DiT-IC: Aligned Diffusion Transformer for Efficient Image Compression

Diffusion-based image compression has recently shown outstanding perceptual fidelity, yet its practicality is hindered by prohibitive sampling overhead and high memory usage.Most existing diffusion codecs employ UNet architectures, where hierarchical downsampling forces diffusion to operate in shallow latent spaces (typically with only $8\times$ spatial downscaling), resulting in excessive computation.In contrast, conventional VAE-based codecs work in much deeper latent domains ($16\times$–$64\times$ downscaled), motivating a key question:Can diffusion operate effectively in such compact latent spaces without compromising reconstruction quality?To address this, we introduce DiT-IC—an Aligned Diffusion Transformer for Image Compression—which replaces the UNet with a Diffusion Transformer capable of performing diffusion in latent space entirely at $32\times$ downscaled resolution.DiT-IC adapts a pretrained text-to-image multi-step DiT into a single-step reconstruction model through three key alignment mechanisms:(1) a variance-guided reconstruction flow that adapts denoising strength to latent uncertainty for efficient reconstruction;(2) a self-distillation alignment that enforces consistency with encoder-defined latent geometry to enable one-step diffusion; and(3) a latent-conditioned guidance that replaces text prompts with semantically aligned latent conditions, enabling text-free inference.With these designs, DiT-IC achieves state-of-the-art perceptual quality while offering up to 30× faster decoding and drastically lower memory usage than existing diffusion-based codecs. Remarkably, it can reconstruct $2048\times2048$ images on a 16 GB laptop GPU. Code will be released.