hub / github.com/NVlabs/imaginaire
github.com/NVlabs/imaginaire @main sqlite
1,306 symbols
3,744 edges
162 files
763 documented · 58%
README
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 NVIDIA Research Inquiries.
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 |
World-to-world Translation
| Algorithm Name | Feature | Publication |
|---|---|---|
| wc-vid2vid | Improve vid2vid on view consistency and long-term consistency. | Mallya et. al. ECCV 2020 |
| GANcraft | Convert semantic block worlds to realistic-looking worlds. | Hao et. al. ICCV 2021 |
Core symbols most depended-on inside this repo
tensor2imcalled by 72
imaginaire/utils/visualization/common.py
sum
called by 63
imaginaire/generators/fs_vid2vid.py
world2local
called by 57
imaginaire/model_utils/gancraft/mc_utils.py
conv
called by 40
imaginaire/third_party/flow_net/flownet2/networks/submodules.py
apply
called by 30
imaginaire/layers/weight_norm.py
eval
called by 30
imaginaire/trainers/gancraft.py
norm
called by 27
imaginaire/third_party/flow_net/flow_net.py
is_master
called by 23
imaginaire/utils/distributed.py
Shape
Languages
Python100%
Modules by API surface
imaginaire/layers/conv.py59 symbols
imaginaire/trainers/base.py47 symbols
imaginaire/layers/residual.py45 symbols
imaginaire/evaluation/caption/clip.py40 symbols
imaginaire/generators/fs_vid2vid.py34 symbols
imaginaire/trainers/vid2vid.py29 symbols
imaginaire/layers/activation_norm.py28 symbols
imaginaire/model_utils/fs_vid2vid.py26 symbols
imaginaire/third_party/upfirdn2d/upfirdn2d.py25 symbols
imaginaire/model_utils/gancraft/mc_utils.py24 symbols
imaginaire/evaluation/pretrained.py24 symbols
imaginaire/generators/gancraft_base.py22 symbols
Dependencies from manifests, versioned
Pillow8.3.2 · 1×
nvidia-ml-py37.352.0 · 1×
requests2.25.1 · 1×
scipy1.3.3 · 1×
tqdm4.35.0 · 1×
For agents
$ claude mcp add imaginaire \
-- python -m otcore.mcp_server <graph>