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+videoretalking/docs/static/images/teaser.png filter=lfs diff=lfs merge=lfs -text
+videoretalking/docs/static/videos/Ablation.mp4 filter=lfs diff=lfs merge=lfs -text
+videoretalking/docs/static/videos/Comparison.mp4 filter=lfs diff=lfs merge=lfs -text
+videoretalking/docs/static/videos/Results_in_the_wild.mp4 filter=lfs diff=lfs merge=lfs -text
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videoretalking/.gitignore

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+*.pkl
+*.jpg
+*.pth
+*.pyc
+__pycache__
+*.h5
+*.pyc
+*.mkv
+*.gif
+*.webm
+checkpoints/*
+results/*
+temp/*
+segments.txt
+.DS_Store

videoretalking/CODE_OF_CONDUCT.md

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+# Code of Conduct
+
+## Our Pledge
+
+In the interest of fostering an open and welcoming environment, we as contributors and maintainers pledge to make participation in our project and our community a harassment-free experience for everyone, regardless of age, body size, disability, ethnicity, gender identity and expression, level of experience, education, socio-economic status, nationality, personal appearance, race, religion, or sexual identity and orientation.
+
+## Our Standards
+
+Examples of behavior that contributes to creating a positive environment include:
+
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videoretalking/LICENSE

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videoretalking/README.md

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+<div align="center">
+
+<h2>VideoReTalking <br/> <span style="font-size:12px">Audio-based Lip Synchronization for Talking Head Video Editing in the Wild</span> </h2> 
+
+  <a href='https://arxiv.org/abs/2211.14758'><img src='https://img.shields.io/badge/ArXiv-2211.14758-red'></a> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;<a href='https://vinthony.github.io/video-retalking/'><img src='https://img.shields.io/badge/Project-Page-Green'></a> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/vinthony/video-retalking/blob/main/quick_demo.ipynb)&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
+[![Replicate](https://replicate.com/cjwbw/video-retalking/badge)](https://replicate.com/cjwbw/video-retalking)
+
+<div>
+    <a target='_blank'>Kun Cheng <sup>*,1,2</sup> </a>&emsp;
+    <a href='https://vinthony.github.io/' target='_blank'>Xiaodong Cun <sup>*,2</a>&emsp;
+    <a href='https://yzhang2016.github.io/yongnorriszhang.github.io/' target='_blank'>Yong Zhang <sup>2</sup></a>&emsp;
+    <a href='https://menghanxia.github.io/' target='_blank'>Menghan Xia <sup>2</sup></a>&emsp;
+    <a href='https://feiiyin.github.io/' target='_blank'>Fei Yin <sup>2,3</sup></a>&emsp;<br/>
+    <a href='https://web.xidian.edu.cn/mrzhu/en/index.html' target='_blank'>Mingrui Zhu <sup>1</sup></a>&emsp;
+    <a href='https://xuanwangvc.github.io/' target='_blank'>Xuan Wang <sup>2</sup></a>&emsp;
+    <a href='https://juewang725.github.io/' target='_blank'>Jue Wang <sup>2</sup></a>&emsp;
+    <a href='https://web.xidian.edu.cn/nnwang/en/index.html' target='_blank'>Nannan Wang <sup>1</sup></a>
+</div>
+<br>
+<div>
+    <sup>1</sup> Xidian University &emsp; <sup>2</sup> Tencent AI Lab &emsp; <sup>3</sup> Tsinghua University
+</div>
+<br>
+<i><strong><a href='https://sa2022.siggraph.org/' target='_blank'>SIGGRAPH Asia 2022 Conference Track</a></strong></i>
+<br>
+<br>
+<img src="https://opentalker.github.io/video-retalking/static/images/teaser.png" width="768px">
+
+
+<div align="justify">  <BR> We present VideoReTalking, a new system to edit the faces of a real-world talking head video according to input audio, producing a high-quality and lip-syncing output video even with a different emotion. Our system disentangles this objective into three sequential tasks:
+  
+ <BR> (1) face video generation with a canonical expression
+<BR> (2) audio-driven lip-sync and 
+  <BR> (3) face enhancement for improving photo-realism. 
+  
+ <BR>  Given a talking-head video, we first modify the expression of each frame according to the same expression template using the expression editing network, resulting in a video with the canonical expression. This video, together with the given audio, is then fed into the lip-sync network to generate a lip-syncing video. Finally, we improve the photo-realism of the synthesized faces through an identity-aware face enhancement network and post-processing. We use learning-based approaches for all three steps and all our modules can be tackled in a sequential pipeline without any user intervention.</div>
+<BR>
+
+<p>
+<img alt='pipeline' src="./docs/static/images/pipeline.png?raw=true" width="768px"><br>
+<em align='center'>Pipeline</em>
+</p>
+
+</div>
+
+## Results in the Wild （contains audio）
+https://user-images.githubusercontent.com/4397546/224310754-665eb2dd-aadc-47dc-b1f9-2029a937b20a.mp4
+
+
+
+
+## Environment
+```
+git clone https://github.com/vinthony/video-retalking.git
+cd video-retalking
+conda create -n video_retalking python=3.8
+conda activate video_retalking
+
+conda install ffmpeg
+
+# Please follow the instructions from https://pytorch.org/get-started/previous-versions/
+# This installation command only works on CUDA 11.1
+pip install torch==1.9.0+cu111 torchvision==0.10.0+cu111 -f https://download.pytorch.org/whl/torch_stable.html
+
+pip install -r requirements.txt
+```
+
+## Quick Inference
+
+#### Pretrained Models
+Please download our [pre-trained models](https://drive.google.com/drive/folders/18rhjMpxK8LVVxf7PI6XwOidt8Vouv_H0?usp=share_link) and put them in `./checkpoints`.
+
+<!-- We also provide some [example videos and audio](https://drive.google.com/drive/folders/14OwbNGDCAMPPdY-l_xO1axpUjkPxI9Dv?usp=share_link). Please put them in `./examples`. -->
+
+#### Inference
+
+```
+python3 inference.py \
+  --face examples/face/1.mp4 \
+  --audio examples/audio/1.wav \
+  --outfile results/1_1.mp4
+```
+This script includes data preprocessing steps. You can test any talking face videos without manual alignment. But it is worth noting that DNet cannot handle extreme poses.
+
+You can also control the expression by adding the following parameters:
+
+```--exp_img```: Pre-defined expression template. The default is "neutral". You can choose "smile" or an image path.
+
+```--up_face```: You can choose "surprise" or "angry" to modify the expression of upper face with [GANimation](https://github.com/donydchen/ganimation_replicate).
+
+
+
+## Citation
+
+If you find our work useful in your research, please consider citing:
+
+```
+@misc{cheng2022videoretalking,
+        title={VideoReTalking: Audio-based Lip Synchronization for Talking Head Video Editing In the Wild}, 
+        author={Kun Cheng and Xiaodong Cun and Yong Zhang and Menghan Xia and Fei Yin and Mingrui Zhu and Xuan Wang and Jue Wang and Nannan Wang},
+        year={2022},
+        eprint={2211.14758},
+        archivePrefix={arXiv},
+        primaryClass={cs.CV}
+  }
+```
+
+## Acknowledgement
+Thanks to
+[Wav2Lip](https://github.com/Rudrabha/Wav2Lip),
+[PIRenderer](https://github.com/RenYurui/PIRender), 
+[GFP-GAN](https://github.com/TencentARC/GFPGAN), 
+[GPEN](https://github.com/yangxy/GPEN),
+[ganimation_replicate](https://github.com/donydchen/ganimation_replicate),
+[STIT](https://github.com/rotemtzaban/STIT)
+for sharing their code.
+
+
+## Related Work
+- [StyleHEAT: One-Shot High-Resolution Editable Talking Face Generation via Pre-trained StyleGAN (ECCV 2022)](https://github.com/FeiiYin/StyleHEAT)
+- [CodeTalker: Speech-Driven 3D Facial Animation with Discrete Motion Prior (CVPR 2023)](https://github.com/Doubiiu/CodeTalker)
+- [SadTalker: Learning Realistic 3D Motion Coefficients for Stylized Audio-Driven Single Image Talking Face Animation (CVPR 2023)](https://github.com/Winfredy/SadTalker)
+- [DPE: Disentanglement of Pose and Expression for General Video Portrait Editing (CVPR 2023)](https://github.com/Carlyx/DPE)
+- [3D GAN Inversion with Facial Symmetry Prior (CVPR 2023)](https://github.com/FeiiYin/SPI/)
+- [T2M-GPT: Generating Human Motion from Textual Descriptions with Discrete Representations (CVPR 2023)](https://github.com/Mael-zys/T2M-GPT)
+
+##  Disclaimer
+
+This is not an official product of Tencent. 
+
+```
+1. Please carefully read and comply with the open-source license applicable to this code before using it. 
+2. Please carefully read and comply with the intellectual property declaration applicable to this code before using it.
+3. This open-source code runs completely offline and does not collect any personal information or other data. If you use this code to provide services to end-users and collect related data, please take necessary compliance measures according to applicable laws and regulations (such as publishing privacy policies, adopting necessary data security strategies, etc.). If the collected data involves personal information, user consent must be obtained (if applicable). Any legal liabilities arising from this are unrelated to Tencent.
+4. Without Tencent's written permission, you are not authorized to use the names or logos legally owned by Tencent, such as "Tencent." Otherwise, you may be liable for your legal responsibilities.
+5. This open-source code does not have the ability to directly provide services to end-users. If you need to use this code for further model training or demos, as part of your product to provide services to end-users, or for similar use, please comply with applicable laws and regulations for your product or service. Any legal liabilities arising from this are unrelated to Tencent.
+6. It is prohibited to use this open-source code for activities that harm the legitimate rights and interests of others (including but not limited to fraud, deception, infringement of others' portrait rights, reputation rights, etc.), or other behaviors that violate applicable laws and regulations or go against social ethics and good customs (including providing incorrect or false information, spreading pornographic, terrorist, and violent information, etc.). Otherwise, you may be liable for your legal responsibilities.
+
+```
+## All Thanks To Our Contributors 
+
+<a href="https://github.com/OpenTalker/video-retalking/graphs/contributors">
+  <img src="https://contrib.rocks/image?repo=OpenTalker/video-retalking" />
+</a>

videoretalking/cog.yaml

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+# Configuration for Cog ⚙️
+# Reference: https://github.com/replicate/cog/blob/main/docs/yaml.md
+
+build:
+  gpu: true
+  system_packages:
+    - "libgl1-mesa-glx"
+    - "libglib2.0-0"
+    - "ffmpeg"
+  python_version: "3.11"
+  python_packages:
+    - "torch==2.0.1"
+    - "torchvision==0.15.2"
+    - "basicsr==1.4.2"
+    - "kornia==0.5.1"
+    - "face-alignment==1.3.4"
+    - "ninja==1.10.2.3"
+    - "einops==0.4.1"
+    - "facexlib==0.2.5"
+    - "librosa==0.9.2"
+    - "cmake==3.27.7"
+    - "numpy==1.23.4"
+  run:
+    - pip install dlib
+    - mkdir -p /root/.pyenv/versions/3.11.6/lib/python3.11/site-packages/facexlib/weights/ && wget --output-document "/root/.pyenv/versions/3.11.6/lib/python3.11/site-packages/facexlib/weights/detection_Resnet50_Final.pth" "https://github.com/xinntao/facexlib/releases/download/v0.1.0/detection_Resnet50_Final.pth"
+    - mkdir -p /root/.pyenv/versions/3.11.6/lib/python3.11/site-packages/facexlib/weights/ && wget --output-document "/root/.pyenv/versions/3.11.6/lib/python3.11/site-packages/facexlib/weights/parsing_parsenet.pth" "https://github.com/xinntao/facexlib/releases/download/v0.2.2/parsing_parsenet.pth"
+    - mkdir -p /root/.cache/torch/hub/checkpoints/ && wget --output-document "/root/.cache/torch/hub/checkpoints/s3fd-619a316812.pth" "https://www.adrianbulat.com/downloads/python-fan/s3fd-619a316812.pth"
+    - mkdir -p /root/.cache/torch/hub/checkpoints/ && wget --output-document "/root/.cache/torch/hub/checkpoints/2DFAN4-cd938726ad.zip" "https://www.adrianbulat.com/downloads/python-fan/2DFAN4-cd938726ad.zip"
+predict: "predict.py:Predictor"

