# HiDream-O1-Image: A Natively Unified Image Generative Foundation Model with Pixel-level Unified Transformer

arXiv: https://arxiv.org/abs/2605.11061 · HTML: https://mlsanity.com/paper/2605.11061/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- Builds on 52 of its 66 references that are in the map.
- First posted on arXiv 11 May 2026.

## Builds on (most cited first, 30 of 52)

- [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)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Scalable Diffusion Models with Transformers](https://mlsanity.com/paper/2212.09748/) (2022)
- [RoFormer: Enhanced Transformer with Rotary Position Embedding](https://mlsanity.com/paper/2104.09864/) (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)
- [Qwen3-VL Technical Report](https://mlsanity.com/paper/2511.21631/) (2025)
- [GLU Variants Improve Transformer](https://mlsanity.com/paper/2002.05202/) (2020)
- [Root Mean Square Layer Normalization](https://mlsanity.com/paper/1910.07467/) (2019)
- [SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features](https://mlsanity.com/paper/2502.14786/) (2025)
- [Qwen-Image Technical Report](https://mlsanity.com/paper/2508.02324/) (2025)
- [FLUX.1 Kontext: Flow Matching for In-Context Image Generation and Editing in Latent Space](https://mlsanity.com/paper/2506.15742/) (2025)
- [GenEval: An Object-Focused Framework for Evaluating Text-to-Image Alignment](https://mlsanity.com/paper/2310.11513/) (2023)
- [Emerging Properties in Unified Multimodal Pretraining](https://mlsanity.com/paper/2505.14683/) (2025)
- [PixArt-α: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis](https://mlsanity.com/paper/2310.00426/) (2023)
- [Janus-Pro: Unified Multimodal Understanding and Generation with Data and Model Scaling](https://mlsanity.com/paper/2501.17811/) (2025)
- [Emu3: Next-Token Prediction is All You Need](https://mlsanity.com/paper/2409.18869/) (2024)
- [Show-o: One Single Transformer to Unify Multimodal Understanding and Generation](https://mlsanity.com/paper/2408.12528/) (2024)
- [Improved Distribution Matching Distillation for Fast Image Synthesis](https://mlsanity.com/paper/2405.14867/) (2024)
- [Flow-GRPO: Training Flow Matching Models via Online RL](https://mlsanity.com/paper/2505.05470/) (2025)
- [ELLA: Equip Diffusion Models with LLM for Enhanced Semantic Alignment](https://mlsanity.com/paper/2403.05135/) (2024)
- [Step1X-Edit: A Practical Framework for General Image Editing](https://mlsanity.com/paper/2504.17761/) (2025)
- [Diffusion Art or Digital Forgery? Investigating Data Replication in Diffusion Models](https://mlsanity.com/paper/2212.03860/) (2022)
- [OmniGen: Unified Image Generation](https://mlsanity.com/paper/2409.11340/) (2024)
- [The Faiss library](https://mlsanity.com/paper/2401.08281/) (2024)
- [BLIP3-o: A Family of Fully Open Unified Multimodal Models-Architecture, Training and Dataset](https://mlsanity.com/paper/2505.09568/) (2025)
- [Hunyuan-DiT: A Powerful Multi-Resolution Diffusion Transformer with Fine-Grained Chinese Understanding](https://mlsanity.com/paper/2405.08748/) (2024)

