HiDream-I1-Fast

Image: HiDream-I1 Demo

HiDream-I1 is a new open-source image generative foundation model with 17B parameters that achieves state-of-the-art image generation quality within seconds.

For more features and to experience the full capabilities of our product, please visit https://vivago.ai/.

Project Updates#

• 📝 May 28, 2025: We've released our technical report HiDream-I1: A High-Efficient Image Generative Foundation Model with Sparse Diffusion Transformer.
• 🚀 April 28, 2025: We've open-sourced the instruction-based-image-editing model HiDream-E1-Full. Experience at https://huggingface.co/spaces/HiDream-ai/HiDream-E1-Full!.

Key Features#

• ✨ Superior Image Quality - Produces exceptional results across multiple styles including photorealistic, cartoon, artistic, and more. Achieves state-of-the-art HPS v2.1 score, which aligns with human preferences.
• 🎯 Best-in-Class Prompt Following - Achieves industry-leading scores on GenEval and DPG benchmarks, outperforming all other open-source models.
• 🔓 Open Source - Released under the MIT license to foster scientific advancement and enable creative innovation.
• 💼 Commercial-Friendly - Generated images can be freely used for personal projects, scientific research, and commercial applications.

Quick Start#

Please make sure you have installed Flash Attention. We recommend CUDA version 12.4 for the manual installation.

pip install -r requirements.txt

Clone the GitHub repo:

git clone https://github.com/HiDream-ai/HiDream-I1

Then you can run the inference scripts to generate images:

# For full model inference
python ./inference.py --model_type full

# For distilled dev model inference
python ./inference.py --model_type dev

# For distilled fast model inference
python ./inference.py --model_type fast

Note: The inference script will automatically download meta-llama/Meta-Llama-3.1-8B-Instruct model files. If you encounter network issues, you can download these files ahead of time and place them in the appropriate cache directory to avoid download failures during inference.

Gradio Demo#

We also provide a Gradio demo for interactive image generation. You can run the demo with:

python gradio_demo.py 

Evaluation Metrics#

DPG-Bench#

GenEval#

HPSv2.1 benchmark#

License Agreement#

The Transformer models in this repository are licensed under the MIT License. The VAE is from FLUX.1 [schnell], and the text encoders from google/t5-v1_1-xxl and meta-llama/Meta-Llama-3.1-8B-Instruct. Please follow the license terms specified for these components. You own all content you create with this model. You can use your generated content freely, but you must comply with this license agreement. You are responsible for how you use the models. Do not create illegal content, harmful material, personal information that could harm others, false information, or content targeting vulnerable groups.

Acknowledgements#

• The VAE component is from FLUX.1 [schnell], licensed under Apache 2.0.
• The text encoders are from google/t5-v1_1-xxl (licensed under Apache 2.0) and meta-llama/Meta-Llama-3.1-8B-Instruct (licensed under the Llama 3.1 Community License Agreement).

Citation#

@article{hidreami1technicalreport,
  title={HiDream-I1: A High-Efficient Image Generative Foundation Model with Sparse Diffusion Transformer},
  author={Cai, Qi and Chen, Jingwen and Chen, Yang and Li, Yehao and Long, Fuchen and Pan, Yingwei and Qiu, Zhaofan and Zhang, Yiheng and Gao, Fengbin and Xu, Peihan and others},
  journal={arXiv preprint arXiv:2505.22705},
  year={2025}
}