HiDream-I1: A High-Efficient Image Generative Foundation Model with Sparse Diffusion Transformer
Qi Cai, Jingwen Chen, Yang Chen, Yehao Li, Fuchen Long, Yingwei Pan, Zhaofan Qiu, Yiheng Zhang, Fengbin Gao, Peihan Xu, Yimeng Wang, Kai Yu, Wenxuan Chen, Ziwei Feng, Zijian Gong, Jianzhuang Pan, Yi Peng, Rui Tian, Siyu Wang, Bo Zhao, Ting Yao, Tao Mei

TL;DR
HiDream-I1 is a high-efficiency, 17-billion-parameter image generative model utilizing a sparse Diffusion Transformer architecture, enabling fast, high-quality image generation and editing with multiple variants for flexible use.
Contribution
The paper introduces HiDream-I1, a novel sparse Diffusion Transformer-based model with dynamic MoE architecture, offering state-of-the-art image quality with reduced computational cost and inference time.
Findings
Achieves state-of-the-art image quality within seconds.
Provides multiple model variants for flexible accessibility.
Enables precise instruction-based image editing.
Abstract
Recent advancements in image generative foundation models have prioritized quality improvements but often at the cost of increased computational complexity and inference latency. To address this critical trade-off, we introduce HiDream-I1, a new open-source image generative foundation model with 17B parameters that achieves state-of-the-art image generation quality within seconds. HiDream-I1 is constructed with a new sparse Diffusion Transformer (DiT) structure. Specifically, it starts with a dual-stream decoupled design of sparse DiT with dynamic Mixture-of-Experts (MoE) architecture, in which two separate encoders are first involved to independently process image and text tokens. Then, a single-stream sparse DiT structure with dynamic MoE architecture is adopted to trigger multi-model interaction for image generation in a cost-efficient manner. To support flexiable accessibility with…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
- 🤗HiDream-ai/HiDream-I1-Devmodel· 593 dl· ♡ 171593 dl♡ 171
- 🤗HiDream-ai/HiDream-E1-Fullmodel· 122 dl· ♡ 209122 dl♡ 209
- 🤗HiDream-ai/HiDream-I1-Fullmodel· 30k dl· ♡ 98830k dl♡ 988
- 🤗HiDream-ai/HiDream-I1-Fastmodel· 55k dl· ♡ 10355k dl♡ 103
- 🤗HiDream-ai/HiDream-E1-1model· 542 dl· ♡ 208542 dl♡ 208
- 🤗RedbeardNZ/HiDream-I1-Fastmodel
- 🤗RedbeardNZ/HiDream-I1-Fullmodel· 1 dl1 dl
- 🤗RedbeardNZ/HiDream-I1-Devmodel
- 🤗RedbeardNZ/HiDream-E1-1model
- 🤗RedbeardNZ/HiDream-E1-Fullmodel· 11 dl11 dl
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsGenerative Adversarial Networks and Image Synthesis · Computer Graphics and Visualization Techniques · Digital Humanities and Scholarship
MethodsAttention Is All You Need · Linear Layer · Mixture of Experts · Dense Connections · Softmax · Diffusion · Position-Wise Feed-Forward Layer · Absolute Position Encodings · Label Smoothing · Multi-Head Attention
