PromptEnhancer

PromptEnhancer-32B

README

License: apache-2.0

Model Details

Model Description

PromptEnhancerV2 is a specialized text-to-image prompt rewriting model that employs chain-of-thought reasoning to enhance user prompts.

Model type: Vision-Language Model for Prompt Enhancement
Language(s) (NLP): Chinese (zh), English (en)
License: Apache-2.0
Finetuned from model: Qwen/Qwen2.5-VL-32B-Instruct

Model Sources

Repository: https://github.com/ximinng/PromptEnhancer
Paper: https://arxiv.org/abs/2509.04545
Homepage: https://hunyuan-promptenhancer.github.io/

How to Get Started with the Model

1. Clone the repository::

bash
git clone https://github.com/ximinng/PromptEnhancer.git
cd PromptEnhancer
pip install -r requirements.txt

2. Model Download:

bash
huggingface-cli download PromptEnhancer/PromptEnhancer-32B --local-dir ./models/promptenhancer-32b

3. Use the model:

python
from inference.prompt_enhancer_v2 import PromptEnhancerV2

# Initialize the model
models_root_path = "./models/promptenhancer-32b"
enhancer = PromptEnhancerV2(models_root_path=models_root_path, device_map="auto")

# Enhance a prompt (Chinese or English)
user_prompt = "韩系插画风女生头像，粉紫色短发+透明感腮红，侧光渲染。"
enhanced_prompt = enhancer.predict(
    prompt_cot=user_prompt,
    device="cuda"
)

print("Enhanced:", enhanced_prompt)

Evaluation

The model is evaluated on the T2I-Keypoints-Eval dataset, which contains diverse text-to-image prompts across various categories and languages.

Citation

If you find this model useful, please consider citing:

BibTeX:

bibtex
@article{promptenhancer,
  title={PromptEnhancer: A Simple Approach to Enhance Text-to-Image Models via Chain-of-Thought Prompt Rewriting},
  author={Wang, Linqing and Xing, Ximing and Cheng, Yiji and Zhao, Zhiyuan and Donghao, Li and Tiankai, Hang and Zhenxi, Li and Tao, Jiale and Wang, QiXun and Li, Ruihuang and Chen, Comi and Li, Xin and Wu, Mingrui and Deng, Xinchi and Gu, Shuyang and Wang, Chunyu and Lu, Qinglin},
  journal={arXiv preprint arXiv:2509.04545},
  year={2025}
}

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Model Details

Model Provider

PromptEnhancer

Model Tree

Base

Qwen/Qwen2.5-VL-32B-Instruct

Fine-tuned

this model

Input Modalities

TextImage

Output Modalities

Text

Supported Functionality

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README

License: apache-2.0

Model Details

Model Description

PromptEnhancerV2 is a specialized text-to-image prompt rewriting model that employs chain-of-thought reasoning to enhance user prompts.

Model type: Vision-Language Model for Prompt Enhancement
Language(s) (NLP): Chinese (zh), English (en)
License: Apache-2.0
Finetuned from model: Qwen/Qwen2.5-VL-32B-Instruct

Model Sources

Repository: https://github.com/ximinng/PromptEnhancer
Paper: https://arxiv.org/abs/2509.04545
Homepage: https://hunyuan-promptenhancer.github.io/

How to Get Started with the Model

1. Clone the repository::

bash
git clone https://github.com/ximinng/PromptEnhancer.git
cd PromptEnhancer
pip install -r requirements.txt

2. Model Download:

bash
huggingface-cli download PromptEnhancer/PromptEnhancer-32B --local-dir ./models/promptenhancer-32b

3. Use the model:

python
from inference.prompt_enhancer_v2 import PromptEnhancerV2

# Initialize the model
models_root_path = "./models/promptenhancer-32b"
enhancer = PromptEnhancerV2(models_root_path=models_root_path, device_map="auto")

# Enhance a prompt (Chinese or English)
user_prompt = "韩系插画风女生头像，粉紫色短发+透明感腮红，侧光渲染。"
enhanced_prompt = enhancer.predict(
    prompt_cot=user_prompt,
    device="cuda"
)

print("Enhanced:", enhanced_prompt)

Evaluation

The model is evaluated on the T2I-Keypoints-Eval dataset, which contains diverse text-to-image prompts across various categories and languages.

Citation

If you find this model useful, please consider citing:

BibTeX:

bibtex
@article{promptenhancer,
  title={PromptEnhancer: A Simple Approach to Enhance Text-to-Image Models via Chain-of-Thought Prompt Rewriting},
  author={Wang, Linqing and Xing, Ximing and Cheng, Yiji and Zhao, Zhiyuan and Donghao, Li and Tiankai, Hang and Zhenxi, Li and Tao, Jiale and Wang, QiXun and Li, Ruihuang and Chen, Comi and Li, Xin and Wu, Mingrui and Deng, Xinchi and Gu, Shuyang and Wang, Chunyu and Lu, Qinglin},
  journal={arXiv preprint arXiv:2509.04545},
  year={2025}
}