regarding IPAdapter: I'm not familiar with it, but from the code it looks like they just run cross-attention again and sum the two attention outputs. feels a bit weird to me, because the attention probabilities add up to 2 instead of 1. and they scale the bonus attention output only instead of lerping. it'd make more sense to me to formulate it as a cross-cross attention (Q against cat([key0, key1]) and cat([val0, val1])), but maybe they wanted it to begin as a no-op at the start of training or something. anyway.. yes, all of that should work fine with HDiT. the paper doesn't implement cross-attention, but it can be added in the standard way (e.g. like stable-diffusion) or as self-cross attention (e.g. DeepFloyd IF or Imagen).
I'd recommend though to make use of HDiT's mapping network. in our attention blocks, the input gets AdaNormed against the condition from the mapping network. this is currently used to convey stuff like class conditions, Karras augmentation conditions and timestep embeddings. but it supports conditioning on custom (single-token) conditions of your choosing. so you could use this to condition on an image embed (this would give you the same image-conditioning control as IPAdapter but via a simpler mechanism).
But generally, most other UIs support it. It has serious limitations though, for example it center-crops the input to 224x224px. (which is enough for a surprisingly large amount of uses, but not enough for many others)
A latent space CLIPVision model would be cool too. Presumably you could leverage the semantic richness of the latent space to efficiently train a more powerful CLIPVision. I don’t know whether anyone has tried this. Maybe there is a good reason for that.
Thanks for your work - it’s been impactful since the early days of the project.
Excited to see where we get to this year.
this year I'm hoping for efficiency and small models! even if it's proprietary. if our work can reduce some energy usage behind closed doors that'd still be a good outcome.
Energy efficiency is key - Especially with some of these extremely inefficient (wasteful, even) features like real-time canvas.
Good luck - Let us know if/how we can help.
I see your headline speed comparison is to "Pixel-space DiT-B/4" - but how does your model compare to the likes of SDXL? I gather they spent $$$$$$ on training etc, so I'd understand if direct comparisons don't make sense.
And do you have any results on things that are traditionally challenging for generative AI, like clocks and mirrors?
activations are small! so you can enjoy bigger batch sizes. this is due to the 4x patching we do on the ingress to the model, and the effectiveness of neighbourhood attention in joining patches at the seams.
the model's inductive biases are pretty different than (for example) a convolutional UNet's. the innermost levels seem to train easily, so images can have good global coherence early in training.
there's no convolutions! so you don't need to worry about artifacts stemming from convolution padding, or having canvas edge padding artifacts leak an implicit position bias.
we can finally see what high-resolution diffusion outputs look like _without_ latents! personally I think current latent VAEs don't _really_ achieve the high resolutions they claim (otherwise fine details like text would survive a VAE roundtrip faithfully); it's common to see latent diffusion outputs with smudgy skin or blurry fur. what I'd like to see in the future of latent diffusion is to listen to the Emu paper and use more channels, or a less ambitious upsample.
it's a transformer! so we can try applying to it everything we know about transformers, like sigma reparameterisation or multimodality. some tricks like masked training will require extra support in [NATTEN](https://github.com/SHI-Labs/NATTEN), but we're very happy with its featureset and performance so far.
but honestly I'm most excited about the efficiency. there's too little work on making pretraining possible at GPU-poor scale. so I was very happy to see HDiT could succeed at small-scale tasks within the resources I had at home (you can get nice oxford flowers samples at 256x256px with half an hour on a 4090). I think with models that are better fits for the problem, perhaps we can get good results with smaller models. and I'd like to see big tech go that direction too!
-Alex Birch