛠️ Users can access and install Control-Net via Mikubill's GitHub page using a URL and the web UI's extension tab.
  • 📂 The installation process involves copying and pasting the URL, installing from the URL, and applying the changes.
  • 📱 Downloading and installing the model for the Control-Net is done through Hugging Face, where users find and download the required files.
  • 🎨 Control-Net's 'Open Pose' function allows users to reproduce a pose from an image or a stick-figure by extracting the pose.
  • 🌐 'CannyEdge' is another key function of Control-Net that extracts line art from an image and generates based on that line art.
  • 🖌️ The 'invert input color' feature is useful for correcting AI's perception when working with line art drawn on a white canvas.
  • 🎭 Control-Net has various other functions like MLSD, Normal Map, Depth, and more, each specialized for different aspects of image generation and manipulation.
  • 👗 VTubers and content creators can use Control-Net to easily create and change character designs and clothing samples for their virtual personas.

Q & A

  • What was the main challenge in generating character poses before the introduction of Control-Net?

    -Before Control-Net, generating character poses required using complex 'spells' or creating poses with 3D drawing software and then converting those images into a pose, which was a more troublesome method.

  • What is Control-Net and how does it simplify the process of pose generation?

    -Control-Net is a revolutionary technology released by Iliasviel in February 2023 that simplifies the process of pose generation by allowing users to easily take a desired pose without the need for complex methods or multiple trials in Gacha.

  • What is the 'SD-WebUI-ControlNet' and how is it used?

    -SD-WebUI-ControlNet is an expansion by Mikubill that allows users to run Control-Net directly on the web UI, making it easier to generate images with specific poses and characteristics.

  • How can one install the 'SD-WebUI-ControlNet' on their system?

    -To install 'SD-WebUI-ControlNet', users need to download and install it from Mikubill's GitHub page, access the web UI, open the extension tab, and use the 'Install from URL' feature to input the URL for the Extensions Git Repository.

  • What is the significance of the 'Automatic 1111' version mentioned in the script?

    -'Automatic 1111' is the local version that has already been installed on the user's system, which is necessary for the functioning of the Control-Net on the web UI.

  • How does one install the model for the Control Net?

    -To install the model for the Control Net, users need to access Hugging Face, search for 'Control Net', download the required files, and then place them into the appropriate folders within the Web UI Install folder.

  • What is the 'Open Pose' function in Control-Net, and how is it used?

    -'Open Pose' is a function in Control-Net that allows users to reproduce a pose from an image. It can extract the pose from a stick-figure or an image and reflect it in the generated result.

  • What is the 'CannyEdge' function and its application in the script?

    -'CannyEdge' is a line extraction function that can create a strong sense of line art in the generated image. It is used to extract lines from a reference image and generate results based on the extracted line art.

  • How does the 'invert input color' function work in the Control-Net?

    -The 'invert input color' function reverses black and white, correcting the AI's recognition error when dealing with black line art on a white canvas. This helps to ensure that the line art is not treated as the background during the coloring process.

  • What other functions are available in Control-Net besides 'Open Pose' and 'CannyEdge'?

    -Control-Net offers several other functions including MLSD (Multi-Scale Line Segment Detector) for straight line extraction, Normal Map for surface uneven detection, Depth for extracting image depth, Holistically Nested Edge Detection for repainting, Pixel Difference Network for clear line drawing, and Segmentation for color difference extraction.

  • How does the Control-Net technology impact the process of character and background design?

    -Control-Net greatly impacts character and background design by allowing for more precise and efficient generation of poses and line art. It simplifies the design process, making it easier to experiment with different poses, outfits, and artistic styles without extensive manual drawing.

