StyleSwin VS Deep-Exemplar-based-Video-Colorization

Compare StyleSwin vs Deep-Exemplar-based-Video-Colorization and see what are their differences.

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StyleSwin Deep-Exemplar-based-Video-Colorization
1 4
462 317
1.3% -
0.0 0.0
about 1 year ago over 1 year ago
Python Python
MIT License 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.

StyleSwin

Posts with mentions or reviews of StyleSwin. We have used some of these posts to build our list of alternatives and similar projects.

Deep-Exemplar-based-Video-Colorization

Posts with mentions or reviews of Deep-Exemplar-based-Video-Colorization. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-10-19.

What are some alternatives?

When comparing StyleSwin and Deep-Exemplar-based-Video-Colorization you can also consider the following projects:

pix2pixHD - Synthesizing and manipulating 2048x1024 images with conditional GANs

CycleGAN - Software that can generate photos from paintings, turn horses into zebras, perform style transfer, and more.

pytorch-CycleGAN-and-pix2pix - Image-to-Image Translation in PyTorch

Few-Shot-Patch-Based-Training - The official implementation of our SIGGRAPH 2020 paper Interactive Video Stylization Using Few-Shot Patch-Based Training

anycost-gan - [CVPR 2021] Anycost GANs for Interactive Image Synthesis and Editing

mmagic - OpenMMLab Multimodal Advanced, Generative, and Intelligent Creation Toolbox. Unlock the magic 🪄: Generative-AI (AIGC), easy-to-use APIs, awsome model zoo, diffusion models, for text-to-image generation, image/video restoration/enhancement, etc.

Anime2Sketch - A sketch extractor for anime/illustration.

OASIS - Official implementation of the paper "You Only Need Adversarial Supervision for Semantic Image Synthesis" (ICLR 2021)

ArtGAN - ArtGAN + WikiArt: This work presents a series of new approaches to improve GAN for conditional image synthesis and we name the proposed model as “ArtGAN”.

TraVeLGAN_with_perceptual_loss - The implementation code of Thesis project which entitled "Photo-to-Emoji Transformation with TraVeLGAN and Perceptual Loss" as a final project in my master study.

TFill - [CVPR 2022]: Bridging Global Context Interactions for High-Fidelity Image Completion

HyperGAN - Composable GAN framework with api and user interface