Yeah, the main motivation was trying to find a way to enable transformers to do high-resolution image synthesis: transformers are known to scale well to extreme, multi-billion parameter scales and typically offer superior coherency & composition in image generation, but current architectures are too expensive to train at scale for high-resolution inputs.
By using a hierarchical architecture and local attention at high-resolution scales (but retaining global attention at low-resolution scales), it becomes viable to apply transformers at these scales.
Additionally, this architecture can now directly be trained on megapixel-scale inputs and generate high-quality results without having to progressively grow the resolution over the training or applying other "tricks" typically needed to make models at these resolutions work well.
Yeah, the main motivation was trying to find a way to enable transformers to do high-resolution image synthesis: transformers are known to scale well to extreme, multi-billion parameter scales and typically offer superior coherency & composition in image generation, but current architectures are too expensive to train at scale for high-resolution inputs.
By using a hierarchical architecture and local attention at high-resolution scales (but retaining global attention at low-resolution scales), it becomes viable to apply transformers at these scales. Additionally, this architecture can now directly be trained on megapixel-scale inputs and generate high-quality results without having to progressively grow the resolution over the training or applying other "tricks" typically needed to make models at these resolutions work well.