arXiv:2601.08303v1 Announce Type: new 
Abstract: 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.

قدمت SnapGen++ إطار عمل جديد يستخدم المحولات الانتشارية (DiTs) لتمكين توليد صور عالية الدقة بكفاءة على الأجهزة المحمولة وأجهزة الحافة، مما يعالج التكاليف العالية للحوسبة والذاكرة التي أعاقت نشرها على الأجهزة.

SnapGen++ ha presentado un nuevo marco que utiliza transformadores de difusión (DiTs) para permitir la generación eficiente de imágenes de alta fidelidad en dispositivos móviles y de borde, abordando los altos costos computacionales y de memoria que han obstaculizado el despliegue en dispositivos.

SnapGen++ a introduit un nouveau cadre utilisant des transformateurs de diffusion (DiTs) pour permettre une génération d'images haute fidélité efficace sur des appareils mobiles et en périphérie, répondant aux coûts computationnels et de mémoire élevés qui ont entravé le déploiement sur appareil.

SnapGen++ has introduced a new framework leveraging diffusion transformers (DiTs) to enable efficient high-fidelity image generation on mobile and edge devices, addressing the high computational and memory costs that have hindered on-device deployment.

SnapGen++: Unleashing Diffusion Transformers for Efficient High-Fidelity Image Generation on Edge Devices

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