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

arXiv: https://arxiv.org/abs/2311.16567 · HTML: https://mlsanity.com/paper/2311.16567/

## Key facts

- Cited by 42 papers in the map (data through 14 Oct 2026).
- Placed in Image generation, text-to-image and editing, one of 14 directions in Diffusion models.
- Builds on 55 of its 64 references that are in the map.
- First posted on arXiv 28 Nov 2023.

## Placed in

- Diffusion models / [Image generation, text-to-image and editing](https://mlsanity.com/diffusion/image-generation-text-to-image-and-editing/)

## Builds on (most cited first, 30 of 55)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [Scalable Diffusion Models with Transformers](https://mlsanity.com/paper/2212.09748/) (2022)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis](https://mlsanity.com/paper/2307.01952/) (2023)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models](https://mlsanity.com/paper/2112.10741/) (2021)
- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
- [DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation](https://mlsanity.com/paper/2208.12242/) (2022)
- [Elucidating the Design Space of Diffusion-Based Generative Models](https://mlsanity.com/paper/2206.00364/) (2022)
- [Prompt-to-Prompt Image Editing with Cross Attention Control](https://mlsanity.com/paper/2208.01626/) (2022)
- [An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion](https://mlsanity.com/paper/2208.01618/) (2022)
- [DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps](https://mlsanity.com/paper/2206.00927/) (2022)
- [SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations](https://mlsanity.com/paper/2108.01073/) (2021)
- [GLU Variants Improve Transformer](https://mlsanity.com/paper/2002.05202/) (2020)
- [Align your Latents: High-Resolution Video Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2304.08818/) (2023)
- [AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning](https://mlsanity.com/paper/2307.04725/) (2023)
- [T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.08453/) (2023)
- [Reproducible scaling laws for contrastive language-image learning](https://mlsanity.com/paper/2212.07143/) (2022)
- [Deep Networks with Stochastic Depth](https://mlsanity.com/paper/1603.09382/) (2016)
- [DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models](https://mlsanity.com/paper/2211.01095/) (2022)
- [eDiff-I: Text-to-Image Diffusion Models with an Ensemble of Expert Denoisers](https://mlsanity.com/paper/2211.01324/) (2022)

## Built on by (most cited first, 30 of 42)

- [SANA-Sprint: One-Step Diffusion with Continuous-Time Consistency Distillation](https://mlsanity.com/paper/2503.09641/) (2025)
- [Lux Post Facto: Learning Portrait Performance Relighting with Conditional Video Diffusion and a Hybrid Dataset](https://mlsanity.com/paper/2503.14485/) (2025)
- [Diffusion Models Meet Remote Sensing: Principles, Methods, and Perspectives](https://mlsanity.com/paper/2404.08926/) (2024)
- [OBS-Diff: Accurate Pruning For Diffusion Models in One-Shot](https://mlsanity.com/paper/2510.06751/) (2025)
- [ELT: Elastic Looped Transformers for Visual Generation](https://mlsanity.com/paper/2604.09168/) (2026)
- [H3AE: High Compression, High Speed, and High Quality AutoEncoder for Video Diffusion Models](https://mlsanity.com/paper/2504.10567/) (2025)
- [Neodragon: Mobile Video Generation using Diffusion Transformer](https://mlsanity.com/paper/2511.06055/) (2025)
- [SD3.5-Flash: Distribution-Guided Distillation of Generative Flows](https://mlsanity.com/paper/2509.21318/) (2025)
- [Learnable Sparsity for Vision Generative Models](https://mlsanity.com/paper/2412.02852/) (2024)
- [Taming Diffusion Transformer for Efficient Mobile Video Generation in Seconds](https://mlsanity.com/paper/2507.13343/) (2025)
- [PocketSR: The Super-Resolution Expert in Your Pocket Mobiles](https://mlsanity.com/paper/2510.03012/) (2025)
- [MoViE: Mobile Diffusion for Video Editing](https://mlsanity.com/paper/2412.06578/) (2024)
- [Foresight: Adaptive Layer Reuse for Accelerated and High-Quality Text-to-Video Generation](https://mlsanity.com/paper/2506.00329/) (2025)
- [Communicate Less, Synthesize the Rest: Latency-aware Intent-based Generative Semantic Multicasting with Diffusion Models](https://mlsanity.com/paper/2411.02334/) (2024)
- [Skrr: Skip and Re-use Text Encoder Layers for Memory Efficient Text-to-Image Generation](https://mlsanity.com/paper/2502.08690/) (2025)
- [MobileI2V: Fast and High-Resolution Image-to-Video on Mobile Devices](https://mlsanity.com/paper/2511.21475/) (2025)
- [DreamLite: A Lightweight On-Device Unified Model for Image Generation and Editing](https://mlsanity.com/paper/2603.28713/) (2026)
- [A-SDM: Accelerating Stable Diffusion through Model Assembly and Feature Inheritance Strategies](https://mlsanity.com/paper/2406.00210/) (2024)
- [Consistent and Controllable Image Animation with Motion Linear Diffusion Transformers](https://mlsanity.com/paper/2508.07246/) (2025)
- [TRIM: Scalable 3D Gaussian Diffusion Inference with Temporal and Spatial Trimming](https://mlsanity.com/paper/2511.16642/) (2025)
- [Upcycling Text-to-Image Diffusion Models for Multi-Task Capabilities](https://mlsanity.com/paper/2503.11905/) (2025)
- [Flow Diverse and Efficient: Learning Momentum Flow Matching via Stochastic Velocity Field Sampling](https://mlsanity.com/paper/2506.08796/) (2025)
- [NanoSD: Edge Efficient Foundation Model for Real Time Image Restoration](https://mlsanity.com/paper/2601.09823/) (2026)
- [Cross-Space Distillation: Teaching One-Step Students with Modern Diffusion Teachers](https://mlsanity.com/paper/2606.32020/) (2026)
- [Accelerating Diffusion Sampling via Exploiting Local Transition Coherence](https://mlsanity.com/paper/2503.09675/) (2025)
- [Distilling Textual Priors from LLM to Efficient Image Fusion](https://mlsanity.com/paper/2504.07029/) (2025)
- [V.I.P. : Iterative Online Preference Distillation for Efficient Video Diffusion Models](https://mlsanity.com/paper/2508.03254/) (2025)
- [EdgeDiT: Hardware-Aware Diffusion Transformers for Efficient On-Device Image Generation](https://mlsanity.com/paper/2603.28405/) (2026)
- [BlazeEdit: Generalist Image Editing on Mobile Devices with Image-to-Image Diffusion Models](https://mlsanity.com/paper/2605.28067/) (2026)
- [Designing Parameter and Compute Efficient Diffusion Transformers using Distillation](https://mlsanity.com/paper/2502.14226/) (2025)

## Abstract (arXiv)

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 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 sub-second inference speed for generating a 512×512 image on mobile devices, establishing a new state of the art.