Full Fine Tuning / DreamBooth of FLUX yields way better results than LoRA training as expected, overfitting and bleeding reduced a lot, check oldest comment for more information, images LoRA vs Fine Tuned full checkpoint

Full Fine Tuning / DreamBooth of FLUX yields way better results than LoRA training as expected, overfitting and bleeding reduced a lot, check oldest comment for more information, images LoRA vs Fine Tuned full checkpoint

Configs and Full Experiments

• Full configs and grid files shared here : https://www.patreon.com/posts/kohya-flux-fine-112099700

• Quality 1 Config better than Quality 2

• Quality 2 is only for low VRAM GPUs

• Always use higher VRAM config if you are on higher VRAM machine

Details

• I am still rigorously testing different hyperparameters and comparing impact of each one to find the best workflow

• So far done 16 different full trainings and completing 8 more at the moment

• I am using my poor overfit 15 images dataset for experimentation (4th image)

• I have already proven that when I use a better dataset it becomes many times betters and generate expressions perfectly

• Here example case : https://www.reddit.com/r/FluxAI/comments/1ffz9uc/tried_expressions_with_flux_lora_training_with_my/

Conclusions

• When the results are analyzed, Fine Tuning is way lesser overfit and more generalized and better quality

• In first 2 images, it is able to change hair color and add beard much better, means lesser overfit

• In the third image, you will notice that the armor is much better, thus lesser overfit

• I noticed that the environment and clothings are much lesser overfit and better quality

Disadvantages

• Kohya still doesn't have FP8 training, thus 24 GB GPUs gets a huge speed drop

• Moreover, 48 GB GPUs has to use Fused Back Pass optimization, thus have some speed drop

• 16 GB GPUs gets way more aggressive speed drop due to lack of FP8

• Clip-L and T5 trainings still not supported

Speeds

• Rank 1 Fast Config - uses 27.5 GB VRAM, 6.28 second / it (LoRA is 4.85 second / it)

• Rank 1 Slower Config - uses 23.1 GB VRAM, 14.12 second / it (LoRA is 4.85 second / it)

• Rank 1 Slowest Config - uses 15.5 GB VRAM, 39 second / it (LoRA is 6.05 second / it)

Final Info

• Saved checkpoints are FP16 and thus 23.8 GB (no Clip-L or T5 trained)

• According to the Kohya, applied optimizations doesn't change quality so all configs are ranked as Rank 1 at the moment

• I am still testing whether these optimizations make any impact on quality or not

• I am still trying to find improved hyper parameters

• All trainings are done at 1024x1024, thus reducing resolution would improve speed, reduce VRAM, but also reduce quality

• Hopefully when FP8 training arrived I think even 12 GB will be able to fully fine tune very well with good speeds