>I do a deep-dive over all of the LoRA training settings in Kohya, and test every setting.

⌚ Timestamps
0:00:00 – introduction
0:01:33 – about the chapters
0:02:09 – testing methodology
0:09:05 – 1. Captioning
0:12:00 – 2. Source Model
0:13:41 – 3. Number of Repeats for training data
0:17:31 – 4. Number of Repeats for Reg data
0:18:57 – 5. Batch size
0:21:39 – 6. Max Train Steps
0:25:08 – 7. Epochs
0:26:47 – 8. Mixed Precision
0:28:18 – 9. Save Precision
0:29:16 – 10. Learning Rate Scheduler
0:31:20 – 11. Learning Rate and Learning Rate Warmup
0:31:39 – 12. Optimizer
0:34:06 – support the channel
0:35:02 – 13. Network Rank and Network Alpha
0:37:42 – 14. Max Resolution
0:41:55 – 15. Gradient Accumulation Steps
0:43:29 – 16. Clip Skip
0:44:55 – Multiple LoRA training script
0:53:09 – Tensorboard while training
0:54:04 – Tensorboard after training
0:55:56 – How to use Tensorboard
0:59:36 – using LoRA inspector script

🔗 links:
LoRA training parameters by bmaltais on github
https://github.com/bmaltais/kohya_ss/wiki/LoRA-training-parameters

Tutorial: Dreambooth LoRA training using Kohya_SS by Desi_Cafe on civitai
https://civitai.com/articles/391/tutorial-dreambooth-lora-training-using-kohyass

Explanation of number of steps (EPOCHs):
https://rentry.org/59xed3#number-of-steps epochs

Network Rank and Network Dimension
https://github.com/bmaltais/kohya_ss/wiki/LoRA-training-parameters#network-rank-dimension

Alpha and Dimensions: Two Wild Settings of Training LoRA in Stable Diffusion
https://ashejunius.com/alpha-and-dimensions-two-wild-settings-of-training-lora-in-stable-diffusion-d7ad3e3a3b0a

My LoRA Training Batch Converter on github
https://github.com/robertJene/LoRA-Training-Batch-Converter

LoRA inspector script on github:
https://github.com/rockerBOO/lora-inspector#install

LoRA inspector helper from my github:
https://github.com/robertJene/LoRA-Inspector-Helper/tree/main

🎶 soundtrack music is on my music channel:
https://www.youtube.com/watch?v=wYXout69WMY&list=PLY7R58WfueSrN58yXsmvBdGUP0psFl9XP<br />
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