https://github.com/fork123aniket/text-to-image-synthesis-using-stackgans
Implementation of StackGANs for text-to-image generation in Tensorflow
https://github.com/fork123aniket/text-to-image-synthesis-using-stackgans
gans generative-adversarial-network generative-model image-generation keras-neural-networks keras-tensorflow stackgan tensorflow text-to-image-generation text-to-image-synthesis
Last synced: 2 months ago
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Implementation of StackGANs for text-to-image generation in Tensorflow
- Host: GitHub
- URL: https://github.com/fork123aniket/text-to-image-synthesis-using-stackgans
- Owner: fork123aniket
- License: mit
- Created: 2022-11-01T10:03:03.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2023-05-01T12:03:38.000Z (about 3 years ago)
- Last Synced: 2025-01-16T06:25:23.145Z (over 1 year ago)
- Topics: gans, generative-adversarial-network, generative-model, image-generation, keras-neural-networks, keras-tensorflow, stackgan, tensorflow, text-to-image-generation, text-to-image-synthesis
- Language: Python
- Homepage:
- Size: 577 KB
- Stars: 3
- Watchers: 2
- Forks: 0
- Open Issues: 1
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Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# StackGANs-based Text-to-Image Generation
This repository provides Stage-wise implementation of StackGANs to produce photo-realistic images from given text. The Stage-1 GAN sketches the primitive shape and colors of the object based on the given text description, yielding Stage-1 low-resolution images. On the other hand, The Stage-2 GAN takes Stage-1 results and text descriptions as inputs and generates high-resolution images with photo-realistic details. Moreover, it is able to rectify defects in Stage-1 results and add compelling details with the refinement process.
## Requirements
- `Python`
- `tensorflow`
- `keras`
- `numpy`
- `pandas`
- `PIL`
- `matplotlib`
## Usage
### Data
The training of StackGAN has been performed on CUB dataset. CUB contains 200 bird species with 11,788 images. Since 80% of birds in this dataset have object-image size ratios of less than 0.5, as a pre-processing step, cropping has been executed for all images to ensure that bounding boxes of birds have greater-than-0.75 object-image size ratios. The dataset can either be downloaded from [***here***](https://drive.google.com/open?id=0B3y_msrWZaXLT1BZdVdycDY5TEE) or can be obtained by running `wget http://www.vision.caltech.edu/visipedia-data/CUB-200-2011/CUB_200_2011.tgz` command.
### Training and Testing
- To train ***Stage-1 StackGAN*** : run `Train_Stage_1_GAN.py`
- To test ***Stage-2 StackGAN*** : run `Train_Stage_2_GAN.py`
- To see the ***Stage-1 StackGAN*** and ***Stage-2 StackGAN*** implementations, please check `Stage_1_GAN.py` and `Stage_2_GAN.py` respectively.
- All hyperparameters to control training and testing of ***StackGANs*** are provided in `Train_Stage_1_GAN.py` and `Train_Stage_2_GAN.py` files.
## Results
The eventual outcomes of both ***Stage-1 StackGAN*** and ***Stage-2 StackGAN*** can be seen against each given input text in the following attached image:-
