Discrete Diffusion Bridges for Spatiotemporally Aligned Image Translation and Generation

We propose Discrete Diffusion Bridges (DDB), a novel frame-work designed to resolve the fundamental spatiotemporal misalignmentof standard discrete diffusion in image translation and generation. Bycorrupting data into a pure mask state via a random schedule, the con-ventional forward process induces a twofold misalignment: spatially, thispure-mask destination entirely discards the rich structural priors of thesource image; temporally, the random masking order inherently contra-dicts the “easy-first, hard-last” decoding mechanism used during infer-ence. To address this, DDB constructs a direct and efficient trajectorybetween domains. Spatially, we introduce a hybrid absorption mechanismthat redefines the absorbing state to a stochastic mixture of mask andsource tokens, effectively injecting source prior as spatial anchors into thelatent space. Temporally, we design an information-guided noise schedulethat quantifies semantic variation to prioritize the corruption of high-information regions at earlier timesteps. This ensures the model learnsto resolve difficult semantic changes using robust context from invariantregions. Extensive experiments validate the versatility and robustnessof our framework across diverse generative paradigms. DDB effectivelybalances edit alignment with structural fidelity across both text-guidedsemantic manipulation and pure structural image translation, while in-herently complementing text-to-image generation and guaranteeing ro-bust high-quality decoding under extremely low sampling steps. Codeand models are available at https://github.com/HKU-HealthAI/DDB.