HiDream-I1: An Open-Source High-Efficient Image Generative Foundation Model
Cai Qi, Yehao Li, Yingwei Pan, Ting Yao, Tao Mei
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 varied model capabilities, we provide HiDream-I1 in three variants: HiDream-I1-Full, HiDream-I1-Dev, and HiDream-I1-Fast. Furthermore, we go beyond the typical text-to-image generation and remould HiDream-I1 with additional image conditions to perform precise instruction-based editing on given images, yielding a new image editing model namely HiDream-E1. We have open-sourced all the codes and model weights of HiDream-I1 and HiDream-E1: https://github.com/HiDream-ai/HiDream-I1 and https://github.com/HiDream-ai/HiDream-E1. These models quickly gained strong traction in the community, ranking among the top globally on the Hugging Face Models Trending list within just one week of launch. In under a month, it surpassed 280,000 downloads and has been officially integrated into the Diffusers library. It is now widely adopted by leading community tools and products, including ComfyUI, Recraft, WaveSpeedAI, fal.ai, and Pruna AI - reflecting the model's growing impact across the open-source AI ecosystem.