style-transfer-pytorch VS Neural-Tile

Compare style-transfer-pytorch vs Neural-Tile and see what are their differences.

Neural-Tile

A better tiling script for Neural-Style (by ProGamerGov)
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style-transfer-pytorch Neural-Tile
6 1
468 37
- -
3.9 0.0
12 months ago over 7 years ago
Python Shell
MIT License -
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

style-transfer-pytorch

Posts with mentions or reviews of style-transfer-pytorch. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-07-17.

Neural-Tile

Posts with mentions or reviews of Neural-Tile. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-01-30.

What are some alternatives?

When comparing style-transfer-pytorch and Neural-Tile you can also consider the following projects:

cupscale - Image Upscaling GUI based on ESRGAN

flownet2-pytorch - Pytorch implementation of FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks

prism - High Resolution Style Transfer in PyTorch with Color Control and Mixed Precision :art:

neural-style-pt - PyTorch implementation of neural style transfer algorithm

pytorch-neural-style-transfer - Reconstruction of the original paper on neural style transfer (Gatys et al.). I've additionally included reconstruction scripts which allow you to reconstruct only the content or the style of the image - for better understanding of how NST works.

pytorch-AdaIN - Unofficial pytorch implementation of 'Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization' [Huang+, ICCV2017]