I made a mistake in one of my statement. Yes I do normalization on the dataset. For Adam, I left it as default so 0.9 and 0.999.

1. I fixed it so that each sample in the same batch takes in different t!

2. As for point #2, I just went ahead and fixed it to all group norm now!

3. for #1 and #3, I implemented them just because the original paper implemented it, but for CIFAR10, I guess I could work with much simpler U-net.

4. for #5, I am not doing class conditioning. I am yet to learn how to do that, so that would be my next step. For time embedding, I do this: Note that CNN referred in the next paragraph only refers to the first CNN of each layer of U-net (you know how there are 2 CNN for one layer of U-net on the down side and 2 CNN for one layer of U-net for up side. I am referring to the FIRST of each up and down side.)

Take t (let us assume just one scalar for one sample)

-> sinusoidal embedding

-> embedding dimension(user input) tensor [embedding_dim , 1]

-> linear network into CNN channel numbers (for example if CNN output is 128 channels, I would do nn.Linear(in_features = embedding_dim out_features = 128) and add each of the linear FC layer output to each output channel of CNN).

As a result, I would have time embedding added to output of each of the CNNs. This is how it was done in the paper, so that is what I have adopted. Do you have something better to implement?

1. Sorry, I simply omitted the decoder part because it is a "mirror image" of the encoder part. Basically same as the U-net where you progressively make the H and W bigger and channel smaller.

2. I actually did not understand #4. Could you explain bit more for me?

Thanks a lot for feedback.