NVlabs/imaginaire
NVIDIA PyTorch GAN library with distributed and mixed precision support
| repo name | NVlabs/imaginaire |
| repo link | https://github.com/NVlabs/imaginaire |
| homepage | http://imaginaire.cc/ |
| language | Python |
| size (curr.) | 66778 kB |
| stars (curr.) | 1700 |
| created | 2020-07-15 |
| license | Other |
Imaginaire
Docs | License | Installation | Model Zoo
Imaginaire is a pytorch library that contains optimized implementation of several image and video synthesis methods developed at NVIDIA.
License
Imaginaire is released under NVIDIA Software license. For commercial use, please consult researchinquiries@nvidia.com
What’s inside?
We have a tutorial for each model. Click on the model name, and your browser should take you to the tutorial page for the project.
Supervised Image-to-Image Translation
| Algorithm Name | Feature | Publication |
|---|---|---|
| pix2pixHD | Learn a mapping that converts a semantic image to a high-resolution photorealistic image. | Wang et. al. CVPR 2018 |
| SPADE | Improve pix2pixHD on handling diverse input labels and delivering better output quality. | Park et. al. CVPR 2019 |
Unsupervised Image-to-Image Translation
| Algorithm Name | Feature | Publication |
|---|---|---|
| UNIT | Learn a one-to-one mapping between two visual domains. | Liu et. al. NeurIPS 2017 |
| MUNIT | Learn a many-to-many mapping between two visual domains. | Huang et. al. ECCV 2018 |
| FUNIT | Learn a style-guided image translation model that can generate translations in unseen domains. | Liu et. al. ICCV 2019 |
| COCO-FUNIT | Improve FUNIT with a content-conditioned style encoding scheme for style code computation. | Saito et. al. ECCV 2020 |
Video-to-video Translation
| Algorithm Name | Feature | Publication |
|---|---|---|
| vid2vid | Learn a mapping that converts a semantic video to a photorealistic video. | Wang et. al. NeurIPS 2018 |
| fs-vid2vid | Learn a subject-agnostic mapping that converts a semantic video and an example image to a photoreslitic video. | Wang et. al. NeurIPS 2019 |
| wc-vid2vid | Improve vid2vid on view consistency and long-term consistency. | Mallya et. al. ECCV 2020 |
