HiDream-O1-Image: A Natively Unified Image Generative Foundation Model with Pixel-level Unified Transformer
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 disjoin...
| Authors | Qi Cai, Jingwen Chen, Chengmin Gao et al. |
|---|---|
| Primary category | cs.CV |
| Published | 11 May 2026 |
| Last updated at source | 11 May 2026 |
Connected in the knowledge graph
- HiDream-ai/HiDream-O1-Image-DevModel · cited by
- HiDream-ai/HiDream-O1-Image-Dev-2604Model · cited by
- HiDream-ai/HiDream-O1-ImageModel · cited by
Models and datasets referencing this paper
Silytics shows public metadata from arXiv and links to the original. Information may be incomplete or out of date. Last refreshed 17 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.