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license: mit
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# This is **NOT A GUIDE** for LoRA training, and should NEVER be treated as.
# This serves as a onging documentation that I share some of the personal opinions or interesting findings where I learned or discovered during my journey in LoRA training.
To start with, LoRA training could be easy and quick with simple dataset preparation and training setup; but surely that's not what I'm satisfied with.
Based on my personal experience, preparing a LoRA's dataset properly is more important than using complicated and fancy training techniques. Just like cooking, you can't make any tasty dishes using materials without good quality.
### Dataset Preparation
In this section, I'll outline the steps I typically follow to prepare the dataset for a LoRA model. I'll skip the "How to Get Image Data" part since there are numerous tools and methods available for obtaining image data.
1. Remove Non-Static Image Files:
After gathering all the necessary image data for a LoRA, remove all non-static image files (e.g., .mp4, .wav, .gif, etc.). Then, convert all non-.png format images into .png.
2. Remove Duplicates:
Take some time to go through the images and remove any duplicates. This is particularly important for large datasets, as duplicates can occur frequently.
3. Upscale Low-Resolution Images:
Upscale all low-resolution images to at least 2k resolution. This step is crucial for improving image quality and ensuring better performance during training. Let's consider an example:
- We have two identical PNG images, one with dimensions 907x823 pixels and the other with dimensions 3624x3288 pixels, achieved by a simple 4x upscale from SD WebUI.
- Now, let's crop them to roughly 1k resolution (960x832 pixels in this case) using SD WebUI's Auto-size Crop:
- Notice the difference? The image with 4x upscale has sharper edges compared to the one without any upscale, resulting in a clean and less blurry appearance when cropped into 1k resolution.
- So, why is this important for LoRA learning?
Inside the LoRA training script, there's a function called [bucket](https://github.com/bmaltais/kohya_ss/wiki/LoRA-training-parameters#enable-buckets) that automatically crops your image data to a size close to the training resolution specified in the training setup.
In this case, the pattern inside a clean image could be learned quickly; and for a somewhat low-resolution image that comes in the first place, upscaling helps sharpen the blurry lines so that the object inside is more easier to be recognized.
4. To be Continued ...
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