https://github.com/vishal-v/stackgan
TensorFlow implementation of "Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks" by Han Zhang, et al.
https://github.com/vishal-v/stackgan
conditioning-augmentation cub-200 gans generative-adversarial-network keras stack-gan tensorflow-2
Last synced: about 1 month ago
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TensorFlow implementation of "Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks" by Han Zhang, et al.
- Host: GitHub
- URL: https://github.com/vishal-v/stackgan
- Owner: Vishal-V
- License: mit
- Created: 2019-08-22T18:25:20.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2020-04-01T08:55:26.000Z (about 5 years ago)
- Last Synced: 2025-03-30T20:33:53.727Z (2 months ago)
- Topics: conditioning-augmentation, cub-200, gans, generative-adversarial-network, keras, stack-gan, tensorflow-2
- Language: Python
- Homepage: https://arxiv.org/abs/1612.03242
- Size: 202 KB
- Stars: 34
- Watchers: 2
- Forks: 9
- Open Issues: 5
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# StackGAN
### Text to Photo-Realistic Image Synthesis
---
#### Dependencies
```
tensorflow==2.1.0
numpy==1.16.4
absl_py==0.7.0
matplotlib==2.2.3
pandas==0.23.4
Pillow==6.1.0
```
#### Downloads
- To download all the dependencies, simply execute
```
pip install -r requirements.txt
```
- To download the CUB 200 dataset, simply execute the `data_download.py` file
```
python data_download.py
```
- Download the Char-RNN-CNN embeddings from this link: [**download link**](https://drive.google.com/file/d/0B3y_msrWZaXLT1BZdVdycDY5TEE) and unzip it in place.
```
unzip birds.zip
```
#### Training
- The `model.py` file contains the bare minimum code to run the stage 1 and stage 2 architecture. It automatically stores the weights after the specified/default number of epochs have completed. Note that the weights will be stored at the same directory level as `model.py`.
```
python model.py
```
#### Architecture
- Stage 1
- Text Encoder Network
- Text description to a 1024 dimensional text embedding
- Learning Deep Representations of Fine-Grained Visual Descriptions [Arxiv Link](https://arxiv.org/abs/1605.05395)
- Conditioning Augmentation Network
- Adds randomness to the network
- Produces more image-text pairs
- Generator Network
- Discriminator Network
- Embedding Compressor Network
- Outputs a 64x64 image
#
- Stage 2
- Text Encoder Network
- Conditioning Augmentation Network
- Generator Network
- Discriminator Network
- Embedding Compressor Network
- Outputs a 256x256 image
---
#### Reference Papers
1. **StackGAN: Text to photo-realistic image synthesis** [[Arxiv Link](https://arxiv.org/pdf/1612.03242.pdf)]
2. **Improved Techniques for Training GANs** [[Arxiv Link](https://arxiv.org/pdf/1606.03498.pdf)]
3. **Generative Adversarial Text to Image Synthesis** [[Arxiv Link](https://arxiv.org/pdf/1605.05396.pdf)]
4. **Learning Deep Representations of Fine-Grained Visual Descriptions** [[Arxiv Link](https://arxiv.org/abs/1605.05395)]
---
#### Note
This is the code I have submitted to TensorFlow for Google Summer of Code. Hence the attributions and the License is for "TensorFlow Authors" and not "Vishal V". This code is under the MIT License.