No.

How would a model get trained on that? You'd have to pass in the entire repository for each sample. It's prohibitively difficult to create that sort of model.

If you want that, you'll have to build tooling on top of a text model (ie. an application that calls a model repeatedly), that takes a prompt and breaks it up into per-file prompts, then incrementally generates the files passing the context of previous files, and the 'context' would be too large, so you'd get large scale consistency errors.

Broadly speaking the number of tokens = the size of the text it can generate.

With small models, the number is trivial (code fragment), so generally speaking 'generate an entirely application' one-step models currently don't exist.

That said, stable diffusion has proved that you can iterate in latent space and use a VAE to upscale to larger sizes to reduce the over all model size while still having output that is ~order of magnitude larger than the latent space.

...so it's not totally out of the question that's coming.

However, right now? no.