videoretalking/docs/index.html

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+<!DOCTYPE html>
+<html>
+<head>
+  <meta charset="utf-8">
+  <!-- Meta tags for social media banners, these should be filled in appropriately as they are your "business card" -->
+  <!-- Replace the content tag with appropriate information -->
+  <meta content="VideoReTalking: Audio-based Lip Synchronization for Talking Head Video Editing In the Wild"
+  property="og:title">
+  <meta content="VideoReTalking: Audio-based Lip Synchronization for Talking Head Video Editing In the Wild"
+    name="description" property="og:description">
+  <meta content="https://vinthony.github.io/video-retalking/" property="og:url">
+  <!-- Path to banner image, should be in the path listed below. Optimal dimenssions are 1200X630-->
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+
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+  <title>VideoRetalking</title>
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+<body>
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+  <section class="hero">
+    <div class="hero-body">
+      <div class="container is-max-desktop">
+        <div class="columns is-centered">
+          <div class="column has-text-centered">
+            <h1 class="xtitle is-1 publication-title">VideoReTalking: Audio-based Lip Synchronization for Talking Head Video Editing In the Wild</h1>
+            <br/>
+            <div class="is-size-5 publication-authors">
+              <!-- Paper authors -->
+              <span class="author-block">
+                <a href="#" target="_blank">Kun Cheng</a><sup>*,1,2</sup></span>
+                <span class="author-block">
+                  <a href="https://vinthony.github.io" target="_blank">Xiaodong Cun</a><sup>*,2</sup></span>
+                  <span class="author-block">
+                    <a href="https://yzhang2016.github.io" target="_blank">Yong Zhang</a><sup>2</sup>
+                  </span>
+                  <span class="author-block">
+                    <a href="https://menghanxia.github.io/" target="_blank">Menghan Xia</a><sup>2</sup>
+                  </span>
+                  <span class="author-block">
+                    <a href="https://feiiyin.github.io/" target="_blank">Fei Yin</a><sup>2,3</sup>
+                  </span>
+                </br>
+                  <span class="author-block">
+                    <a href="https://web.xidian.edu.cn/mrzhu/en/index.html" target="_blank">Mingrui Zhu</a><sup>1</sup>
+                  </span>
+                  <span class="author-block">
+                    <a href="https://xuanwangvc.github.io/" target="_blank">Xuan Wang</a><sup>2</sup>
+                  </span>
+                  <span class="author-block">
+                    <a href="https://juewang725.github.io/" target="_blank">Jue Wang</a><sup>2</sup>
+                  </span>
+                  <span class="author-block">
+                    <a href="https://web.xidian.edu.cn/nnwang/en/index.html" target="_blank">Nannan Wang</a><sup>1</sup>
+                  </span>
+                  </div>
+                  <br/>
+                  <div class="is-size-5 publication-authors">
+                    <span class="author-block">
+                      <sup>1</sup> Xidian University &nbsp;&nbsp;&nbsp;
+                      <sup>2</sup> Tencent AI Lab &nbsp;&nbsp;&nbsp;
+                      <sup>3</sup> Tsinghua University
+                      <br>SIGGRAPH Asia 2022 (Conference Track)</span>
+                    <span class="eql-cntrb"><small><br><sup>*</sup>Indicates Equal Contribution</small></span>
+                  </div>
+
+                  <div class="column has-text-centered">
+                    <div class="publication-links">
+                         <!-- Arxiv PDF link -->
+                      <span class="link-block">
+                        <a href="https://arxiv.org/pdf/2211.14758.pdf" target="_blank"
+                        class="external-link ">
+                        <span class="icon">
+                          <i class="fas fa-file-pdf"></i>
+                        </span>
+                        <span>Paper</span>
+                      </a>
+                    </span>
+
+                  <!-- Github link -->
+                  <span class="link-block">
+                    <a href="https://github.com/vinthony/video-retalking/" target="_blank"
+                    class="external-link ">
+                    <span class="icon">
+                      <i class="fab fa-github"></i>
+                    </span>
+                    <span>Code</span>
+                  </a>
+                </span>
+
+                <!-- ArXiv abstract Link -->
+                <span class="link-block">
+                  <a href="https://arxiv.org/abs/2211.14758" target="_blank"
+                  class="external-link ">
+                  <span class="icon">
+                    <i class="ai ai-arxiv"></i>
+                  </span>
+                  <span>arXiv</span>
+                </a>
+              </span>
+            </div>
+          </div>
+        </div>
+      </div>
+    </div>
+  </div>
+</section>
+
+
+<!-- Teaser video-->
+<section class="hero teaser">
+  <div class="container is-max-desktop">
+    <div class="hero-body-img">
+      <img src="./static/images/teaser.png" width="80%">
+    </div>
+  </div>
+</section>
+<!-- End teaser video -->
+
+<!-- Paper abstract -->
+<section class="section hero is-light">
+  <div class="container is-max-desktop">
+    <div class="columns is-centered has-text-centered">
+      <div class="column is-four-fifths">
+        <h2 class="title is-3">Abstract</h2>
+        <div class="content has-text-justified">
+          <p>
+            We present VideoReTalking, a new system to edit the faces of a real-world talking head video according to input audio, 
+        producing a high-quality and lip-syncing output video even with a different emotion. Our system disentangles this objective 
+        into three sequential tasks: (1) face video generation with a canonical expression; (2) audio-driven lip-sync; and 
+        (3) face enhancement for improving photo-realism. Given a talking-head video, we first modify the expression of each frame 
+        according to the same expression template using the expression editing network, resulting in a video with the canonical 
+        expression. This video, together with the given audio, is then fed into the lip-sync network to generate a lip-syncing video.
+        Finally, we improve the photo-realism of the synthesized faces through an identity-aware face enhancement network and 
+        post-processing. We use learning-based approaches for all three steps and all our modules can be tackled in a sequential 
+        pipeline without any user intervention.
+          </p>
+        </div>
+      </div>
+    </div>
+  </div>
+</section>
+<!-- End paper abstract -->
+
+
+
+
+
+
+<!-- Youtube video -->
+<section class="hero is-small is-light">
+  <div class="hero-body">
+    <div class="container">
+      <!-- Paper video. -->
+      <h2 class="title is-3">Pipeline</h2>
+      <div class="columns is-centered has-text-centered">
+        <div class="column is-four-fifths">
+          
+          <div class="hero-body-img">
+            <!-- Youtube embed code here -->
+            <img width='80%' src="static/images/pipeline.png">
+          </div>
+        </div>
+      </div>
+    </div>
+  </div>
+</section>
+<!-- End youtube video -->
+
+
+<!-- Youtube video -->
+<section class="hero is-small is-light">
+  <div class="hero-body">
+    <div class="container">
+      <!-- Paper video. -->
+      <h2 class="title is-3"><strong>Video1</strong>: Video Results in the Wild.</h2>
+      <div class="columns is-centered has-text-centered">
+        <div class="column is-four-fifths">
+          
+          <video controls=""  width="100%">
+                    <!-- t=0.001 is a hack to make iPhone show video thumbnail -->
+                    <source src="./static/videos/Results_in_the_wild.mp4#t=0.001" type="video/mp4">
+                </video>
+        </div>
+      </div>
+    </div>
+  </div>
+</section>
+<!-- End youtube video
+
+  !-- Youtube video -->
+<section class="hero is-small is-light">
+  <div class="hero-body">
+    <div class="container">
+      <!-- Paper video. -->
+      <h2 class="title is-3"><strong>Video2</strong>: Comparison with SOTA Methods.</h2>
+      <div class="columns is-centered has-text-centered">
+        <div class="column is-four-fifths">
+          
+          <video controls=""  width="100%">
+                    <!-- t=0.001 is a hack to make iPhone show video thumbnail -->
+                    <source src="./static/videos/Comparison.mp4#t=0.001" type="video/mp4">
+                </video>
+        </div>
+      </div>
+    </div>
+  </div>
+</section>
+
+  <section class="hero is-small is-light">
+    <div class="hero-body">
+      <div class="container">
+        <!-- Paper video. -->
+        <h2 class="title is-3"><strong>Video3</strong>: Ablation Study on Different Modules. </h2>
+        <div class="columns is-centered has-text-centered">
+          <div class="column is-four-fifths">
+            
+            <video controls=""  width="100%">
+                      <!-- t=0.001 is a hack to make iPhone show video thumbnail -->
+                      <source src="./static/videos/Ablation.mp4#0.001" type="video/mp4">
+                  </video>
+          </div>
+        </div>
+      </div>
+    </div>
+  </section>
+
+<!--BibTex citation -->
+  <section class="section" id="BibTeX">
+    <div class="container is-max-desktop content">
+      <h2 class="title">BibTeX</h2>
+      <pre><code>@misc{videoretalking,
+        title={VideoReTalking: Audio-based Lip Synchronization for Talking Head Video Editing In the Wild}, 
+        author={Kun Cheng and Xiaodong Cun and Yong Zhang and Menghan Xia and Fei Yin and Mingrui Zhu and Xuan Wang and Jue Wang and Nannan Wang},
+        year={2022},
+        eprint={2211.14758},
+        archivePrefix={arXiv},
+        primaryClass={cs.CV}
+  }</code></pre>
+    </div>
+</section>
+<!--End BibTex citation -->
+
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videoretalking/docs/static/css/fontawesome.all.min.css

@@ -0,0 +1,5 @@
+/*!
+ * Font Awesome Free 5.15.1 by @fontawesome - https://fontawesome.com
+ * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License)
+ */
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+}
+a:hover:after{
+  color:black;
+  /*color: #111;*/
+  text-decoration: none;
+  top:0%;
+}
+.entry{
+    position: relative;
+    top:0;
+    left: 20px;
+    margin-top: 5px;
+}
+
+.posts > .post{
+    border-bottom: 0;
+    padding-bottom:0em;
+    padding-top: 10px;
+    margin-bottom: 5px;
+}
+.papertitle{
+    margin-top: 0px;
+    font-weight: 600;
+    font-size:16px;
+    font-style: italic;
+    /*font-style: italic;*/
+    /*height: 2.6em*/
+}

