vid2vid | Pytorch implementation of our method for high-resolution | Machine Learning library

 by   NVIDIA Python Version: Current License: Non-SPDX

kandi X-RAY | vid2vid Summary

kandi X-RAY | vid2vid Summary

vid2vid is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Generative adversarial networks applications. vid2vid has no bugs, it has no vulnerabilities and it has medium support. However vid2vid build file is not available and it has a Non-SPDX License. You can download it from GitHub.

Pytorch implementation for high-resolution (e.g., 2048x1024) photorealistic video-to-video translation. It can be used for turning semantic label maps into photo-realistic videos, synthesizing people talking from edge maps, or generating human motions from poses. The core of video-to-video translation is image-to-image translation. Some of our work in that space can be found in pix2pixHD and SPADE. Video-to-Video Synthesis Ting-Chun Wang1, Ming-Yu Liu1, Jun-Yan Zhu2, Guilin Liu1, Andrew Tao1, Jan Kautz1, Bryan Catanzaro1 1NVIDIA Corporation, 2MIT CSAIL In Neural Information Processing Systems (NeurIPS) 2018.

            kandi-support Support

              vid2vid has a medium active ecosystem.
              It has 8248 star(s) with 1194 fork(s). There are 250 watchers for this library.
              It had no major release in the last 6 months.
              There are 100 open issues and 65 have been closed. On average issues are closed in 158 days. There are 4 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of vid2vid is current.

            kandi-Quality Quality

              vid2vid has 0 bugs and 0 code smells.

            kandi-Security Security

              vid2vid has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              vid2vid code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              vid2vid has a Non-SPDX License.
              Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.

            kandi-Reuse Reuse

              vid2vid releases are not available. You will need to build from source code and install.
              vid2vid has no build file. You will be need to create the build yourself to build the component from source.
              Installation instructions, examples and code snippets are available.
              vid2vid saves you 2432 person hours of effort in developing the same functionality from scratch.
              It has 5299 lines of code, 345 functions and 65 files.
              It has medium code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed vid2vid and discovered the below as its top functions. This is intended to give you an instant insight into vid2vid implemented functionality, and help decide if they suit your requirements.
            • Train the model
            • Prepare data
            • Parse ids
            • Create and return the model object
            • Parse arguments
            • Forward computation
            • Generate a grid of inputs
            • Resample an image
            • Create a 2d triangular grid
            • Compute the loss of the loss function
            • Read a PNG file
            • Create a list of images from a directory
            • Generate the inference
            • Create a dataset
            • Compute the flow
            • Saves images
            • Parse tensorflow tensorflow distributions
            • Parse weights and bias
            • Load pretrained model
            • Parse flatetfusion module
            • Saves all of the tensors to disk
            • Parse tensors
            • Load the first frame in the network
            • Label colormap
            • Create and return a model object
            • Adds arguments for the given module
            • Performs the forward computation
            Get all kandi verified functions for this library.

            vid2vid Key Features

            No Key Features are available at this moment for vid2vid.

            vid2vid Examples and Code Snippets

            No Code Snippets are available at this moment for vid2vid.

            Community Discussions


            How to use a customized dataset for training with PyTorch/few-shot-vid2vid
            Asked 2020-Mar-03 at 01:13

            I’d like to use my own dataset created from the FaceForensics footage with few-show-vid2vid. So I generated image sequences with ffmpeg and keypoints with dlib. When I try to start the training script, I get the following error. What exactly is the problem? The provided small dataset was working for me.



            Answered 2020-Mar-03 at 01:13

            for i in range(67):

            This is incorrect, you should be using range(68) for 68 face landmarks. You can verify this with python -c "for i in range(67): print(i)" which will only count from 0 to 66 (67 total numbers). python -c "for i in range(68): print(i)" will count from 0 to 67 (68 items) and get the whole face landmark set.



            When I run deep learning training code on Google Colab, do the resulting weights and biases get saved somewhere?
            Asked 2020-Jan-07 at 15:38

            I am training some deep learning code from this repository on a Google Colab notebook. The training is ongoing and seems like it is going to take a day or two.

            I am new to deep learning, but my question:

            Once the Google Colab notebook has finished running the training script, does this mean that the resulting weights and biases will be hard written to a model somewhere (in the repository folder that I have on my Google Drive), and therefore I can then run the code on any test data I like at any point in the future? Or, once I close the Google Colab notebook, do I lose the weight and bias information and would have to run the training script again if I wanted to use the neural network?

            I realise that this might depend on the details of the script (again, the repository is here), but I thought that there might be a general way that these things work also.

            Any help in understanding would be greatly appreciated.



            Answered 2020-Jan-07 at 15:31

            No; Colab comes with no built-in checkpointing; any saving must be done by the user - so unless the repository code does so, it's up to you.

            Note that the repo would need to figure out how to connect to a remote server (or connect to your local device) for data transfer; skimming through its, there's no such thing.

            How to save model? See this SO; for a minimal version - the most common, and a reliable option is to "mount" your Google Drive onto Colab, and point save/load paths to direct


            Community Discussions, Code Snippets contain sources that include Stack Exchange Network


            No vulnerabilities reported

            Install vid2vid

            Install python libraries dominate and requests.
            If you plan to train with face datasets, please install dlib.
            If you plan to train with pose datasets, please install DensePose and/or OpenPose.
            Clone this repo:
            Docker Image If you have difficulty building the repo, a docker image can be found in the docker folder.


            For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .
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          • CLI

            gh repo clone NVIDIA/vid2vid

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