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】
TLDRThe video script introduces a revolutionary AI technology called Control-Net, developed by Iliasviel, which simplifies the process of generating character poses in web UI. It allows users to install and use Control-Net through Mikubill's SD-WebUI-ControlNet extension, making it easier to produce images that reflect desired poses and characteristics. The script provides a step-by-step guide on installation, model setup, and utilization of various functions like Open Pose and CannyEdge for precise image generation and character design, highlighting the transformative impact of Control-Net on the field of image generative AI.
Takeaways
- 🚀 Introduction of Control-Net by Iliasviel in February 2023 revolutionized the way poses are generated for characters, making it significantly easier compared to previous methods.
- 📱 Mikubill's 'SD-WebUI-ControlNet' expansion allows for the use of Control-Net directly within the web UI, enhancing user experience and workflow efficiency.
- 🔧 Installation of the local version, Automatic 1111, is a prerequisite for using the Control-Net feature within the web UI.
- 🛠️ The process of installing Control-Net involves accessing Mikubill's GitHub page, copying the URL, and following a series of steps to integrate it into the web UI.
- 📂 The installation also requires the addition of specific model files from Hugging Face, which are placed in the appropriate folders within the Web UI Install directory.
- 🎨 Control-Net's Open Pose function enables the reproduction of a pose from a simple stick-figure or an image, providing artists with a versatile tool for character design.
- 🌟 The CannyEdge function is another key feature of Control-Net, offering line extraction capabilities that can enhance the sense of line art in generated images.
- 🔄 Control-Net can be thought of as an additional order to the prompt, allowing for greater precision and control over the generation results.
- 🖌️ The detected map, which is the stick-figure extracted by Control-Net, can be saved and used independently for further refinement or design work.
- 🎭 Control-Net's versatility extends to various other functions like MLSD, Normal Map, Depth, and more, each specialized for different aspects of image generation and manipulation.
- 👗 The technology is particularly beneficial for character design, background creation, and can even simplify the process of changing outfits for VTubers using Live2D.
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 a troublesome method of either writing 'spells' to induce a pose or creating a pose with 3D drawing software and then using an image-to-image process. This often involved multiple attempts in Gacha to achieve the desired result.
Who released the revolutionary Control-Net technology in February 2023?
-Iliasviel released the Control-Net technology, which made pose generation significantly easier.
What is the 'SD-WebUI-ControlNet' that is mentioned in the script?
-SD-WebUI-ControlNet is an expansion developed by Mikubill that allows users to run Control-Net on the web UI, simplifying the process of pose generation.
How does one install the 'SD-WebUI-ControlNet' on the web UI?
-To install 'SD-WebUI-ControlNet', one needs to access Mikubill's GitHub page, copy the URL, and paste it into the 'Install from URL' tab in the extension section of the web UI. After pressing 'Install' and waiting for the process to complete, the user should see 'SD Web UI ControlNets' in the list and then apply and restart the web UI.
What is the purpose of installing the Control Net model from Hugging Face?
-The Control Net model from Hugging Face is necessary to provide the functionality for the web UI to execute the pose generation and manipulation tasks. It includes various files that are downloaded and inserted into the Extensions folder in the Web UI Install folder.
How does the Open Pose function of Control-Net work?
-The Open Pose function 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, effectively copying the pose onto a different character or in a different artistic style.
What is CannyEdge and how is it used in the context of Control-Net?
-CannyEdge is a line extraction function that can create a strong sense of line art in the generated images. It is used to extract lines from a reference image and generate a result based on the extracted line art, which can be particularly useful for character design and illustration.
What is the significance of the 'detected map' in the Control-Net workflow?
-The 'detected map' is the outcome of using the Control-Net's line extraction functions. It is an image that represents the extracted lines or edges from the original image, which can be saved and used as a reference for further generation tasks, such as painting or redesigning characters.
How does the pre-processor and model relationship work in Control-Net?
-In Control-Net, the pre-processor is used to pre-process the input, such as extracting lines or poses from an image. The model then uses this processed information to generate the final result. The pre-processor and model are considered a 'set' and need to be chosen based on the type of input used in the control net.
What are some other functions of Control-Net besides Open Pose and CannyEdge?
-Control-Net offers various 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, Fake Scribble for creating illustrations from photos, and Segmentation for color-based composition creation.
How does the Control-Net technology impact the efficiency of character design and illustration?
