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speeds are very resolution dependent. I am getting 1.1 it/s with 512 but around 5 it/s with 1024, training Flux Lora. Would you expect the parameters like LR to be the same for both, or have you seen that you need other parameters at higher resolutions?
Hey guys i have question, i. trained lora with my dataset, and it went great, i saw there is some lora's out there like photorealistic loras that people did, can i train again my dataset image with those lora? or you cant do that, if i understand correctly lora it's to train specific things in the model right?
you could, by merging the lora into the Flux checkpoint first, and then train on that. Whether this improves the result for the Flux realistic Lora is open to debate, but your theory is correct. I have trained in the past on an SDXL checkpoint merged like that, because I knew I wanted to use it with another Lora that negatively affected my lora, unless trained like that
i still see no reason to train a full fintetuning and get a 23GB model compared to a few MB for a LoRA. I already have around 20 LoRA's' and the results are very good. But will it be possible to extract a loRA's from the finteuning model? because that could be interesting.
whether finetunes are still better than direct Lora/Dora training with Flux I think is currently unknown. With SDXL it was the case. It could well be that a high-rank Dora is now good enough and you won't see a difference anymore.
now Flux training became fun instead of a chore. you can run 3000 steps in 1 hour, which is equivalent to 6000 steps on SDXL because SDXL benefited from regularization images a lot. so Flux is now faster to test than SDXL, because SDXL couldn't handle low resolution training
I was trying to find a fast generation method like this and thought it might be Schnell, but apparently Schnell is not good for personal photo generation: https://x.com/levelsio/status/1833562269281616287