I'm wondering if the Dreambooth method would be effective for training with 10,000 high-quality imag
I'm wondering if the Dreambooth method would be effective for training with 10,000 high-quality images. Based on the publicly available SDXL base model, there are various fine-tuning methods such as Dreambooth, LoRA, HyperNetwork, and Textual Inversion. From what I know, Dreambooth seems to be the most appropriate. I would appreciate it if you could share any specifically recommended methods for the final training process you emphasized, or if there are any noteworthy precedents for training with such a large dataset.
(P.S. I've watched all of your fine-tuning tutorial videos on YouTube multiple times. I'm a big fan! Thank you!)
(P.S. I've watched all of your fine-tuning tutorial videos on YouTube multiple times. I'm a big fan! Thank you!)
