This is the Windows app named Video Diffusion - Pytorch whose latest release can be downloaded as 0.6.0.zip. It can be run online in the free hosting provider OnWorks for workstations.
Download and run online this app named Video Diffusion - Pytorch 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
Video Diffusion - Pytorch
DESCRIPTION
Implementation of Video Diffusion Models, Jonathan Ho's new paper extending DDPMs to Video Generation - in Pytorch. Implementation of Video Diffusion Models, Jonathan Ho's new paper extending DDPMs to Video Generation - in Pytorch. It uses a special space-time factored U-net, extending generation from 2D images to 3D videos. 14k for difficult moving mnist (converging much faster and better than NUWA) - wip. Any new developments for text-to-video synthesis will be centralized at Imagen-pytorch. For conditioning on text, they derived text embeddings by first passing the tokenized text through BERT-large. You can also directly pass in the descriptions of the video as strings, if you plan on using BERT-base for text conditioning. This repository also contains a handy Trainer class for training on a folder of gifs. Each gif must be of the correct dimensions image_size and num_frames.
Features
- From 2D images to 3D videos
- Co-training Images and Video
- Sample videos (as gif files) will be saved to ./results periodically, as are the diffusion model parameters
- You can also directly pass in the descriptions of the video as strings
- Implementation of Video Diffusion Models, Jonathan Ho's new paper
- It uses a special space-time factored U-net
Programming Language
Python
Categories
This is an application that can also be fetched from https://sourceforge.net/projects/video-diffusion-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.
