Accelerating Multi-Condition T2I Generation via Adaptive Condition Offloading and Pruning

Text-to-image (T2I) generation using multiple conditions enables fine-grained user control on the generated image. Yet, incorporating multi-condition inputs incurs substantial computation and communication overhead, due to additional preprocessing subtasks and control optimizations. It hence leads to unacceptable generation latency. In this paper, we propose an end-edge collaborative system design to accelerate multi-condition T2I generation through adaptive condition offloading and pruning. Extensive offline profiling reveal that, different conditions exhibit significant diversity in computation and communication costs. To this end, we propose a $\mathit{\text{Subtask}}\mathit{\text{Manager}}$ that jointly optimizes condition inference offloading and bandwidth allocation using a heuristic algorithm, balancing local and edge execution delays to minimize overall preprocessing latency. Then, we design a lightweight feature-driven $\mathit{\text{Conditioning}}\mathit{\text{Scale}}\mathit{\text{Estimator}}$ that evaluates the contribution of each condition by analyzing its feature activation strength and overlap with other conditions. This allows adaptive conditioning scale selection and pruning of insignificant conditions, thereby accelerating the denoising process. Extensive experimental results show that our system reduces latency by nearly 25% and improves 6% average generation quality, outperforming other benchmarks.