ComfyUI : NEW Official ControlNet Models are released! Here is my tutorial on how to use them.

Scott Detweiler
20 Aug 202315:59

TLDRIn this informative video, the host introduces the newly released sdxl official control net models, emphasizing their availability and gradual release. The video provides a step-by-step guide on installing the necessary manager, fetching models from the Hugging Face repository, and utilizing preprocessors. The host also demonstrates the application of control net models and preprocessors, such as the candy edge detector and depth map, in creating detailed and customized images. The process is showcased through the Comfortable UI, highlighting the flexibility and potential of these tools in generating unique visual content.

Takeaways

  • 🚀 The official Control Net models for SDXL are now available and will be released gradually.
  • 🛠️ It is highly recommended to install the manager for handling custom nodes in Comfy.
  • 🔄 The manager installation involves cloning the repository from GitHub and setting up the local installation.
  • 📚 The video provides a quick guide on how to install the manager and use it to install custom nodes.
  • 🔍 Control Net preprocessors are essential for preparing images for the Control Net models.
  • 🎨 The video demonstrates the use of different preprocessors like the Candy Edge detector and depth maps for enhancing image details.
  • 📱 The Control Net models can be installed from the Hugging Face repository and are organized based on their architecture.
  • 🌐 The SDXL models, referred to as Control Loras, are smaller and more memory-efficient due to their architectural design.
  • 🔧 The video outlines the process of installing SDXL models using the manager and the 'get clone' feature.
  • 🎢 The workflow involves using Control Net models in conjunction with preprocessors to create detailed and contextually relevant images.
  • 📈 The video also discusses the use of various settings like strength, start, and end to control the application of the Control Net models.

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 platform.

  • 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 custom nodes.

  • How does one install the manager?

    -To install the manager, one needs to go to the GitHub repository, use the 'code' button to clone the repository, and follow the instructions for their respective operating system.

  • Where can the control net models be found?

    -The control net models can be found in the official Hugging Face repository.

  • What is the purpose of the preprocessors?

    -The preprocessors are used to process images before they are used with the control net models, such as creating depth maps or edge detectors.

  • How does one install the control net models?

    -The control net models can be installed through the manager by searching for 'control net' and selecting the appropriate package to install.

  • What is the significance of the 'control net apply advanced' node?

    -The 'control net apply advanced' node allows for more control over the application of the control net, such as adjusting the strength of the control net's influence at different stages of the process.

  • How can one use multiple control nets in a workflow?

    -By duplicating the control net model and hooking it up in a chain, one can use multiple control nets in a workflow, each with its own preprocessor or image input.

  • What is the role of the latent node in the process?

    -The latent node represents the latent space in which the model generates the image. It needs to be a certain size (1024 or larger for sdxl) and is used in conjunction with the VAE (Variational Autoencoder) for image generation.

  • How does the depth map preprocessor affect the image generation?

    -The depth map preprocessor provides information about the depth or distance of objects in the image relative to the camera. It can be used to guide the model in generating images that maintain the perspective and depth information from the original image.

  • What is the purpose of the 'start' and 'end' settings in the control net apply advanced node?

    -The 'start' and 'end' settings allow the user to control at what point in the image generation process the control net's influence begins and ends, providing more fine-tuned control over the final image.

Outlines

00:00

🚀 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 the manager for handling custom nodes and guides the audience through the installation process. Scotty corrects a previous mistake regarding the use of 'get clone' instead of 'fetch' and proceeds to demonstrate how to install the manager and retrieve the models from the Hugging Face repository. He also discusses the necessity of installing preprocessors and provides a brief overview of the steps involved.

05:01

🛠️ Exploring Control Net Preprocessors and Installation

Scotty delves into the functionality of control net preprocessors, highlighting their role in enhancing the quality of generated images. He demonstrates how to use the Comfortable UI (Comfy UI) manager to install various preprocessors, including edge detectors and depth maps. The speaker explains the difference between normal maps and depth maps, and how combining different preprocessors can improve the detail and accuracy of the final output. He also discusses the process of installing the SDXL models from the Hugging Face repository and organizing them within the Comfy UI for easy access.

