Conv bias or not with Instance Normalization? It is well known that Conv layers that are followed by BatchNorm ones should not have bias due to BatchNorm having a bias term.

Using InstanceNorm however, the statistics are instance-specific rather than batch-specific yet there are still are two learnable parameters $\gamma$ and $\beta$, where $\beta$ is a learnable bias.

Naturally, Conv layers followed by InstanceNorm layers should also not use bias.

Though some well-renowned implementations on github insist on using Conv bias when followed by InstanceNorm.

What's your take on this?
Bias should not be used before InstanceNorm exactly as before BatchNorm. Whether you set affine to True or False, these layers first normalize the input, so any added bias is discarded.