MobileStyleGAN.pytorch VS random_face

Compare MobileStyleGAN.pytorch vs random_face and see what are their differences.

random_face

A simple python library for fast image generation of people who do not exist. (by bes-dev)
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MobileStyleGAN.pytorch random_face
1 3
656 176
- -
0.0 0.0
about 2 years ago almost 2 years ago
Python Python
Apache License 2.0 Apache License 2.0
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.

MobileStyleGAN.pytorch

Posts with mentions or reviews of MobileStyleGAN.pytorch. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-04-13.

random_face

Posts with mentions or reviews of random_face. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-04-13.

What are some alternatives?

When comparing MobileStyleGAN.pytorch and random_face you can also consider the following projects:

PINTO_model_zoo - A repository for storing models that have been inter-converted between various frameworks. Supported frameworks are TensorFlow, PyTorch, ONNX, OpenVINO, TFJS, TFTRT, TensorFlowLite (Float32/16/INT8), EdgeTPU, CoreML.

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”.

APDrawingGAN - Code for APDrawingGAN: Generating Artistic Portrait Drawings from Face Photos with Hierarchical GANs (CVPR 2019 Oral)

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

DeceiveD - [NeurIPS 2021] Deceive D: Adaptive Pseudo Augmentation for GAN Training with Limited Data