Lora Training using only ComfyUI!!
TLDRMarcus introduces a new method for training Lora models exclusively within ComfyUI, eliminating the need for external platforms like Kaggle or Google Colab. He outlines the process of creating a dataset, generating text captions for images, and using the magic node 'ljr Lora' for training. The video demonstrates the simplicity and efficiency of this approach, showing that all steps, from data preparation to model training, can be done within ComfyUI. Marcus emphasizes the ease of use and the potential for users to create custom Lora models without any coding or complex setup.
Takeaways
- 🚀 ComfyUI now supports full training of Lora models without the need for external platforms like Kaggle or Google Collab.
- 🔍 Training begins with creating a dataset of images, which should be placed in a specifically named folder within a 'databased' directory.
- 🎨 The dataset should consist of at least 25 different sketches in PNG format, which will be used to define the style for the Lora model.
- đź”— The GitHub repository for the Lora training node is provided by Larry Jane, and it is essential to use the correct version of Scorch CU 121.
- đź“ť Text captions for each image are generated using the Lora caption node and the W14 tagger, which helps the training process by describing the content of each image.
- 🔄 The training process involves using the 'magic node', which is a significant update in ComfyUI, allowing for the creation of Lora models within the platform.
- đź“Ś Key options for training include defining the checkpoint name, path to the image dataset, batch size, max training epochs, and output directory.
- 🔢 The training process saves a Lora model after every set of images (e.g., every 10 images), resulting in a model with no numbers in its name.
- 🎥 The video provides a demonstration of the entire workflow, from uploading the sketches to generating a Lora model using ComfyUI.
- đź’ˇ The presenter, Markus, emphasizes the simplicity and efficiency of training Lora models exclusively within ComfyUI, highlighting its user-friendly interface and capabilities.
- đź”— Links to the GitHub repository and further resources are provided in the video description for viewers to explore and utilize in their own projects.
Q & A
What is the main topic of the video?
-The main topic of the video is training fully trained Lora models exclusively within the ComfyUI environment.
Who is the speaker of the video?
-The speaker of the video is Marcus.
What does the speaker mention as a replacement for previous training methods?
-The speaker mentions that there is no more need for Coya, Kaggle, or Google Collab as the training can be done entirely within ComfyUI.
What is the first step in training a Lora model according to the video?
-The first step is to create a dataset of images, which will be used to train the AI.
How many sketches does the speaker recommend for a dataset?
-The speaker recommends a minimum of 25 sketches for a dataset, but 50 is used in the demonstration.
What is the significance of the folder name in the training process?
-The folder name is significant because it is used as the base for the training, and the node works off this structure.
What is the purpose of creating text captions for the images?
-The text captions help the AI understand what is in each image during the training process, giving it a better idea of what it's looking at.
What is the 'magic node' referred to in the video?
-The 'magic node' is the node used for the actual training of the Lora model within the ComfyUI.
How often does the training process save a Lora model?
-The training process saves a Lora model every 10 images, or after an epoch (EPO).
What is the final output of the training process?
-The final output is a fully trained Lora model with no numbers in the name, located in the 'models/Lora' directory.
How long did it take to train the Lora model in the video?
-It took 23 minutes and 20 seconds to train the Lora model in the video.
Outlines
🚀 Introduction to Training AI Models in Comfy UI
The speaker, Marcus, introduces the topic of training AI models, specifically Luras, within the Comfy UI environment. He emphasizes the convenience of this method as it eliminates the need for external platforms like Kaggle or Google Colab. Marcus outlines the process of creating a dataset of images and preparing text captions for each image, which are essential for training the AI. He also mentions the importance of having a specific version of the Scorch CU 121 for this process and provides a link to the GitHub repository for the required node, 'Allora Trading by Larry Jane mine'. The speaker guides the audience through the initial setup, including organizing the data set and installing the necessary nodes for training.
đź“š Detailed Training Process with Magic Node
In this paragraph, Marcus delves into the detailed process of training AI models using the 'Magic Node' in Comfy UI. He explains the various options available within the node, such as checkpoint name, image set path, batch size, max training epochs, and output settings. The speaker clarifies the importance of correctly specifying the path to the image dataset and provides specific instructions on how to set up the node for training. He also describes the training process, including how the AI analyzes each image and creates text files, and how it saves progress at regular intervals. Marcus demonstrates the efficiency of this process, highlighting that it can be completed entirely within Comfy UI without the need for external resources.
🎨 Training with Sketches and Final Output
Marcus concludes the video by showcasing the application of the training process using a set of sketches. He explains how to use the trained AI model to generate Luras based on these sketches, emphasizing the flexibility in choosing images for training. The speaker also provides a brief demonstration of the training process in action, showing how the AI identifies and categorizes images. He then presents the final output, a Lura model named after the folder it was trained in, and mentions that the process took approximately 23 minutes. Marcus assures the audience that the training was done solely within Comfy UI, without any external platforms. He also teases future content by promising to include a few images of trained Luras in the video description.
Mindmap
Keywords
đź’ˇComfyUI
đź’ˇLora
đź’ˇDataset
đź’ˇText Captions
đź’ˇTraining
đź’ˇGitHub
đź’ˇScorch CU 121
đź’ˇljr Laura
đź’ˇCheckpoint
đź’ˇEPO
đź’ˇModels Lura
Highlights
Training fully trained Lora models exclusively in ComfyUI is now possible, eliminating the need for external platforms like Kaggle or Google Colab.
The process begins by creating a dataset of images, which are the core of the training data for the AI.
A minimum of 25 to 50 images is recommended for the dataset, with no requirement for uniform sizing or format.
The images must be stored in a specifically named folder within the database directory for the node to recognize and utilize them effectively.
A fresh installation of ComfyUI with a specific version of Scorch CU 121 is necessary for the training process to function correctly.
The Lora Caption node is used to generate text captions for each image, providing the AI with a description of the content.
The W14 Tagger node is used in conjunction with the Lora Caption node to enhance the training process.
The Magic Node, ljr e, is responsible for the actual training of the Lora model within ComfyUI.
Configurable options within the Magic Node allow for customization of the training process, including checkpoint naming, image set path, batch size, and epoch settings.
The training process involves iterating through the images in sets, saving a Lora model after each set is processed.
The final output is a fully trained Lora model without any numerical identifiers in its name, simplifying identification and use.
The entire training workflow is contained within ComfyUI, from data set preparation to model generation.
The prompt system in ComfyUI allows for the direct application of trained Lora models to new data.
The video provides a step-by-step guide on how to achieve Lora training using only ComfyUI, making it accessible to a broader audience.
The presenter, Markus, demonstrates the training of two different Lora models using sketch-style images, showcasing the versatility of the method.
Links to necessary nodes and additional resources will be provided in the video description for viewers to replicate the process.
This method represents a significant advancement in AI training, simplifying the process and making it more user-friendly.