Open yxuhan opened 4 months 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)
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.