# SenseFlow: Scaling Distribution Matching for Flow-based Text-to-Image Distillation

arXiv: https://arxiv.org/abs/2506.00523 · HTML: https://mlsanity.com/paper/2506.00523/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- Placed in Diffusion foundations and sampling, one of 14 directions in Diffusion models.
- Builds on 39 of its 48 references that are in the map.
- First posted on arXiv 31 May 2025.

## Placed in

- Diffusion models / [Diffusion foundations and sampling](https://mlsanity.com/diffusion/diffusion-foundations-and-sampling/)

## Builds on (most cited first, 30 of 39)

- [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)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis](https://mlsanity.com/paper/2307.01952/) (2023)
- [Scaling Rectified Flow Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2403.03206/) (2024)
- [LAION-5B: An open large-scale dataset for training next generation image-text models](https://mlsanity.com/paper/2210.08402/) (2022)
- [DreamFusion: Text-to-3D using 2D Diffusion](https://mlsanity.com/paper/2209.14988/) (2022)
- [SAM 2: Segment Anything in Images and Videos](https://mlsanity.com/paper/2408.00714/) (2024)
- [Consistency Models](https://mlsanity.com/paper/2303.01469/) (2023)
- [ProlificDreamer: High-Fidelity and Diverse Text-to-3D Generation with Variational Score Distillation](https://mlsanity.com/paper/2305.16213/) (2023)
- [ImageReward: Learning and Evaluating Human Preferences for Text-to-Image Generation](https://mlsanity.com/paper/2304.05977/) (2023)
- [One-step Diffusion with Distribution Matching Distillation](https://mlsanity.com/paper/2311.18828/) (2023)
- [Pick-a-Pic: An Open Dataset of User Preferences for Text-to-Image Generation](https://mlsanity.com/paper/2305.01569/) (2023)
- [GenEval: An Object-Focused Framework for Evaluating Text-to-Image Alignment](https://mlsanity.com/paper/2310.11513/) (2023)
- [Human Preference Score v2: A Solid Benchmark for Evaluating Human Preferences of Text-to-Image Synthesis](https://mlsanity.com/paper/2306.09341/) (2023)
- [Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference](https://mlsanity.com/paper/2310.04378/) (2023)
- [Diffusion Model Alignment Using Direct Preference Optimization](https://mlsanity.com/paper/2311.12908/) (2023)
- [Adversarial Diffusion Distillation](https://mlsanity.com/paper/2311.17042/) (2023)
- [Improved Distribution Matching Distillation for Fast Image Synthesis](https://mlsanity.com/paper/2405.14867/) (2024)
- [Consistency Trajectory Models: Learning Probability Flow ODE Trajectory of Diffusion](https://mlsanity.com/paper/2310.02279/) (2023)
- [Knowledge Distillation in Iterative Generative Models for Improved Sampling Speed](https://mlsanity.com/paper/2101.02388/) (2021)
- [Simplifying, Stabilizing and Scaling Continuous-Time Consistency Models](https://mlsanity.com/paper/2410.11081/) (2024)
- [Fast High-Resolution Image Synthesis with Latent Adversarial Diffusion Distillation](https://mlsanity.com/paper/2403.12015/) (2024)
- [SDXL-Lightning: Progressive Adversarial Diffusion Distillation](https://mlsanity.com/paper/2402.13929/) (2024)
- [Diff-Instruct: A Universal Approach for Transferring Knowledge From Pre-trained Diffusion Models](https://mlsanity.com/paper/2305.18455/) (2023)
- [Diffusion-GAN: Training GANs with Diffusion](https://mlsanity.com/paper/2206.02262/) (2022)
- [AM-RADIO: Agglomerative Vision Foundation Model -- Reduce All Domains Into One](https://mlsanity.com/paper/2312.06709/) (2023)

