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

• 🚀 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:

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

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}
}