In summary, convolutional VAE and batch normalization seem very useful for fast training of VAE models for image data. Unfortunately there appear to be some Theano related issues with the current implementation of batch normalization, which cause the generated computation graph to ~100x slower for the convolutional version than the feedforward version. Eventually the convolutional version should be very efficient, but for now training times of two or three days are not as useful as the 40 minute training times for the feedforward model.
Semi-supervised VAE is also a very promising avenue for learning better generative models, but implementing the model proposed earlier will take time after the course is over. For now, all the code remains posted at https://github.com/kastnerkyle/ift6266h15 , with improvements to both batch normalization and the convolutional model, and soon semi-supervised VAE.