NextFlow🚀: Unified Sequential Modeling Activates Multimodal Understanding and Generation

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🚀 Overview

NextFlow is a unified decoder-only autoregressive transformer trained on 6T interleaved text-image tokens. It bridges the gap between understanding and generation within a single architecture, redefining sequential modeling.

• 🌟 Unified Architecture: Seamlessly integrates multimodal generation, editing, and understanding in one decoder-only transformer, removing the need for separate diffusion or LLM backbones.
• 🌟 Next-Scale Prediction: A hierarchical prediction paradigm enables generating 1024×1024 images in just 5 seconds—significantly faster than comparable AR models.
• 🌟 SOTA Performance: Achieves state-of-the-art scores on DPG (88.32) and ImgEdit (4.49), matching specialized diffusion models in quality while retaining LLM reasoning capabilities.
• 🌟 Advanced Capabilities: Unlocks native Chain-of-Thought (CoT) reasoning, in-context editing, and interleaved generation without re-encoding overhead.

🎨 Demo

High-Fidelity Generation

NextFlow produces high-fidelity visuals with exceptional prompt adherence, adeptly handling complex spatial relationships and cultural nuances.

<div align="center"> <img src="assets/demo_t2i.jpg" alt="Text to Image Demo" style="width: 95%;" /> </div>

Complex Instruction Following

The model demonstrates precise alignment between text and images, ensuring accurate representation of detailed descriptions.

<div align="left"> <img src="assets/demo_t2i_2.jpg" alt="Text to Image Demo" style="width: 100%;" /> </div>

Image Editing

NextFlow supports precise, instruction-based editing. It modifies specific regions, styles, or attributes based on natural language commands while preserving the original structure and background consistency.

<div align="center"> <img src="assets/demo_edit.jpg" alt="Image editing Demo" style="width: 90%;" /> </div>

CoT Reasoning

By handling interleaved sequences naturally, the model employs Chain-of-Thought reasoning to refine prompts and plan before generating visual content.

<div align="center"> <img src="assets/t2i_thinking.png" alt="Interleaved Demo" style="width: 80%;" /> </div>

Interleaved Generation

Leveraging robust in-context learning, NextFlow performs zero-shot image editing and subject-driven generation effortlessly.

<div align="left"> <img src="assets/interleavedc.png" alt="Editing Demo" style="width: 100%;" /> </div>

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🏆 Benchmark Evaluation

EditCanvas Benchmark

To overcome dataset limitations, we introduce EditCanvas, a rigorous benchmark covering Traditional Editing and Subject-Driven Generation across 56 tasks with over 5,000 high-quality samples. </br>

<div align="center"> <img src="assets/EditCanvas.jpg" alt="EditCanvas Results" style="width: 100%;" /> </div>

Comparison with SOTA

We compare NextFlow against leading unified models (Bagel, Emu3.5) and specialized diffusion models. On the DPG benchmark, NextFlow RL scores 88.32, matching Qwen-Image and outperforming all other models. On ImgEdit, it sets a new state-of-the-art with a score of 4.49. </br>

<div align="center"> <img src="assets/radar_performance.png" alt="Radar Chart Comparison" style="width: 90%;" /> </div>

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📖 Introduction of NextFlow

NextFlow represents a paradigm shift in autoregressive visual generation. By treating images as hierarchical structures, we achieve specialized diffusion model's quality while keep LLM's reasoning.

Model Architecture: Decoder-Only Transformer

Initialized from Qwen2.5-VL-7B, NextFlow extends the standard LLM architecture for visual token prediction. We utilize a Unified Tokenizer, Scale Reweighting, and Self-Correction with Residual Features to stabilize large-scale corpus training and achieve high performance.

<div align="center"> <img src="assets/framework.jpg" alt="frame work" style="width: 60%;" /> </div>

Training Odyssey

Our pipeline is validated on 6 trillion tokens, ensuring robust multimodal capabilities.

• Alignment & Pre-Training: Large-scale training on text, image-text pairs, and interleaved data.
• Reinforcement Learning (RL): We introduce a prefix-tuning strategy for Group Reward Policy Optimization (GRPO), focusing on coarse-scale "prefixes" to stabilize global structure optimization.

<div align="center"> <img src="assets/training_pipeline.jpg" alt="Training Pipeline" style="width: 85%;" /> </div>

Inference Efficiency

NextFlow is highly efficient, enabling thegeneration of 1024 × 1024 images in just 5 seconds—orders of magnitude faster than comparableAR models. And NextFlowrequires 6× fewer FLOPs than MMDiT-based diffusion models at 1024² resolution. Its next-scale approach enables dynamic resolution generation without the typical computational costs of autoregression.

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Citation

@article{zhang2026nextflow,
  title={NextFlow: Unified Sequential Modeling Activates Multimodal Understanding and Generation},
  author={Zhang, Huichao and Qu, Liao and Liu, Yiheng and Chen, Hang and Song, Yangyang and Dong, Yongsheng and Sun, Shikun and Li, Xian and Wang, Xu and Jiang, Yi and others},
  journal={arXiv preprint arXiv:2601.02204},
  year={2026}
}

@article{sun2026var,
  title={VAR RL Done Right: Tackling Asynchronous Policy Conflicts in Visual Autoregressive Generation},
  author={Sun, Shikun and Qu, Liao and Zhang, Huichao and Liu, Yiheng and Song, Yangyang and Li, Xian and Wang, Xu and Jiang, Yi and Du, Daniel K and Wu, Xinglong and others},
  journal={arXiv preprint arXiv:2601.02256},
  year={2026}
}