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. The process starts by creating a dataset of images and generating text captions for each. With a specific Scorch CU 121 version, users can train Lora models using the 'ljr Lora' node. The training saves checkpoints at set intervals, resulting in a fully trained Lora model. The video showcases the simplicity and effectiveness of this approach, with examples of generated sketches and the final Lora model.
Takeaways
- 🚀 ComfyUI now supports full training of Lora models without the need for external platforms like Kaggle or Google Colab.
- 📂 To begin, create a dataset of images and place them in a folder named 'dataset' followed by the name of your Lora model.
- 🖼️ The images in the dataset can be of different sizes and should be in PNG format.
- 🔗 The GitHub link for the node is provided in the video description, which is essential for training Lora models within ComfyUI.
- 📝 Generate text captions for each image in the dataset, which will help the training process by providing context for the images.
- 📋 Ensure that you have the correct version of Scorch CU 121 for the training process to work properly.
- 🔄 Use the Lora caption node and the W14 tagger to process the images and text, creating text files for each image.
- 🎯 The magic node (ljr Lora) is used for the actual training of the Lora model within ComfyUI.
- 🛠️ Configure the training settings, including checkpoint name, image set path, 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), until the full dataset is trained.
- 🎉 The final Lora model is saved without numbers in the name, using the name specified in the training settings.
Q & A
What is the main topic of the video?
-The main topic of the video is training Lora models using only the ComfyUI platform without the need for external tools like Kaggle or Google Colab.
Who is the presenter of the video?
-The presenter of the video is Marcus.
What is the significance of the GitHub link mentioned in the video?
-The GitHub link leads to the repository containing the node 'allora trading' by Larry Jane, which is crucial for training Lora models within the ComfyUI environment.
What type of images are required to create a dataset for training?
-A dataset for training requires a collection of images, which can be of different types of sketches in PNG format and do not have to be of the same size.
How important is the naming of the folder containing the images?
-The naming of the folder is very important as it must be named 'more s' followed by the name of the Lora model being trained, as this structure is recognized by the training node.
What is the purpose of creating text captions for each image in the dataset?
-Text captions help describe what is in each image, providing the training process with additional context and understanding of the content of the images.
What is the role of the 'W 14 tagger' in the training process?
-The 'W 14 tagger' is used to analyze each image in the dataset and create a text file that describes the content of each picture, aiding in the training of the Lora model.
What are the key options available in the 'magic node' for training Lora models?
-Key options include checkpoint name, path to images, dataset size, max training epochs, save frequency, output name, and output directory.
How often does the training process save a Lora model?
-The training process saves a Lora model after every epoch, which is a set of 10 images, allowing for incremental progress and model retrieval.
What is the final output of the training process?
-The final output is a fully trained Lora model without any numbers in its name, saved in the 'models Lura' directory, along with corresponding text files for each image in the dataset.
How does the video demonstrate the training workflow?
-The video demonstrates the training workflow by showing the setup and execution of the 'magic node' with a dataset of sketches, including the creation of text captions, and the training process itself.
Outlines
🚀 Introduction to Training AI Models in Comfy UI
The paragraph introduces the audience to a new method of training AI models, specifically Luras, within the Comfy UI environment. The speaker, Marcus, emphasizes the convenience of this approach as it eliminates the need for external platforms like Kaggle or Google Colab. The process begins with creating a dataset of images, which are then organized into a specific folder structure. Marcus mentions the importance of using a specific version of the Comfy UI and outlines the steps for generating text captions for each image, which are crucial for the AI training process. The speaker also provides information about the GitHub repository where the necessary nodes for this process can be found.
📚 Configuring the Training Process in Comfy UI
In this paragraph, Marcus delves into the specifics of configuring the AI training process within the Comfy UI. He introduces the 'magic node' and explains the various options and parameters required for training, such as the checkpoint name, image dataset path, batch size, and the number of epochs. The speaker also discusses the process of saving the AI model at different stages of training, providing details on how the system handles the saving of epochs and the naming convention for the AI models. Marcus demonstrates the ease of use and the streamlined workflow of training AI models in Comfy UI, highlighting the efficiency and user-friendliness of the platform.
🎨 Training AI with Sketches and Direct Integration
Marcus showcases the application of the training process by using a set of sketches to create a sketch-style AI model. He explains how to use the text captions with the images in the dataset and how to integrate them directly into the Comfy UI for training. The speaker provides examples of different sketches used for training and emphasizes the flexibility of the process, as any image can be used as long as it is part of the dataset. Marcus also demonstrates the training process in action, showing how the AI model is created and saved within the Comfy UI environment. The paragraph concludes with a brief mention of the results and the potential for further exploration and development using this method.
🎥 Conclusion and Preview of Future AI Fuzz Videos
The final paragraph wraps up the video with a summary of the process and a preview of upcoming content. Marcus reiterates the ease and efficiency of training AI models within the Comfy UI and provides a link to the GitHub repository for the nodes used in the training process. He also teases future AI Fuzz videos, promising to showcase more trained AI models and provide additional insights into the training process. The paragraph ends with a call to action for viewers to look forward to the next video in the series, creating anticipation and engagement.
Mindmap
Keywords
💡ComfyUI
💡Lora
💡GitHub
💡Dataset
💡Text Captions
💡Scorch CU 121
💡ljr Lora
💡WG 14 Tagger
💡Checkpoint
💡EPO
💡Output Directory
💡Prompt
Highlights
ComfyUI now supports full training of Lora models without the need for external platforms like Kaggle or Google Colab.
Training begins by creating a dataset of images, which are the core of the training process.
The images should be in PNG format and can vary in size, with a minimum of 25 recommended for a comprehensive dataset.
The dataset folder must be named following the structure 'dataset_name_of_your_lore' to be recognized by the training node.
Text captions are generated for each image to provide the Lora model with context during training.
A specific version of Scorch CU 121 is required for the training process to function properly.
The Lora training node, 'ljr Laura', is introduced as a powerful tool for ComfyUI users.
The 'WG 14 tagger' node is used to analyze the images and create text files, which can be downloaded from the manager.
Default settings on the 'WG 14 tagger' are sufficient for generating descriptions of the images.
The 'magic node', 'ljr train', is responsible for the actual training of the Lora model within ComfyUI.
Checkpoints, image paths, batch size, and save options are configurable within the 'ljr train' node.
The training process saves a Lora model every epoch, allowing for incremental progress and review.
The final Lora model is saved without numbers in the name, corresponding to the folder name it was trained in.
The entire training workflow, from setup to completion, is managed within ComfyUI, emphasizing its comprehensive capabilities.
The video provides a step-by-step guide on how to train a Lora model using ComfyUI, showcasing its user-friendly interface and functionality.
The presenter, Markus, demonstrates the training of two different Lora models with unique datasets, illustrating the versatility of the platform.
A link to the GitHub repository for the 'ljr Laura' node will be provided in the video description for viewers to access.