PromptEnhancer

PromptEnhancer-Img2img-Edit

README

License: apache-2.0

Model Details

Model Description

PromptEnhancerV2 (Img2Img Edit) is a specialized vision-language prompt rewriting model that employs chain-of-thought reasoning to enhance user editing instructions with visual context.

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-Img2img-Edit --local-dir ./models/promptenhancer-img2img-edit

3. Use the model:

python
from inference.prompt_enhancer_img2img import PromptEnhancerImg2Img

# Initialize the model
models_root_path = "./models/promptenhancer-img2img-edit"
enhancer = PromptEnhancerImg2Img(model_path=models_root_path, device_map="auto")

# Enhance an editing instruction with image context (Chinese or English)
edit_instruction = "去掉图片底部的水印，保留主体不变"
image_path = "./examples/sample_image.png"

enhanced_prompt = enhancer.predict(
    edit_instruction=edit_instruction,
    image_path=image_path,
    temperature=0.1,
    top_p=0.9,
    max_new_tokens=2048
)

print("Enhanced:", enhanced_prompt)

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 (Img2Img Edit) is a specialized vision-language prompt rewriting model that employs chain-of-thought reasoning to enhance user editing instructions with visual context.

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-Img2img-Edit --local-dir ./models/promptenhancer-img2img-edit

3. Use the model:

python
from inference.prompt_enhancer_img2img import PromptEnhancerImg2Img

# Initialize the model
models_root_path = "./models/promptenhancer-img2img-edit"
enhancer = PromptEnhancerImg2Img(model_path=models_root_path, device_map="auto")

# Enhance an editing instruction with image context (Chinese or English)
edit_instruction = "去掉图片底部的水印，保留主体不变"
image_path = "./examples/sample_image.png"

enhanced_prompt = enhancer.predict(
    edit_instruction=edit_instruction,
    image_path=image_path,
    temperature=0.1,
    top_p=0.9,
    max_new_tokens=2048
)

print("Enhanced:", enhanced_prompt)

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}
}