videoretalking/docs/static/js/bulma-carousel.js

@@ -0,0 +1,2371 @@
+(function webpackUniversalModuleDefinition(root, factory) {
+	if(typeof exports === 'object' && typeof module === 'object')
+		module.exports = factory();
+	else if(typeof define === 'function' && define.amd)
+		define([], factory);
+	else if(typeof exports === 'object')
+		exports["bulmaCarousel"] = factory();
+	else
+		root["bulmaCarousel"] = factory();
+})(typeof self !== 'undefined' ? self : this, function() {
+return /******/ (function(modules) { // webpackBootstrap
+/******/ 	// The module cache
+/******/ 	var installedModules = {};
+/******/
+/******/ 	// The require function
+/******/ 	function __webpack_require__(moduleId) {
+/******/
+/******/ 		// Check if module is in cache
+/******/ 		if(installedModules[moduleId]) {
+/******/ 			return installedModules[moduleId].exports;
+/******/ 		}
+/******/ 		// Create a new module (and put it into the cache)
+/******/ 		var module = installedModules[moduleId] = {
+/******/ 			i: moduleId,
+/******/ 			l: false,
+/******/ 			exports: {}
+/******/ 		};
+/******/
+/******/ 		// Execute the module function
+/******/ 		modules[moduleId].call(module.exports, module, module.exports, __webpack_require__);
+/******/
+/******/ 		// Flag the module as loaded
+/******/ 		module.l = true;
+/******/
+/******/ 		// Return the exports of the module
+/******/ 		return module.exports;
+/******/ 	}
+/******/
+/******/
+/******/ 	// expose the modules object (__webpack_modules__)
+/******/ 	__webpack_require__.m = modules;
+/******/
+/******/ 	// expose the module cache
+/******/ 	__webpack_require__.c = installedModules;
+/******/
+/******/ 	// define getter function for harmony exports
+/******/ 	__webpack_require__.d = function(exports, name, getter) {
+/******/ 		if(!__webpack_require__.o(exports, name)) {
+/******/ 			Object.defineProperty(exports, name, {
+/******/ 				configurable: false,
+/******/ 				enumerable: true,
+/******/ 				get: getter
+/******/ 			});
+/******/ 		}
+/******/ 	};
+/******/
+/******/ 	// getDefaultExport function for compatibility with non-harmony modules
+/******/ 	__webpack_require__.n = function(module) {
+/******/ 		var getter = module && module.__esModule ?
+/******/ 			function getDefault() { return module['default']; } :
+/******/ 			function getModuleExports() { return module; };
+/******/ 		__webpack_require__.d(getter, 'a', getter);
+/******/ 		return getter;
+/******/ 	};
+/******/
+/******/ 	// Object.prototype.hasOwnProperty.call
+/******/ 	__webpack_require__.o = function(object, property) { return Object.prototype.hasOwnProperty.call(object, property); };
+/******/
+/******/ 	// __webpack_public_path__
+/******/ 	__webpack_require__.p = "";
+/******/
+/******/ 	// Load entry module and return exports
+/******/ 	return __webpack_require__(__webpack_require__.s = 5);
+/******/ })
+/************************************************************************/
+/******/ ([
+/* 0 */
+/***/ (function(module, __webpack_exports__, __webpack_require__) {
+
+"use strict";
+/* unused harmony export addClasses */
+/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "d", function() { return removeClasses; });
+/* unused harmony export show */
+/* unused harmony export hide */
+/* unused harmony export offset */
+/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "e", function() { return width; });
+/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "b", function() { return height; });
+/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "c", function() { return outerHeight; });
+/* unused harmony export outerWidth */
+/* unused harmony export position */
+/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "a", function() { return css; });
+/* harmony import */ var __WEBPACK_IMPORTED_MODULE_0__type__ = __webpack_require__(2);
+
+
+var addClasses = function addClasses(element, classes) {
+	classes = Array.isArray(classes) ? classes : classes.split(' ');
+	classes.forEach(function (cls) {
+		element.classList.add(cls);
+	});
+};
+
+var removeClasses = function removeClasses(element, classes) {
+	classes = Array.isArray(classes) ? classes : classes.split(' ');
+	classes.forEach(function (cls) {
+		element.classList.remove(cls);
+	});
+};
+
+var show = function show(elements) {
+	elements = Array.isArray(elements) ? elements : [elements];
+	elements.forEach(function (element) {
+		element.style.display = '';
+	});
+};
+
+var hide = function hide(elements) {
+	elements = Array.isArray(elements) ? elements : [elements];
+	elements.forEach(function (element) {
+		element.style.display = 'none';
+	});
+};
+
+var offset = function offset(element) {
+	var rect = element.getBoundingClientRect();
+	return {
+		top: rect.top + document.body.scrollTop,
+		left: rect.left + document.body.scrollLeft
+	};
+};
+
+// returns an element's width
+var width = function width(element) {
+	return element.getBoundingClientRect().width || element.offsetWidth;
+};
+// returns an element's height
+var height = function height(element) {
+	return element.getBoundingClientRect().height || element.offsetHeight;
+};
+
+var outerHeight = function outerHeight(element) {
+	var withMargin = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : false;
+
+	var height = element.offsetHeight;
+	if (withMargin) {
+		var style = window.getComputedStyle(element);
+		height += parseInt(style.marginTop) + parseInt(style.marginBottom);
+	}
+	return height;
+};
+
+var outerWidth = function outerWidth(element) {
+	var withMargin = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : false;
+
+	var width = element.offsetWidth;
+	if (withMargin) {
+		var style = window.getComputedStyle(element);
+		width += parseInt(style.marginLeft) + parseInt(style.marginRight);
+	}
+	return width;
+};
+
+var position = function position(element) {
+	return {
+		left: element.offsetLeft,
+		top: element.offsetTop
+	};
+};
+
+var css = function css(element, obj) {
+	if (!obj) {
+		return window.getComputedStyle(element);
+	}
+	if (Object(__WEBPACK_IMPORTED_MODULE_0__type__["b" /* isObject */])(obj)) {
+		var style = '';
+		Object.keys(obj).forEach(function (key) {
+			style += key + ': ' + obj[key] + ';';
+		});
+
+		element.style.cssText += style;
+	}
+};
+
+/***/ }),
+/* 1 */
+/***/ (function(module, __webpack_exports__, __webpack_require__) {
+
+"use strict";
+/* harmony export (immutable) */ __webpack_exports__["a"] = detectSupportsPassive;
+function detectSupportsPassive() {
+	var supportsPassive = false;
+
+	try {
+		var opts = Object.defineProperty({}, 'passive', {
+			get: function get() {
+				supportsPassive = true;
+			}
+		});
+
+		window.addEventListener('testPassive', null, opts);
+		window.removeEventListener('testPassive', null, opts);
+	} catch (e) {}
+
+	return supportsPassive;
+}
+
+/***/ }),
+/* 2 */
+/***/ (function(module, __webpack_exports__, __webpack_require__) {
+
+"use strict";
+/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "a", function() { return isFunction; });
+/* unused harmony export isNumber */
+/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "c", function() { return isString; });
+/* unused harmony export isDate */
+/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "b", function() { return isObject; });
+/* unused harmony export isEmptyObject */
+/* unused harmony export isNode */
+/* unused harmony export isVideo */
+/* unused harmony export isHTML5 */
+/* unused harmony export isIFrame */
+/* unused harmony export isYoutube */
+/* unused harmony export isVimeo */
+var _typeof = typeof Symbol === "function" && typeof Symbol.iterator === "symbol" ? function (obj) { return typeof obj; } : function (obj) { return obj && typeof Symbol === "function" && obj.constructor === Symbol && obj !== Symbol.prototype ? "symbol" : typeof obj; };
+
+var isFunction = function isFunction(unknown) {
+	return typeof unknown === 'function';
+};
+var isNumber = function isNumber(unknown) {
+	return typeof unknown === "number";
+};
+var isString = function isString(unknown) {
+	return typeof unknown === 'string' || !!unknown && (typeof unknown === 'undefined' ? 'undefined' : _typeof(unknown)) === 'object' && Object.prototype.toString.call(unknown) === '[object String]';
+};
+var isDate = function isDate(unknown) {
+	return (Object.prototype.toString.call(unknown) === '[object Date]' || unknown instanceof Date) && !isNaN(unknown.valueOf());
+};
+var isObject = function isObject(unknown) {
+	return (typeof unknown === 'function' || (typeof unknown === 'undefined' ? 'undefined' : _typeof(unknown)) === 'object' && !!unknown) && !Array.isArray(unknown);
+};
+var isEmptyObject = function isEmptyObject(unknown) {
+	for (var name in unknown) {
+		if (unknown.hasOwnProperty(name)) {
+			return false;
+		}
+	}
+	return true;
+};
+
+var isNode = function isNode(unknown) {
+	return !!(unknown && unknown.nodeType === HTMLElement | SVGElement);
+};
+var isVideo = function isVideo(unknown) {
+	return isYoutube(unknown) || isVimeo(unknown) || isHTML5(unknown);
+};
+var isHTML5 = function isHTML5(unknown) {
+	return isNode(unknown) && unknown.tagName === 'VIDEO';
+};
+var isIFrame = function isIFrame(unknown) {
+	return isNode(unknown) && unknown.tagName === 'IFRAME';
+};
+var isYoutube = function isYoutube(unknown) {
+	return isIFrame(unknown) && !!unknown.src.match(/\/\/.*?youtube(-nocookie)?\.[a-z]+\/(watch\?v=[^&\s]+|embed)|youtu\.be\/.*/);
+};
+var isVimeo = function isVimeo(unknown) {
+	return isIFrame(unknown) && !!unknown.src.match(/vimeo\.com\/video\/.*/);
+};
+
+/***/ }),
+/* 3 */
+/***/ (function(module, __webpack_exports__, __webpack_require__) {
+
+"use strict";
+var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }();
+
+function _toConsumableArray(arr) { if (Array.isArray(arr)) { for (var i = 0, arr2 = Array(arr.length); i < arr.length; i++) { arr2[i] = arr[i]; } return arr2; } else { return Array.from(arr); } }
+
+function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } }
+
+var EventEmitter = function () {
+  function EventEmitter() {
+    var events = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : [];
+
+    _classCallCheck(this, EventEmitter);
+
+    this.events = new Map(events);
+  }
+
+  _createClass(EventEmitter, [{
+    key: "on",
+    value: function on(name, cb) {
+      var _this = this;
+
+      this.events.set(name, [].concat(_toConsumableArray(this.events.has(name) ? this.events.get(name) : []), [cb]));
+
+      return function () {
+        return _this.events.set(name, _this.events.get(name).filter(function (fn) {
+          return fn !== cb;
+        }));
+      };
+    }
+  }, {
+    key: "emit",
+    value: function emit(name) {
+      for (var _len = arguments.length, args = Array(_len > 1 ? _len - 1 : 0), _key = 1; _key < _len; _key++) {
+        args[_key - 1] = arguments[_key];
+      }
+
+      return this.events.has(name) && this.events.get(name).map(function (fn) {
+        return fn.apply(undefined, args);
+      });
+    }
+  }]);
+
+  return EventEmitter;
+}();
+
+/* harmony default export */ __webpack_exports__["a"] = (EventEmitter);
+
+/***/ }),
+/* 4 */
+/***/ (function(module, __webpack_exports__, __webpack_require__) {
+
+"use strict";
+var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }();
+
+function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } }
+
+var Coordinate = function () {
+	function Coordinate() {
+		var x = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : 0;
+		var y = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : 0;
+
+		_classCallCheck(this, Coordinate);
+
+		this._x = x;
+		this._y = y;
+	}
+
+	_createClass(Coordinate, [{
+		key: 'add',
+		value: function add(coord) {
+			return new Coordinate(this._x + coord._x, this._y + coord._y);
+		}
+	}, {
+		key: 'sub',
+		value: function sub(coord) {
+			return new Coordinate(this._x - coord._x, this._y - coord._y);
+		}
+	}, {
+		key: 'distance',
+		value: function distance(coord) {
+			var deltaX = this._x - coord._x;
+			var deltaY = this._y - coord._y;
+
+			return Math.sqrt(Math.pow(deltaX, 2) + Math.pow(deltaY, 2));
+		}
+	}, {
+		key: 'max',
+		value: function max(coord) {
+			var x = Math.max(this._x, coord._x);
+			var y = Math.max(this._y, coord._y);
+
+			return new Coordinate(x, y);
+		}
+	}, {
+		key: 'equals',
+		value: function equals(coord) {
+			if (this == coord) {
+				return true;
+			}
+			if (!coord || coord == null) {
+				return false;
+			}
+			return this._x == coord._x && this._y == coord._y;
+		}
+	}, {
+		key: 'inside',
+		value: function inside(northwest, southeast) {
+			if (this._x >= northwest._x && this._x <= southeast._x && this._y >= northwest._y && this._y <= southeast._y) {
+
+				return true;
+			}
+			return false;
+		}
+	}, {
+		key: 'constrain',
+		value: function constrain(min, max) {
+			if (min._x > max._x || min._y > max._y) {
+				return this;
+			}
+
+			var x = this._x,
+			    y = this._y;
+
+			if (min._x !== null) {
+				x = Math.max(x, min._x);
+			}
+			if (max._x !== null) {
+				x = Math.min(x, max._x);
+			}
+			if (min._y !== null) {
+				y = Math.max(y, min._y);
+			}
+			if (max._y !== null) {
+				y = Math.min(y, max._y);
+			}
+
+			return new Coordinate(x, y);
+		}
+	}, {
+		key: 'reposition',
+		value: function reposition(element) {
+			element.style['top'] = this._y + 'px';
+			element.style['left'] = this._x + 'px';
+		}
+	}, {
+		key: 'toString',
+		value: function toString() {
+			return '(' + this._x + ',' + this._y + ')';
+		}
+	}, {
+		key: 'x',
+		get: function get() {
+			return this._x;
+		},
+		set: function set() {
+			var value = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : 0;
+
+			this._x = value;
+			return this;
+		}
+	}, {
+		key: 'y',
+		get: function get() {
+			return this._y;
+		},
+		set: function set() {
+			var value = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : 0;
+
+			this._y = value;
+			return this;
+		}
+	}]);
+
+	return Coordinate;
+}();
+
+/* harmony default export */ __webpack_exports__["a"] = (Coordinate);
+
+/***/ }),
+/* 5 */
+/***/ (function(module, __webpack_exports__, __webpack_require__) {
+
+"use strict";
+Object.defineProperty(__webpack_exports__, "__esModule", { value: true });
+/* harmony import */ var __WEBPACK_IMPORTED_MODULE_0__utils_index__ = __webpack_require__(6);
+/* harmony import */ var __WEBPACK_IMPORTED_MODULE_1__utils_css__ = __webpack_require__(0);
+/* harmony import */ var __WEBPACK_IMPORTED_MODULE_2__utils_type__ = __webpack_require__(2);
+/* harmony import */ var __WEBPACK_IMPORTED_MODULE_3__utils_eventEmitter__ = __webpack_require__(3);
+/* harmony import */ var __WEBPACK_IMPORTED_MODULE_4__components_autoplay__ = __webpack_require__(7);
+/* harmony import */ var __WEBPACK_IMPORTED_MODULE_5__components_breakpoint__ = __webpack_require__(9);
+/* harmony import */ var __WEBPACK_IMPORTED_MODULE_6__components_infinite__ = __webpack_require__(10);
+/* harmony import */ var __WEBPACK_IMPORTED_MODULE_7__components_loop__ = __webpack_require__(11);
+/* harmony import */ var __WEBPACK_IMPORTED_MODULE_8__components_navigation__ = __webpack_require__(13);
+/* harmony import */ var __WEBPACK_IMPORTED_MODULE_9__components_pagination__ = __webpack_require__(15);
+/* harmony import */ var __WEBPACK_IMPORTED_MODULE_10__components_swipe__ = __webpack_require__(18);
+/* harmony import */ var __WEBPACK_IMPORTED_MODULE_11__components_transitioner__ = __webpack_require__(19);
