Update README.md

README.md

@@ -15,10 +15,10 @@ Based on my personal experience, preparing a LoRA's dataset properly is more imp
    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:
+    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.
+    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.