I'm actually more curious if we could parse the underlying logic that ultimately it emulates to merge those images together.
It 'looks like' something kind of sophisticated is being modelled with AI but there's some nice algorithms hidden in there.
The training principle of CLIP is very simple, but intuitively understanding how the diffusion prior maps between semantically similar textual and visual representations is a bit more unclear (if that's even a well-formulated question!)
> While our model can render a wide variety of text prompts zero-shot, it can can have difficulty producing realistic im ages for complex prompts. Therefore, we provide our model with editing capabilities in addition to zero-shot generation, which allows humans to iteratively improve model samples until they match more complex prompts. Specifically, we fine-tune our model to perform image inpainting, finding that it is capable of making realistic edits to existing images using natural language prompts.
Unless that only applies to GLIDE and not to DAL-E?
More like 'averaging them' and finding variations from vast inputs.
Which is more a long the lines of what I mean.