+/* harmony import */ var __WEBPACK_IMPORTED_MODULE_12__defaultOptions__ = __webpack_require__(22);
+/* harmony import */ var __WEBPACK_IMPORTED_MODULE_13__templates__ = __webpack_require__(23);
+/* harmony import */ var __WEBPACK_IMPORTED_MODULE_14__templates_item__ = __webpack_require__(24);
+var _extends = Object.assign || function (target) { for (var i = 1; i < arguments.length; i++) { var source = arguments[i]; for (var key in source) { if (Object.prototype.hasOwnProperty.call(source, key)) { target[key] = source[key]; } } } return target; };
+
+var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }();
+
+function _defineProperty(obj, key, value) { if (key in obj) { Object.defineProperty(obj, key, { value: value, enumerable: true, configurable: true, writable: true }); } else { obj[key] = value; } return obj; }
+
+function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } }
+
+function _possibleConstructorReturn(self, call) { if (!self) { throw new ReferenceError("this hasn't been initialised - super() hasn't been called"); } return call && (typeof call === "object" || typeof call === "function") ? call : self; }
+
+function _inherits(subClass, superClass) { if (typeof superClass !== "function" && superClass !== null) { throw new TypeError("Super expression must either be null or a function, not " + typeof superClass); } subClass.prototype = Object.create(superClass && superClass.prototype, { constructor: { value: subClass, enumerable: false, writable: true, configurable: true } }); if (superClass) Object.setPrototypeOf ? Object.setPrototypeOf(subClass, superClass) : subClass.__proto__ = superClass; }
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+var bulmaCarousel = function (_EventEmitter) {
+  _inherits(bulmaCarousel, _EventEmitter);
+
+  function bulmaCarousel(selector) {
+    var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {};
+
+    _classCallCheck(this, bulmaCarousel);
+
+    var _this = _possibleConstructorReturn(this, (bulmaCarousel.__proto__ || Object.getPrototypeOf(bulmaCarousel)).call(this));
+
+    _this.element = Object(__WEBPACK_IMPORTED_MODULE_2__utils_type__["c" /* isString */])(selector) ? document.querySelector(selector) : selector;
+    // An invalid selector or non-DOM node has been provided.
+    if (!_this.element) {
+      throw new Error('An invalid selector or non-DOM node has been provided.');
+    }
+    _this._clickEvents = ['click', 'touch'];
+
+    // Use Element dataset values to override options
+    var elementConfig = _this.element.dataset ? Object.keys(_this.element.dataset).filter(function (key) {
+      return Object.keys(__WEBPACK_IMPORTED_MODULE_12__defaultOptions__["a" /* default */]).includes(key);
+    }).reduce(function (obj, key) {
+      return _extends({}, obj, _defineProperty({}, key, _this.element.dataset[key]));
+    }, {}) : {};
+    // Set default options - dataset attributes are master
+    _this.options = _extends({}, __WEBPACK_IMPORTED_MODULE_12__defaultOptions__["a" /* default */], options, elementConfig);
+
+    _this._id = Object(__WEBPACK_IMPORTED_MODULE_0__utils_index__["a" /* uuid */])('slider');
+
+    _this.onShow = _this.onShow.bind(_this);
+
+    // Initiate plugin
+    _this._init();
+    return _this;
+  }
+
+  /**
+   * Initiate all DOM element containing datePicker class
+   * @method
+   * @return {Array} Array of all datePicker instances
+   */
+
+
+  _createClass(bulmaCarousel, [{
+    key: '_init',
+
+
+    /****************************************************
+     *                                                  *
+     * PRIVATE FUNCTIONS                                *
+     *                                                  *
+     ****************************************************/
+    /**
+     * Initiate plugin instance
+     * @method _init
+     * @return {Slider} Current plugin instance
+     */
+    value: function _init() {
+      this._items = Array.from(this.element.children);
+
+      // Load plugins
+      this._breakpoint = new __WEBPACK_IMPORTED_MODULE_5__components_breakpoint__["a" /* default */](this);
+      this._autoplay = new __WEBPACK_IMPORTED_MODULE_4__components_autoplay__["a" /* default */](this);
+      this._navigation = new __WEBPACK_IMPORTED_MODULE_8__components_navigation__["a" /* default */](this);
+      this._pagination = new __WEBPACK_IMPORTED_MODULE_9__components_pagination__["a" /* default */](this);
+      this._infinite = new __WEBPACK_IMPORTED_MODULE_6__components_infinite__["a" /* default */](this);
+      this._loop = new __WEBPACK_IMPORTED_MODULE_7__components_loop__["a" /* default */](this);
+      this._swipe = new __WEBPACK_IMPORTED_MODULE_10__components_swipe__["a" /* default */](this);
+
+      this._build();
+
+      if (Object(__WEBPACK_IMPORTED_MODULE_2__utils_type__["a" /* isFunction */])(this.options.onReady)) {
+        this.options.onReady(this);
+      }
+
+      return this;
+    }
+
+    /**
+     * Build Slider HTML component and append it to the DOM
+     * @method _build
+     */
+
+  }, {
+    key: '_build',
+    value: function _build() {
+      var _this2 = this;
+
+      // Generate HTML Fragment of template
+      this.node = document.createRange().createContextualFragment(Object(__WEBPACK_IMPORTED_MODULE_13__templates__["a" /* default */])(this.id));
+      // Save pointers to template parts
+      this._ui = {
+        wrapper: this.node.firstChild,
+        container: this.node.querySelector('.slider-container')
+
+        // Add slider to DOM
+      };this.element.appendChild(this.node);
+      this._ui.wrapper.classList.add('is-loading');
+      this._ui.container.style.opacity = 0;
+
+      this._transitioner = new __WEBPACK_IMPORTED_MODULE_11__components_transitioner__["a" /* default */](this);
+
+      // Wrap all items by slide element
+      this._slides = this._items.map(function (item, index) {
+        return _this2._createSlide(item, index);
+      });
+
+      this.reset();
+
+      this._bindEvents();
+
+      this._ui.container.style.opacity = 1;
+      this._ui.wrapper.classList.remove('is-loading');
+    }
+
+    /**
+     * Bind all events
+     * @method _bindEvents
+     * @return {void}
+     */
+
+  }, {
+    key: '_bindEvents',
+    value: function _bindEvents() {
+      this.on('show', this.onShow);
+    }
+  }, {
+    key: '_unbindEvents',
+    value: function _unbindEvents() {
+      this.off('show', this.onShow);
+    }
+  }, {
+    key: '_createSlide',
+    value: function _createSlide(item, index) {
+      var slide = document.createRange().createContextualFragment(Object(__WEBPACK_IMPORTED_MODULE_14__templates_item__["a" /* default */])()).firstChild;
+      slide.dataset.sliderIndex = index;
+      slide.appendChild(item);
+      return slide;
+    }
+
+    /**
+     * Calculate slider dimensions
+     */
+
+  }, {
+    key: '_setDimensions',
+    value: function _setDimensions() {
+      var _this3 = this;
+
+      if (!this.options.vertical) {
+        if (this.options.centerMode) {
+          this._ui.wrapper.style.padding = '0px ' + this.options.centerPadding;
+        }
+      } else {
+        this._ui.wrapper.style.height = Object(__WEBPACK_IMPORTED_MODULE_1__utils_css__["c" /* outerHeight */])(this._slides[0]) * this.slidesToShow;
+        if (this.options.centerMode) {
+          this._ui.wrapper.style.padding = this.options.centerPadding + ' 0px';
+        }
+      }
+
+      this._wrapperWidth = Object(__WEBPACK_IMPORTED_MODULE_1__utils_css__["e" /* width */])(this._ui.wrapper);
+      this._wrapperHeight = Object(__WEBPACK_IMPORTED_MODULE_1__utils_css__["c" /* outerHeight */])(this._ui.wrapper);
+
+      if (!this.options.vertical) {
+        this._slideWidth = Math.ceil(this._wrapperWidth / this.slidesToShow);
+        this._containerWidth = Math.ceil(this._slideWidth * this._slides.length);
+        this._ui.container.style.width = this._containerWidth + 'px';
+      } else {
+        this._slideWidth = Math.ceil(this._wrapperWidth);
+        this._containerHeight = Math.ceil(Object(__WEBPACK_IMPORTED_MODULE_1__utils_css__["c" /* outerHeight */])(this._slides[0]) * this._slides.length);
+        this._ui.container.style.height = this._containerHeight + 'px';
+      }
+
+      this._slides.forEach(function (slide) {
+        slide.style.width = _this3._slideWidth + 'px';
+      });
+    }
+  }, {
+    key: '_setHeight',
+    value: function _setHeight() {
+      if (this.options.effect !== 'translate') {
+        this._ui.container.style.height = Object(__WEBPACK_IMPORTED_MODULE_1__utils_css__["c" /* outerHeight */])(this._slides[this.state.index]) + 'px';
+      }
+    }
+
+    // Update slides classes
+
+  }, {
+    key: '_setClasses',
+    value: function _setClasses() {
+      var _this4 = this;
+
+      this._slides.forEach(function (slide) {
+        Object(__WEBPACK_IMPORTED_MODULE_1__utils_css__["d" /* removeClasses */])(slide, 'is-active is-current is-slide-previous is-slide-next');
+        if (Math.abs((_this4.state.index - 1) % _this4.state.length) === parseInt(slide.dataset.sliderIndex, 10)) {
+          slide.classList.add('is-slide-previous');
+        }
+        if (Math.abs(_this4.state.index % _this4.state.length) === parseInt(slide.dataset.sliderIndex, 10)) {
+          slide.classList.add('is-current');
+        }
+        if (Math.abs((_this4.state.index + 1) % _this4.state.length) === parseInt(slide.dataset.sliderIndex, 10)) {
+          slide.classList.add('is-slide-next');
+        }
+      });
+    }
+
+    /****************************************************
+     *                                                  *
+     * GETTERS and SETTERS                              *
+     *                                                  *
+     ****************************************************/
+
+    /**
+     * Get id of current datePicker
+     */
+
+  }, {
+    key: 'onShow',
+
+
+    /****************************************************
+     *                                                  *
+     * EVENTS FUNCTIONS                                 *
+     *                                                  *
+     ****************************************************/
+    value: function onShow(e) {
+      this._navigation.refresh();
+      this._pagination.refresh();
+      this._setClasses();
+    }
+
+    /****************************************************
+     *                                                  *
+     * PUBLIC FUNCTIONS                                 *
+     *                                                  *
+     ****************************************************/
+
+  }, {
+    key: 'next',
+    value: function next() {
+      if (!this.options.loop && !this.options.infinite && this.state.index + this.slidesToScroll > this.state.length - this.slidesToShow && !this.options.centerMode) {
+        this.state.next = this.state.index;
+      } else {
+        this.state.next = this.state.index + this.slidesToScroll;
+      }
+      this.show();
+    }
+  }, {
+    key: 'previous',
+    value: function previous() {
+      if (!this.options.loop && !this.options.infinite && this.state.index === 0) {
+        this.state.next = this.state.index;
+      } else {
+        this.state.next = this.state.index - this.slidesToScroll;
+      }
+      this.show();
+    }
+  }, {
+    key: 'start',
+    value: function start() {
+      this._autoplay.start();
+    }
+  }, {
+    key: 'pause',
+    value: function pause() {
+      this._autoplay.pause();
+    }
+  }, {
+    key: 'stop',
+    value: function stop() {
+      this._autoplay.stop();
+    }
+  }, {
+    key: 'show',
+    value: function show(index) {
+      var force = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : false;
+
+      // If all slides are already visible then return
+      if (!this.state.length || this.state.length <= this.slidesToShow) {
+        return;
+      }
+
+      if (typeof index === 'Number') {
+        this.state.next = index;
+      }
+
+      if (this.options.loop) {
+        this._loop.apply();
+      }
+      if (this.options.infinite) {
+        this._infinite.apply();
+      }
+
+      // If new slide is already the current one then return
+      if (this.state.index === this.state.next) {
+        return;
+      }
+
+      this.emit('before:show', this.state);
+      this._transitioner.apply(force, this._setHeight.bind(this));
+      this.emit('after:show', this.state);
+
+      this.emit('show', this);
+    }
+  }, {
+    key: 'reset',
+    value: function reset() {
+      var _this5 = this;
+
+      this.state = {
+        length: this._items.length,
+        index: Math.abs(this.options.initialSlide),
+        next: Math.abs(this.options.initialSlide),
+        prev: undefined
+      };
+
+      // Fix options
+      if (this.options.loop && this.options.infinite) {
+        this.options.loop = false;
+      }
+      if (this.options.slidesToScroll > this.options.slidesToShow) {
+        this.options.slidesToScroll = this.slidesToShow;
+      }
+      this._breakpoint.init();
+
+      if (this.state.index >= this.state.length && this.state.index !== 0) {
+        this.state.index = this.state.index - this.slidesToScroll;
+      }
+      if (this.state.length <= this.slidesToShow) {
+        this.state.index = 0;
+      }
+
+      this._ui.wrapper.appendChild(this._navigation.init().render());
+      this._ui.wrapper.appendChild(this._pagination.init().render());
+
+      if (this.options.navigationSwipe) {
+        this._swipe.bindEvents();
+      } else {
+        this._swipe._bindEvents();
+      }
+
+      this._breakpoint.apply();
+      // Move all created slides into slider
+      this._slides.forEach(function (slide) {
+        return _this5._ui.container.appendChild(slide);
+      });
+      this._transitioner.init().apply(true, this._setHeight.bind(this));
+
+      if (this.options.autoplay) {
+        this._autoplay.init().start();
+      }
+    }
+
+    /**
+     * Destroy Slider
+     * @method destroy
+     */
+
+  }, {
+    key: 'destroy',
+    value: function destroy() {
+      var _this6 = this;
+
+      this._unbindEvents();
+      this._items.forEach(function (item) {
+        _this6.element.appendChild(item);
+      });
+      this.node.remove();
+    }
+  }, {
+    key: 'id',
+    get: function get() {
+      return this._id;
+    }
+  }, {
+    key: 'index',
+    set: function set(index) {
+      this._index = index;
+    },
+    get: function get() {
+      return this._index;
+    }
+  }, {
+    key: 'length',
+    set: function set(length) {
+      this._length = length;
+    },
+    get: function get() {
+      return this._length;
+    }
+  }, {
+    key: 'slides',
+    get: function get() {
+      return this._slides;
+    },
+    set: function set(slides) {
+      this._slides = slides;
+    }
+  }, {
+    key: 'slidesToScroll',
+    get: function get() {
+      return this.options.effect === 'translate' ? this._breakpoint.getSlidesToScroll() : 1;
+    }
+  }, {
+    key: 'slidesToShow',
+    get: function get() {
+      return this.options.effect === 'translate' ? this._breakpoint.getSlidesToShow() : 1;
+    }
+  }, {
+    key: 'direction',
+    get: function get() {
+      return this.element.dir.toLowerCase() === 'rtl' || this.element.style.direction === 'rtl' ? 'rtl' : 'ltr';
+    }
+  }, {
+    key: 'wrapper',
+    get: function get() {
