This is the Windows app named Make-A-Video - Pytorch (wip) whose latest release can be downloaded as 0.2.0.zip. It can be run online in the free hosting provider OnWorks for workstations.
Download and run online this app named Make-A-Video - Pytorch (wip) with OnWorks for free.
Follow these instructions in order to run this app:
- 1. Downloaded this application in your PC.
- 2. Enter in our file manager https://www.onworks.net/myfiles.php?username=XXXXX with the username that you want.
- 3. Upload this application in such filemanager.
- 4. Start any OS OnWorks online emulator from this website, but better Windows online emulator.
- 5. From the OnWorks Windows OS you have just started, goto our file manager https://www.onworks.net/myfiles.php?username=XXXXX with the username that you want.
- 6. Download the application and install it.
- 7. Download Wine from your Linux distributions software repositories. Once installed, you can then double-click the app to run them with Wine. You can also try PlayOnLinux, a fancy interface over Wine that will help you install popular Windows programs and games.
Wine is a way to run Windows software on Linux, but with no Windows required. Wine is an open-source Windows compatibility layer that can run Windows programs directly on any Linux desktop. Essentially, Wine is trying to re-implement enough of Windows from scratch so that it can run all those Windows applications without actually needing Windows.
SCREENSHOTS
Ad
Make-A-Video - Pytorch (wip)
DESCRIPTION
Implementation of Make-A-Video, new SOTA text to video generator from Meta AI, in Pytorch. They combine pseudo-3d convolutions (axial convolutions) and temporal attention and show much better temporal fusion. The pseudo-3d convolutions isn't a new concept. It has been explored before in other contexts, say for protein contact prediction as "dimensional hybrid residual networks". The gist of the paper comes down to, take a SOTA text-to-image model (here they use DALL-E2, but the same learning points would easily apply to Imagen), make a few minor modifications for attention across time and other ways to skimp on the compute cost, do frame interpolation correctly, get a great video model out. Passing in images (if one were to pretrain on images first), both temporal convolution and attention will be automatically skipped. In other words, you can use this straightforwardly in your 2d Unet and then port it over to a 3d Unet once that phase of the training is done.
Features
- The temporal modules are initialized to output identity as the paper had done
- You can also control the two modules so that when fed 3-dimensional features, it only does training spatially
- Full SpaceTimeUnet that is agnostic to images or video training, and where even if video is passed in, time can be ignored
- Passing in images (if one were to pretrain on images first), both temporal convolution and attention will be automatically skipped
- The gist of the paper comes down to, take a SOTA text-to-image model
- Implementation of Make-A-Video, new SOTA text to video generator from Meta AI, in Pytorch
Programming Language
Python
Categories
This is an application that can also be fetched from https://sourceforge.net/projects/make-a-video-pytorch.mirror/. It has been hosted in OnWorks in order to be run online in an easiest way from one of our free Operative Systems.