Outlines

00:00

🚀 Introduction to Control-Net and Mikubill's SD-WebUI-ControlNet

This paragraph introduces the revolutionary Control-Net technology released by Iliasviel in February 2023, which simplifies the process of posing characters in web UI. It explains the traditional, cumbersome methods of achieving desired poses using 'spells' or 3D drawing software, and contrasts this with the ease offered by Control-Net. The speaker shares their experience using Mikubill's 'SD-WebUI-ControlNet', an extension that facilitates the use of Control-Net on the web UI. The paragraph provides a step-by-step guide on how to download, install, and apply the Control-Net, including accessing GitHub, installing the extension, and applying the model. It emphasizes the breakthrough nature of this technology in the field of character posing and image generation.

05:01

🎨 Understanding the Control-Net's Pre-Processor and Model Functions

This paragraph delves into the specifics of how the Control-Net's pre-processor and model functions work together to achieve desired outputs. It explains the concept of pre-processing as the extraction of essential elements from an image, such as poses or line art, and how this process is necessary when using certain images but not others. The paragraph uses the 'Open Pose' and 'CannyEdge' functions as examples to illustrate how the Control-Net can extract and utilize information from a source image to generate new images. It also touches on the practical applications of these functions in character design and game development, highlighting the efficiency and creativity that Control-Net brings to the design process.

10:03

🛠️ Exploring Additional Control-Net Functions and Their Applications

In this paragraph, the speaker discusses a variety of additional functions available within the Control-Net, such as MLSD for straight line extraction, Normal Map for surface unevenness detection, Depth for image depth extraction, and others like Holistically Nested Edge Detection and Pixel Difference Network. Each function is briefly explained, along with its potential applications in different fields such as character illustration, background design, and material design. The paragraph emphasizes the versatility of Control-Net and how it can assist both artists and designers in their creative processes, from refining character poses to generating detailed backgrounds and textures. The speaker also shares personal insights on which functions are most useful for specific types of design work, providing a comprehensive overview of the capabilities of Control-Net.

Mindmap

Keywords

💡Control-Net

Control-Net is a revolutionary technology introduced in the video that simplifies the process of generating images with specific poses or characteristics. It allows users to generate images by extracting features from existing images or line drawings, rather than relying solely on textual prompts. This breakthrough significantly enhances the precision and control over the generative process, as demonstrated by the ability to reproduce a stick-figure's pose or extract line art for more detailed character designs.

💡SD-WebUI-ControlNet

SD-WebUI-ControlNet is an expansion that enables the use of Control-Net directly within a web UI environment. This tool is highlighted in the video as a convenient way to harness the power of Control-Net without the need for complex setups or extensive technical knowledge. It streamlines the process of installing and applying the Control-Net technology, making it accessible for a broader range of users.

💡Pose Reproduction

Pose reproduction refers to the ability to generate an image where the character adopts a specific pose, as desired by the user. In the context of the video, this is achieved through the use of Control-Net, which can extract and replicate poses from simple stick-figure drawings or more complex illustrations. This feature is particularly useful for artists and designers looking to create images with dynamic and accurate poses.

💡Line Art

Line art in the video refers to the black and white drawings that focus on the轮廓 and structure of characters or objects, without shading or color. The Control-Net's CannyEdge function is used to extract line art from an image, which can then be used as a basis for coloring and further design work. This process simplifies the task of creating detailed illustrations and can be particularly helpful for those who excel at drawing outlines but may struggle with coloring or shading.

💡Pre-processor

A pre-processor, as discussed in the video, is a function within the Control-Net that processes the input before it is used for image generation. This can involve tasks such as extracting lines or edges from an image, which are then used to guide the generative process. The choice of pre-processor depends on the type of input and the desired outcome, with options like Open Pose or CannyEdge being used for different purposes.

💡Model

In the context of the video, a model refers to the specific algorithm or set of instructions within the Control-Net that is used to generate the final image. Different models are designed for different tasks, such as reproducing poses, extracting line art, or focusing on depth and detail. The model is selected based on the user's requirements and the type of input they are providing.

💡Detected Map

A detected map, as described in the video, is the output of the Control-Net's line extraction functions, which visually represents the structural elements extracted from an input image. This map is essentially a visual guide that shows the lines and contours identified by the pre-processor, such as Open Pose or CannyEdge. These maps can be saved and used as references for further image generation or design work.

