SnapGen++: Unleashing Diffusion Transformers for Efficient High-Fidelity Image Generation on Edge Devices
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.
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Dongting Hu
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2026-02-11T22:17:26.079Z
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Dongting Hu
timmy11hu
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timmy11hu
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Aarush Gupta
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Magzhan Gabidolla
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Arpit Sahni
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Huseyin Coskun
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Yanyu Li
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Yerlan Idelbayev
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Ahsan Mahmood
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Aleksei Lebedev
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Dishani Lahiri
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Anujraaj Goyal
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Ju Hu
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Mingming Gong
696708d3c5e371f6b235d119
Sergey Tulyakov
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Anil Kag
claimed_verified
2026-03-17T08:31:42.050Z
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Anil Kag
akag-snap
user
akag-snap
SnapGen++: Unleashing Diffusion Transformers for Efficient High-Fidelity Image Generation on Edge Devices
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.
An efficient diffusion transformer framework for mobile and edge devices that maintains high-generation quality while reducing computational costs through compact architecture, elastic training, and knowledge-guided distillation.
diffusion transformers DiTs sparse attention mechanism elastic training supernetwork knowledge-guided distribution matching distillation step-distillation pipeline teacher models high-fidelity generation low-latency generation