This is a PyTorch implementaion for a tool called PixelBrush that generates an artistic image using a given description. This implementation based on Jaile's work and a PyTorch implemention of Generative Adversarial Text-to-Image Synthesis paper.

• pytorch
• visdom
• h5py
• PIL
• numpy
• skip-thoughts

We used the images from The Oxford Paintings Dataset. We creating descriptions using Neuraltalk2.

The images names and descriptions we use are in data/oxford/vis_oxford.json file.

1. Create text embedding using skip-thoughts, and the file create_embedding_oxford_uniskip_biskip.py. (You can skip this step by using the files in data/oxford).
2. Download the oxford dataset.
3. Create hd5 dataset using convert_oxford_to_hd5_script.py (You can skip this step by downloading hd5 files from drive).
4. run runtime.py

1. Download hd5 files from drive.
2. run runtime.py with the following parameters:

• type: choose an architecture (simple | normal | deep)
• inference = True
• split = 10
• pre_trained_gen = "pre-trained-models/{}_200.pth"