💡Installation Procedure

The installation procedure outlined in the video is the step-by-step process for setting up and configuring the Control-Net and its related tools within the web UI. This includes downloading and installing the necessary extensions, models, and pre-processors, as well as configuring the settings to enable the desired functionalities. Following this procedure ensures that users can effectively utilize the Control-Net for image generation.

💡Generative AI

Generative AI refers to artificial intelligence systems that are capable of creating new content, such as images, music, or text. In the video, the focus is on image generative AI, which uses algorithms to produce visual content based on input data. The Control-Net technology is a significant advancement in this field, allowing for more precise control over the generative process and enabling users to produce images that closely match their intended design or pose.

💡Character Design

Character design involves creating the visual appearance and personality of characters for various forms of media, such as animation, video games, and illustration. The video highlights how Control-Net can be used to enhance character design by providing tools for pose reproduction and line art extraction, which can help designers quickly generate and refine their ideas, as well as create multiple variations of a character with ease.

💡Live2D

Live2D is a software suite that allows for the creation of two-dimensional, anime-style characters that can be posed and animated in a seemingly three-dimensional space. In the video, the presenter mentions that Control-Net can be useful for VTubers and other content creators who use Live2D, as it simplifies the process of creating sample clothing or design changes for their virtual characters.

Highlights

Introduction of Control-Net, a revolutionary technology released by Iliasviel in February 2023 that simplifies pose generation.

Mikubill's 'SD-WebUI-ControlNet' allows for easier implementation of Control-Net on the web UI.

Local version Automatic 1111 is required for the installation of Control-Net on the web UI.

Instructions for downloading and installing Control-Net from Mikubill's GitHub page.

The process of installing the Control-Net extension through the web UI's extension tab.

Verification of successful installation through the 'Installed in~' message and the presence of SD Web UI 'ControlNets' in the list.

Explanation of installing the model for the Control Net from Hugging Face and downloading necessary files.

The importance of placing the downloaded files in the correct folder structure within the Web UI Install folder.

Restarting the Web UI and confirming the presence of Control-Net on the script.

Demonstration of generating an image from a stick-figure using the open pose function of Control-Net.

Explanation of how the pre-processor and model work together as a set, depending on the type of image used in the control net.

Introduction to CannyEdge, a line extraction function within Control-Net, and its setup for saving 'detected maps'.

Use of CannyEdge to generate an image with a strong sense of line art from a sample image.

The ability to leave painting to AI by using the detected-map generated from line art.

How the 'invert input color' function can be used to correct AI's recognition of black and white in line art.

The potential of Control-Net in game development for refining character designs and creating new design products.

Overview of other functions of Control-Net, such as MLSD, Normal Map, Depth, Holistically Nested Edge Detection, Pixel Difference Network, and Fake Scribble.

The practical applications of Control-Net for artists and designers, including ease of changing poses and clothing in character illustrations.

The impact of Control-Net on VTubers using Live2D for easily creating sample clothing changes.

Conclusion on the revolutionary impact of Control-Net on image generative AI and its ease of operation.

Casual Browsing

【やってみよう！】LoRAの使い方&簡単な作り方（kohya's GUI）

2024-03-27 14:40:02

簡単に好きなポーズを表現！controlnetの導入方法と基本的な使い方【Stable Diffusion】

2024-04-21 15:25:01

Midjourney・にじジャーニーの使い方と始め方を解説！AIイラスト自動生成のやり方【ミッドジャーニー / iPhone / iPad 】

2024-04-05 19:35:01

日本一わかりやすいStableDiffusion WebUI AUTOMATIC1111(ローカル版)のインストール方法と基本的な使い方

2024-03-30 15:00:01

ネガティブエンベッドの導入と使用方法解説！もう長いプロンプトはおしまい！【Stable Diffusion】

2024-04-15 04:25:01

日本一わかりやすいLoRA学習！sd-scripts導入から学習実行まで解説！東北ずん子LoRAを作ってみよう！【Stable Diffusion】

2024-03-27 17:10:00