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You need to loop back the generated output to provide context for the next output, just like how GPT-3 works. This at least establishes the needed data to train

You need to loop back the generated output to provide context for the next output, just like how GPT-3 works. This at least establishes the needed data to train for consistent outputs. Given a deep well-trained network, the high-level meta-data that needs to be consistent across scenes is just a small set and the network will discover this just as GPT-3 did.

dougabug 5y ago
Right. In the literature this is typically referred to as autoregressive sequence generation, and you can use it generate a whole story consistent with a given prompt (of any length), or even a whole image where you give it the first N seed pixels.