+      return this._ui.wrapper;
+    }
+  }, {
+    key: 'wrapperWidth',
+    get: function get() {
+      return this._wrapperWidth || 0;
+    }
+  }, {
+    key: 'container',
+    get: function get() {
+      return this._ui.container;
+    }
+  }, {
+    key: 'containerWidth',
+    get: function get() {
+      return this._containerWidth || 0;
+    }
+  }, {
+    key: 'slideWidth',
+    get: function get() {
+      return this._slideWidth || 0;
+    }
+  }, {
+    key: 'transitioner',
+    get: function get() {
+      return this._transitioner;
+    }
+  }], [{
+    key: 'attach',
+    value: function attach() {
+      var _this7 = this;
+
+      var selector = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : '.slider';
+      var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {};
+
+      var instances = new Array();
+
+      var elements = Object(__WEBPACK_IMPORTED_MODULE_2__utils_type__["c" /* isString */])(selector) ? document.querySelectorAll(selector) : Array.isArray(selector) ? selector : [selector];
+      [].forEach.call(elements, function (element) {
+        if (typeof element[_this7.constructor.name] === 'undefined') {
+          var instance = new bulmaCarousel(element, options);
+          element[_this7.constructor.name] = instance;
+          instances.push(instance);
+        } else {
+          instances.push(element[_this7.constructor.name]);
+        }
+      });
+
+      return instances;
+    }
+  }]);
+
+  return bulmaCarousel;
+}(__WEBPACK_IMPORTED_MODULE_3__utils_eventEmitter__["a" /* default */]);
+
+/* harmony default export */ __webpack_exports__["default"] = (bulmaCarousel);
+
+/***/ }),
+/* 6 */
+/***/ (function(module, __webpack_exports__, __webpack_require__) {
+
+"use strict";
+/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "a", function() { return uuid; });
+/* unused harmony export isRtl */
+/* unused harmony export defer */
+/* unused harmony export getNodeIndex */
+/* unused harmony export camelize */
+function _toConsumableArray(arr) { if (Array.isArray(arr)) { for (var i = 0, arr2 = Array(arr.length); i < arr.length; i++) { arr2[i] = arr[i]; } return arr2; } else { return Array.from(arr); } }
+
+var uuid = function uuid() {
+	var prefix = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : '';
+	return prefix + ([1e7] + -1e3 + -4e3 + -8e3 + -1e11).replace(/[018]/g, function (c) {
+		return (c ^ crypto.getRandomValues(new Uint8Array(1))[0] & 15 >> c / 4).toString(16);
+	});
+};
+var isRtl = function isRtl() {
+	return document.documentElement.getAttribute('dir') === 'rtl';
+};
+
+var defer = function defer() {
+	this.promise = new Promise(function (resolve, reject) {
+		this.resolve = resolve;
+		this.reject = reject;
+	}.bind(this));
+
+	this.then = this.promise.then.bind(this.promise);
+	this.catch = this.promise.catch.bind(this.promise);
+};
+
+var getNodeIndex = function getNodeIndex(node) {
+	return [].concat(_toConsumableArray(node.parentNode.children)).indexOf(node);
+};
+var camelize = function camelize(str) {
+	return str.replace(/-(\w)/g, toUpper);
+};
+
+/***/ }),
+/* 7 */
+/***/ (function(module, __webpack_exports__, __webpack_require__) {
+
+"use strict";
+/* harmony import */ var __WEBPACK_IMPORTED_MODULE_0__utils_eventEmitter__ = __webpack_require__(3);
+/* harmony import */ var __WEBPACK_IMPORTED_MODULE_1__utils_device__ = __webpack_require__(8);
+var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }();
+
+function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } }
+
+function _possibleConstructorReturn(self, call) { if (!self) { throw new ReferenceError("this hasn't been initialised - super() hasn't been called"); } return call && (typeof call === "object" || typeof call === "function") ? call : self; }
+
+function _inherits(subClass, superClass) { if (typeof superClass !== "function" && superClass !== null) { throw new TypeError("Super expression must either be null or a function, not " + typeof superClass); } subClass.prototype = Object.create(superClass && superClass.prototype, { constructor: { value: subClass, enumerable: false, writable: true, configurable: true } }); if (superClass) Object.setPrototypeOf ? Object.setPrototypeOf(subClass, superClass) : subClass.__proto__ = superClass; }
+
+
+
+
+var onVisibilityChange = Symbol('onVisibilityChange');
+var onMouseEnter = Symbol('onMouseEnter');
+var onMouseLeave = Symbol('onMouseLeave');
+
+var defaultOptions = {
+	autoplay: false,
+	autoplaySpeed: 3000
+};
+
+var Autoplay = function (_EventEmitter) {
+	_inherits(Autoplay, _EventEmitter);
+
+	function Autoplay(slider) {
+		_classCallCheck(this, Autoplay);
+
+		var _this = _possibleConstructorReturn(this, (Autoplay.__proto__ || Object.getPrototypeOf(Autoplay)).call(this));
+
+		_this.slider = slider;
+
+		_this.onVisibilityChange = _this.onVisibilityChange.bind(_this);
+		_this.onMouseEnter = _this.onMouseEnter.bind(_this);
+		_this.onMouseLeave = _this.onMouseLeave.bind(_this);
+		return _this;
+	}
+
+	_createClass(Autoplay, [{
+		key: 'init',
+		value: function init() {
+			this._bindEvents();
+			return this;
+		}
+	}, {
+		key: '_bindEvents',
+		value: function _bindEvents() {
+			document.addEventListener('visibilitychange', this.onVisibilityChange);
+			if (this.slider.options.pauseOnHover) {
+				this.slider.container.addEventListener(__WEBPACK_IMPORTED_MODULE_1__utils_device__["a" /* pointerEnter */], this.onMouseEnter);
+				this.slider.container.addEventListener(__WEBPACK_IMPORTED_MODULE_1__utils_device__["b" /* pointerLeave */], this.onMouseLeave);
+			}
+		}
+	}, {
+		key: '_unbindEvents',
+		value: function _unbindEvents() {
+			document.removeEventListener('visibilitychange', this.onVisibilityChange);
+			this.slider.container.removeEventListener(__WEBPACK_IMPORTED_MODULE_1__utils_device__["a" /* pointerEnter */], this.onMouseEnter);
+			this.slider.container.removeEventListener(__WEBPACK_IMPORTED_MODULE_1__utils_device__["b" /* pointerLeave */], this.onMouseLeave);
+		}
+	}, {
+		key: 'start',
+		value: function start() {
+			var _this2 = this;
+
+			this.stop();
+			if (this.slider.options.autoplay) {
+				this.emit('start', this);
+				this._interval = setInterval(function () {
+					if (!(_this2._hovering && _this2.slider.options.pauseOnHover)) {
+						if (!_this2.slider.options.centerMode && _this2.slider.state.next >= _this2.slider.state.length - _this2.slider.slidesToShow && !_this2.slider.options.loop && !_this2.slider.options.infinite) {
+							_this2.stop();
+						} else {
+							_this2.slider.next();
+						}
+					}
+				}, this.slider.options.autoplaySpeed);
+			}
+		}
+	}, {
+		key: 'stop',
+		value: function stop() {
+			this._interval = clearInterval(this._interval);
+			this.emit('stop', this);
+		}
+	}, {
+		key: 'pause',
+		value: function pause() {
+			var _this3 = this;
+
+			var speed = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : 0;
+
+			if (this.paused) {
+				return;
+			}
+			if (this.timer) {
+				this.stop();
+			}
+			this.paused = true;
+			if (speed === 0) {
+				this.paused = false;
+				this.start();
+			} else {
+				this.slider.on('transition:end', function () {
+					if (!_this3) {
+						return;
+					}
+					_this3.paused = false;
+					if (!_this3.run) {
+						_this3.stop();
+					} else {
+						_this3.start();
+					}
+				});
+			}
+		}
+	}, {
+		key: 'onVisibilityChange',
+		value: function onVisibilityChange(e) {
+			if (document.hidden) {
+				this.stop();
+			} else {
+				this.start();
+			}
+		}
+	}, {
+		key: 'onMouseEnter',
+		value: function onMouseEnter(e) {
+			this._hovering = true;
+			if (this.slider.options.pauseOnHover) {
+				this.pause();
+			}
+		}
+	}, {
+		key: 'onMouseLeave',
+		value: function onMouseLeave(e) {
+			this._hovering = false;
+			if (this.slider.options.pauseOnHover) {
+				this.pause();
+			}
+		}
+	}]);
+
+	return Autoplay;
+}(__WEBPACK_IMPORTED_MODULE_0__utils_eventEmitter__["a" /* default */]);
+
+/* harmony default export */ __webpack_exports__["a"] = (Autoplay);
+
+/***/ }),
+/* 8 */
+/***/ (function(module, __webpack_exports__, __webpack_require__) {
+
+"use strict";
+/* unused harmony export isIE */
+/* unused harmony export isIETouch */
+/* unused harmony export isAndroid */
+/* unused harmony export isiPad */
+/* unused harmony export isiPod */
+/* unused harmony export isiPhone */
+/* unused harmony export isSafari */
+/* unused harmony export isUiWebView */
+/* unused harmony export supportsTouchEvents */
+/* unused harmony export supportsPointerEvents */
+/* unused harmony export supportsTouch */
+/* unused harmony export pointerDown */
+/* unused harmony export pointerMove */
+/* unused harmony export pointerUp */
+/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "a", function() { return pointerEnter; });
+/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "b", function() { return pointerLeave; });
+var isIE = window.navigator.pointerEnabled || window.navigator.msPointerEnabled;
+var isIETouch = window.navigator.msPointerEnabled && window.navigator.msMaxTouchPoints > 1 || window.navigator.pointerEnabled && window.navigator.maxTouchPoints > 1;
+var isAndroid = navigator.userAgent.match(/(Android);?[\s\/]+([\d.]+)?/);
+var isiPad = navigator.userAgent.match(/(iPad).*OS\s([\d_]+)/);
+var isiPod = navigator.userAgent.match(/(iPod)(.*OS\s([\d_]+))?/);
+var isiPhone = !navigator.userAgent.match(/(iPad).*OS\s([\d_]+)/) && navigator.userAgent.match(/(iPhone\sOS)\s([\d_]+)/);
+var isSafari = navigator.userAgent.toLowerCase().indexOf('safari') >= 0 && navigator.userAgent.toLowerCase().indexOf('chrome') < 0 && navigator.userAgent.toLowerCase().indexOf('android') < 0;
+var isUiWebView = /(iPhone|iPod|iPad).*AppleWebKit(?!.*Safari)/i.test(navigator.userAgent);
+
+var supportsTouchEvents = !!('ontouchstart' in window);
+var supportsPointerEvents = !!('PointerEvent' in window);
+var supportsTouch = supportsTouchEvents || window.DocumentTouch && document instanceof DocumentTouch || navigator.maxTouchPoints; // IE >=11
+var pointerDown = !supportsTouch ? 'mousedown' : 'mousedown ' + (supportsTouchEvents ? 'touchstart' : 'pointerdown');
+var pointerMove = !supportsTouch ? 'mousemove' : 'mousemove ' + (supportsTouchEvents ? 'touchmove' : 'pointermove');
+var pointerUp = !supportsTouch ? 'mouseup' : 'mouseup ' + (supportsTouchEvents ? 'touchend' : 'pointerup');
+var pointerEnter = supportsTouch && supportsPointerEvents ? 'pointerenter' : 'mouseenter';
+var pointerLeave = supportsTouch && supportsPointerEvents ? 'pointerleave' : 'mouseleave';
+
+/***/ }),
+/* 9 */
+/***/ (function(module, __webpack_exports__, __webpack_require__) {
+
+"use strict";
+var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }();
+
+function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } }
+
+var onResize = Symbol('onResize');
+
+var Breakpoints = function () {
+	function Breakpoints(slider) {
+		_classCallCheck(this, Breakpoints);
+
+		this.slider = slider;
+		this.options = slider.options;
+
+		this[onResize] = this[onResize].bind(this);
+
+		this._bindEvents();
+	}
+
+	_createClass(Breakpoints, [{
+		key: 'init',
+		value: function init() {
+			this._defaultBreakpoint = {
+				slidesToShow: this.options.slidesToShow,
+				slidesToScroll: this.options.slidesToScroll
+			};
+			this.options.breakpoints.sort(function (a, b) {
+				return parseInt(a.changePoint, 10) > parseInt(b.changePoint, 10);
+			});
+			this._currentBreakpoint = this._getActiveBreakpoint();
+
+			return this;
+		}
+	}, {
+		key: 'destroy',
+		value: function destroy() {
+			this._unbindEvents();
+		}
+	}, {
+		key: '_bindEvents',
+		value: function _bindEvents() {
+			window.addEventListener('resize', this[onResize]);
+			window.addEventListener('orientationchange', this[onResize]);
+		}
+	}, {
+		key: '_unbindEvents',
+		value: function _unbindEvents() {
+			window.removeEventListener('resize', this[onResize]);
+			window.removeEventListener('orientationchange', this[onResize]);
+		}
+	}, {
+		key: '_getActiveBreakpoint',
+		value: function _getActiveBreakpoint() {
+			//Get breakpoint for window width
+			var _iteratorNormalCompletion = true;
+			var _didIteratorError = false;
+			var _iteratorError = undefined;
+
+			try {
+				for (var _iterator = this.options.breakpoints[Symbol.iterator](), _step; !(_iteratorNormalCompletion = (_step = _iterator.next()).done); _iteratorNormalCompletion = true) {
+					var point = _step.value;
+
+					if (point.changePoint >= window.innerWidth) {
+						return point;
+					}
+				}
+			} catch (err) {
+				_didIteratorError = true;
+				_iteratorError = err;
+			} finally {
+				try {
+					if (!_iteratorNormalCompletion && _iterator.return) {
+						_iterator.return();
+					}
+				} finally {
+					if (_didIteratorError) {
+						throw _iteratorError;
+					}
+				}
+			}
+
+			return this._defaultBreakpoint;
+		}
+	}, {
+		key: 'getSlidesToShow',
+		value: function getSlidesToShow() {
+			return this._currentBreakpoint ? this._currentBreakpoint.slidesToShow : this._defaultBreakpoint.slidesToShow;
+		}
+	}, {
+		key: 'getSlidesToScroll',
+		value: function getSlidesToScroll() {
+			return this._currentBreakpoint ? this._currentBreakpoint.slidesToScroll : this._defaultBreakpoint.slidesToScroll;
+		}
+	}, {
+		key: 'apply',
+		value: function apply() {
+			if (this.slider.state.index >= this.slider.state.length && this.slider.state.index !== 0) {
+				this.slider.state.index = this.slider.state.index - this._currentBreakpoint.slidesToScroll;
+			}
+			if (this.slider.state.length <= this._currentBreakpoint.slidesToShow) {
+				this.slider.state.index = 0;
+			}
+
+			if (this.options.loop) {
+				this.slider._loop.init().apply();
+			}
+
+			if (this.options.infinite) {
+				this.slider._infinite.init().apply();
+			}
+
+			this.slider._setDimensions();
+			this.slider._transitioner.init().apply(true, this.slider._setHeight.bind(this.slider));
+			this.slider._setClasses();
+
+			this.slider._navigation.refresh();
+			this.slider._pagination.refresh();
+		}
+	}, {
+		key: onResize,
+		value: function value(e) {
+			var newBreakPoint = this._getActiveBreakpoint();
+			if (newBreakPoint.slidesToShow !== this._currentBreakpoint.slidesToShow) {
+				this._currentBreakpoint = newBreakPoint;
+				this.apply();
+			}
+		}
+	}]);
+
+	return Breakpoints;
+}();
+
+/* harmony default export */ __webpack_exports__["a"] = (Breakpoints);
+
+/***/ }),
+/* 10 */
+/***/ (function(module, __webpack_exports__, __webpack_require__) {
+
+"use strict";
+var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }();
+
+function _toConsumableArray(arr) { if (Array.isArray(arr)) { for (var i = 0, arr2 = Array(arr.length); i < arr.length; i++) { arr2[i] = arr[i]; } return arr2; } else { return Array.from(arr); } }
+
+function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } }
+
+var Infinite = function () {
+	function Infinite(slider) {
+		_classCallCheck(this, Infinite);
+
+		this.slider = slider;
+	}
+
+	_createClass(Infinite, [{
+		key: 'init',
+		value: function init() {
+			if (this.slider.options.infinite && this.slider.options.effect === 'translate') {
+				if (this.slider.options.centerMode) {
+					this._infiniteCount = Math.ceil(this.slider.slidesToShow + this.slider.slidesToShow / 2);
+				} else {
+					this._infiniteCount = this.slider.slidesToShow;
+				}
+
+				var frontClones = [];
+				var slideIndex = 0;
