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hey, it's how many discrete levels each value of an image channel can take. For instance, for a black-and-white image (i.e. there is only a single channel) and color-levels equal to 2, the image is binary (each pixel value is equal to either one or two). If color-levels is 255 and image is coloured (i.e. there are 3 channels), you get a usual colored image.
from pixelcnn.
from pixelcnn.
I don't know if my understanding is correct: taking a color image as an example, each channel in each position of the image corresponds to 256 probability values, and the index corresponding to the maximum probability value is taken as the result of image generation (or the prediction result during the training stage).
yea, you are absolutely right here
note that teacher forcing is used during the training
from pixelcnn.
from pixelcnn.
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from pixelcnn.