ComfyUI : NEW Official ControlNet Models are released! Here is my tutorial on how to use them.
TLDRThe video script introduces the release of official control net models for the sdxl platform. It guides viewers on installing the necessary manager, fetching models from the Hugging Face repository, and utilizing preprocessors. The tutorial showcases the process of integrating control net models and preprocessors into the workflow, demonstrating their application through various examples and settings. The video emphasizes the importance of selecting appropriate models and preprocessors for the desired outcome, and encourages experimentation with different control net configurations to achieve the best results.
Takeaways
- 🚀 Introduction of the official control net models for the community.
- 🛠️ Importance of installing the manager for easy management of custom nodes.
- 🔄 Correcting the mistake from a previous video, emphasizing the use of 'git clone' instead of 'get clone'.
- 📦 Installation of the manager through Git and adding it to the local custom nodes directory.
- 🔍 Using the manager to search and install control net packages from the repository.
- 🎨 Understanding the difference between UI packages and workflow packages, opting for the latter to learn the process.
- 🔧 Installation of preprocessors, which are essential for the control net to function properly.
- 🌐 Fetching the sdxl models from the Hugging Face repository and understanding the architecture behind 'control Loras'.
- 📱 Alternative installation methods for different platforms, like using a 'model paths.yaml' file for easier management.
- 🖼️ Demonstration of the preprocessors in action, such as the candy Edge detector and depth map.
- 🎮 Customizing the control net process by adjusting settings like strength, start, and end points for the application of the depth map.
Q & A
What is the main topic of the video?
-The main topic of the video is the introduction and usage of the official control net models for the sdxl, including their installation and application in a graphic interface.
What is the first step in using the control net models?
-The first step is to install the manager, which is highly recommended for managing the custom nodes and models.
What was the mistake made in the previous video regarding the installation process?
-The mistake made in the previous video was saying to 'get fetch' instead of 'get cloned'.
Where can the models be obtained from?
-The models can be obtained from the sdxl official Hugging Face repository.
What are the two main types of control net preprocessors mentioned in the video?
-The two main types of control net preprocessors mentioned are the candy Edge detector and the depth map.
Why are the control net models designed to be smaller and more memory efficient?
-The control net models are designed to be smaller and more memory efficient due to their architectural implementation, which allows for better performance and usability.
How can the user organize the control net models?
-The user can organize the control net models by placing them into specific folders within their comfy installation directory, such as 'control net' or 'custom nodes'.
What is the purpose of the control net apply advanced option?
-The control net apply advanced option allows users to have more control over the application of the control net, including the ability to adjust settings like strength, start, and end points for the processing.
How does the video demonstrate the use of the control net models?
-The video demonstrates the use of the control net models by loading an image, applying a depth map preprocessor, and then using the control net model to generate a new image based on the depth map and user prompts.
What is the significance of the scheduler setting in the video?
-The scheduler setting is significant as it determines the mathematical model used to pull information out of the latent space, affecting the final output of the generated image.
What was the final result of using the control net models in the video?
-The final result was an image of an alien cyborg female on an alien ship, which adhered to the depth map and outline of the original image while incorporating the user's prompt.
Outlines
🚀 Introduction to SDXL Control Net Models
The speaker, Scotty, introduces the availability of official SDXL control net models and outlines the process for their installation and use. He emphasizes the importance of installing a manager for handling custom nodes and guides the audience through the installation process, correcting a previous mistake regarding the use of 'fetch' instead of 'clone'. Scotty also mentions the need to install preprocessors and provides a brief overview of the steps involved in setting up the environment for using control net models.
🛠️ Exploring Control Net Preprocessors and Custom Nodes
Scotty delves into the functionality of control net preprocessors, demonstrating their use with various images. He explains the difference between a depth map and a normal map, and how they can be combined for enhanced results. The speaker also discusses the installation of control net models from the Hugging Face repository, highlighting the importance of selecting the appropriate model based on system memory. He further illustrates the process of integrating these models into the workflow and the benefits of using the manager for streamlined installation.
🎨 Applying Control Net Models in the Creative Process
In this section, Scotty focuses on the practical application of control net models in creating images. He describes the process of loading preprocessed images, such as depth maps, into the control net and discusses the use of positive and negative encoders. The speaker also explains the role of conditioning in the image generation process and provides insights into the settings and parameters that can be adjusted for different outcomes. He emphasizes the flexibility of control net models and the potential for creating complex and detailed images.
🌟 Finalizing the Image with Control Net and VAE
Scotty concludes the video by demonstrating the final steps in generating an image using control net models. He discusses the use of a latent space model, the importance of selecting an appropriate sampler, and the role of the scheduler in the image generation process. The speaker also highlights the ability to control the influence of the depth map at different stages of the generation process. He provides a prompt for creating an alien cyborg female on an alien ship and showcases the resulting image, acknowledging the support of the channel's sponsors and encouraging viewer feedback.
Mindmap
Keywords
💡sdxl official control net models
💡manager
💡Hugging Face repository
💡preprocessors
💡custom nodes
💡Candy Edge detector
💡depth map
💡control net apply
💡latent
💡CFG
💡sampler
Highlights
Introduction of the official control net models and their availability.
Recommendation to install the manager for handling custom nodes efficiently.
Instructions on installing the manager using git and cloning the repository correctly.
Explanation of the process to install custom nodes from the sdxl official Hugging Face repository.
Importance of preprocessors in the workflow and how to install them.
Demonstration of installing control net preprocessors and their role in the process.
Clarification on the use of control net models and their architectural implementation.
Instructions on installing sdxl models from Hugging Face and organizing them in the Comfy interface.
Discussion on the use of different preprocessors like candy edge detector and depth map.
Explanation of how control nets work in combination with other elements like encoders and samplers.
Demonstration of the creative process using control nets, including setting up prompts and parameters.
Importance of graph neatness and organization in the Comfy interface for better workflow.
In-depth look at how to use control nets effectively, including the use of strength, start, and end settings.
Practical example of transforming an image using control nets, demonstrating the potential of the technology.
Discussion on the potential of control nets for various applications and their impact on creative processes.