PITI VS OASIS

Compare PITI vs OASIS and see what are their differences.

PITI

PITI: Pretraining is All You Need for Image-to-Image Translation (by PITI-Synthesis)
Computer Vision image-generation image-synthesis image-to-image-translation pretraining
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OASIS

Official implementation of the paper "You Only Need Adversarial Supervision for Semantic Image Synthesis" (ICLR 2021) (by boschresearch)
semantic-image-synthesis Gan image-to-image-translation Computer Vision multi-modal generative-adversarial-networks Deep Learning Pytorch image-generation label-to-image-translation oasis iclr2021 Machine Learning bcai
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PITI OASIS
1 1
471 317
- 2.5%
2.0 10.0
11 months ago over 1 year ago
Python Python
MIT License GNU Affero General Public License v3.0
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.

PITI

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

OASIS

Posts with mentions or reviews of OASIS. We have used some of these posts to build our list of alternatives and similar projects.
  • StyleGAN-NADA: Blind Training and Other Wonders
    1 project | dev.to | 7 Dec 2022
    Conclusion So this is how StyleGAN-NADA, a CLIP-guided zero-shot method for Non-Adversarial Domain Adaptation of image generators, works. Although the StyleGAN-NADA is focused on StyleGAN, it can be applied to other generative architectures such as OASIS and many others.

What are some alternatives?

When comparing PITI and OASIS you can also consider the following projects:

cycle-diffusion - [ICCV 2023] A latent space for stochastic diffusion models

Deep-Exemplar-based-Video-Colorization - The source code of CVPR 2019 paper "Deep Exemplar-based Video Colorization".

concept-ablation - Ablating Concepts in Text-to-Image Diffusion Models (ICCV 2023)

Keras-GAN - Keras implementations of Generative Adversarial Networks.

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

AdamP - AdamP: Slowing Down the Slowdown for Momentum Optimizers on Scale-invariant Weights (ICLR 2021)

pix2pixHD - Synthesizing and manipulating 2048x1024 images with conditional GANs

clean-fid - PyTorch - FID calculation with proper image resizing and quantization steps [CVPR 2022]

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

Anime2Sketch - A sketch extractor for anime/illustration.

VLDet - [ICLR 2023] PyTorch implementation of VLDet （https://arxiv.org/abs/2211.14843）

PITI vs cycle-diffusion OASIS vs Deep-Exemplar-based-Video-Colorization PITI vs concept-ablation OASIS vs Keras-GAN OASIS vs pytorch-CycleGAN-and-pix2pix OASIS vs AdamP OASIS vs pix2pixHD OASIS vs clean-fid OASIS vs anycost-gan OASIS vs Anime2Sketch OASIS vs VLDet