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A comprehensive collection of research papers and resources on diffusion&flow based models, systematically organized by application and architecture. It highlights cutting-edge advances in flow-guided diffusion techniques for image, video, and multimodal generation.

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Diffusion and Flow Models Research

A curated collection of current and important research in Diffusion and Flow-based generative models. This repository compiles foundational concepts, groundbreaking papers, and practical applications in this rapidly evolving field—covering inference acceleration, model optimization, image editing, and personalized generation.

📌 Note: This collection is actively maintained with the most significant and up-to-date research in the field.

⭐ Rating

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Contents

⭐ Milestone Models

Seedream 4.0: Toward Next-generation Multimodal Image Generation ⭐⭐⭐⭐⭐
[Paper]

Qwen-Image ⭐⭐⭐⭐
[Paper] [Project Page]

Z-Image: An Efficient Image Generation Foundation Model with Single-Stream Diffusion Transformer ⭐⭐⭐⭐⭐
[Paper] [Project Page]

FLUX.1 Kontext: Flow Matching for In-Context Image Generation and Editing in Latent Space ⭐⭐⭐⭐⭐
[Paper]

⚡️ Inference Acceleration

Timestep Embedding Tells: It’s Time to Cache for Video Diffusion Model ⭐⭐⭐⭐⭐
[Paper] [Project Page]

MagCache: Fast Video Generation with Magnitude-Aware Cache ⭐⭐⭐⭐⭐
[Paper] [Project Page]

Training-free Diffusion Acceleration with Bottleneck Sampling ⭐⭐⭐
[Paper] [Project Page]

NAMI: Efficient Image Generation via Progressive Rectified Flow Transformers ⭐⭐
[Paper]

SVDQUANT: ABSORBING OUTLIERS BY LOW-RANK COMPONENTS FOR 4-BIT DIFFUSION MODELS ⭐⭐⭐⭐
[Paper]

From Reusing to Forecasting: Accelerating Diffusion Models with TaylorSeers ⭐⭐⭐
[Paper] [Project Page]

⚙️ Model Optimization

SAGEATTENTION: ACCURATE 8-BIT ATTENTION FOR PLUG-AND-PLAY INFERENCE ACCELERATION ⭐⭐⭐⭐
[Paper] [Project Page]

SageAttention2: Efficient Attention with Thorough Outlier Smoothing and Per-thread INT4 Quantization ⭐⭐⭐⭐
[Paper]

SageAttention3: Microscaling FP4 Attention for Inference and An Exploration of 8-bit Training ⭐⭐⭐⭐
[Paper]

FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness ⭐⭐⭐⭐⭐
[Paper] [Project Page]

FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning ⭐⭐⭐⭐⭐
[Paper]

FlashAttention-3: Fast and Accurate Attention with Asynchrony and Low-precision ⭐⭐⭐⭐⭐
[Paper]

🧪 Model Distillation

SANA-Sprint: One-Step Diffusion with Continuous-Time Consistency Distillation ⭐⭐⭐
[Paper] [Project Page]

One-step Diffusion with Distribution Matching Distillation ⭐⭐⭐⭐⭐
[Paper]

Improved Distribution Matching Distillation for Fast Image Synthesis ⭐⭐⭐⭐
[Paper]

DECOUPLED DMD: CFG AUGMENTATION AS THE SPEAR, DISTRIBUTION MATCHING AS THE SHIELD ⭐⭐⭐⭐⭐
[Paper] [Project Page]

Distribution Matching Distillation Meets Reinforcement Learning ⭐⭐⭐⭐⭐
[Paper]

📊 Reinforcement Learning

Distribution Matching Distillation Meets Reinforcement Learning ⭐⭐⭐⭐⭐
[Paper]

X-Omni: Reinforcement Learning Makes Discrete Autoregressive Image Generative Models Great Again ⭐⭐⭐⭐
[Paper]

Flow-GRPO: Training Flow Matching Models via Online RL ⭐⭐⭐⭐
[Paper] [Project Page]

🌊 Flow Matching Concepts

FLOW MATCHING FOR GENERATIVE MODELING ⭐⭐⭐⭐⭐
[Paper]

Z-Image: An Efficient Image Generation Foundation Model with Single-Stream Diffusion Transformer ⭐⭐⭐⭐⭐
[Paper] [Project Page]

🎨 Image Editing

FireFlow: Fast Inversion of Rectified Flow for Image Semantic Editing
[Paper] [Project Page]

FlowEdit: Inversion-Free Text-Based Editing Using Pre-Trained Flow Models ⭐⭐
[Paper] [Project Page]

HiDream-I1: A High-Efficient Image Generative Foundation Model with Sparse Diffusion Transformer ⭐⭐⭐
[Paper] [Project Page]

👤 Personalized - ID Preserving Image Generation

PuLID: Pure and Lightning ID Customization via Contrastive Alignment ⭐⭐⭐⭐
[Paper] [Project Page]

InfiniteYou: Flexible Photo Recrafting While Preserving Your Identity ⭐⭐⭐
[Paper] [Project Page]

WithAnyone: Towards Controllable and ID-Consistent Image Generation ⭐⭐
[Project Page]

LoRAShop: Training-Free Multi-Concept Image Generation and Editing with Rectified Flow Transformers ⭐⭐
[Paper] [Project Page]

FluxSpace: Disentangled Semantic Editing in Rectified Flow Transformers ⭐⭐
[Paper]

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A comprehensive collection of research papers and resources on diffusion&flow based models, systematically organized by application and architecture. It highlights cutting-edge advances in flow-guided diffusion techniques for image, video, and multimodal generation.

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