DepGraph-U: Dependency-Aware Execution Pruning for Unified Multimodal Inference

Unified multimodal models (UMMs) bring understanding and generation into a single Transformer, reducing the need for separate specialist models. A shared inference implementation can nevertheless execute operators that the current request does not consume. Even when a valid image cache exists, or a caller requests pixels rather than text, unused producers and vocabulary projections can still execute. We use output-dependent liveness to identify work that remains executable but is unnecessary for the requested output or required future state. To address this, we propose DepGraph-U, a dependency-aware execution framework that uses audited output and state dependencies to guide guarded inference rewrites, without further weight modification. Cache-Aware Dependency Dispatch (CADD) bypasses upstream producers on safe cache hits, and an image-only interface omits unused vocabulary projection. For understanding, graph-compatible KV policies distinguish preserving dynamic layer selection from enabling earlier reuse under a stable graph. We evaluate DepGraph-U across two Show-o2 scales and eight benchmarks. The complete system achieves 2.88–3.21× generation speedup over native Show-o2, with quality trade-offs from the selected approximation policies. Against the reconstructed understanding reference, Exact-DLS preserves all 24,769 decoded responses with 1.15–2.34× speedup. Across the six tested understanding tasks, 80.8% of Exact-DLS examples finish before incremental reuse, explaining why cache compatibility alone does not determine realized latency.