Training A Public FLUX Style LoRA — All Process Will Be Shared

I started training a public LoRA style (2 seperate training each on 4x A6000).

Experimenting captions vs non-captions. So we will see which yields best results for style training on FLUX.

Generated captions with multi-GPU batch Joycaption app.

I am showing 5 examples of what Joycaption generates on FLUX dev. Left images are the original style images from the dataset.

I used my multi-GPU Joycaption APP (used 8x A6000 for ultra fast captioning) : https://www.patreon.com/posts/110613301

I used my Gradio batch caption editor to edit some words and add activation token as ohwx 3d render : https://www.patreon.com/posts/108992085

The no caption dataset uses only ohwx 3d render as caption

I am using my newest 4x_GPU_Rank_1_SLOW_Better_Quality.json on 4X A6000 GPU and train 500 epochs — 114 images : https://www.patreon.com/posts/110879657

Total step count is being 500 * 114 / 4 (4x GPU — batch size 1) = 14250

Taking 37 hours currently if I don’t terminate early

Will save a checkpoint once every 25 epochs

Full Windows Kohya LoRA training tutorial : https://youtu.be/nySGu12Y05k

Full cloud tutorial I am still editing

Hopefully will share trained LoRA on Hugging Face and CivitAI along with full dataset including captions.

I got permission to share dataset but can’t be used commercially.

Also I will hopefully share full workflow in the CivitAI and Hugging Face LoRA pages.