meshed-memory-transformer VS stargan-v2

I could use some up-to-date models (e.g, this one: https://github.com/aimagelab/meshed-memory-transformer), but all those I looked into require pre-processing step of features/bounding-boxes generation. The problem is that I can't use an off-the shelf bounding-box extraction model as it would not perform well on the dataset I have (images are not like COCO at all). So I was wondering if there is a relatively up-to-date architecture that I can use that will not require this processing step. That is, an implementation that requires only inputs (images) and outputs (sentences).

This version of StarGAN2 (coined as 'Post-modern Style Transfer') is intended mostly for fellow artists, who rarely look at scientific metrics, but rather need a working creative tool. At least, this is what I use nearly daily myself. Here are few pieces, made with it: Terminal Blink, Occurro, etc. Tested on Pytorch 1.4-1.8. Sequence-to-video conversions require FFMPEG. For more explicit details refer to the original implementation.