video diffusion pytorch

We have hosted the application video diffusion pytorch in order to run this application in our online workstations with Wine or directly.

Quick description about video diffusion pytorch:

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.
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