Implementation of NÜWA, state of the art attention network for text to video synthesis, in Pytorch

Related tags

Deep Learningdeep-learningtransformersartificial-intelligenceattention-mechanismtext-to-video
Overview

NÜWA - Pytorch (wip)

Implementation of NÜWA, state of the art attention network for text to video synthesis, in Pytorch. This repository will be populated in the case that Microsoft does not open source the code by end of December. It may also contain an extension into video and audio, using a dual decoder approach.

DeepReader

Citations

@misc{wu2021nuwa,
    title   = {N\"UWA: Visual Synthesis Pre-training for Neural visUal World creAtion}, 
    author  = {Chenfei Wu and Jian Liang and Lei Ji and Fan Yang and Yuejian Fang and Daxin Jiang and Nan Duan},
    year    = {2021},
    eprint  = {2111.12417},
    archivePrefix = {arXiv},
    primaryClass = {cs.CV}
}
Comments
  • Question about generated videos?

    Question about generated videos?

    There are a lot of negative numbers and very small decimals (like 5e-1). But the loss degrades normally when training. Is that a normal situation? How can I make the result visible?

    opened by Fitzwong 0
  • Why the video does not pass through the encoder?

    Why the video does not pass through the encoder?

    Hi! lucidrains. Thanks for providing a great repo which is convenient to understand the NUWA paper.
    I have a question as follows: In the NUWA paper, we can see that the inputs of the Encoder are caption tokens (caption condition) and the video tokens (3DNA condition). So, in my eye, the video tokens sequence should fully self-attend in the Encoder, right? And then, the outputs condition the Decoder. The Decoder provided by you is as following. 截屏2022-05-12 上午11 07 12. It has causal self-attention and text-condition as we expected. But from the definition in paper, the condition contains the text-condition and 3DNA condition, and these two condition the Decoder. Is my opinion right? I am just curious about the condition in the NUWA paper. The Encoder in your repo is only the Text-Encoder, but the video does not pass through the encoder to condition the Encoder.

    Looking forward to your reply! Thanks!

    opened by Wang-Xiaodong1899 0
  • Questions about function forward() in NUWA please.

    Questions about function forward() in NUWA please.

    I'm confused me that, in function forward() of class NUWA, the ground-truth video is fed to transformer and calculate the output video, which is different from function generate().

    frame_embeddings = self.video_transformer(
            frame_embeddings,  # calculated from ground-truth video
            context = text_embeds,
            context_mask = text_mask
        )

    So when training NUWA, the loss comes from logits. But the logits are not only from text, but ground-truth video (only one transformer layer, different from the auto-regressive model in generate function). Is that some kind of cheating when training? Or should I generate logits in the same way as in generate(), and then calculate loss to train?

    opened by Fitzwong 1
  • Type of dataset for training VQ-GAN

    Type of dataset for training VQ-GAN

    Hi,

    First, thanks a lot for the amazing work! I have one question regarding the training of the VQ-GAN, do you recommend training it on a dataset similar to the dataset the nuwa model will be trained? What I mean is, if I want to train nuwa to generate sport videos based on text, do I need to also train the VQ-GAN on a sport dataset?

    Thanks a lot

    opened by antonibigata 0
  • Pseudocode for 3DNA?

    Pseudocode for 3DNA?

    me no comprendai le complex einops 😢

    Can someone give the 3DNA pseudocode to illustrate what's going on 🤗

    (Also how did lucidrains bang out thousands of lines of code in a few weeks - is he confirmed to be human? 🤔)

    opened by neel04 4
Releases(0.7.7a)
Owner
Phil Wang
Working with Attention. It's all we need
Phil Wang
GitHub Repository
Hepsiburada - Hepsiburada Urun Bilgisi Cekme

Hepsiburada Urun Bilgisi Cekme from hepsiburada import Marka nike = Marka("nike"

Ilker Manap 8 Oct 26, 2022
OpenPose: Real-time multi-person keypoint detection library for body, face, hands, and foot estimation

Build Type Linux MacOS Windows Build Status OpenPose has represented the first real-time multi-person system to jointly detect human body, hand, facia

25.7k Jan 09, 2023
Instantaneous Motion Generation for Robots and Machines.

