Awesome
Introduction
This is the source code of our TOMM 2023 paper "LFR-GAN: Local Feature Refinement based Generative Adversarial Network for Text-to-Image Generation". Please cite the following paper if you use our code.
Zijun Deng, Xiangteng He and Yuxin Peng*, "LFR-GAN: Local Feature Refinement based Generative Adversarial Network for Text-to-Image Generation", ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), 2023.
Dependencies
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Python 3.7
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CUDA 1.11.0
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PyTorch 1.7.1
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gcc 7.5.0
Run the following commands to install the same dependencies as our experiments.
conda install -c pytorch pytorch=1.7.1 torchvision cudatoolkit=11.0
pip install git+https://github.com/openai/CLIP.git
pip install -r requirements.txt
Data Preparation
Download the image data, pretrained models, and parser tool that we used from the link (password: 2fsx) and unzip them to corresponding folders.
Generate Image
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For bird images:
python code/main.py --cfg=code/cfg/eval_bird.yml --lafite=pretrained_models/birds.pkl
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For flower images:
python code/main.py --cfg=code/cfg/eval_flower.yml --lafite=pretrained_models/flower.pkl
Train Model
You can also train the models by yourself.
# pretrained_models/bird_netG_epoch_700.pth
python code/main_DMGAN.py --cfg code/cfg/bird_DMGAN.yml --gpu 0
# pretrained_models/flower_netG_epoch_325.pth
python code/main_DMGAN.py --cfg code/cfg/flower_DMGAN.yml --gpu 0
For the training of Lafite models, please refer to https://github.com/drboog/Lafite.
Evaluate
For the evaluation, please refer to Lafite.
For any questions, feel free to contact us (dengzijun57@gmail.com).
Welcome to our Laboratory Homepage for more information about our papers, source codes, and datasets.