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Dreambooth is a training technique for training text to image models in a single subject, is not the same as training text to image or a text to image lora. You can read more about it here. Is not in all the examples but you can train the text encoders with any of them, it's just optional, there are some full finetunes that also trained the text encoders, it's supposed to bring better results but that really depends on what you're training. You can also do more spcifics training with the advanced scripts like DoRA or B-LoRA About the VAE, no, you can't just add the parameters of it because it's a different model with a different kind of training, the unet/transformer model uses the embeddings from the text encoders to generate images, the VAE it's used for encoding/decoding the images into latents/images. Finally the training scripts are for learning and to improve them, so you can just read them and try to understand what's being done and you can add more specific parts for your needs. |
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Hi,
I’m trying to find the best approach for SD fine-tuning and I’m little-bit confused. Most if the scripts have the same structure with small changes between them.
I saw this options:
Am I right? Thus are the differences between the scripts?
I also saw that dreambooth have some spacial dataset class but I didn’t understood what spacial there.
Beside that there are script for the vae training, can’t I just add the parameters of the vae to the optimizer in one of the scripts above?
thank you
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