One of the biggest improvements to text models like Image/Parti which are much better than Stable Diffusion is that they typically either train a much larger te

One of the biggest improvements to text models like Image/Parti which are much better than Stable Diffusion is that they typically either train a much larger text model from scratch, or plug in a very good pre-trained text model like T5. This instantly upgrades their images. It turns out that the image models already understand the low-level details of images well, and it's high-level reasoning/composition that is screwing their samples up - which is due to the poor text embedding they receive as a summary. (You can't generate a good image of 'a blue ball left of a red ball' if your text embedding is confused what 'left' is, no matter how high quality you are able to generate 'blue ball' or 'red ball' individually.) So, I would not be surprised if hands are improved by simply using a much better text model. (And why not plug in GPT-3 or PaLM for even better results...) That said, hands are also like text-inside-images in being small, highly variable, and intrinsically difficult. And we know that the larger models solve text rendering simply by scaling up an OOM or 2 past SD. (You can see this from the text samples in the papers, and also more recently from Stability's Deep Floyd samples they've been teasing on Twitter.) So either way, scaling will probably solve hands soon.