Comments (2)
Equation 8 uses the U-Net ϵθ(xt, t, z) trained in training process to generate x_t+1 from x_t. However, as far as i can see, the ϵθ was trained for denoising from x_t to generate x_t-1.
Let me first say that the U-Net predicts the noise within the image, which can be thought of as a direction of change from
However, your intuition is not wrong that Eq 8 is between
Thinking of the limit where
More specificly, ϵθ is used to predict noise that already exist in x_t, why Stochastic encoder uses the noise that is predicted to be exist currently by ϵθ to map the picture to latent space?
I'm not clear about this question. In general, the stochastic encoder turns image
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Thanks for your reply!
I understand what Stochastic encoder are trying to do in this model.
But I'm still confused by the arugment that changing direction of x_t to x_t+1 is same with that of x_t-1 to x_t.
Any mathmatical provement can be provided to illustrate the process? Or can you provide me with any other papers used the same process you might have refered while doing your work.
I am deeply grateful for your help!
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