SDMetrics VS StyleFlow

Compare SDMetrics vs StyleFlow and see what are their differences.

SDMetrics

Metrics to evaluate quality and efficacy of synthetic datasets. (by sdv-dev)

StyleFlow

StyleFlow: Attribute-conditioned Exploration of StyleGAN-generated Images using Conditional Continuous Normalizing Flows (ACM TOG 2021) (by RameenAbdal)
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SDMetrics StyleFlow
2 6
192 2,389
2.6% -
8.9 0.0
8 days ago about 1 year ago
Python Python
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.

SDMetrics

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

StyleFlow

Posts with mentions or reviews of StyleFlow. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-06-06.

What are some alternatives?

When comparing SDMetrics and StyleFlow you can also consider the following projects:

stylegan2-ada-pytorch - StyleGAN2-ADA - Official PyTorch implementation

DashcamCleaner - Censor identifiable information in videos, in particular dashcam recordings in Germany.

SAM - Official Implementation for "Only a Matter of Style: Age Transformation Using a Style-Based Regression Model" (SIGGRAPH 2021) https://arxiv.org/abs/2102.02754

ALAE - [CVPR2020] Adversarial Latent Autoencoders

StyleGAN_PyTorch - The implementation of StyleGAN on PyTorch 1.0.1

sofgan - [TOG 2022] SofGAN: A Portrait Image Generator with Dynamic Styling

restyle-encoder - Official Implementation for "ReStyle: A Residual-Based StyleGAN Encoder via Iterative Refinement" (ICCV 2021) https://arxiv.org/abs/2104.02699