+				for (var i = this.slider.state.length; i > this.slider.state.length - 1 - this._infiniteCount; i -= 1) {
+					slideIndex = i - 1;
+					frontClones.unshift(this._cloneSlide(this.slider.slides[slideIndex], slideIndex - this.slider.state.length));
+				}
+
+				var backClones = [];
+				for (var _i = 0; _i < this._infiniteCount + this.slider.state.length; _i += 1) {
+					backClones.push(this._cloneSlide(this.slider.slides[_i % this.slider.state.length], _i + this.slider.state.length));
+				}
+
+				this.slider.slides = [].concat(frontClones, _toConsumableArray(this.slider.slides), backClones);
+			}
+			return this;
+		}
+	}, {
+		key: 'apply',
+		value: function apply() {}
+	}, {
+		key: 'onTransitionEnd',
+		value: function onTransitionEnd(e) {
+			if (this.slider.options.infinite) {
+				if (this.slider.state.next >= this.slider.state.length) {
+					this.slider.state.index = this.slider.state.next = this.slider.state.next - this.slider.state.length;
+					this.slider.transitioner.apply(true);
+				} else if (this.slider.state.next < 0) {
+					this.slider.state.index = this.slider.state.next = this.slider.state.length + this.slider.state.next;
+					this.slider.transitioner.apply(true);
+				}
+			}
+		}
+	}, {
+		key: '_cloneSlide',
+		value: function _cloneSlide(slide, index) {
+			var newSlide = slide.cloneNode(true);
+			newSlide.dataset.sliderIndex = index;
+			newSlide.dataset.cloned = true;
+			var ids = newSlide.querySelectorAll('[id]') || [];
+			ids.forEach(function (id) {
+				id.setAttribute('id', '');
+			});
+			return newSlide;
+		}
+	}]);
+
+	return Infinite;
+}();
+
+/* harmony default export */ __webpack_exports__["a"] = (Infinite);
+
+/***/ }),
+/* 11 */
+/***/ (function(module, __webpack_exports__, __webpack_require__) {
+
+"use strict";
+/* harmony import */ var __WEBPACK_IMPORTED_MODULE_0__utils_dom__ = __webpack_require__(12);
+var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }();
+
+function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } }
+
+
+
+var Loop = function () {
+	function Loop(slider) {
+		_classCallCheck(this, Loop);
+
+		this.slider = slider;
+	}
+
+	_createClass(Loop, [{
+		key: "init",
+		value: function init() {
+			return this;
+		}
+	}, {
+		key: "apply",
+		value: function apply() {
+			if (this.slider.options.loop) {
+				if (this.slider.state.next > 0) {
+					if (this.slider.state.next < this.slider.state.length) {
+						if (this.slider.state.next > this.slider.state.length - this.slider.slidesToShow && Object(__WEBPACK_IMPORTED_MODULE_0__utils_dom__["a" /* isInViewport */])(this.slider._slides[this.slider.state.length - 1], this.slider.wrapper)) {
+							this.slider.state.next = 0;
+						} else {
+							this.slider.state.next = Math.min(Math.max(this.slider.state.next, 0), this.slider.state.length - this.slider.slidesToShow);
+						}
+					} else {
+						this.slider.state.next = 0;
+					}
+				} else {
+					if (this.slider.state.next <= 0 - this.slider.slidesToScroll) {
+						this.slider.state.next = this.slider.state.length - this.slider.slidesToShow;
+					} else {
+						this.slider.state.next = 0;
+					}
+				}
+			}
+		}
+	}]);
+
+	return Loop;
+}();
+
+/* harmony default export */ __webpack_exports__["a"] = (Loop);
+
+/***/ }),
+/* 12 */
+/***/ (function(module, __webpack_exports__, __webpack_require__) {
+
+"use strict";
+/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "a", function() { return isInViewport; });
+var isInViewport = function isInViewport(element, html) {
+	var rect = element.getBoundingClientRect();
+	html = html || document.documentElement;
+	return rect.top >= 0 && rect.left >= 0 && rect.bottom <= (window.innerHeight || html.clientHeight) && rect.right <= (window.innerWidth || html.clientWidth);
+};
+
+/***/ }),
+/* 13 */
+/***/ (function(module, __webpack_exports__, __webpack_require__) {
+
+"use strict";
+/* harmony import */ var __WEBPACK_IMPORTED_MODULE_0__templates_navigation__ = __webpack_require__(14);
+/* harmony import */ var __WEBPACK_IMPORTED_MODULE_1__utils_detect_supportsPassive__ = __webpack_require__(1);
+var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }();
+
+function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } }
+
+
+
+
+var Navigation = function () {
+	function Navigation(slider) {
+		_classCallCheck(this, Navigation);
+
+		this.slider = slider;
+
+		this._clickEvents = ['click', 'touch'];
+		this._supportsPassive = Object(__WEBPACK_IMPORTED_MODULE_1__utils_detect_supportsPassive__["a" /* default */])();
+
+		this.onPreviousClick = this.onPreviousClick.bind(this);
+		this.onNextClick = this.onNextClick.bind(this);
+		this.onKeyUp = this.onKeyUp.bind(this);
+	}
+
+	_createClass(Navigation, [{
+		key: 'init',
+		value: function init() {
+			this.node = document.createRange().createContextualFragment(Object(__WEBPACK_IMPORTED_MODULE_0__templates_navigation__["a" /* default */])(this.slider.options.icons));
+			this._ui = {
+				previous: this.node.querySelector('.slider-navigation-previous'),
+				next: this.node.querySelector('.slider-navigation-next')
+			};
+
+			this._unbindEvents();
+			this._bindEvents();
+
+			this.refresh();
+
+			return this;
+		}
+	}, {
+		key: 'destroy',
+		value: function destroy() {
+			this._unbindEvents();
+		}
+	}, {
+		key: '_bindEvents',
+		value: function _bindEvents() {
+			var _this = this;
+
+			this.slider.wrapper.addEventListener('keyup', this.onKeyUp);
+			this._clickEvents.forEach(function (clickEvent) {
+				_this._ui.previous.addEventListener(clickEvent, _this.onPreviousClick);
+				_this._ui.next.addEventListener(clickEvent, _this.onNextClick);
+			});
+		}
+	}, {
+		key: '_unbindEvents',
+		value: function _unbindEvents() {
+			var _this2 = this;
+
+			this.slider.wrapper.removeEventListener('keyup', this.onKeyUp);
+			this._clickEvents.forEach(function (clickEvent) {
+				_this2._ui.previous.removeEventListener(clickEvent, _this2.onPreviousClick);
+				_this2._ui.next.removeEventListener(clickEvent, _this2.onNextClick);
+			});
+		}
+	}, {
+		key: 'onNextClick',
+		value: function onNextClick(e) {
+			if (!this._supportsPassive) {
+				e.preventDefault();
+			}
+
+			if (this.slider.options.navigation) {
+				this.slider.next();
+			}
+		}
+	}, {
+		key: 'onPreviousClick',
+		value: function onPreviousClick(e) {
+			if (!this._supportsPassive) {
+				e.preventDefault();
+			}
+
+			if (this.slider.options.navigation) {
+				this.slider.previous();
+			}
+		}
+	}, {
+		key: 'onKeyUp',
+		value: function onKeyUp(e) {
+			if (this.slider.options.keyNavigation) {
+				if (e.key === 'ArrowRight' || e.key === 'Right') {
+					this.slider.next();
+				} else if (e.key === 'ArrowLeft' || e.key === 'Left') {
+					this.slider.previous();
+				}
+			}
+		}
+	}, {
+		key: 'refresh',
+		value: function refresh() {
+			// let centerOffset = Math.floor(this.options.slidesToShow / 2);
+			if (!this.slider.options.loop && !this.slider.options.infinite) {
+				if (this.slider.options.navigation && this.slider.state.length > this.slider.slidesToShow) {
+					this._ui.previous.classList.remove('is-hidden');
+					this._ui.next.classList.remove('is-hidden');
+					if (this.slider.state.next === 0) {
+						this._ui.previous.classList.add('is-hidden');
+						this._ui.next.classList.remove('is-hidden');
+					} else if (this.slider.state.next >= this.slider.state.length - this.slider.slidesToShow && !this.slider.options.centerMode) {
+						this._ui.previous.classList.remove('is-hidden');
+						this._ui.next.classList.add('is-hidden');
+					} else if (this.slider.state.next >= this.slider.state.length - 1 && this.slider.options.centerMode) {
+						this._ui.previous.classList.remove('is-hidden');
+						this._ui.next.classList.add('is-hidden');
+					}
+				} else {
+					this._ui.previous.classList.add('is-hidden');
+					this._ui.next.classList.add('is-hidden');
+				}
+			}
+		}
+	}, {
+		key: 'render',
+		value: function render() {
+			return this.node;
+		}
+	}]);
+
+	return Navigation;
+}();
+
+/* harmony default export */ __webpack_exports__["a"] = (Navigation);
+
+/***/ }),
+/* 14 */
+/***/ (function(module, __webpack_exports__, __webpack_require__) {
+
+"use strict";
+/* harmony default export */ __webpack_exports__["a"] = (function (icons) {
+	return "<div class=\"slider-navigation-previous\">" + icons.previous + "</div>\n<div class=\"slider-navigation-next\">" + icons.next + "</div>";
+});
+
+/***/ }),
+/* 15 */
+/***/ (function(module, __webpack_exports__, __webpack_require__) {
+
+"use strict";
+/* harmony import */ var __WEBPACK_IMPORTED_MODULE_0__templates_pagination__ = __webpack_require__(16);
+/* harmony import */ var __WEBPACK_IMPORTED_MODULE_1__templates_pagination_page__ = __webpack_require__(17);
+/* harmony import */ var __WEBPACK_IMPORTED_MODULE_2__utils_detect_supportsPassive__ = __webpack_require__(1);
+var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }();
+
+function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } }
+
+
+
+
+
+var Pagination = function () {
+	function Pagination(slider) {
+		_classCallCheck(this, Pagination);
+
+		this.slider = slider;
+
+		this._clickEvents = ['click', 'touch'];
+		this._supportsPassive = Object(__WEBPACK_IMPORTED_MODULE_2__utils_detect_supportsPassive__["a" /* default */])();
+
+		this.onPageClick = this.onPageClick.bind(this);
+		this.onResize = this.onResize.bind(this);
+	}
+
+	_createClass(Pagination, [{
+		key: 'init',
+		value: function init() {
+			this._pages = [];
+			this.node = document.createRange().createContextualFragment(Object(__WEBPACK_IMPORTED_MODULE_0__templates_pagination__["a" /* default */])());
+			this._ui = {
+				container: this.node.firstChild
+			};
+
+			this._count = Math.ceil((this.slider.state.length - this.slider.slidesToShow) / this.slider.slidesToScroll);
+
+			this._draw();
+			this.refresh();
+
+			return this;
+		}
+	}, {
+		key: 'destroy',
+		value: function destroy() {
+			this._unbindEvents();
+		}
+	}, {
+		key: '_bindEvents',
+		value: function _bindEvents() {
+			var _this = this;
+
+			window.addEventListener('resize', this.onResize);
+			window.addEventListener('orientationchange', this.onResize);
+
+			this._clickEvents.forEach(function (clickEvent) {
+				_this._pages.forEach(function (page) {
+					return page.addEventListener(clickEvent, _this.onPageClick);
+				});
+			});
+		}
+	}, {
+		key: '_unbindEvents',
+		value: function _unbindEvents() {
+			var _this2 = this;
+
+			window.removeEventListener('resize', this.onResize);
+			window.removeEventListener('orientationchange', this.onResize);
+
+			this._clickEvents.forEach(function (clickEvent) {
+				_this2._pages.forEach(function (page) {
+					return page.removeEventListener(clickEvent, _this2.onPageClick);
+				});
+			});
+		}
+	}, {
+		key: '_draw',
+		value: function _draw() {
+			this._ui.container.innerHTML = '';
+			if (this.slider.options.pagination && this.slider.state.length > this.slider.slidesToShow) {
+				for (var i = 0; i <= this._count; i++) {
+					var newPageNode = document.createRange().createContextualFragment(Object(__WEBPACK_IMPORTED_MODULE_1__templates_pagination_page__["a" /* default */])()).firstChild;
+					newPageNode.dataset.index = i * this.slider.slidesToScroll;
+					this._pages.push(newPageNode);
+					this._ui.container.appendChild(newPageNode);
+				}
+				this._bindEvents();
+			}
+		}
+	}, {
+		key: 'onPageClick',
+		value: function onPageClick(e) {
+			if (!this._supportsPassive) {
+				e.preventDefault();
+			}
+
+			this.slider.state.next = e.currentTarget.dataset.index;
+			this.slider.show();
+		}
+	}, {
+		key: 'onResize',
+		value: function onResize() {
+			this._draw();
+		}
+	}, {
+		key: 'refresh',
+		value: function refresh() {
+			var _this3 = this;
+
+			var newCount = void 0;
+
+			if (this.slider.options.infinite) {
+				newCount = Math.ceil(this.slider.state.length - 1 / this.slider.slidesToScroll);
+			} else {
+				newCount = Math.ceil((this.slider.state.length - this.slider.slidesToShow) / this.slider.slidesToScroll);
+			}
+			if (newCount !== this._count) {
+				this._count = newCount;
+				this._draw();
+			}
+
+			this._pages.forEach(function (page) {
+				page.classList.remove('is-active');
+				if (parseInt(page.dataset.index, 10) === _this3.slider.state.next % _this3.slider.state.length) {
+					page.classList.add('is-active');
+				}
+			});
+		}
+	}, {
+		key: 'render',
+		value: function render() {
+			return this.node;
+		}
+	}]);
+
+	return Pagination;
+}();
+
+/* harmony default export */ __webpack_exports__["a"] = (Pagination);
+
+/***/ }),
+/* 16 */
+/***/ (function(module, __webpack_exports__, __webpack_require__) {
+
+"use strict";
+/* harmony default export */ __webpack_exports__["a"] = (function () {
+	return "<div class=\"slider-pagination\"></div>";
+});
+
+/***/ }),
+/* 17 */
+/***/ (function(module, __webpack_exports__, __webpack_require__) {
+
+"use strict";
+/* harmony default export */ __webpack_exports__["a"] = (function () {
+  return "<div class=\"slider-page\"></div>";
+});
+
+/***/ }),
+/* 18 */
+/***/ (function(module, __webpack_exports__, __webpack_require__) {
+
+"use strict";
+/* harmony import */ var __WEBPACK_IMPORTED_MODULE_0__utils_coordinate__ = __webpack_require__(4);
+/* harmony import */ var __WEBPACK_IMPORTED_MODULE_1__utils_detect_supportsPassive__ = __webpack_require__(1);
+var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }();
+
+function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } }
+
+
+
+
+var Swipe = function () {
+	function Swipe(slider) {
+		_classCallCheck(this, Swipe);
+
+		this.slider = slider;
+
+		this._supportsPassive = Object(__WEBPACK_IMPORTED_MODULE_1__utils_detect_supportsPassive__["a" /* default */])();
+
+		this.onStartDrag = this.onStartDrag.bind(this);
+		this.onMoveDrag = this.onMoveDrag.bind(this);
+		this.onStopDrag = this.onStopDrag.bind(this);
+
+		this._init();
+	}
+
+	_createClass(Swipe, [{
+		key: '_init',
+		value: function _init() {}
+	}, {
+		key: 'bindEvents',
+		value: function bindEvents() {
+			var _this = this;
+
+			this.slider.container.addEventListener('dragstart', function (e) {
+				if (!_this._supportsPassive) {
+					e.preventDefault();
+				}
+			});
+			this.slider.container.addEventListener('mousedown', this.onStartDrag);
+			this.slider.container.addEventListener('touchstart', this.onStartDrag);
+
+			window.addEventListener('mousemove', this.onMoveDrag);
+			window.addEventListener('touchmove', this.onMoveDrag);
+
+			window.addEventListener('mouseup', this.onStopDrag);
+			window.addEventListener('touchend', this.onStopDrag);
+			window.addEventListener('touchcancel', this.onStopDrag);
+		}
+	}, {
+		key: 'unbindEvents',
+		value: function unbindEvents() {
+			var _this2 = this;
+
+			this.slider.container.removeEventListener('dragstart', function (e) {
+				if (!_this2._supportsPassive) {
+					e.preventDefault();
+				}
+			});
+			this.slider.container.removeEventListener('mousedown', this.onStartDrag);
+			this.slider.container.removeEventListener('touchstart', this.onStartDrag);
+
+			window.removeEventListener('mousemove', this.onMoveDrag);
+			window.removeEventListener('touchmove', this.onMoveDrag);
+
+			window.removeEventListener('mouseup', this.onStopDrag);
+			window.removeEventListener('mouseup', this.onStopDrag);
+			window.removeEventListener('touchcancel', this.onStopDrag);
+		}
+
+		/**
+   * @param {MouseEvent|TouchEvent}
+   */
