One of the major hurdles in AI-driven image modeling is the inability to account for the diversity in image content complexity effectively. The tokenization methods so far used are static compression ratios where all images are treated equally, and the complexities of images are not considered. Due to this reason, complex images get over-compressed and…

Latent diffusion models are advanced techniques for generating high-resolution images by compressing visual data into a latent space using visual tokenizers. These tokenizers reduce computational demands while retaining essential details. However, such models suffer from a critical challenge: increasing the dimensions of the token feature increases reconstruction quality but decreases image generation quality. It thus…

Vision Transformers (ViTs) have become a cornerstone in computer vision, offering strong performance and adaptability. However, their large size and computational demands create challenges, particularly for deployment on devices with limited resources. Models like FLUX Vision Transformers, with billions of parameters, require substantial storage and memory, making them impractical for many use cases. These limitations…

Autoregressive (AR) models have changed the field of image generation, setting new benchmarks in producing high-quality visuals. These models break down the image creation process into sequential steps, each token generated based on prior tokens, creating outputs with exceptional realism and coherence. Researchers have widely adopted AR techniques for computer vision, gaming, and digital content…