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.

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:

import torch
from transformers import PreTrainedTokenizerFast, LlamaForCausalLM
from diffusers import HiDreamImagePipeline
tokenizer_4 = PreTrainedTokenizerFast.from_pretrained("meta-llama/Meta-Llama-3.1-8B-Instruct")
text_encoder_4 = LlamaForCausalLM.from_pretrained(
    "meta-llama/Meta-Llama-3.1-8B-Instruct",
    output_hidden_states=True,
    output_attentions=True,
    torch_dtype=torch.bfloat16,
)

pipe = HiDreamImagePipeline.from_pretrained(
    "HiDream-ai/HiDream-I1-Full",  # "HiDream-ai/HiDream-I1-Dev" | "HiDream-ai/HiDream-I1-Fast"
    tokenizer_4=tokenizer_4,
    text_encoder_4=text_encoder_4,
    torch_dtype=torch.bfloat16,
)

pipe = pipe.to('cuda')

image = pipe(
    'A cat holding a sign that says "HiDream.ai".',
    height=1024,
    width=1024,
    guidance_scale=5.0,  # 0.0 for Dev&Fast
    num_inference_steps=50,  # 28 for Dev and 16 for Fast
    generator=torch.Generator("cuda").manual_seed(0),
).images[0]
image.save("output.png")

Evaluation Metrics

DPG-Bench

GenEval

HPSv2.1 benchmark

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}
}