pretrained hypernetwork model about hyperkohaku HOT 12 OPEN

Still training……
QQ

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It should be usable
But "how to get reasonable result" still need more research

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Still training……

@KohakuBlueleaf are you still training the loras or are you already training HyperDreamBooth?

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Still training……

@KohakuBlueleaf are you still training the loras or are you already training HyperDreamBooth?

Hyperdreambooth

You can actually skip stage I
Directly train the hypernetwork is doable
Already tried on LyCORIS repo

The problem is I need someone to help me to implement something like "mask face" trick for train on face
Or something like that

I have not much experience about DreamBooth so cannot get a good training method for pre-optimized loras

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If you skip stage 1, what do you use as ground truth weights during the hypernetwork training (Figure 2, phase 1 in the paper)? Do you randomly initialize them? I am trying to understand it from your code but I am a bit lost

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If you skip stage 1, what do you use as ground truth weights during the hypernetwork training (Figure 2, phase 1 in the paper)? Do you randomly initialize them? I am trying to understand it from your code but I am a bit lost

Actually you only need the diffusion loss
Weight loss is jot necessary
But it will give you better ( if your pre-optimized weights is good) result

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@KohakuBlueleaf If the stage 1 is skipped, should we set the add_constant flag to True in the HyperDream?

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@KohakuBlueleaf If the stage 1 is skipped, should we set the add_constant flag to True in the HyperDream?

I think yes
But I am not sure if it will work
Will check my code

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@KohakuBlueleaf Hi is this project stop now?

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@KohakuBlueleaf Hi is this project stop now?

Not, but I just don't have time to let it keep moving...
I have literally over 10 proj on hand....

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Thank you for your hard work. Is the hyperdreambooth project a low priority among the 10 ongoing projects? @KohakuBlueleaf

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Thank you for your hard work. Is the hyperdreambooth project a low priority among the 10 ongoing projects? @KohakuBlueleaf

Yes it is relatively low

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