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About Stochastic encoder #51
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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
I'm not clear about this question. In general, the stochastic encoder turns image |
Thanks for your reply! |
Thanks for your excellent work! It is very inspiring!
I have a question about Stochastic encoder.
Equation 8 in the paper is described as the reverse of equation 1. 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.
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?
Thanks for your answering!
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