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when do we use objective "pred_x_start"? #291
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To clarify, the p(x(t-1)|xt) is the denoise step in most papers, which may be unncessary as discussed. As Eq. 9 in https://arxiv.org/pdf/2107.00630.pdf, the well trained model by "predict_x_start" is already capable of producing x0 from xt. |
I had the same question. I read a couple of blogs, but there's no ones that clarify this issues. |
Hello, Do you figure it out? I have the same question too. I don't know which paper should I read:( |
We can use the objective "pred_x_start" as self-condition. As described in the annotated code, 'if doing self-conditioning, 50% of the time, predict x_start from current set of times and condition with unet with that this technique will slow down training by 25%, but seems to lower FID significantly'. |
Hi diffusion developers,
Thank you for the open source development!
I have a naive question about the objective "pred_x_start". If we use this objective, after training we have a model that can directly denoise from any timestep xt to x0. In this case, what is the purpose of reverse diffusion process with >1 timesteps?
There are essentially two possible outcomes after training:
Best,
Leo
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