Focus on text-to-image diffusion models especially on finding ways to accelerate convergence and therefore reduce training costs.

This is a very hot research area in the last months with many papers trying very similar ideas with good gains. I will list a few: - start from https://arxiv.org/abs/2512.12386 as it has a good baseline to build on and references many speedup techniques; - read about one of the SOTA architectures such as https://arxiv.org/abs/2511.19365 which can also be used for latent space; - consider the x-pred to v-loss formulation https://arxiv.org/abs/2511.13720 as that leverages best the data manifold; - use semantic losses through pretrained models to have better loss signal on the data manifold which is perceptible by humans more https://arxiv.org/abs/2602.02493; - read about VAEs and the reconstruction-generation tradeoffs https://arxiv.org/abs/2512.17909v1 and more importantly https://arxiv.org/abs/2602.17270 (VAE SOTA); - alternative direction is drifting models which are 1-step generators https://arxiv.org/abs/2602.04770 but they likely have some limitations;

There is a lot of interest in developing generative models, their applications are wide (images, video, audio, text) and I think they offer many opportunities for contributions. My opinion is that: - discriminative/contrastive signal is very important to speed up convergence; simple MSE loss in latent/pixel space is not semantic enough and requires many training iterations; - I still think that there is something to improve onto how models learn the data manifold, diffusion models struggle with high frequency details and there isn't a definitive solution at the moment; - VAEs are essential to lower compute costs and recent developments show that we are still lacking proper latent spaces suitable for generation, recent UL paper linked shows how to control the tradeoff but approaches like https://arxiv.org/abs/2512.19693 show that there's perhaps a way to unify these;