10:02

🎨 Applying Control Net Models and Preprocessors

The speaker illustrates the application of control net models and preprocessors in the creation process. He explains how to load and apply a depth map preprocessor to an image, and how to use the control net model in conjunction with it. Scotty also discusses the use of positive and negative encoders, and how they are utilized in the generation process. He provides a step-by-step guide on setting up the control net model, including the selection of appropriate settings for the latent space and the choice of sampler and scheduler. The speaker emphasizes the importance of adhering to the depth map throughout the generation process.

15:03

🌟 Finalizing the Image with Control Net Settings

Scotty concludes the tutorial by discussing the final steps in using control net models. He explains how to adjust the strength of the control net application at different stages of the image generation process, allowing for greater control over the final output. The speaker provides a practical example of generating an image of an alien cyborg female on a spaceship, discussing the impact of the prompt and the adherence to the depth map. He encourages viewers to experiment with different control net models and settings, and thanks the audience for their support, promising to share more in future videos.

Mindmap

Keywords

💡sdxl official control net models

These are specialized AI models released by the sdxl organization, designed to enhance the capabilities of AI systems by providing control over various aspects of image generation and processing. In the context of the video, these models are used to create and modify images with specific characteristics, adhering to certain conditions or structures.

💡manager

In the context of the video, the manager refers to a tool or software utility that simplifies the process of managing and installing custom nodes and models within an AI system. It is a crucial component for users who want to streamline their workflow and easily integrate new functionalities.

💡Hugging Face repository

Hugging Face is a platform that hosts a repository of machine learning models, including those for natural language processing and computer vision. In the video, it is mentioned as the source for obtaining the sdxl official control net models, indicating its role as a central hub for AI model distribution.

💡preprocessors

Preprocessors are tools or algorithms used to prepare or modify data before it is fed into a machine learning model. In the context of the video, preprocessors are utilized to transform images into a format that the control net models can understand and process effectively.

💡control net

Control net is a term used in the video to describe a specific type of AI model that allows for the conditioning and manipulation of image data. It is used to guide the AI in generating images that adhere to certain structural or stylistic guidelines.

💡Candy Edge detector

The Candy Edge detector is a specific type of preprocessor mentioned in the video, which is used to detect and highlight the edges within an image. It is one of the tools available for users to refine the input data before it is processed by the AI models.

💡depth map

A depth map is a visual representation that encodes the distance of objects from the camera in an image. It is used in the video to provide the AI with information about the relative distances of different elements within the image, which can be crucial for creating realistic and accurate image transformations.

💡latent

In the context of AI and machine learning, a latent refers to a set of values or features that are not directly observable but can be inferred from the data. In the video, a latent is used as an intermediate representation of the image data that the AI models can manipulate to generate new images.

💡CFG

CFG, or Configuration, is a parameter in AI models that controls the level of freedom the model has in interpreting the input. A lower CFG value allows for more creative and less predictable outputs, while a higher value results in more accurate but potentially less varied results.

💡sampler

A sampler in the context of AI and machine learning is a method or algorithm used to select or generate data points from a larger set. In the video, the sampler is responsible for how the AI pulls information out of the latent space to create the final image.

💡scheduler

A scheduler in the context of AI model training and inference is a mechanism that controls the learning rate or other hyperparameters over time. It helps to optimize the training process or the generation of outputs by adjusting these parameters dynamically.

Highlights

Introduction of the official control net models for the community.

Recommendation to install the manager for easier handling of custom nodes.

Instructions on how to correctly install the manager using the git clone method.

Explanation of the availability of various custom node packages for control net.

Importance of choosing the right package that allows for the creation of custom workflows.

Demonstration of installing the control net preprocessors from the Hugging Face repository.

Clarification on the use of control net preprocessors and their role in the workflow.

Discussion on the efficiency of the new control Lora's architecture.

Instructions on installing the sdxl models from the Hugging Face repository.

Use of the extra model paths.yaml file for easier management of models.

Explanation of the function and application of the candy edge detector preprocessor.

Comparison between normal maps and depth maps, and their distinct uses.

Demonstration of the depth map's application in enhancing the detail of images.

Process of setting up the control net model and its integration with the workflow.

Importance of using the correct control net model and preprocessor for the task.

Explanation of the conditioning aspect of the control net and its significance.

Demonstration of the creative process using control nets and the resulting image output.

Adjustment of control net strength and the impact on the final image.

Final demonstration of the prompt 'alien cyborg female on an alien ship' and its visual outcome.

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