Maybe they keep it up just so that they have something in txt2img space? It may not be the best, or even good, but you don't know that until you try it, and until then, it just enhances the value of the OpenAI platform. E.g. if you're building something backed by OpenAI LLMs, and are thinking about future txt2img integration, the existence of Dall-E might stop you from "shopping around" txt2img services in advance.

The way I see it, they don't need txt2img at this moment - GPT-4 ensures they're the top #1 name both in the industry and in AI-related news stories. But it doesn't mean they won't come back to it. Couple observations:

- OpenAI isn't a "release early, release often" shop. They might be already working on something, but they'll release it only when it is a qualitative improvement over everyone else (or at least Dall-E).

- A bunch of hobbyists is doing all their work for free anyway. Stable Diffusion itself may not be SOTA, but the totality of hundreds of different fine-tunes on Civitai very much is. With all those models being shared in the open and relatively easy/cheap to recreate, it would make sense for OpenAI to just stand by and watch, and only invest resources once hobbyists hit a plateau.

- Looking at those Civitai models, it seems to me that OpenAI could beat txt2img SOTA easily, at any moment, by taking (or re-creating, depending on the license) the best five to ten SD derivatives, and put them behind GPT-4, or even GPT-3.5, fine-tuned to 1) choose the best SD derivative for user's prompt, and 2) transform user's prompt to set of parameters (positive & negative prompts, diffuser algo, numeric params) crafted with choice from 1) in mind. It's a black box. On the Internet, no one can tell you're an ensemble model.

- They could even be doing it as we speak - addition of function calls is aligned with this direction, fine-tuning for good prompt generation is mostly a txt2txt exercise, and again, hobbyists around the world are busy building a high-quality human-curated data set of {what I want}x{model + positive prompt + negative prompt + diffuser + other params} -> {is this any good?}. If I were them, I'd just mine this and not say anything.

- Overall, I think that in txt2img space, currently the hard part isn't the "img" part, but the "txt" part. OpenAI has a huge advantage here, and as long as its true, they're in position to instantly overtake everyone else in this space. That is, they have an "Ultimate attack" charged and ready, and are patiently waiting for a good moment to trigger it.

- Didn't they hint that GPT-4 successor will be multimodal? That could end up being their comeback to txt2img. And img2txt. And a bunch of other modalities.

EDIT: As if on cue, the very thing I was speculating about above is being discussed wrt. LLMs right now:

- https://news.ycombinator.com/item?id=36413296 - GPT-4 is 8 GPTs in a trench coat

- https://news.ycombinator.com/item?id=36413768 - 3-4 orders of magnitude efficiency (size vs effect) improvement in code generation, if your training data isn't garbage

And in both threads, people bring up older papers and discuss the merits of combining smaller specialized models into a more generic whole.