LORA + Checkpoint Model Training GUIDE - Get the BEST RESULTS super easy

The video begins with an introduction to training AI models, specifically LoRAs and models, for achieving impressive results. The speaker emphasizes the ease of obtaining good results with proper training and introduces a Discord channel for support and community interaction. The process of training is explained, where an input photo is transformed into noise and then reconstructed into a new image. The importance of selecting the right images for training is discussed, including images with different facial expressions, fashion styles, and lighting conditions. The video also touches on the challenges of training AI to recognize small objects like faces and the need for high-quality images for effective training.

The second paragraph delves into the specifics of image selection and quality for training AI models. It highlights the need for a variety of images that capture different emotions, fashion styles, and hairstyles. The importance of image quality is stressed, with a focus on sharpness and clarity to facilitate the AI's learning process. The paragraph also discusses the significance of using descriptive keywords in text files to enable variability and adaptability in the AI's training. The differences between LoRAs and models are explained, with LoRAs being smaller, versatile add-ons, and models being larger, more consistent, and suitable for complex themes like architecture.

This paragraph addresses the number of images required for training, which depends on the complexity of the subject. It suggests that fewer images are needed for training faces due to their consistent structure, while more complex subjects like architectural styles require a larger dataset. The concept of steps and epochs in the training process is clarified, with steps being repetitions per image and epochs representing model generations. The paragraph also provides guidance on determining the number of steps per epoch based on the size of the dataset and the desired training depth.

The focus of this paragraph is on the optimal image size for training AI models, recommending a minimum size of 512x512 pixels. It advises against cropping images to a square ratio to avoid losing important training data. The paragraph also discusses the software's automatic creation of training buckets for different resolutions and ratios. The video suggests using high-resolution images for better training results, especially when upscaling, but cautions that higher resolutions can slow down the training process due to increased GPU power requirements.

The speaker introduces various tools for image preparation, such as Google Images for sourcing and a tool called 'Bulk Resize' for resizing images. The paragraph outlines a recommended folder structure for organizing training materials and provides detailed instructions for installing and setting up the Koyasha software for model training. It also covers the installation of necessary components like Python, Git, and Visual Studio, and the activation of GPU acceleration for faster training.

This paragraph emphasizes the importance of accurately captioning images and refining keywords to guide the AI training process. It introduces a tool called 'Boru Data Set Tag Manager' for managing and editing keywords. The video demonstrates how to use the tool to review and adjust keywords for each image, ensuring they align with the desired training outcomes. The paragraph also discusses selecting a base model for training, recommending the use of the Stable Diffusion 1.5 model for its suitability as a training source.

The final paragraph covers setting the training parameters in the Koyasha software, including batch size, number of epochs, and image resolution. It advises on troubleshooting potential issues like running out of VRAM and suggests remedies such as reducing the batch size or image resolution. The speaker shares a 'merge trick' for improving the trained model's performance by combining it with a more advanced model using the Checkpoint Merger tool. The video concludes with a call to join the speaker's Discord for further assistance and an invitation to like the video.