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MobileDiffusion: Instant Text-to-Image Generation on Mobile Devices

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arxiv 2311.16567 v2 pith:BZZYV5ZZ submitted 2023-11-28 cs.CV

Yang Zhao , Yanwu Xu , Zhisheng Xiao , Haolin Jia , Tingbo Hou This is my paper · ORCID
classification cs.CV
keywords mobilediffusionmodeldevicesinferencemobiletechniquestext-to-imagearchitecture
verification ladder T0 review T1 audit T2 compute T3 formal
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abstract

The deployment of large-scale text-to-image diffusion models on mobile devices is impeded by their substantial model size and slow inference speed. In this paper, we propose \textbf{MobileDiffusion}, a highly efficient text-to-image diffusion model obtained through extensive optimizations in both architecture and sampling techniques. We conduct a comprehensive examination of model architecture design to reduce redundancy, enhance computational efficiency, and minimize model's parameter count, while preserving image generation quality. Additionally, we employ distillation and diffusion-GAN finetuning techniques on MobileDiffusion to achieve 8-step and 1-step inference respectively. Empirical studies, conducted both quantitatively and qualitatively, demonstrate the effectiveness of our proposed techniques. MobileDiffusion achieves a remarkable \textbf{sub-second} inference speed for generating a $512\times512$ image on mobile devices, establishing a new state of the art.

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Cited by 6 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

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    Injecting spatial importance maps (e.g., CFG delta) into the OBS Hessian improves subject preservation in pruned diffusion models at high sparsity, but gains over the baseline are small and without error bars.

  2. ELT: Elastic Looped Transformers for Visual Generation

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    Weight-shared looped transformers trained with intra-loop self-distillation match MaskGIT-class FID/FVD at roughly 4x fewer parameters and support any-time inference across loop counts.

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

    cs.CV 2026-01 conditional novelty 6.0 of 10

    A compact elastic diffusion transformer with adaptive sparse attention and knowledge-guided distribution-matching distillation achieves 4-step 1K image generation on a phone in roughly 1.8 seconds.

  4. On-device Sora: Enabling Training-Free Diffusion-based Text-to-Video Generation for Mobile Devices

    cs.CV 2025-02 conditional novelty 6.0 of 10

    A training-free pipeline makes diffusion text-to-video generation run on an iPhone 15 Pro with quality close to GPU output, at the cost of slower generation.

  5. Memory-Efficient Visual Autoregressive Modeling with Scale-Aware KV Cache Compression

    cs.LG 2025-05 conditional novelty 5.0 of 10

    ScaleKV cuts KV cache memory for Visual Autoregressive text-to-image generation to 10% by classifying layers as drafters or refiners per scale and pruning low-attention tokens while keeping benchmark scores nearly unchanged.

  6. Improving Joint Embedding Predictive Architecture with Diffusion Noise

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    Injecting EDM-style noise into masked-token position embeddings and adding two auxiliary losses improves I-JEPA's linear-probing accuracy by about 1.5 points on ImageNet-1K.

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