Kohya brought massive improvements to FLUX LoRA (as low as 4 GB GPUs) and DreamBooth / Fine-Tuning (as low as 6 GB GPUs) training (Patreon)

You can download all configs and full instructions

> https://www.patreon.com/posts/112099700 - Fine Tuning post

> https://www.patreon.com/posts/110879657 - LoRA post

Kohya brought massive improvements to FLUX LoRA and DreamBooth / Fine-Tuning (min 6GB GPU) training.

Now as low as 4GB GPUs can train FLUX LoRA with decent quality and 24GB and below GPUs got a huge speed boost when doing Full DreamBooth / Fine-Tuning training

You need minimum 4GB GPU to do a FLUX LoRA training and minimum 6 GB GPU to do FLUX DreamBooth / Full Fine-Tuning training. It is just mind blowing.

You can download all configs and full instructions > https://www.patreon.com/posts/112099700

The above post also has 1-click installers and downloaders for Windows, RunPod and Massed Compute

The model downloader scripts also updated and downloading 30+GB models takes total 1 minute on Massed Compute

You can read the recent updates here : https://github.com/kohya-ss/sd-scripts/tree/sd3?tab=readme-ov-file#recent-updates

This is the Kohya GUI branch : https://github.com/bmaltais/kohya_ss/tree/sd3-flux.1

Key thing to reduce VRAM usage is using block swap

Kohya implemented the logic of OneTrainer to improve block swapping speed significantly and now it is supported for LoRAs as well

Now you can do FP16 training with LoRAs on 24 GB and below GPUs

Now you can train a FLUX LoRA on a 4 GB GPU - key is FP8, block swap and using certain layers training (remember single layer LoRA training)

It took me more than 1 day to test all newer configs, their VRAM demands, their relative step speeds and prepare the configs :)