Optimized for efficient inference with reduced memory footprint. Loading a checkpoint is bit-identical to quantizing the dense bf16 transformer on the fly, so quality matches the runtime int8/fp8 Dtype path exactly. Same-seed LPIPS vs the bf16 model (lower is better): 0.291 INT8, 0.278 FP8 (50-step suite means; all 28 per-case pairs pass per scheme).
Samples
Prompt: "cute sloth typing on a computer"
INT8
INT8
FP8
FP8
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.
✨ 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.
Then you can run the inference scripts to generate images:
python
1# For full model inference2python ./inference.py --model_type full
34# For distilled dev model inference5python ./inference.py --model_type dev
67# For distilled fast model inference8python ./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
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 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
bibtex
1@article{hidreami1technicalreport,
2 title={HiDream-I1: A High-Efficient Image Generative Foundation Model with Sparse Diffusion Transformer},
3 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},
4 journal={arXiv preprint arXiv:2505.22705},
5 year={2025}
6}
Quantized transformer checkpoints (this repo)
This repo adds pre-quantized diffusion transformer checkpoints for HiDream-ai/HiDream-I1-Full,
built with torchao dynamic activation quantization from the dense bf16 transformer. The
official model card above is unchanged from the source repo.
Files:
HiDream-I1-Full-INT8.pt (19.7 GB)
HiDream-I1-Full-FP8.pt (17.1 GB)
Details:
int8: Int8DynamicActivationInt8WeightConfig (per-token activation, per-channel weight, torch._int_mm).
The routed MoE expert Linears quantize cleanly: they only ever see the concatenated
image+text token stream, so the torch._int_mm M>16 minimum never binds.
fp8: Float8DynamicActivationFloat8WeightConfig with PerRow granularity (e4m3, torch._scaled_mm).
The loader must floor the dynamic activation scale (activation_value_lb=1e-12 on torchao 0.13+)
so all-zero activation token rows cannot produce a zero scale.
Loading a checkpoint is bit-identical to quantizing the dense bf16 transformer on the fly;
the checkpoint skips the dense load and quantize step.
Validated against same-seed dense bf16 renders over 28 cases per scheme (SSIM, LPIPS-vgg,
CLIP delta, non-finite and black-frame checks): all 28 per-case pairs pass per scheme; INT8 LPIPS mean 0.291 / SSIM 0.877, FP8 LPIPS mean 0.278 / SSIM 0.852 (50-step trajectory-divergence band; CLIP delta means 0.007-0.008). INT8 additionally verified bit-identical to on-the-fly quantize across all 1615 state dict tensors (1073 quantized, max abs diff 0.0). torch 2.10 and torchao 0.17.
Samples
Prompt: "cute sloth typing on a computer" (1024x1024, family default steps/guidance, seeds 0-2).
int8
fp8
Pre-cast fp8 text encoder (this repo)
HiDream-I1-Full-text_encoder_4-FP8.pt (8.6 GB) is the pipeline's text_encoder_4 (LlamaForCausalLM,
assembled from the standalone unsloth/Meta-Llama-3.1-8B-Instruct mirror the pipeline expects) with the
layerwise fp8 storage cast Unsloth Studio applies at load time, saved pre-cast:
Loading it is bit-identical to downloading the dense encoder and casting on load
(verified tensor for tensor: 291 tensors, 225 cast to fp8 storage).
Cuts the text-encoder download from 16.1 GB to 8.6 GB and halves the fp8 pipeline
assembly time (24.3 s vs 48.0 s measured on B200).
Plain-tensor state dict: loads with torch.load(weights_only=True). Metadata records
scheme fp8, component text_encoder_4, base unsloth/Meta-Llama-3.1-8B-Instruct.
