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】
TLDRControl-Netは、Stable Diffusionのプラグインで、画像のスタイルを移転し制御できます。この技術を使うと、好みのポーズを取ることができます。インストール方法は、Mikubillの「SD-WebUI-ControlNet」をダウンロードし、Web UIの拡張タブからインストールします。モデルもダウンロードして、設定フォルダに配置する必要があります。Control-Netを使うと、スティックフィギュアからポーズを再現したり、線画を用いたりすることができます。これにより、AIで絵を描くことができます。
Takeaways
- 🚀 Introduction of Control-Net: A revolutionary technology released by Iliasviel in February 2023, allowing for easier pose generation in AI.
- 📦 Installation of Mikubill's 'SD-WebUI-ControlNet': A guide on downloading, installing, and applying the Control-Net extension on the web UI.
- 🔍 Accessing GitHub and Hugging Face: Instructions on navigating to Mikubill's GitHub page and searching for Control-Net on Hugging Face for model downloads.
- 🧩 Model Installation: A step-by-step guide on placing the downloaded models into the correct folder structure for the Web UI.
- 🏋️ Open Pose Function: A demonstration of how to use the Open Pose function to reproduce a pose from an image or a stick-figure.
- 🎨 CannyEdge Function: Explanation of the CannyEdge function for line extraction and how it can be used to generate images with a strong line art aesthetic.
- 📸 Detected Map: Information on how to save and use detected maps for line art and other functions within the Control-Net.
- 🖌️ Coloring from Line Art: Instructions on how to use the Control-Net to color line art while maintaining its integrity.
- 🛠️ Additional Functions: Brief overview of other functions like MLSD, Normal Map, Depth, and more, and their potential uses in character and background design.
- 👗 VTuber Applications: Mention of how Control-Net can assist VTubers with creating clothing samples and changing outfits easily for Live2D.
- 🎉 Revolution in AI Image Generation: A conclusion emphasizing the impact of Control-Net on the ease and precision of pose and design generation in AI.
Q & A
What was the main challenge in making a character pose in the web UI before the introduction of Control-Net?
-Before Control-Net, users had to either write 'spells' to induce a pose or create a pose using 3D drawing software and then combine that image-to-image, which was a more troublesome method.
What is Control-Net and how did it change the process of character posing?
-Control-Net is a revolutionary technology introduced by Iliasviel in February 2023 that allows users to achieve desired character poses more easily, eliminating the need for the more complex methods previously used.
How can one install the Control-Net extension on the web UI?
-To install the Control-Net extension, users need to access Mikubill's GitHub page, copy the URL, and paste it into the Extensions Git Repository URL field in the web UI's extension tab. After pressing Install and waiting for the process to complete, the extension should be ready to use.
What is the purpose of the model files downloaded from Hugging Face in the Control-Net setup?
-The model files downloaded from Hugging Face are essential components of the Control-Net system. They are placed in the Extensions folder within the Web UI Install folder to enable the various functionalities of Control-Net, such as pose reproduction and line extraction.
The Open Pose function allows users to reproduce a pose from an image by extracting the outline of a stick-figure or by reading the pose from a sample image, effectively mirroring the pose in the generated content.
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What is the CannyEdge function in Control-Net and how is it used?
-CannyEdge is a line extraction function that can create a strong sense of line art in the generated images. It is particularly useful for character illustrations and can be used to save the 'detected map,' which is the extracted line art from the reference image.
What is the significance of the 'invert input color' option in the Control-Net settings?
-The 'invert input color' option is used when generating images from line art. It reverses black and white, correcting the AI's tendency to treat the black parts as the background, thus ensuring that the line art is not broken when the AI paints over it.
How does the Control-Net impact the efficiency of character design?
-Control-Net significantly improves the efficiency of character design by allowing for the accurate tracing of poses and line art from reference images. This enables designers to focus more on the creative aspects and quickly iterate through different design ideas.
What are some other functions of Control-Net besides Open Pose and CannyEdge?
-Other functions of Control-Net include 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 can Control-Net be beneficial for VTubers using Live2D?
-Control-Net can help VTubers easily create sample designs for clothing changes by accurately tracing poses and line art. These designs can then be slightly adjusted and imported into Live2D for use in their virtual performances.
What makes Control-Net a 'revolution of image generative AI'?
