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hamadichihaoui / BIRD

This is the official implementation of "Blind Image Restoration via Fast Diffusion Inversion"
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Why DDIM can ensure the generated images within the manifold of realistic images? #12

Open yxuhan opened 5 months ago

yxuhan commented 5 months ago

Thanks for the paper and the code!

In a recent paper, it demonstrates that using DDIM inversion, one can embed a dog face into a Diffusion Model trained solely on FFHQ (a famous high-quality face image dataset). See https://arxiv.org/pdf/2310.09213v1.

In my experiments, I also find one can embed a very blurry face image into a Diffusion Model trained on FFHQ via DDIM inversion. So I wonder why DDIM can ensure the generated images within the manifold of realistic images in the proposed method?

Thanks in advance.

hamadichihaoui commented 1 month ago

Hi, Sorry for the very late reply. In our case we are inverting the blurry image assuming that there is a blur operator that generates it. which is a strong regularization that makes the inversion favors a clean image over outputting the same blurry image. In the paper you mentioned, as far as I understood the authors invert the blurry image as if it is a clean image (they don't take into consideration blur model during the inversion)