Generative Patch-Nearest-Neighbor

With GPNN, you can generate a random sample from a given image in few seconds. For example:

GPNN can perform many other tasks, such as image generation, conditional inpainting, structural analogies, image retargeting, collage, and more. Currently, this implementation supports only the first three tasks.

python -m pip install -r requirements.txt

This code will run on cuda gpu if available. Running on cpu is by specifying '--not_cuda'.

If running this code on your machine is too exhausting, or you want to get quick results, you may:

• Generate smaller image by specifying '--out_size <int>'
• Use very fast approximate-nearest-neighbor method (faiss), by specifying '--faiss'. Install faiss by the following:

python -m pip install faiss-gpu

Notice that this method is different from the normalized distance matrix presented in the original paper.

To generate a random sample, run:

python random_sample.py -in <image_path>

You may control the variation degree of the new sample by adjusting the noise level '--sigma'. Default is 0.75.

To generate a new image where the content of one image is constructed into the structure of another image, run:

python structural_analogies.py -a <first_image_path> -b <second_image_path>

where the first image is the content and the second is the structure.

To generate color-guided inpainting recovery, use an inpainted image where the inpainted area color is similar to your target recovery color (in the same image):

python inpainting.py -in <image_path> -m <mask_path>

the mask is in the following format - ones in the pixels where the inpainted area is, and zeros elsewhere.

Generated images will be saved in './output' folder. To change that, specify '-out <dir_path>'.

In each of the tasks, there are many parameters to adjust. A full list may be obtained by specifying '--help'.