-Control-Net is considered a revolution because it introduces a level of control and precision in image generation that was previously unattainable. By allowing users to guide the generation process with specific poses, line art, and other extracted features, it opens up new possibilities for creativity and efficiency in design work.
Outlines
🚀 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 generating poses for characters. It explains the traditional, cumbersome methods of achieving desired poses, such as writing 'spells' or using 3D drawing software. The speaker expresses excitement about using Mikubill's 'SD-WebUI-ControlNet', an expanded tool that facilitates the use of Control-Net on the web UI. The paragraph details the installation process of this tool, including downloading and installing it, accessing Mikubill's GitHub page, and applying necessary restarts. It also briefly mentions the need to install a model for the Control-Net by accessing Hugging Face, downloading specific files, and placing them in the correct folder within the Web UI Install directory.
📸 Understanding the Pre-Processor and Model Relationship in Control-Net
This section delves into the relationship between the pre-processor and the model within the Control-Net framework. It uses the analogy of ordering at a ramen shop to explain how additional specifications (like 'more harder or more oily') can influence the final product, similar to how the pre-processor refines the input before the model generates the output. The paragraph clarifies that no pre-processor is needed when using a stick-figure image because the 'stick' is already extracted, whereas a pre-processor is necessary for regular images to extract the 'stick' for pose reproduction. It then introduces 'CannyEdge', another function of the Control-Net that extracts line art from an image, and explains how to set up the system to save the 'detected map', which is the stick-figure image produced by the Control-Net. The section concludes with a practical demonstration of using CannyEdge to generate an image with a strong line art aesthetic.
🎨 Advanced Functions and Applications of Control-Net in Art and Design
This paragraph discusses the advanced functions of Control-Net beyond Open Pose and CannyEdge, and their potential applications in art and design. It introduces various models such as MLSD for straight line extraction, Normal Map for surface unevenness detection, Depth for extracting image depth, and Holistically Nested Edge Detection for repainting. The speaker also mentions Pixel Difference Network, Fake Scribble for creating illustrations from photos, and Segmentation for室内设计. The paragraph emphasizes the efficiency and precision that Control-Net brings to character design and background creation, allowing for accurate tracing and generation of images that were previously challenging with traditional methods. It also touches on the benefits for VTubers and game developers in creating character samples and designs. The speaker concludes by reiterating the transformative impact of Control-Net on image generative AI and its value in enhancing creative processes.
Mindmap
Keywords
💡Control-Net
💡SD-WebUI-ControlNet
💡Pose
💡CannyEdge
💡Pre-processor
💡Model
💡Hugging Face
💡GitHub
💡Installation
💡Web UI
💡Git Repository
💡Line Art
Highlights
Control-Netの導入により、より簡単にポーズを指定できるようになりました。
Control-NetはIliasvielが開発した革新的な技術です。
Mikubillの「SD-WebUI-ControlNet」を使用してControl-NetをWeb UIで実行できます。
Control-Netのインストールには、Automatic1111のインストールが必要です。
Control-Netをインストールするために、GitHubのMikubillページにアクセスし、URLをコピーする必要があります。
Web UIの拡張タブからControl-Netをインストールし、モデルを設定することができます。
Control-NetのモデルとしてOpen Poseが使用されます。
Open Poseは、画像からポーズを再現するのに役立ちます。
CannyEdgeは、線を抽出する機能で、線画の強調などに使用できます。
CannyEdgeを使用すると、画像から検出された「検出マップ」を保存できます。
Control-Netを使用すると、線画から絵画を生成することができます。
Control-Netは、キャラクターデザインやゲーム開発での使用にも適しています。
Control-Netのスクリプトには、他にもMLSDやDepth、Segmentationなどの機能があります。
Control-Netは、VTuberの衣装変更などのデザインにも役立ちます。
Control-Netの導入により、以前に比べてより正確に画像をトレースすることができます。
Control-Netは、画像生成AIの革命的な進歩と言われています。
Control-Netを使用することで、より効率的なキャラクターデザインが可能になります。
背景や建物の創作には、MLSDやDepth、Segmentationが便利です。
Control-Netは、通常の2Dイラストでは使用されないような機能も含んでいます。
Control-Netの導入により、より多様な創作が可能となり、創造的なアイデアを洗い出すことができます。
Control-Netの使い方について、次回もご紹介します。