As to the second, it is possible to just replace the "A" data, to recycle "B", but this usually results in identity leak, so it's best to start a new model with your required pair.
FYI - I've tried all greyscale images as one of the faces for training, result was poor (color wise and sharpness wise) swapping on a color video at 250k iters, original model. B&W images were also of poor quality due to being extracted from a film from the 1930's.
All greyscale/poor quality on one side is not-ideal and will almost always lead to failure. This is due to the inability for the model to learn sufficient information from the poor quality source data.