DeepFloyd IF: open-source text-to-image model

> Stability AI releases DeepFloyd IF, a powerful text-to-image model

Hope not. This is a worse license.

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This does outperform Stable Diffusion 2.1, but uses a different architecture and requires more memory and compute. Stable Diffusion runs its denoising process in a compressed "latent space" which is how it was able to be so compute-efficient compared to other diffusion models. It also uses the (relatively) small text encoder from OpenAI's CLIP model to encode user prompts. Both of these optimizations meant that it could run much faster compared to say, DALLE or Imagen, but it didn't follow complicated user prompts especially well and had trouble with things like counting and text-rendering.

DeepFloyd IF is based on Google's Imagen model, which has two key differences from Stable Diffusion: (1) it denoises in pixel space instead of a compressed latent space, and (2) it uses a 10x larger pretrained text encoder (T5-XXL-1.1) compared to SD's CLIP encoder. (1) allows it to better render high-frequency details and text, and (2) allows it to understand complex prompts much better. These improvements come at the cost of multiple times more memory usage and compute requirements compared to SD, though.

In terms of "will it replace SD?"—in the short term I think yes. But I still think latent diffusion models are the future. For example, Stability is gearing up to release Stable Diffusion XL right now, a larger version of the original SD that does higher fidelity and higher resolution generations. I wouldn't be surprised if it takes the crown back from DeepFloyd when it releases, but I guess we'll have to see.

As far as I can tell from Emad's discord and twitter discussion, the idea appears to be to make this a "research" release, and therefore the worse license.

At a later point the model will be renamed "StableIf", and released with a similar license to StableDiffusion.

I'm quite curious how much of the improvement on text rendering is from the switch to pixel-space diffusion vs. the switch to a much larger pretrained text encoder. I'm leaning towards the latter, which then raises the question of what happens when you try training Stable Diffusion with T5-XXL-1.1 as the text encoder instead of CLIP — does it gain the ability to do text well?