danielroich / PTI

Official Implementation for "Pivotal Tuning for Latent-based editing of Real Images" (ACM TOG 2022) https://arxiv.org/abs/2106.05744

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The more smile, the more black

tengshaofeng opened this issue · comments

tbq_0a186d611d3e2493bfb35ec396390806_age
tbq_1803151818-00006002_org_smile
thanks for your great work. Did you find the phenomenon above?

Thanks!

Yes, I have, I think it is connected to the fact that the above image is more "out of domain" compared to the second image.
I found you need to add more "power" to the editing directions in order to edit OOD images.

This is also a finding from earlier papers, you can see it in our quantitative analysis.

PTI mitigates this pheromone but does not solve it completely

hope this helps

@danielroich thanks for your reply patiently. Did you means that I should collect more images which covers the OOD images, and
learn the new directions on the images?

Something like that, use the same edit directions, but add more "power" to the tuning be feeding it with few-shot fine-tuning instead of one-shot as the classic PTI does

So when fine-tuning, should I fixed some layers?

No
Add more images
Try using the multi_id_coach on several images of the same person