Update README.md

README.md

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 license: mit
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-# This is **NOT A GUIDE** for LoRA training, and you should NEVER treat it as.
+# 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 proper dataset preparation and training setup; but I'm sure most would not be satisfied with a LoRA that is barely useable.
+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.
 
-- To train a quality driven LoRA, dataset preparation is the first step that I'd put special care on.
+ - ### 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 907*823 pixels, the other with 3624*3288 pixels using simple 4x-upscale from SD WebUI.