+
+	}, {
+		key: 'onStartDrag',
+		value: function onStartDrag(e) {
+			if (e.touches) {
+				if (e.touches.length > 1) {
+					return;
+				} else {
+					e = e.touches[0];
+				}
+			}
+
+			this._origin = new __WEBPACK_IMPORTED_MODULE_0__utils_coordinate__["a" /* default */](e.screenX, e.screenY);
+			this.width = this.slider.wrapperWidth;
+			this.slider.transitioner.disable();
+		}
+
+		/**
+   * @param {MouseEvent|TouchEvent}
+   */
+
+	}, {
+		key: 'onMoveDrag',
+		value: function onMoveDrag(e) {
+			if (this._origin) {
+				var point = e.touches ? e.touches[0] : e;
+				this._lastTranslate = new __WEBPACK_IMPORTED_MODULE_0__utils_coordinate__["a" /* default */](point.screenX - this._origin.x, point.screenY - this._origin.y);
+				if (e.touches) {
+					if (Math.abs(this._lastTranslate.x) > Math.abs(this._lastTranslate.y)) {
+						if (!this._supportsPassive) {
+							e.preventDefault();
+						}
+						e.stopPropagation();
+					}
+				}
+			}
+		}
+
+		/**
+   * @param {MouseEvent|TouchEvent}
+   */
+
+	}, {
+		key: 'onStopDrag',
+		value: function onStopDrag(e) {
+			if (this._origin && this._lastTranslate) {
+				if (Math.abs(this._lastTranslate.x) > 0.2 * this.width) {
+					if (this._lastTranslate.x < 0) {
+						this.slider.next();
+					} else {
+						this.slider.previous();
+					}
+				} else {
+					this.slider.show(true);
+				}
+			}
+			this._origin = null;
+			this._lastTranslate = null;
+		}
+	}]);
+
+	return Swipe;
+}();
+
+/* harmony default export */ __webpack_exports__["a"] = (Swipe);
+
+/***/ }),
+/* 19 */
+/***/ (function(module, __webpack_exports__, __webpack_require__) {
+
+"use strict";
+/* harmony import */ var __WEBPACK_IMPORTED_MODULE_0__transitions_fade__ = __webpack_require__(20);
+/* harmony import */ var __WEBPACK_IMPORTED_MODULE_1__transitions_translate__ = __webpack_require__(21);
+var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }();
+
+function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } }
+
+
+
+
+var Transitioner = function () {
+	function Transitioner(slider) {
+		_classCallCheck(this, Transitioner);
+
+		this.slider = slider;
+		this.options = slider.options;
+
+		this._animating = false;
+		this._animation = undefined;
+
+		this._translate = new __WEBPACK_IMPORTED_MODULE_1__transitions_translate__["a" /* default */](this, slider, slider.options);
+		this._fade = new __WEBPACK_IMPORTED_MODULE_0__transitions_fade__["a" /* default */](this, slider, slider.options);
+	}
+
+	_createClass(Transitioner, [{
+		key: 'init',
+		value: function init() {
+			this._fade.init();
+			this._translate.init();
+			return this;
+		}
+	}, {
+		key: 'isAnimating',
+		value: function isAnimating() {
+			return this._animating;
+		}
+	}, {
+		key: 'enable',
+		value: function enable() {
+			this._animation && this._animation.enable();
+		}
+	}, {
+		key: 'disable',
+		value: function disable() {
+			this._animation && this._animation.disable();
+		}
+	}, {
+		key: 'apply',
+		value: function apply(force, callback) {
+			// If we don't force refresh and animation in progress then return
+			if (this._animating && !force) {
+				return;
+			}
+
+			switch (this.options.effect) {
+				case 'fade':
+					this._animation = this._fade;
+					break;
+				case 'translate':
+				default:
+					this._animation = this._translate;
+					break;
+			}
+
+			this._animationCallback = callback;
+
+			if (force) {
+				this._animation && this._animation.disable();
+			} else {
+				this._animation && this._animation.enable();
+				this._animating = true;
+			}
+
+			this._animation && this._animation.apply();
+
+			if (force) {
+				this.end();
+			}
+		}
+	}, {
+		key: 'end',
+		value: function end() {
+			this._animating = false;
+			this._animation = undefined;
+			this.slider.state.index = this.slider.state.next;
+			if (this._animationCallback) {
+				this._animationCallback();
+			}
+		}
+	}]);
+
+	return Transitioner;
+}();
+
+/* harmony default export */ __webpack_exports__["a"] = (Transitioner);
+
+/***/ }),
+/* 20 */
+/***/ (function(module, __webpack_exports__, __webpack_require__) {
+
+"use strict";
+/* harmony import */ var __WEBPACK_IMPORTED_MODULE_0__utils_css__ = __webpack_require__(0);
+var _extends = Object.assign || function (target) { for (var i = 1; i < arguments.length; i++) { var source = arguments[i]; for (var key in source) { if (Object.prototype.hasOwnProperty.call(source, key)) { target[key] = source[key]; } } } return target; };
+
+var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }();
+
+function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } }
+
+
+
+var Fade = function () {
+	function Fade(transitioner, slider) {
+		var options = arguments.length > 2 && arguments[2] !== undefined ? arguments[2] : {};
+
+		_classCallCheck(this, Fade);
+
+		this.transitioner = transitioner;
+		this.slider = slider;
+		this.options = _extends({}, options);
+	}
+
+	_createClass(Fade, [{
+		key: 'init',
+		value: function init() {
+			var _this = this;
+
+			if (this.options.effect === 'fade') {
+				this.slider.slides.forEach(function (slide, index) {
+					Object(__WEBPACK_IMPORTED_MODULE_0__utils_css__["a" /* css */])(slide, {
+						position: 'absolute',
+						left: 0,
+						top: 0,
+						bottom: 0,
+						'z-index': slide.dataset.sliderIndex == _this.slider.state.index ? 0 : -2,
+						opacity: slide.dataset.sliderIndex == _this.slider.state.index ? 1 : 0
+					});
+				});
+			}
+			return this;
+		}
+	}, {
+		key: 'enable',
+		value: function enable() {
+			var _this2 = this;
+
+			this._oldSlide = this.slider.slides.filter(function (slide) {
+				return slide.dataset.sliderIndex == _this2.slider.state.index;
+			})[0];
+			this._newSlide = this.slider.slides.filter(function (slide) {
+				return slide.dataset.sliderIndex == _this2.slider.state.next;
+			})[0];
+			if (this._newSlide) {
+				this._newSlide.addEventListener('transitionend', this.onTransitionEnd.bind(this));
+				this._newSlide.style.transition = this.options.duration + 'ms ' + this.options.timing;
+				if (this._oldSlide) {
+					this._oldSlide.addEventListener('transitionend', this.onTransitionEnd.bind(this));
+					this._oldSlide.style.transition = this.options.duration + 'ms ' + this.options.timing;
+				}
+			}
+		}
+	}, {
+		key: 'disable',
+		value: function disable() {
+			var _this3 = this;
+
+			this._oldSlide = this.slider.slides.filter(function (slide) {
+				return slide.dataset.sliderIndex == _this3.slider.state.index;
+			})[0];
+			this._newSlide = this.slider.slides.filter(function (slide) {
+				return slide.dataset.sliderIndex == _this3.slider.state.next;
+			})[0];
+			if (this._newSlide) {
+				this._newSlide.removeEventListener('transitionend', this.onTransitionEnd.bind(this));
+				this._newSlide.style.transition = 'none';
+				if (this._oldSlide) {
+					this._oldSlide.removeEventListener('transitionend', this.onTransitionEnd.bind(this));
+					this._oldSlide.style.transition = 'none';
+				}
+			}
+		}
+	}, {
+		key: 'apply',
+		value: function apply(force) {
+			var _this4 = this;
+
+			this._oldSlide = this.slider.slides.filter(function (slide) {
+				return slide.dataset.sliderIndex == _this4.slider.state.index;
+			})[0];
+			this._newSlide = this.slider.slides.filter(function (slide) {
+				return slide.dataset.sliderIndex == _this4.slider.state.next;
+			})[0];
+
+			if (this._oldSlide && this._newSlide) {
+				Object(__WEBPACK_IMPORTED_MODULE_0__utils_css__["a" /* css */])(this._oldSlide, {
+					opacity: 0
+				});
+				Object(__WEBPACK_IMPORTED_MODULE_0__utils_css__["a" /* css */])(this._newSlide, {
+					opacity: 1,
+					'z-index': force ? 0 : -1
+				});
+			}
+		}
+	}, {
+		key: 'onTransitionEnd',
+		value: function onTransitionEnd(e) {
+			if (this.options.effect === 'fade') {
+				if (this.transitioner.isAnimating() && e.target == this._newSlide) {
+					if (this._newSlide) {
+						Object(__WEBPACK_IMPORTED_MODULE_0__utils_css__["a" /* css */])(this._newSlide, {
+							'z-index': 0
+						});
+						this._newSlide.removeEventListener('transitionend', this.onTransitionEnd.bind(this));
+					}
+					if (this._oldSlide) {
+						Object(__WEBPACK_IMPORTED_MODULE_0__utils_css__["a" /* css */])(this._oldSlide, {
+							'z-index': -2
+						});
+						this._oldSlide.removeEventListener('transitionend', this.onTransitionEnd.bind(this));
+					}
+				}
+				this.transitioner.end();
+			}
+		}
+	}]);
+
+	return Fade;
+}();
+
+/* harmony default export */ __webpack_exports__["a"] = (Fade);
+
+/***/ }),
+/* 21 */
+/***/ (function(module, __webpack_exports__, __webpack_require__) {
+
+"use strict";
+/* harmony import */ var __WEBPACK_IMPORTED_MODULE_0__utils_coordinate__ = __webpack_require__(4);
+/* harmony import */ var __WEBPACK_IMPORTED_MODULE_1__utils_css__ = __webpack_require__(0);
+var _extends = Object.assign || function (target) { for (var i = 1; i < arguments.length; i++) { var source = arguments[i]; for (var key in source) { if (Object.prototype.hasOwnProperty.call(source, key)) { target[key] = source[key]; } } } return target; };
+
+var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }();
+
+function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } }
+
+
+
+
+var Translate = function () {
+	function Translate(transitioner, slider) {
+		var options = arguments.length > 2 && arguments[2] !== undefined ? arguments[2] : {};
+
+		_classCallCheck(this, Translate);
+
+		this.transitioner = transitioner;
+		this.slider = slider;
+		this.options = _extends({}, options);
+
+		this.onTransitionEnd = this.onTransitionEnd.bind(this);
+	}
+
+	_createClass(Translate, [{
+		key: 'init',
+		value: function init() {
+			this._position = new __WEBPACK_IMPORTED_MODULE_0__utils_coordinate__["a" /* default */](this.slider.container.offsetLeft, this.slider.container.offsetTop);
+			this._bindEvents();
+			return this;
+		}
+	}, {
+		key: 'destroy',
+		value: function destroy() {
+			this._unbindEvents();
+		}
+	}, {
+		key: '_bindEvents',
+		value: function _bindEvents() {
+			this.slider.container.addEventListener('transitionend', this.onTransitionEnd);
+		}
+	}, {
+		key: '_unbindEvents',
+		value: function _unbindEvents() {
+			this.slider.container.removeEventListener('transitionend', this.onTransitionEnd);
+		}
+	}, {
+		key: 'enable',
+		value: function enable() {
+			this.slider.container.style.transition = this.options.duration + 'ms ' + this.options.timing;
+		}
+	}, {
+		key: 'disable',
+		value: function disable() {
+			this.slider.container.style.transition = 'none';
+		}
+	}, {
+		key: 'apply',
+		value: function apply() {
+			var _this = this;
+
+			var maxOffset = void 0;
+			if (this.options.effect === 'translate') {
+				var slide = this.slider.slides.filter(function (slide) {
+					return slide.dataset.sliderIndex == _this.slider.state.next;
+				})[0];
+				var slideOffset = new __WEBPACK_IMPORTED_MODULE_0__utils_coordinate__["a" /* default */](slide.offsetLeft, slide.offsetTop);
+				if (this.options.centerMode) {
+					maxOffset = new __WEBPACK_IMPORTED_MODULE_0__utils_coordinate__["a" /* default */](Math.round(Object(__WEBPACK_IMPORTED_MODULE_1__utils_css__["e" /* width */])(this.slider.container)), Math.round(Object(__WEBPACK_IMPORTED_MODULE_1__utils_css__["b" /* height */])(this.slider.container)));
+				} else {
+					maxOffset = new __WEBPACK_IMPORTED_MODULE_0__utils_coordinate__["a" /* default */](Math.round(Object(__WEBPACK_IMPORTED_MODULE_1__utils_css__["e" /* width */])(this.slider.container) - Object(__WEBPACK_IMPORTED_MODULE_1__utils_css__["e" /* width */])(this.slider.wrapper)), Math.round(Object(__WEBPACK_IMPORTED_MODULE_1__utils_css__["b" /* height */])(this.slider.container) - Object(__WEBPACK_IMPORTED_MODULE_1__utils_css__["b" /* height */])(this.slider.wrapper)));
+				}
+				var nextOffset = new __WEBPACK_IMPORTED_MODULE_0__utils_coordinate__["a" /* default */](Math.min(Math.max(slideOffset.x * -1, maxOffset.x * -1), 0), Math.min(Math.max(slideOffset.y * -1, maxOffset.y * -1), 0));
+				if (this.options.loop) {
+					if (!this.options.vertical && Math.abs(this._position.x) > maxOffset.x) {
+						nextOffset.x = 0;
+						this.slider.state.next = 0;
+					} else if (this.options.vertical && Math.abs(this._position.y) > maxOffset.y) {
+						nextOffset.y = 0;
+						this.slider.state.next = 0;
+					}
+				}
+
+				this._position.x = nextOffset.x;
+				this._position.y = nextOffset.y;
+				if (this.options.centerMode) {
+					this._position.x = this._position.x + this.slider.wrapperWidth / 2 - Object(__WEBPACK_IMPORTED_MODULE_1__utils_css__["e" /* width */])(slide) / 2;
+				}
+
+				if (this.slider.direction === 'rtl') {
+					this._position.x = -this._position.x;
+					this._position.y = -this._position.y;
+				}
+				this.slider.container.style.transform = 'translate3d(' + this._position.x + 'px, ' + this._position.y + 'px, 0)';
+
+				/**
+     * update the index with the nextIndex only if
+     * the offset of the nextIndex is in the range of the maxOffset
+     */
+				if (slideOffset.x > maxOffset.x) {
+					this.slider.transitioner.end();
+				}
+			}
+		}
+	}, {
+		key: 'onTransitionEnd',
+		value: function onTransitionEnd(e) {
+			if (this.options.effect === 'translate') {
+
+				if (this.transitioner.isAnimating() && e.target == this.slider.container) {
+					if (this.options.infinite) {
+						this.slider._infinite.onTransitionEnd(e);
+					}
+				}
+				this.transitioner.end();
+			}
+		}
+	}]);
+
+	return Translate;
+}();
+
+/* harmony default export */ __webpack_exports__["a"] = (Translate);
+
+/***/ }),
+/* 22 */
+/***/ (function(module, __webpack_exports__, __webpack_require__) {
+
+"use strict";
+var defaultOptions = {
+  initialSlide: 0,
+  slidesToScroll: 1,
+  slidesToShow: 1,
+
+  navigation: true,
+  navigationKeys: true,
+  navigationSwipe: true,
+
+  pagination: true,
+
+  loop: false,
+  infinite: false,
+
+  effect: 'translate',
+  duration: 300,
+  timing: 'ease',
+
+  autoplay: false,
+  autoplaySpeed: 3000,
+  pauseOnHover: true,
+  breakpoints: [{
+    changePoint: 480,
+    slidesToShow: 1,
+    slidesToScroll: 1
+  }, {
+    changePoint: 640,
+    slidesToShow: 2,
+    slidesToScroll: 2
+  }, {
+    changePoint: 768,
+    slidesToShow: 3,
+    slidesToScroll: 3
+  }],
+
+  onReady: null,
+  icons: {
+    'previous': '<svg viewBox="0 0 50 80" xml:space="preserve">\n      <polyline fill="currentColor" stroke-width=".5em" stroke-linecap="round" stroke-linejoin="round" points="45.63,75.8 0.375,38.087 45.63,0.375 "/>\n    </svg>',
+    'next': '<svg viewBox="0 0 50 80" xml:space="preserve">\n      <polyline fill="currentColor" stroke-width=".5em" stroke-linecap="round" stroke-linejoin="round" points="0.375,0.375 45.63,38.087 0.375,75.8 "/>\n    </svg>'
+  }
+};
+
+/* harmony default export */ __webpack_exports__["a"] = (defaultOptions);
+
+/***/ }),
+/* 23 */
+/***/ (function(module, __webpack_exports__, __webpack_require__) {
+
+"use strict";
+/* harmony default export */ __webpack_exports__["a"] = (function (id) {
+  return "<div id=\"" + id + "\" class=\"slider\" tabindex=\"0\">\n    <div class=\"slider-container\"></div>\n  </div>";
+});
+
+/***/ }),
+/* 24 */
+/***/ (function(module, __webpack_exports__, __webpack_require__) {
+
+"use strict";
+/* harmony default export */ __webpack_exports__["a"] = (function () {
+  return "<div class=\"slider-item\"></div>";
+});
+
+/***/ })
+/******/ ])["default"];
+});

