Instella-T2I: Pushing the Limits of 1D Discrete Latent Space Image Generation
Journal:
arXiv
Published Date:
Jun 26, 2025
Abstract
Image tokenization plays a critical role in reducing the computational
demands of modeling high-resolution images, significantly improving the
efficiency of image and multimodal understanding and generation. Recent
advances in 1D latent spaces have reduced the number of tokens required by
eliminating the need for a 2D grid structure. In this paper, we further advance
compact discrete image representation by introducing 1D binary image latents.
By representing each image as a sequence of binary vectors, rather than using
traditional one-hot codebook tokens, our approach preserves high-resolution
details while maintaining the compactness of 1D latents. To the best of our
knowledge, our text-to-image models are the first to achieve competitive
performance in both diffusion and auto-regressive generation using just 128
discrete tokens for images up to 1024x1024, demonstrating up to a 32-fold
reduction in token numbers compared to standard VQ-VAEs. The proposed 1D binary
latent space, coupled with simple model architectures, achieves marked
improvements in speed training and inference speed. Our text-to-image models
allow for a global batch size of 4096 on a single GPU node with 8 AMD MI300X
GPUs, and the training can be completed within 200 GPU days. Our models achieve
competitive performance compared to modern image generation models without any
in-house private training data or post-training refinements, offering a
scalable and efficient alternative to conventional tokenization methods.