-Control-Net significantly improves the efficiency of character design and illustration by allowing for the accurate tracing of poses and lines from reference images, easy generation of coloring images from line art, and the ability to quickly iterate on character designs and poses. It simplifies the design process and allows artists to focus more on creativity rather than technical details.
What potential applications can Control-Net have in game development and VTuber content creation?
-In game development, Control-Net can be used to quickly generate character designs and backgrounds, as well as to extract and repurpose elements from existing images for new game assets. For VTubers, Control-Net can facilitate the easy creation of sample clothing changes and character variations, allowing for more dynamic and varied content based on the distribution needs.
Outlines
🚀 Introduction to Control-Net and its Implementation
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 using 'spells' or 3D drawing software, and contrasts this with the ease of use provided by Control-Net. The speaker demonstrates the installation and use of Mikubill's 'SD-WebUI-ControlNet', a web UI expansion that facilitates the use of Control-Net. The process involves downloading and installing the necessary components, accessing GitHub, and navigating through the web UI to activate the Control-Net functionality. The paragraph emphasizes the breakthrough nature of Control-Net and its impact on character pose generation.
📚 Understanding Control-Net's Pre-Processors and Models
This paragraph delves into the specifics of how Control-Net functions, particularly focusing on the relationship between pre-processors and models. 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 Control-Net refines AI-generated images based on certain inputs. The paragraph clarifies the roles of pre-processors in image preparation and how they differ depending on whether a stick-figure or a regular image is used. It also highlights the use of 'Open Pose' for pose reproduction and introduces 'CannyEdge', a line extraction function. The speaker guides the audience through setting up the 'detected map' saving feature and demonstrates how to use CannyEdge to generate line art-based images, emphasizing the efficiency and potential applications in character design and game development.
🎨 Exploring Advanced Features of Control-Net
This paragraph discusses additional functions of Control-Net beyond Open Pose and CannyEdge. It introduces various models such as MLSD for straight line extraction, Normal Map for surface unevenness detection, Depth for depth extraction, and Holistically Nested Edge Detection for outline strength and weakness identification. The paragraph also covers Pixel Difference Network, Fake Scribble for image-to-drawing conversion, and Segmentation for室内设计. It provides insights into the practical applications of these features in character illustration, background creation, and material design. The speaker shares personal experiences and recommendations on which features to use for specific design tasks, highlighting the transformative impact of Control-Net on the creative process. The paragraph concludes by reiterating the ease of pose generation with Control-Net and its benefits for content creators, designers, and VTubers.
Mindmap
Keywords
💡Control-Net
💡Mikubill's SD-WebUI-ControlNet
💡Hugging Face
💡Open Pose
💡CannyEdge
💡Pre-processor
💡Model
💡Detected Map
💡Installation Procedure
💡VTuber
💡Character Design
Highlights
Introduction of the revolutionary Control-Net technology by Iliasviel in February 2023, which simplifies the process of making a character take a desired pose.
The use of Mikubill's 'SD-WebUI-ControlNet' to facilitate the operation of Control-Net on the web UI.
The installation process of the local version, Automatic 1111, and its necessity for using the Control-Net.
Accessing Mikubill's GitHub page for the installation of the Control-Net extension.
The detailed steps for installing the Control-Net from URL in the web UI's extension tab.
Verification of successful installation through the appearance of 'SD Web UI ControlNets' in the list.
Instructions for applying changes and restarting the web UI to activate the Control-Net.
The process of installing the model for the Control-Net by accessing Hugging Face and downloading specific files.
Explanation of the function and application of the 'Open Pose' in reproducing a pose from an image.
Demonstration of extracting a pose from an image and reproducing it using the Control-Net.
The introduction and use of the 'CannyEdge' function for line extraction in image generation.
The ability to save the 'detected map' for further use in the image generation process.
The application of the Control-Net to generate images based on line art, enhancing efficiency in character design.
The use of the 'invert input color' function for correcting recognition errors when using black line art on a white canvas.
The potential of Control-Net in game development for refining character designs.
Explanation of the various models and functions available within the Control-Net, such as MLSD, Normal Map, Depth, and Holistically Nested Edge Detection.
The practical application of the Control-Net in VTuber content creation, such as changing outfits easily.
The transformative impact of Control-Net on image generative AI, allowing for more accurate tracing and creative flexibility.
The summary of the Control-Net's ease of operation and its significant benefits for artists and designers.