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

- [Toward Native Multimodal Modeling: A Roadmap](https://mlsanity.com/paper/2605.25343/) (2026)
- [SenseNova-U1.5: Towards Native Unified Visual Intelligence](https://mlsanity.com/paper/2609.11929/) (2026)
- [Boogu-Image-0.1: Boosting Open Agentic Multimodal Generation via Understanding under a Minimal Budget](https://mlsanity.com/paper/2607.13125/) (2026)
- [An Empirical Study of Training Pixel-Space Text-to-Image Diffusion Models](https://mlsanity.com/paper/2608.16887/) (2026)
- [Compressing Image Style Training into a Single Model Forward](https://mlsanity.com/paper/2606.13809/) (2026)
- [Exploring the Performance Frontier of Compact Unified Image Generation Models](https://mlsanity.com/paper/2608.20334/) (2026)
- [GenFirst: Generation Before Reconstruction for Stable End-to-End Latent Generative Modeling](https://mlsanity.com/paper/2608.29335/) (2026)
- [WeGenBench: A Multidimensional Diagnostic Benchmark towards Text-to-Image Model Optimization](https://mlsanity.com/paper/2606.20100/) (2026)
- [Pixel-Space Diffusion Transformers](https://mlsanity.com/paper/2607.17585/) (2026)
- [The Second LoViF 2026 Challenge on Real-World All-in-One Image Restoration: Methods and Results](https://mlsanity.com/paper/2607.21118/) (2026)
- [PixelUMM: Encoder-Free Unified Image and Video Understanding and Generation](https://mlsanity.com/paper/2609.38597/) (2026)
- [Test-Time Curriculum for Open-Set AIGC Detection](https://mlsanity.com/paper/2608.00559/) (2026)
- [Energy-Guided Flow Matching](https://mlsanity.com/paper/2608.05811/) (2026)
- [MaskFlow: Precise, Consistent and Seamless Regional Image Editing](https://mlsanity.com/paper/2608.06929/) (2026)
- [WithEveryone: Unified Planning and Identity Grounding for Group Image Generation](https://mlsanity.com/paper/2608.20336/) (2026)
- [ReART: Reference-Guided Retrieval and Refinement for Emotion-Aware Art Generation](https://mlsanity.com/paper/2608.22329/) (2026)
- [Scalable Question-Centric Text-to-Image Evaluation: Reliable Ranking, Fine-Grained Diagnosis, and Cost-Aware Routing](https://mlsanity.com/paper/2608.24112/) (2026)
- [Multi2AV-Safety: Benchmarking Safety in Multimodal-to-Audio-Video Generation](https://mlsanity.com/paper/2608.26535/) (2026)
- [RASA: Disentangled Spatial-Motional Priors for Cross-Identity Character Animation](https://mlsanity.com/paper/2608.28219/) (2026)
- [EraseSAE: Surgical Concept Erasure in Text-to-Video Diffusion Models via Sparse Autoencoders](https://mlsanity.com/paper/2609.03629/) (2026)
- [Beyond Visual Quality: Evaluating Physical Consistency under Ego-Motion with EgoGenEval](https://mlsanity.com/paper/2609.11172/) (2026)
- [MindTopo: Can Foundation Models Reason in Topological Space?](https://mlsanity.com/paper/2609.11900/) (2026)
- [Beyond Natural Images: Rethinking AI-Generated Image Detection in Documents](https://mlsanity.com/paper/2609.14352/) (2026)
- [Moonworks Lunara: Modeling Artistic Intelligence](https://mlsanity.com/paper/2609.22272/) (2026)
- [CollageAttack: Exploiting Cross-Modal Alignment Flaws in T2I Models through Spatial Text Composition](https://mlsanity.com/paper/2609.38253/) (2026)
- [CAST: Causal Advantage-Structured Training with Spatially Grounded Compositional Rewards for Diffusion Models](https://mlsanity.com/paper/2609.39441/) (2026)
- [Do Generative Priors Align with Human Naturalness Perception?](https://mlsanity.com/paper/2610.09928/) (2026)
- [From Prompting to Composing: A Spatial Canvas Interface for Poster Generation](https://mlsanity.com/paper/2610.12230/) (2026)
- [Reasoning-Informed Visual Editing](https://mlsanity.com/paper/2610.12343/) (2026)
- [InfoVis: Unified Slide Image Generation and Editing](https://mlsanity.com/paper/2610.16784/) (2026)

## Abstract (arXiv)

The evolution of visual generative models has long been constrained by fragmented architectures relying on disjoint text encoders and external VAEs. In this report, we present HiDream-O1-Image, a natively unified generative foundation model via pixel-space Diffusion Transformer, that pioneers a paradigm shift from modular architectures to an end-to-end in-context visual generation engine. By mapping raw image pixels, text tokens, and task-specific conditions into a single shared token space, HiDream-O1-Image achieves a structural unification of multimodal inputs within an Unified Transformer (UiT) architecture. This native encoding paradigm eliminates the need for separate VAEs or disjoint pre-trained text encoders, allowing the model to treat diverse generation and editing tasks as a consistent in-context reasoning process. Extensive experiments show that HiDream-O1-Image excels across various generation tasks, including text-to-image generation, instruction-based editing, and subject-driven personalization. Notably, with only 8B parameters, HiDream-O1-Image (8B) achieves performance parity with or even surpasses established state-of-the-art models with significantly larger parameters (e.g., 27B Qwen-Image). Crucially, to validate the immense scalability of this paradigm, we successfully scale the architecture up to over 200B parameters. Experimental results demonstrate that this massive-scale version HiDream-O1-Image-Pro (200B+) unlocks unprecedented generative capabilities and superior performance, establishing new state-of-the-art benchmarks. Ultimately, HiDream-O1-Image highlights the immense potential of natively unified architectures and charts a highly scalable path toward next-generation multimodal AI.