FlashU is the first framework to accelerate unified multimodal models by exploiting the distinct neuron sets used for generation vs. understanding.
March 17, 2026
Original Paper
Flash-Unified: A Training-Free and Task-Aware Acceleration Framework for Native Unified Models
arXiv · 2603.15271
The Takeaway
It provides a training-free acceleration method that prunes and skips layers dynamically depending on whether the model is currently 'generating' or 'understanding'. This addresses the massive computational overhead of native unified models (like Show-o) without requiring retraining.
From the abstract
Native unified multimodal models, which integrate both generative and understanding capabilities, face substantial computational overhead that hinders their real-world deployment. Existing acceleration techniques typically employ a static, monolithic strategy, ignoring the fundamental divergence in computational profiles between iterative generation tasks (e.g., image generation) and single-pass understanding tasks (e.g., VQA). In this work, we present the first systematic analysis of unified mo