Comments (2)
Hi,
Thanks for pointing out the mistake in line 91. I have corrected it now.
Regarding reusing the same text embedding here, this is because the same text embedding is input to all the key, value projection matrices in the attention layers.
How to merge two fully fine-tuned networks is an interesting question. I doubt linear least square constraints will be sufficient for that as -- (1) obtaining input output constraint is more difficult for all weights because of its dependency on the sampled noise and previous layers, and (2) non-linearities in the network.
Maybe it can still be solved through gradient descent optimization on the constraints. One of the relevant works might be this.
Maybe fine-tuning can be done such that simple addition of weights leads to model merging. I don't have any specific insight but its an exciting direction to explore :)
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Thanks, that makes perfect sense regarding the text embedding input.
Thanks for the link to the paper. This seams a much more logical way to go - especially due to the non-linearity in the network.
from custom-diffusion.
Related Issues (20)
- Errors in using diffuser version HOT 3
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- Question about the cross attention HOT 1
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- Hi, How toMultiple-Concept Fine-tuning for DreamBooth?
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- Is it possible to train multi joint training with SDXL? diffusers_training_sdxl.py seems impossible for multi joint training. HOT 2
- Question about loss on valid image region during training HOT 1
- Missing text prompts? HOT 2
- Does the diffusers version now work properly? HOT 3
- why the results produced by the model training and verification are inconsistent with the results I test using the saved model weights? HOT 1
- Evaluation:where is the “prompts.json”file?
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