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First of all, congratulations on such a fantastic project. I had a doubt regarding the training models. As I understood, you employed a trained SDE model to perturb the target image into a noise image, and after that, you reverse the stochastic process to get the final image. In that case, we should use the repository of @yang-songhttps://github.com/yang-song/score_sde (this one for PyTorch implementation) to train SDE models instead of https://github.com/ermongroup/ddim right?
Thank you so much.
The text was updated successfully, but these errors were encountered:
Hi @alvarogonjim and @xiaoiker! There are two versions of SDE/diffusion models that are compatible with our method: VP (ddpm, ddim, etc) and VE (score SDE from @yang-song ). You can use either as the pretrained model and then use the corresponding algorithm to reverse the image. The algorithm for VE is described in Algorithm 1 and 2 and the algorithm for VP is in the appendix in Algorithm 3 and 4. Feel free to check both out!
HI @chenlin9
First of all, congratulations on such a fantastic project. I had a doubt regarding the training models. As I understood, you employed a trained SDE model to perturb the target image into a noise image, and after that, you reverse the stochastic process to get the final image. In that case, we should use the repository of @yang-song https://github.com/yang-song/score_sde (this one for PyTorch implementation) to train SDE models instead of https://github.com/ermongroup/ddim right?
Thank you so much.
The text was updated successfully, but these errors were encountered: