Efficient and expressive high-resolution image synthesis via variational autoencoder-enriched transformers with sparse attention mechanisms

We introduce a method for efficient and expressive high-resolution image synthesis, harnessing the power of variational autoencoders (VAEs) and transformers with sparse attention (SA) mechanisms. By utilizing VAEs, we can establish a context-rich vocabulary of image constituents, thereby capturing intricate image features in a superior manner compared with traditional techniques. Subsequently, we employ SA mechanisms within our transformer model, improving computational efficiency while dealing with long sequences inherent to high-resolution images. Extending beyond traditional conditional synthesis, our model successfully integrates both nonspatial and spatial information while also incorporating temporal dynamics, enabling sequential image synthesis. Through rigorous experiments, we demonstrate our method’s effectiveness in semantically guided synthesis of megapixel images. Our findings substantiate this method as a significant contribution to the field of high-resolution image synthesis.