DiT-IC: Aligned Diffusion Transformer for Efficient Image Compression

TL;DR

DiT-IC introduces an aligned diffusion transformer that enables efficient, high-quality image compression in deep latent spaces, significantly reducing decoding time and memory usage compared to existing diffusion codecs.

Contribution

It proposes a novel diffusion transformer architecture with alignment mechanisms for effective diffusion in deep latent spaces, enabling fast, high-quality image reconstruction without text prompts.

Findings

Achieves state-of-the-art perceptual quality in image compression.

Offers up to 30x faster decoding than existing diffusion codecs.

Can reconstruct 2048x2048 images on a 16 GB GPU.

Abstract

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 U-Net architectures, where hierarchical downsampling forces diffusion to operate in shallow latent spaces (typically with only 8x spatial downscaling), resulting in excessive computation. In contrast, conventional VAE-based codecs work in much deeper latent domains (16x - 64x 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 U-Net with a Diffusion Transformer capable of performing diffusion in latent space entirely at 32x 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 30x faster decoding and drastically lower memory usage than existing diffusion-based codecs. Remarkably, it can reconstruct 2048x2048 images on a 16 GB laptop GPU.