HiDream-I1

HiDream-I1

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

• July 16, 2025: We've open-sourced the updated image editing model HiDream-E1-1.
• 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!.
• April 11, 2025: HiDream is now officially supported in the diffusers library. Check out the docs here.
• April 8, 2025: We've launched a Hugging Face Space for HiDream-I1-Dev. Experience our model firsthand at https://huggingface.co/spaces/HiDream-ai/HiDream-I1-Dev!
• April 7, 2025: We've open-sourced the text-to-image model HiDream-I1.

Models

We offer both the full version and distilled models. For more information about the models, please refer to the link under Usage.

Name Script Inference Steps HuggingFace repo HiDream-I1-Full inference.py 50 HiDream-I1-Full HiDream-I1-Dev inference.py 28 HiDream-I1-Dev HiDream-I1-Fast inference.py 16 HiDream-I1-Fast

Quick Start

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

pip install -r requirements.txt
pip install -U flash-attn --no-build-isolation

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 try to automatically download meta-llama/Llama-3.1-8B-Instruct model files. You need to agree to the license of the Llama model on your HuggingFace account and login using huggingface-cli login in order to use the automatic downloader.

Gradio Demo

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

python gradio_demo.py 

Inference with Diffusers

We recommend install Diffusers from source for better compatibility.

pip install git+https://github.com/huggingface/diffusers.git

Then you can inference HiDream-I1 with the following command:

…

Evaluation Metrics

DPG-Bench

Model Overall Global Entity Attribute Relation Other PixArt-alpha 71.11 74.97 79.32 78.60 82.57 76.96 SDXL 74.65 83.27 82.43 80.91 86.76 80.41 DALL-E 3 83.50 90.97 89.61 88.39 90.58 89.83 Flux.1-dev 83.79 85.80 86.79 89.98 90.04 89.90 SD3-Medium 84.08 87.90 91.01 88.83 80.70 88.68 Janus-Pro-7B 84.19 86.90 88.90 89.40 89.32 89.48 CogView4-6B 85.13 83.85 90.35 91.17 91.14 87.29 HiDream-I1 85.89 76.44 90.22 89.48 93.74 91.83

GenEval

Model Overall Single Obj. Two Obj. Counting Colors Position Color attribution SDXL 0.55 0.98 0.74 0.39 0.85 0.15 0.23 PixArt-alpha 0.48 0.98 0.50 0.44 0.80 0.08 0.07 Flux.1-dev 0.66 0.98 0.79 0.73 0.77 0.22 0.45 DALL-E 3 0.67 0.96 0.87 0.47 0.83 0.43 0.45 CogView4-6B 0.73 0.99 0.86 0.66 0.79 0.48 0.58 SD3-Medium 0.74 0.99 0.94 0.72 0.89 0.33 0.60 Janus-Pro-7B 0.80 0.99 0.89 0.59 0.90 0.79 0.66 HiDream-I1 0.83 1.00 0.98 0.79 0.91 0.60 0.72

HPSv2.1 benchmark

Model Averaged Animation Concept-art Painting Photo Stable Diffusion v2.0 26.38 27.09 26.02 25.68 26.73 Midjourney V6 30.29 32.02 30.29 29.74 29.10 SDXL 30.64 32.84 31.36 30.86 27.48 Dall-E3 31.44 32.39 31.09 31.18 31.09 SD3 31.53 32.60 31.82 32.06 29.62 Midjourney V5 32.33 34.05 32.47 32.24 30.56 CogView4-6B 32.31 33.23 32.60 32.89 30.52 Flux.1-dev 32.47 33.87 32.27 32.62 31.11 stable cascade 32.95 34.58 33.13 33.29 30.78 HiDream-I1 33.82 35.05 33.74 33.88 32.61

License

The code in this repository and the HiDream-I1 models are licensed under MIT License.

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}
}