> Regarding the opening image: if it can't correctly put the marks on dice, how can it put eyes, nose and mouth correctly on a human face? It helps if you con

> Regarding the opening image: if it can't correctly put the marks on dice, how can it put eyes, nose and mouth correctly on a human face? It helps if you consider it all as effectively advanced compression. Everything the model can do is limited by its architecture, the number of parameters in the model, and the accuracy and size of the training data. The underlying architecture is a transformer (e.g. GPT3) wired to a (denoising) diffusion model. Current flaws with this approach: - Transformers seem to approach a "bag-of-words" model, often ignoring the ordering of the words. Among other things, this means that text-to-image models are very bad at "binding attributes" [0]. This is why "a boy wearing a red shirt and a girl wearing a black jacket" may fail (putting the colors on the wrong items, for instance). - Autoregressive transformers have no means to correct early mistakes. - Training data is captioned images and the captions are likely noisy and under-specified. Every time it sees a face labeled as "face" - it tries to generate a face from the distribution of _all_ faces in the data. The same goes for the dice. If the dice are just labeled "dice", but don't have a description of how they landed - the model has to guess which angle you're referring to. As a sibling comment points out, this is exacerbated by the relative frequencies of examples of the data in the dataset. [0] https://wikiless.org/wiki/Binding_(linguistics) https://wikiless.org/wiki/Binding_(linguistics)