Lora Training using only ComfyUI!!
TLDRIn this AI Fuzz video, Marcus introduces a new method for training AI models exclusively within Comfy UI, eliminating the need for external platforms like Kaggle or Google Colab. He details the process of creating a dataset of images, generating text captions for each image, and using a specific node called 'ljr Laura' for training. The video demonstrates how to set up the node, configure its options, and initiate the training process, resulting in a fully trained AI model. Marcus emphasizes the ease and efficiency of this workflow, showcasing its potential for users to create custom AI models without relying on external resources.
Takeaways
- ๐ Marcus introduces a new method for training AI models exclusively within the Comfy UI platform, eliminating the need for external resources like Kaggle or Google Colab.
- ๐ The process starts by creating a dataset of images, which must be stored in a folder named 'dataset' for the node to recognize it as a data source.
- ๐จ Different types of sketches can be used to train the AI, and they don't need to be the same size, as long as they are in PNG format.
- ๐ The GitHub link for the node is provided by Larry Jane mine, and it is essential to have a specific version of Scorch CU 121 for the node to function correctly.
- ๐ Text captions are created for each image in the dataset, which helps the AI understand the content of the images during training.
- ๐ The LJR Laura node is used for the actual training of the AI model within the Comfy UI, offering various options for customization.
- ๐ The training process involves saving checkpoints (epochs) at specified intervals, allowing for incremental improvements and model recovery.
- ๐ Once training is complete, the AI model can be used directly within Comfy UI without any additional steps or external platforms.
- ๐ฅ Marcus demonstrates the workflow by training a sketch model and emphasizes that the entire process is done within Comfy UI, showcasing the platform's capabilities.
- ๐ A link to the GitHub repository will be provided in the video description for viewers to access the node and try the process themselves.
- ๐ธ The video concludes with a preview of some trained models and a promise to show more examples in future AI Fuzz videos.
Q & A
What is the main topic of the video?
-The main topic of the video is training AI models, specifically Luras, using a single node in ComfyUI without the need for external platforms like Kaggle or Google Colab.
Who is the presenter of the video?
-The presenter of the video is Marcus.
What is the purpose of the GitHub link mentioned in the video?
-The GitHub link is for the LJR Trading node created by Larry Jane mine, which is used for training Luras in ComfyUI.
What type of images are used to create the dataset for training?
-The dataset for training consists of sketches in PNG format.
How many sketches are recommended for creating a dataset?
-It is recommended to have a minimum of 25 sketches for creating a dataset, although 50 is often used for demonstrations.
What is the significance of the folder naming in the databased folder?
-The folder naming in the databased folder is significant because the node works off this structure, and the folder name should reflect the base checkpoint name for the training.
What is the role of the W14 Tagger in the process?
-The W14 Tagger is used to create text captions for each image in the dataset, which helps the AI understand what's in each picture during training.
What are the key options for the LJR node in ComfyUI?
-The key options for the LJR node include checkpoint name, path to images, dataset size, max training epochs (EPOs), save frequency, output name, and output directory.
How does the training process save progress?
-The training process saves progress every set number of images (e.g., every 10 images), creating a Lura model at each checkpoint.
What is the final output of the training process?
-The final output is a fully trained Lura model saved in the specified output directory, with the name based on the folder name used for the dataset.
How does the video demonstrate the training process?
-The video demonstrates the training process by showing the setup and execution of the LJR node in ComfyUI, including the creation of text captions, the setup of the node, and the actual training process.
Outlines
๐ Introduction to Training AI Models in Comfy UI
Marcus introduces the audience to a new method of training AI models, specifically Luras, exclusively within the Comfy UI platform. He emphasizes the convenience of this approach, as it eliminates the need for external resources such as Kaggle or Google Colab. Marcus outlines the initial steps, which involve creating a dataset of images and organizing them in a specific folder structure that the AI can recognize. He also mentions the importance of having a specific version of the Scorch CU 121 for the process to work correctly.
๐ Preparing the Dataset and Text Captions
The second paragraph delves into the process of preparing the dataset and generating text captions for the images. Marcus explains the need for a minimum of 25 images and demonstrates with a set of 24 sketches. He details the importance of the folder naming convention and the use of the 'database' term in the folder name. The paragraph also covers the use of the Lura caption node and the W14 tagger to create text descriptions for each image, which aids the AI in understanding the content of the images during training.
๐ฏ Training Luras with the Magic Node
Marcus introduces the 'magic node,' which is central to training Luras within Comfy UI. He outlines the various options available within the node, such as checkpoint name, image path, batch size, and the number of epochs. The paragraph explains the process of setting up the node, including the correct path to the image folder and the parameters for saving the trained models. Marcus also shares his personal preferences for certain settings and demonstrates the training process, highlighting the efficiency and ease of training AI models within the Comfy UI environment.
๐ Showcasing the Trained Luras and Conclusion
In the final paragraph, Marcus showcases the results of the training process, presenting the generated Luras and the corresponding text files. He emphasizes the simplicity of the process and the ability to use the trained Luras directly within Comfy UI. The paragraph concludes with a brief mention of the time it took to train and a teaser for future content, promising to share more images and AI fuzz videos in upcoming episodes.
Mindmap
Keywords
๐กAI Fuzz
๐กLoras
๐กComfort UI
๐กDataset
๐กSketch Style
๐กText Captions
๐กGitHub
๐กScorch CU 121
๐กMagic Node
๐กCheckpoint
๐กEpochs
Highlights
Introducing a new method for training AI models using a single node in ComfyUI.
No more need for external platforms like Kaggle or Google Colab for training AI models.
The process begins by creating a dataset of images, which will be used to train the AI.
The images should be in PNG format and can vary in size.
The dataset must be placed in a specifically named folder for the node to recognize it as a data source.
Text captions are created for each image in the dataset to provide context during training.
A fresh install of ComfyUI with a specific version of Scorch CU 121 is required for the training process.
The training node is named 'ljr Laura' and is part of a group of nodes created by Larry Jane mine.
The node allows for the saving of the AI model at specific intervals during training.
The training process is done entirely within ComfyUI, without the need for external triggers or platforms.
The training node has options for setting the checkpoint name, image path, batch size, max training epochs, and output directory.
The training node saves the AI model after every batch of images processed.
The final AI model is saved without numbers in the name, using the name specified in the node settings.
The training process is demonstrated with a set of sketch images to create a sketch style AI model.
The video provides a step-by-step guide on how to use the node for training AI models in ComfyUI.
The video concludes with a demonstration of the AI model in action, using it to generate sketches.