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Hi, I noticed you apply a normalization with on noisy rendered images with a constant (1 + sigma^2). (https://github.com/pals-ttic/sjc/blob/main/adapt_sd.py#L133).
I am confused with the intuition to scale the noisy data with such constant. Is this scaling to keep the deviation of input at 1 ?
The text was updated successfully, but these errors were encountered:
yes and that's how diffusion models are trained in general see https://arxiv.org/abs/2206.00364 table 1
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Hi, I noticed you apply a normalization with on noisy rendered images with a constant (1 + sigma^2). (https://github.com/pals-ttic/sjc/blob/main/adapt_sd.py#L133).
I am confused with the intuition to scale the noisy data with such constant. Is this scaling to keep the deviation of input at 1 ?
The text was updated successfully, but these errors were encountered: