UDiffText - Enhancing Text Synthesis Using Character-aware Models

UDiffText: Innovating Text Synthesis in Images

UDiffText: A Unified Framework for High-quality Text Synthesis in Arbitrary Images offers a revolutionary approach to text synthesis within both synthetic and real-world images. This advanced framework integrates character-aware diffusion models, opening up possibilities for various applications, including scene text editing, arbitrary text generation, and precise text-to-image (T2I) generation.

Project Highlights

UDiffText stands out for its ability to seamlessly synthesize text that appears accurate and harmonious within any given image. This feature is crucial for enhancing tasks such as editing texts in scenes, generating new text within images, and creating high-quality text-to-image translations. The framework is the culmination of cutting-edge research accepted at ECCV2024.

Installation Process

Clone the Repository: Begin by cloning the UDiffText repository from GitHub.
```
git clone https://github.com/ZYM-PKU/UDiffText.git
cd UDiffText
```

Setting Up Environment: Create a new Python environment and install the necessary packages.

conda create -n udiff python=3.11
conda activate udiff
pip install torch==2.1.1 torchvision==0.16.1 --index-url https://download.pytorch.org/whl/cu121
pip install -r requirements.txt

Organize Checkpoints: Establish a directory for checkpoints to store various model components, including AutoEncoders, encoders, and pretrained models.

Training the Framework

Data Preparation: Set up directories for datasets like LAION-OCR, ICDAR13, TextSeg, and SynthText on your disk. Each dataset has specific configuration files to align with their respective structures.
Character-level Encoder Training: Configure pre-training parameters and execute the training script to prepare the character-level encoder.
```
python pretrain.py
```
Model Training: Download the pretrained model and configure training paths to initiate UDiffText model training.
```
python train.py
```

Performance Evaluation

To evaluate UDiffText, users can download checkpoints, set appropriate configuration parameters, and run evaluation scripts to validate performance on datasets.

python test.py

Demo Experience

Users can interact with UDiffText via a local demo by executing the demo script or exploring the online demo provided by Hugging Face.

python demo.py

Access online demo

Acknowledgements and References

The development of UDiffText is supported by publicly available datasets like LAION-OCR. The project leverages open-source resources from Stable Diffusion models, embedded in character-aware diffusion frameworks.

A formal citation is available for those referencing the framework in academic and research contexts. The project is a testament to the ongoing efforts in integrating text synthesis capabilities into diverse imaging contexts, enhancing digital text interaction capabilities.