You can build competitive text-to-image systems without relying on pretrained language models, which could make these systems cheaper and more accessible to build from scratch.
Halcyon shows that text-to-image models don't need large language pretraining to work well. By training from scratch on 100M image-text pairs, Halcyon achieves better results than existing systems while using far less compute—18× to 275× less than competitors. This challenges the assumption that internet-scale language models are essential for image generation.