Post
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Training directly onto the Flux 3 Klein Image distilled model produces better results than finetuning the base!
I've put together a collection (https://huggingface.co/collections/SimpleTuner/flux-3-klein-image-lora-training) containing an experiment log book, a couple character LoRAs, and a 50,000 step diverse photo aesthetics finetune for the distilled variant in both 512px and 1024px resolution buckets.
A training assistant LoRA was produced by training a PEFT LoRA for directly onto the distilled Flux 3 Klein Image model for 30,000 steps on 30,000 real (15k illustrative and 15k photo) images over 8x H100 to break down the dual time distillation objective without introducing much bias into the resulting adapter.
Using that frozen LoRA, two photography LoRAs were trained against the full ~29k photo aesthetics dataset ( webshart/terminusresearch-photo-aesthetics) for 50,000 steps that demonstrate the ability of the training assistant to preserve the original dual time distillation objective.
Additionally, KohakuBlueleaf's KohakuFA (via https://github.com/SimpleTuner-io/KohakuFA-CuTe) was tested on two separate 50,000 step photography training runs to briefly evaluate the differences of the higher precision attention kernels.
I've put together a collection (https://huggingface.co/collections/SimpleTuner/flux-3-klein-image-lora-training) containing an experiment log book, a couple character LoRAs, and a 50,000 step diverse photo aesthetics finetune for the distilled variant in both 512px and 1024px resolution buckets.
A training assistant LoRA was produced by training a PEFT LoRA for directly onto the distilled Flux 3 Klein Image model for 30,000 steps on 30,000 real (15k illustrative and 15k photo) images over 8x H100 to break down the dual time distillation objective without introducing much bias into the resulting adapter.
Using that frozen LoRA, two photography LoRAs were trained against the full ~29k photo aesthetics dataset ( webshart/terminusresearch-photo-aesthetics) for 50,000 steps that demonstrate the ability of the training assistant to preserve the original dual time distillation objective.
Additionally, KohakuBlueleaf's KohakuFA (via https://github.com/SimpleTuner-io/KohakuFA-CuTe) was tested on two separate 50,000 step photography training runs to briefly evaluate the differences of the higher precision attention kernels.