## Built on by (most cited first, 30 of 32)

- [Distribution Matching Distillation Meets Reinforcement Learning](https://mlsanity.com/paper/2511.13649/) (2025)
- [pi-Flow: Policy-Based Few-Step Generation via Imitation Distillation](https://mlsanity.com/paper/2510.14974/) (2025)
- [Flash-DMD: Towards High-Fidelity Few-Step Image Generation with Efficient Distillation and Joint Reinforcement Learning](https://mlsanity.com/paper/2511.20549/) (2025)
- [Salt: Self-Consistent Distribution Matching with Cache-Aware Training for Fast Video Generation](https://mlsanity.com/paper/2604.03118/) (2026)
- [LinVideo: A Post-Training Framework towards O(n) Attention in Efficient Video Generation](https://mlsanity.com/paper/2510.08318/) (2025)
- [LiveTalk: Real-Time Multimodal Interactive Video Diffusion via Improved On-Policy Distillation](https://mlsanity.com/paper/2512.23576/) (2025)
- [Reinforcing Few-step Generators via Reward-Tilted Distribution Matching](https://mlsanity.com/paper/2605.26108/) (2026)
- [Flash-VAED: Plug-and-Play VAE Decoders for Efficient Video Generation](https://mlsanity.com/paper/2602.19161/) (2026)
- [Lens: Rethinking Training Efficiency for Foundational Text-to-Image Models](https://mlsanity.com/paper/2605.21573/) (2026)
- [VDOT: Efficient Unified Video Creation via Optimal Transport Distillation](https://mlsanity.com/paper/2512.06802/) (2025)
- [Mean Flow Distillation: Robust and Stable Distillation for Flow Matching Models](https://mlsanity.com/paper/2606.11155/) (2026)
- [1.x-Distill: Breaking the Diversity, Quality, and Efficiency Barrier in Distribution Matching Distillation](https://mlsanity.com/paper/2604.04018/) (2026)
- [Continuous-Time Distribution Matching for Few-Step Diffusion Distillation](https://mlsanity.com/paper/2605.06376/) (2026)
- [Distribution Matching Distillation without Fake Score Network](https://mlsanity.com/paper/2605.19256/) (2026)
- [Sortblock: Similarity-Aware Feature Reuse for Diffusion Model](https://mlsanity.com/paper/2508.00412/) (2025)
- [Budget-Constrained Step-Level Diffusion Caching](https://mlsanity.com/paper/2606.13496/) (2026)
- [Mage-Flow: An Efficient Native-Resolution Foundation Model for Image Generation and Editing](https://mlsanity.com/paper/2607.19064/) (2026)
- [Reward-Aware Trajectory Shaping for Few-step Visual Generation](https://mlsanity.com/paper/2604.14910/) (2026)
- [Multi-Resolution Flow Matching: Training-Free Diffusion Acceleration via Staged Sampling](https://mlsanity.com/paper/2607.01642/) (2026)
- [DefaultShift: Auditing Semantic Default Shift in Accelerated Text-to-Image Models](https://mlsanity.com/paper/2608.21784/) (2026)
- [Salt++: Context-Aligned Post-Training for Few-Step Streaming Multimodal Generation](https://mlsanity.com/paper/2609.36995/) (2026)
- [Omni-LiveAvatar: Minute-Level Real-Time Streaming Joint Audio-Video Avatar Generation](https://mlsanity.com/paper/2608.13602/) (2026)
- [CrossDistill: Balancing Quality and Diversity via Trajectory-Level Hybrid Few-Step Distillation](https://mlsanity.com/paper/2609.14725/) (2026)
- [IB-Flow: Information Bottleneck-Guided CFG Distillation for Few-Step Text-to-Image Generation](https://mlsanity.com/paper/2607.09133/) (2026)
- [Spectral Amplitude Purification in Distribution Matching for Diffusion Distillation](https://mlsanity.com/paper/2609.29116/) (2026)
- [PDMD: Projected Distribution Matching Distillation for Video Diffusion Models](https://mlsanity.com/paper/2609.35768/) (2026)
- [Waypoint-1.5: A Real-Time Video World Model for Consumer Hardware](https://mlsanity.com/paper/2609.37107/) (2026)
- [SoL-Refiner: Speed-of-Light One-Step Refinement for High-Resolution Video](https://mlsanity.com/paper/2609.37969/) (2026)
- [Enhancing Autoregressive Video Generation via Representation Adversarial Distillation](https://mlsanity.com/paper/2609.40037/) (2026)
- [DMAD: Distribution Matching as Adversarial Distillation for Fast Visual Generation](https://mlsanity.com/paper/2610.02188/) (2026)

## Abstract (arXiv)

The Distribution Matching Distillation (DMD) has been successfully applied to text-to-image diffusion models such as Stable Diffusion (SD) 1.5. However, vanilla DMD suffers from convergence difficulties on large-scale flow-based text-to-image models, such as SD 3.5 and FLUX. In this paper, we first analyze the issues when applying vanilla DMD on large-scale models. Then, to overcome the scalability challenge, we propose implicit distribution alignment (IDA) to constrain the divergence between the generator and the fake distribution. Furthermore, we propose intra-segment guidance (ISG) to relocate the timestep denoising importance from the teacher model. With IDA alone, DMD converges for SD 3.5; employing both IDA and ISG, DMD converges for SD 3.5 and FLUX.1 dev. Together with a scaled VFM-based discriminator, our final model, dubbed SenseFlow, achieves superior performance in distillation for both diffusion based text-to-image models such as SDXL, and flow-matching models such as SD 3.5 Large and FLUX.1 dev. The source code is available at hrefhttps://github.com/XingtongGe/SenseFlowhttps://github.com/XingtongGe/SenseFlow