Hi,
I have been working in a project in which I have to predict an image from an input with shape (timesteps, quantity). “timesteps” has a size of about 2000, while “quantity” about 500.
In order to compress the input, I calculated the root mean square along the first entry, getting a single vector with shape ~500.
I have seen that in the “inference network” page in the Bayesflow documentation it is written that “Image-generation subnets concatenate the condition channel-wise with the target image”.
What I could do I think is to compress the “quantity” vector with, for example, a MLP, then reshape it for broadcasting to the image shape.
However, I was wondering if cross-attention conditioning (used for example in Stable Diffusion) can be used in Bayesflow.
Thank you