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LoRA_training_sneak_peak

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fallenl commited on Mar 28

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@@ -24,7 +24,7 @@ In this section, I'll outline the steps I typically follow to prepare the datase
      ![Comparison](imgs/upscale/comp.png)
    - Now, let's crop them to roughly 1k resolution (960x832 pixels in this case) using SD WebUI's Auto-size Crop:
      ![Cropped Comparison](imgs/upscale/cut-comp.png)
-   - Notice the difference? The image with 4x upscale has sharper edges compared to the one without any upscale, resulting in a cleaner 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.

      ![Comparison](imgs/upscale/comp.png)
    - Now, let's crop them to roughly 1k resolution (960x832 pixels in this case) using SD WebUI's Auto-size Crop:
      ![Cropped Comparison](imgs/upscale/cut-comp.png)
+   - 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.