SnapGen++: Unleashing Diffusion Transformers for Efficient High-Fidelity Image Generation on Edge Devices
Authors: name: Dongting Hu, name: Aarush Gupta, name: Magzhan Gabidolla, name: Arpit Sahni, name: Huseyin Coskun, name: Yanyu Li, name: Yerlan Idelbayev, name: Ahsan Mahmood, name: Aleksei Lebedev, name: Dishani Lahiri, name: Anujraaj Goyal, name: Ju Hu, name: Mingming Gong, name: Sergey Tulyakov, name: Anil Kag
Organization: Snapchat Inc.

Recent advances in diffusion transformers (DiTs) have set new standards in image generation, yet remain impractical for on-device deployment due to their high computational and memory costs. In this work, we present an efficient DiT framework tailored for mobile and edge devices that achieves transformer-level generation quality under strict resource constraints. Our design combines three key components. First, we propose a compact DiT architecture with an adaptive global-local sparse attention mechanism that balances global context modeling and local detail preservation. Second, we propose an elastic training framework that jointly optimizes sub-DiTs of varying capacities within a unified supernetwork, allowing a single model to dynamically adjust for efficient inference across different hardware. Finally, we develop Knowledge-Guided Distribution Matching Distillation, a step-distillation pipeline that integrates the DMD objective with knowledge transfer from few-step teacher models, producing high-fidelity and low-latency generation (e.g., 4-step) suitable for real-time on-device use. Together, these contributions enable scalable, efficient, and high-quality diffusion models for deployment on diverse hardware.