---
license: mit
---
# This is **NOT A GUIDE** for LoRA training, and please NEVER treat it as.

# This serves as a onging project 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
 I'll disscuss several steps that I usually do when I prepare the dataset for a LoRA in order, and I'll skip the "How to Get Image Data" part since there're too many tools/methods available for you to use.
   1. After getting all image data needed for a LoRA, remove all none-static image files (.mp4, .wav, .gif, etc). Then change all none-.png format images into .png.
   2. Spend some time going through the images to see if there're any duplicated images and remove them. This will happen a lot if the dataset is large.
   3. Upscale all low-resolution images to at least 2k resolution. But why? Let's see an example:  
      We have two identical png images, one with 907x823 pixels, the other with 3624x3288 pixels using simple 4x-upscale from SD WebUI.
      <img src="imgs/upscale/comp.png"/>
    Now let's crop them into roughly 1k resolution (960x832 in this case) using SD WebUI's Auto-size Crop:
      <img src="imgs/upscale/cut-comp.png"/>
    See the differences? The image with 4x-upscale has a sharper edges comparing to the one without any upscale, and hence looks more clean and less blurry when getting cropped into 1k resolution. <br />
    So what does it translate to LoRA learning? <br />
    Inside the LoRA training script, we can see a function called [bucket](https://github.com/bmaltais/kohya_ss/wiki/LoRA-training-parameters#enable-buckets). <br />
    It'll automatically crop your image data into a size that's closed to the training resolution that we specify in training setup.