Ruckig Instantaneous Motion Generation for Robots and Machines. Ruckig generates trajectories on-the-fly, allowing robots and machines to react instan

Berscheid 374 Dec 23, 2022
A tool to prepare websites grabbed with wget for local viewing.

makelocal A tool to prepare websites grabbed with wget for local viewing. exapmples After fetching xkcd.com with: wget -r -no-remove-listing -r -N --p

5 Apr 23, 2022
Curved Projection Reformation

Description Assuming that we already know the image of the centerline, we want the lumen to be displayed on a plane, which requires curved projection

夜听残荷 5 Sep 11, 2022
Dynamic Divide-and-Conquer Adversarial Training for Robust Semantic Segmentation （ICCV2021）

Dynamic Divide-and-Conquer Adversarial Training for Robust Semantic Segmentation This is a pytorch project for the paper Dynamic Divide-and-Conquer Ad

DV Lab 29 Nov 21, 2022
Sign Language Transformers (CVPR'20)

Sign Language Transformers (CVPR'20) This repo contains the training and evaluation code for the paper Sign Language Transformers: Sign Language Trans

Necati Cihan Camgoz 164 Dec 30, 2022
DynaTune: Dynamic Tensor Program Optimization in Deep Neural Network Compilation

DynaTune: Dynamic Tensor Program Optimization in Deep Neural Network Compilation This repository is the implementation of DynaTune paper. This folder

4 Nov 02, 2022
Speedy Implementation of Instance-based Learning (IBL) agents in Python

A Python library to create single or multi Instance-based Learning (IBL) agents that are built based on Instance Based Learning Theory (IBLT) 1 Instal

0 Nov 18, 2021
Official implementation of Deep Convolutional Dictionary Learning for Image Denoising.

DCDicL for Image Denoising Hongyi Zheng*, Hongwei Yong*, Lei Zhang, "Deep Convolutional Dictionary Learning for Image Denoising," in CVPR 2021. (* Equ

Z80 91 Dec 21, 2022
Complete system for facial identity system. Include one-shot model, database operation, features visualization, monitoring

Complete system for facial identity system. Include one-shot model, database operation, features visualization, monitoring

2 Dec 28, 2021
A PyTorch implementation of "ANEMONE: Graph Anomaly Detection with Multi-Scale Contrastive Learning", CIKM-21

ANEMONE A PyTorch implementation of "ANEMONE: Graph Anomaly Detection with Multi-Scale Contrastive Learning", CIKM-21 Dependencies python==3.6.1 dgl==

Graph Analysis & Deep Learning Laboratory, GRAND 30 Dec 14, 2022
The implementation of CVPR2021 paper Temporal Query Networks for Fine-grained Video Understanding, by Chuhan Zhang, Ankush Gupta and Andrew Zisserman.

Temporal Query Networks for Fine-grained Video Understanding 📋 This repository contains the implementation of CVPR2021 paper Temporal_Query_Networks

55 Dec 21, 2022
Some toy examples of score matching algorithms written in PyTorch

toy_gradlogp This repo implements some toy examples of the following score matching algorithms in PyTorch: ssm-vr: sliced score matching with variance

Ending Hsiao 21 Dec 26, 2022
An AFL implementation with UnTracer (our coverage-guided tracer)

UnTracer-AFL This repository contains an implementation of our prototype coverage-guided tracing framework UnTracer in the popular coverage-guided fuz

113 Dec 17, 2022
TOOD: Task-aligned One-stage Object Detection, ICCV2021 Oral

One-stage object detection is commonly implemented by optimizing two sub-tasks: object classification and localization, using heads with two parallel branches, which might lead to a certain level of

264 Jan 09, 2023
Collective Multi-type Entity Alignment Between Knowledge Graphs (WWW'20)

CG-MuAlign A reference implementation for "Collective Multi-type Entity Alignment Between Knowledge Graphs", published in WWW 2020. If you find our pa

Bran Zhu 28 Dec 11, 2022
Angular & Electron desktop UI framework. Angular components for native looking and behaving macOS desktop UI (Electron/Web)

Angular Desktop UI This is a collection for native desktop like user interface components in Angular, especially useful for Electron apps. It starts w

Marc J. Schmidt 49 Dec 22, 2022
Official implementation for Scale-Aware Neural Architecture Search for Multivariate Time Series Forecasting

1 SNAS4MTF This repo is the official implementation for Scale-Aware Neural Architecture Search for Multivariate Time Series Forecasting. 1.1 The frame

SZJ 5 Sep 21, 2022
Statistical-Rethinking-with-Python-and-PyMC3 - Python/PyMC3 port of the examples in " Statistical Rethinking A Bayesian Course with Examples in R and Stan" by Richard McElreath

Statistical Rethinking with Python and PyMC3 This repository has been deprecated in favour of this one, please check that repository for updates, for

Osvaldo Martin 786 Dec 29, 2022