videoretalking/docs/static/js/bulma-carousel.min.js

@@ -0,0 +1 @@
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t=this;this.stop(),this.slider.options.autoplay&&(this.emit("start",this),this._interval=setInterval(function(){t._hovering&&t.slider.options.pauseOnHover||(!t.slider.options.centerMode&&t.slider.state.next>=t.slider.state.length-t.slider.slidesToShow&&!t.slider.options.loop&&!t.slider.options.infinite?t.stop():t.slider.next())},this.slider.options.autoplaySpeed))}},{key:"stop",value:function(){this._interval=clearInterval(this._interval),this.emit("stop",this)}},{key:"pause",value:function(){var t=this,e=0<arguments.length&&void 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this._currentBreakpoint?this._currentBreakpoint.slidesToScroll:this._defaultBreakpoint.slidesToScroll}},{key:"apply",value:function(){this.slider.state.index>=this.slider.state.length&&0!==this.slider.state.index&&(this.slider.state.index=this.slider.state.index-this._currentBreakpoint.slidesToScroll),this.slider.state.length<=this._currentBreakpoint.slidesToShow&&(this.slider.state.index=0),this.options.loop&&this.slider._loop.init().apply(),this.options.infinite&&this.slider._infinite.init().apply(),this.slider._setDimensions(),this.slider._transitioner.init().apply(!0,this.slider._setHeight.bind(this.slider)),this.slider._setClasses(),this.slider._navigation.refresh(),this.slider._pagination.refresh()}},{key:s,value:function(t){var e=this._getActiveBreakpoint();e.slidesToShow!==this._currentBreakpoint.slidesToShow&&(this._currentBreakpoint=e,this.apply())}}]),e}();e.a=r},function(t,e,i){"use strict";var n=function(){function n(t,e){for(var i=0;i<e.length;i++){var n=e[i];n.enumerable=n.enumerable||!1,n.configurable=!0,"value"in n&&(n.writable=!0),Object.defineProperty(t,n.key,n)}}return function(t,e,i){return e&&n(t.prototype,e),i&&n(t,i),t}}();var s=function(){function e(t){!function(t,e){if(!(t instanceof e))throw new TypeError("Cannot call a class as a function")}(this,e),this.slider=t}return n(e,[{key:"init",value:function(){if(this.slider.options.infinite&&"translate"===this.slider.options.effect){this.slider.options.centerMode?this._infiniteCount=Math.ceil(this.slider.slidesToShow+this.slider.slidesToShow/2):this._infiniteCount=this.slider.slidesToShow;for(var t=[],e=0,i=this.slider.state.length;i>this.slider.state.length-1-this._infiniteCount;i-=1)e=i-1,t.unshift(this._cloneSlide(this.slider.slides[e],e-this.slider.state.length));for(var n=[],s=0;s<this._infiniteCount+this.slider.state.length;s+=1)n.push(this._cloneSlide(this.slider.slides[s%this.slider.state.length],s+this.slider.state.length));this.slider.slides=[].concat(t,function(t){if(Array.isArray(t)){for(var e=0,i=Array(t.length);e<t.length;e++)i[e]=t[e];return i}return Array.from(t)}(this.slider.slides),n)}return this}},{key:"apply",value:function(){}},{key:"onTransitionEnd",value:function(t){this.slider.options.infinite&&(this.slider.state.next>=this.slider.state.length?(this.slider.state.index=this.slider.state.next=this.slider.state.next-this.slider.state.length,this.slider.transitioner.apply(!0)):this.slider.state.next<0&&(this.slider.state.index=this.slider.state.next=this.slider.state.length+this.slider.state.next,this.slider.transitioner.apply(!0)))}},{key:"_cloneSlide",value:function(t,e){var i=t.cloneNode(!0);return i.dataset.sliderIndex=e,i.dataset.cloned=!0,(i.querySelectorAll("[id]")||[]).forEach(function(t){t.setAttribute("id","")}),i}}]),e}();e.a=s},function(t,e,i){"use strict";var n=i(12),s=function(){function n(t,e){for(var i=0;i<e.length;i++){var n=e[i];n.enumerable=n.enumerable||!1,n.configurable=!0,"value"in n&&(n.writable=!0),Object.defineProperty(t,n.key,n)}}return function(t,e,i){return e&&n(t.prototype,e),i&&n(t,i),t}}();var r=function(){function e(t){!function(t,e){if(!(t instanceof e))throw new TypeError("Cannot call a class as a function")}(this,e),this.slider=t}return s(e,[{key:"init",value:function(){return this}},{key:"apply",value:function(){this.slider.options.loop&&(0<this.slider.state.next?this.slider.state.next<this.slider.state.length?this.slider.state.next>this.slider.state.length-this.slider.slidesToShow&&Object(n.a)(this.slider._slides[this.slider.state.length-1],this.slider.wrapper)?this.slider.state.next=0:this.slider.state.next=Math.min(Math.max(this.slider.state.next,0),this.slider.state.length-this.slider.slidesToShow):this.slider.state.next=0:this.slider.state.next<=0-this.slider.slidesToScroll?this.slider.state.next=this.slider.state.length-this.slider.slidesToShow:this.slider.state.next=0)}}]),e}();e.a=r},function(t,e,i){"use strict";i.d(e,"a",function(){return n});var n=function(t,e){var i=t.getBoundingClientRect();return e=e||document.documentElement,0<=i.top&&0<=i.left&&i.bottom<=(window.innerHeight||e.clientHeight)&&i.right<=(window.innerWidth||e.clientWidth)}},function(t,e,i){"use strict";var n=i(14),s=i(1),r=function(){function n(t,e){for(var i=0;i<e.length;i++){var n=e[i];n.enumerable=n.enumerable||!1,n.configurable=!0,"value"in n&&(n.writable=!0),Object.defineProperty(t,n.key,n)}}return function(t,e,i){return e&&n(t.prototype,e),i&&n(t,i),t}}();var o=function(){function e(t){!function(t,e){if(!(t instanceof e))throw new TypeError("Cannot call a class as a function")}(this,e),this.slider=t,this._clickEvents=["click","touch"],this._supportsPassive=Object(s.a)(),this.onPreviousClick=this.onPreviousClick.bind(this),this.onNextClick=this.onNextClick.bind(this),this.onKeyUp=this.onKeyUp.bind(this)}return r(e,[{key:"init",value:function(){return this.node=document.createRange().createContextualFragment(Object(n.a)(this.slider.options.icons)),this._ui={previous:this.node.querySelector(".slider-navigation-previous"),next:this.node.querySelector(".slider-navigation-next")},this._unbindEvents(),this._bindEvents(),this.refresh(),this}},{key:"destroy",value:function(){this._unbindEvents()}},{key:"_bindEvents",value:function(){var e=this;this.slider.wrapper.addEventListener("keyup",this.onKeyUp),this._clickEvents.forEach(function(t){e._ui.previous.addEventListener(t,e.onPreviousClick),e._ui.next.addEventListener(t,e.onNextClick)})}},{key:"_unbindEvents",value:function(){var e=this;this.slider.wrapper.removeEventListener("keyup",this.onKeyUp),this._clickEvents.forEach(function(t){e._ui.previous.removeEventListener(t,e.onPreviousClick),e._ui.next.removeEventListener(t,e.onNextClick)})}},{key:"onNextClick",value:function(t){this._supportsPassive||t.preventDefault(),this.slider.options.navigation&&this.slider.next()}},{key:"onPreviousClick",value:function(t){this._supportsPassive||t.preventDefault(),this.slider.options.navigation&&this.slider.previous()}},{key:"onKeyUp",value:function(t){this.slider.options.keyNavigation&&("ArrowRight"===t.key||"Right"===t.key?this.slider.next():"ArrowLeft"!==t.key&&"Left"!==t.key||this.slider.previous())}},{key:"refresh",value:function(){this.slider.options.loop||this.slider.options.infinite||(this.slider.options.navigation&&this.slider.state.length>this.slider.slidesToShow?(this._ui.previous.classList.remove("is-hidden"),this._ui.next.classList.remove("is-hidden"),0===this.slider.state.next?(this._ui.previous.classList.add("is-hidden"),this._ui.next.classList.remove("is-hidden")):this.slider.state.next>=this.slider.state.length-this.slider.slidesToShow&&!this.slider.options.centerMode?(this._ui.previous.classList.remove("is-hidden"),this._ui.next.classList.add("is-hidden")):this.slider.state.next>=this.slider.state.length-1&&this.slider.options.centerMode&&(this._ui.previous.classList.remove("is-hidden"),this._ui.next.classList.add("is-hidden"))):(this._ui.previous.classList.add("is-hidden"),this._ui.next.classList.add("is-hidden")))}},{key:"render",value:function(){return this.node}}]),e}();e.a=o},function(t,e,i){"use strict";e.a=function(t){return'<div class="slider-navigation-previous">'+t.previous+'</div>\n<div class="slider-navigation-next">'+t.next+"</div>"}},function(t,e,i){"use strict";var n=i(16),s=i(17),r=i(1),o=function(){function n(t,e){for(var i=0;i<e.length;i++){var n=e[i];n.enumerable=n.enumerable||!1,n.configurable=!0,"value"in n&&(n.writable=!0),Object.defineProperty(t,n.key,n)}}return function(t,e,i){return e&&n(t.prototype,e),i&&n(t,i),t}}();var a=function(){function e(t){!function(t,e){if(!(t instanceof e))throw new TypeError("Cannot call a class as a function")}(this,e),this.slider=t,this._clickEvents=["click","touch"],this._supportsPassive=Object(r.a)(),this.onPageClick=this.onPageClick.bind(this),this.onResize=this.onResize.bind(this)}return o(e,[{key:"init",value:function(){return this._pages=[],this.node=document.createRange().createContextualFragment(Object(n.a)()),this._ui={container:this.node.firstChild},this._count=Math.ceil((this.slider.state.length-this.slider.slidesToShow)/this.slider.slidesToScroll),this._draw(),this.refresh(),this}},{key:"destroy",value:function(){this._unbindEvents()}},{key:"_bindEvents",value:function(){var i=this;window.addEventListener("resize",this.onResize),window.addEventListener("orientationchange",this.onResize),this._clickEvents.forEach(function(e){i._pages.forEach(function(t){return t.addEventListener(e,i.onPageClick)})})}},{key:"_unbindEvents",value:function(){var i=this;window.removeEventListener("resize",this.onResize),window.removeEventListener("orientationchange",this.onResize),this._clickEvents.forEach(function(e){i._pages.forEach(function(t){return t.removeEventListener(e,i.onPageClick)})})}},{key:"_draw",value:function(){if(this._ui.container.innerHTML="",this.slider.options.pagination&&this.slider.state.length>this.slider.slidesToShow){for(var t=0;t<=this._count;t++){var e=document.createRange().createContextualFragment(Object(s.a)()).firstChild;e.dataset.index=t*this.slider.slidesToScroll,this._pages.push(e),this._ui.container.appendChild(e)}this._bindEvents()}}},{key:"onPageClick",value:function(t){this._supportsPassive||t.preventDefault(),this.slider.state.next=t.currentTarget.dataset.index,this.slider.show()}},{key:"onResize",value:function(){this._draw()}},{key:"refresh",value:function(){var e=this,t=void 0;(t=this.slider.options.infinite?Math.ceil(this.slider.state.length-1/this.slider.slidesToScroll):Math.ceil((this.slider.state.length-this.slider.slidesToShow)/this.slider.slidesToScroll))!==this._count&&(this._count=t,this._draw()),this._pages.forEach(function(t){t.classList.remove("is-active"),parseInt(t.dataset.index,10)===e.slider.state.next%e.slider.state.length&&t.classList.add("is-active")})}},{key:"render",value:function(){return this.node}}]),e}();e.a=a},function(t,e,i){"use strict";e.a=function(){return'<div class="slider-pagination"></div>'}},function(t,e,i){"use strict";e.a=function(){return'<div class="slider-page"></div>'}},function(t,e,i){"use strict";var n=i(4),s=i(1),r=function(){function n(t,e){for(var i=0;i<e.length;i++){var n=e[i];n.enumerable=n.enumerable||!1,n.configurable=!0,"value"in n&&(n.writable=!0),Object.defineProperty(t,n.key,n)}}return function(t,e,i){return e&&n(t.prototype,e),i&&n(t,i),t}}();var o=function(){function e(t){!function(t,e){if(!(t instanceof e))throw new TypeError("Cannot call a class as a function")}(this,e),this.slider=t,this._supportsPassive=Object(s.a)(),this.onStartDrag=this.onStartDrag.bind(this),this.onMoveDrag=this.onMoveDrag.bind(this),this.onStopDrag=this.onStopDrag.bind(this),this._init()}return r(e,[{key:"_init",value:function(){}},{key:"bindEvents",value:function(){var e=this;this.slider.container.addEventListener("dragstart",function(t){e._supportsPassive||t.preventDefault()}),this.slider.container.addEventListener("mousedown",this.onStartDrag),this.slider.container.addEventListener("touchstart",this.onStartDrag),window.addEventListener("mousemove",this.onMoveDrag),window.addEventListener("touchmove",this.onMoveDrag),window.addEventListener("mouseup",this.onStopDrag),window.addEventListener("touchend",this.onStopDrag),window.addEventListener("touchcancel",this.onStopDrag)}},{key:"unbindEvents",value:function(){var e=this;this.slider.container.removeEventListener("dragstart",function(t){e._supportsPassive||t.preventDefault()}),this.slider.container.removeEventListener("mousedown",this.onStartDrag),this.slider.container.removeEventListener("touchstart",this.onStartDrag),window.removeEventListener("mousemove",this.onMoveDrag),window.removeEventListener("touchmove",this.onMoveDrag),window.removeEventListener("mouseup",this.onStopDrag),window.removeEventListener("mouseup",this.onStopDrag),window.removeEventListener("touchcancel",this.onStopDrag)}},{key:"onStartDrag",value:function(t){if(t.touches){if(1<t.touches.length)return;t=t.touches[0]}this._origin=new n.a(t.screenX,t.screenY),this.width=this.slider.wrapperWidth,this.slider.transitioner.disable()}},{key:"onMoveDrag",value:function(t){if(this._origin){var e=t.touches?t.touches[0]:t;this._lastTranslate=new n.a(e.screenX-this._origin.x,e.screenY-this._origin.y),t.touches&&Math.abs(this._lastTranslate.x)>Math.abs(this._lastTranslate.y)&&(this._supportsPassive||t.preventDefault(),t.stopPropagation())}}},{key:"onStopDrag",value:function(t){this._origin&&this._lastTranslate&&(Math.abs(this._lastTranslate.x)>.2*this.width?this._lastTranslate.x<0?this.slider.next():this.slider.previous():this.slider.show(!0)),this._origin=null,this._lastTranslate=null}}]),e}();e.a=o},function(t,e,i){"use strict";var n=i(20),s=i(21),r=function(){function n(t,e){for(var i=0;i<e.length;i++){var n=e[i];n.enumerable=n.enumerable||!1,n.configurable=!0,"value"in n&&(n.writable=!0),Object.defineProperty(t,n.key,n)}}return function(t,e,i){return e&&n(t.prototype,e),i&&n(t,i),t}}();var o=function(){function e(t){!function(t,e){if(!(t instanceof e))throw new TypeError("Cannot call a class as a function")}(this,e),this.slider=t,this.options=t.options,this._animating=!1,this._animation=void 0,this._translate=new s.a(this,t,t.options),this._fade=new n.a(this,t,t.options)}return r(e,[{key:"init",value:function(){return this._fade.init(),this._translate.init(),this}},{key:"isAnimating",value:function(){return this._animating}},{key:"enable",value:function(){this._animation&&this._animation.enable()}},{key:"disable",value:function(){this._animation&&this._animation.disable()}},{key:"apply",value:function(t,e){if(!this._animating||t){switch(this.options.effect){case"fade":this._animation=this._fade;break;case"translate":default:this._animation=this._translate}this._animationCallback=e,t?this._animation&&this._animation.disable():(this._animation&&this._animation.enable(),this._animating=!0),this._animation&&this._animation.apply(),t&&this.end()}}},{key:"end",value:function(){this._animating=!1,this._animation=void 0,this.slider.state.index=this.slider.state.next,this._animationCallback&&this._animationCallback()}}]),e}();e.a=o},function(t,e,i){"use strict";var s=i(0),r=Object.assign||function(t){for(var e=1;e<arguments.length;e++){var i=arguments[e];for(var n in i)Object.prototype.hasOwnProperty.call(i,n)&&(t[n]=i[n])}return t},o=function(){function n(t,e){for(var i=0;i<e.length;i++){var n=e[i];n.enumerable=n.enumerable||!1,n.configurable=!0,"value"in n&&(n.writable=!0),Object.defineProperty(t,n.key,n)}}return function(t,e,i){return e&&n(t.prototype,e),i&&n(t,i),t}}();var n=function(){function n(t,e){var i=2<arguments.length&&void 0!==arguments[2]?arguments[2]:{};!function(t,e){if(!(t instanceof e))throw new TypeError("Cannot call a class as a function")}(this,n),this.transitioner=t,this.slider=e,this.options=r({},i)}return o(n,[{key:"init",value:function(){var i=this;return"fade"===this.options.effect&&this.slider.slides.forEach(function(t,e){Object(s.a)(t,{position:"absolute",left:0,top:0,bottom:0,"z-index":t.dataset.sliderIndex==i.slider.state.index?0:-2,opacity:t.dataset.sliderIndex==i.slider.state.index?1:0})}),this}},{key:"enable",value:function(){var e=this;this._oldSlide=this.slider.slides.filter(function(t){return t.dataset.sliderIndex==e.slider.state.index})[0],this._newSlide=this.slider.slides.filter(function(t){return t.dataset.sliderIndex==e.slider.state.next})[0],this._newSlide&&(this._newSlide.addEventListener("transitionend",this.onTransitionEnd.bind(this)),this._newSlide.style.transition=this.options.duration+"ms "+this.options.timing,this._oldSlide&&(this._oldSlide.addEventListener("transitionend",this.onTransitionEnd.bind(this)),this._oldSlide.style.transition=this.options.duration+"ms "+this.options.timing))}},{key:"disable",value:function(){var e=this;this._oldSlide=this.slider.slides.filter(function(t){return t.dataset.sliderIndex==e.slider.state.index})[0],this._newSlide=this.slider.slides.filter(function(t){return t.dataset.sliderIndex==e.slider.state.next})[0],this._newSlide&&(this._newSlide.removeEventListener("transitionend",this.onTransitionEnd.bind(this)),this._newSlide.style.transition="none",this._oldSlide&&(this._oldSlide.removeEventListener("transitionend",this.onTransitionEnd.bind(this)),this._oldSlide.style.transition="none"))}},{key:"apply",value:function(t){var e=this;this._oldSlide=this.slider.slides.filter(function(t){return t.dataset.sliderIndex==e.slider.state.index})[0],this._newSlide=this.slider.slides.filter(function(t){return t.dataset.sliderIndex==e.slider.state.next})[0],this._oldSlide&&this._newSlide&&(Object(s.a)(this._oldSlide,{opacity:0}),Object(s.a)(this._newSlide,{opacity:1,"z-index":t?0:-1}))}},{key:"onTransitionEnd",value:function(t){"fade"===this.options.effect&&(this.transitioner.isAnimating()&&t.target==this._newSlide&&(this._newSlide&&(Object(s.a)(this._newSlide,{"z-index":0}),this._newSlide.removeEventListener("transitionend",this.onTransitionEnd.bind(this))),this._oldSlide&&(Object(s.a)(this._oldSlide,{"z-index":-2}),this._oldSlide.removeEventListener("transitionend",this.onTransitionEnd.bind(this)))),this.transitioner.end())}}]),n}();e.a=n},function(t,e,i){"use strict";var r=i(4),o=i(0),s=Object.assign||function(t){for(var e=1;e<arguments.length;e++){var i=arguments[e];for(var n in i)Object.prototype.hasOwnProperty.call(i,n)&&(t[n]=i[n])}return t},a=function(){function n(t,e){for(var i=0;i<e.length;i++){var n=e[i];n.enumerable=n.enumerable||!1,n.configurable=!0,"value"in n&&(n.writable=!0),Object.defineProperty(t,n.key,n)}}return function(t,e,i){return e&&n(t.prototype,e),i&&n(t,i),t}}();var n=function(){function n(t,e){var i=2<arguments.length&&void 0!==arguments[2]?arguments[2]:{};!function(t,e){if(!(t instanceof e))throw new TypeError("Cannot call a class as a function")}(this,n),this.transitioner=t,this.slider=e,this.options=s({},i),this.onTransitionEnd=this.onTransitionEnd.bind(this)}return a(n,[{key:"init",value:function(){return this._position=new r.a(this.slider.container.offsetLeft,this.slider.container.offsetTop),this._bindEvents(),this}},{key:"destroy",value:function(){this._unbindEvents()}},{key:"_bindEvents",value:function(){this.slider.container.addEventListener("transitionend",this.onTransitionEnd)}},{key:"_unbindEvents",value:function(){this.slider.container.removeEventListener("transitionend",this.onTransitionEnd)}},{key:"enable",value:function(){this.slider.container.style.transition=this.options.duration+"ms "+this.options.timing}},{key:"disable",value:function(){this.slider.container.style.transition="none"}},{key:"apply",value:function(){var e=this,t=void 0;if("translate"===this.options.effect){var i=this.slider.slides.filter(function(t){return t.dataset.sliderIndex==e.slider.state.next})[0],n=new r.a(i.offsetLeft,i.offsetTop);t=this.options.centerMode?new r.a(Math.round(Object(o.e)(this.slider.container)),Math.round(Object(o.b)(this.slider.container))):new r.a(Math.round(Object(o.e)(this.slider.container)-Object(o.e)(this.slider.wrapper)),Math.round(Object(o.b)(this.slider.container)-Object(o.b)(this.slider.wrapper)));var s=new r.a(Math.min(Math.max(-1*n.x,-1*t.x),0),Math.min(Math.max(-1*n.y,-1*t.y),0));this.options.loop&&(!this.options.vertical&&Math.abs(this._position.x)>t.x?(s.x=0,this.slider.state.next=0):this.options.vertical&&Math.abs(this._position.y)>t.y&&(s.y=0,this.slider.state.next=0)),this._position.x=s.x,this._position.y=s.y,this.options.centerMode&&(this._position.x=this._position.x+this.slider.wrapperWidth/2-Object(o.e)(i)/2),"rtl"===this.slider.direction&&(this._position.x=-this._position.x,this._position.y=-this._position.y),this.slider.container.style.transform="translate3d("+this._position.x+"px, "+this._position.y+"px, 0)",n.x>t.x&&this.slider.transitioner.end()}}},{key:"onTransitionEnd",value:function(t){"translate"===this.options.effect&&(this.transitioner.isAnimating()&&t.target==this.slider.container&&this.options.infinite&&this.slider._infinite.onTransitionEnd(t),this.transitioner.end())}}]),n}();e.a=n},function(t,e,i){"use strict";e.a={initialSlide:0,slidesToScroll:1,slidesToShow:1,navigation:!0,navigationKeys:!0,navigationSwipe:!0,pagination:!0,loop:!1,infinite:!1,effect:"translate",duration:300,timing:"ease",autoplay:!1,autoplaySpeed:3e3,pauseOnHover:!0,breakpoints:[{changePoint:480,slidesToShow:1,slidesToScroll:1},{changePoint:640,slidesToShow:2,slidesToScroll:2},{changePoint:768,slidesToShow:3,slidesToScroll:3}],onReady:null,icons:{previous:'<svg viewBox="0 0 50 80" xml:space="preserve">\n      <polyline fill="currentColor" stroke-width=".5em" stroke-linecap="round" stroke-linejoin="round" points="45.63,75.8 0.375,38.087 45.63,0.375 "/>\n    </svg>',next:'<svg viewBox="0 0 50 80" xml:space="preserve">\n      <polyline fill="currentColor" stroke-width=".5em" stroke-linecap="round" stroke-linejoin="round" points="0.375,0.375 45.63,38.087 0.375,75.8 "/>\n    </svg>'}}},function(t,e,i){"use strict";e.a=function(t){return'<div id="'+t+'" class="slider" tabindex="0">\n    <div class="slider-container"></div>\n  </div>'}},function(t,e,i){"use strict";e.a=function(){return'<div class="slider-item"></div>'}}]).default});

videoretalking/docs/static/js/bulma-slider.js

@@ -0,0 +1,461 @@
+(function webpackUniversalModuleDefinition(root, factory) {
+	if(typeof exports === 'object' && typeof module === 'object')
+		module.exports = factory();
+	else if(typeof define === 'function' && define.amd)
+		define([], factory);
+	else if(typeof exports === 'object')
+		exports["bulmaSlider"] = factory();
+	else
+		root["bulmaSlider"] = factory();
+})(typeof self !== 'undefined' ? self : this, function() {
+return /******/ (function(modules) { // webpackBootstrap
+/******/ 	// The module cache
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+/******/ 			Object.defineProperty(exports, name, {
+/******/ 				configurable: false,
+/******/ 				enumerable: true,
+/******/ 				get: getter
+/******/ 			});
+/******/ 		}
+/******/ 	};
+/******/
+/******/ 	// getDefaultExport function for compatibility with non-harmony modules
+/******/ 	__webpack_require__.n = function(module) {
+/******/ 		var getter = module && module.__esModule ?
+/******/ 			function getDefault() { return module['default']; } :
+/******/ 			function getModuleExports() { return module; };
+/******/ 		__webpack_require__.d(getter, 'a', getter);
+/******/ 		return getter;
+/******/ 	};
+/******/
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+/******/ 	__webpack_require__.o = function(object, property) { return Object.prototype.hasOwnProperty.call(object, property); };
+/******/
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+/******/
+/******/ 	// Load entry module and return exports
+/******/ 	return __webpack_require__(__webpack_require__.s = 0);
+/******/ })
+/************************************************************************/
+/******/ ([
+/* 0 */
+/***/ (function(module, __webpack_exports__, __webpack_require__) {
+
+"use strict";
+Object.defineProperty(__webpack_exports__, "__esModule", { value: true });
+/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "isString", function() { return isString; });
+/* harmony import */ var __WEBPACK_IMPORTED_MODULE_0__events__ = __webpack_require__(1);
+var _extends = Object.assign || function (target) { for (var i = 1; i < arguments.length; i++) { var source = arguments[i]; for (var key in source) { if (Object.prototype.hasOwnProperty.call(source, key)) { target[key] = source[key]; } } } return target; };
+
+var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }();
+
+var _typeof = typeof Symbol === "function" && typeof Symbol.iterator === "symbol" ? function (obj) { return typeof obj; } : function (obj) { return obj && typeof Symbol === "function" && obj.constructor === Symbol && obj !== Symbol.prototype ? "symbol" : typeof obj; };
+
+function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } }
+
+function _possibleConstructorReturn(self, call) { if (!self) { throw new ReferenceError("this hasn't been initialised - super() hasn't been called"); } return call && (typeof call === "object" || typeof call === "function") ? call : self; }
+
+function _inherits(subClass, superClass) { if (typeof superClass !== "function" && superClass !== null) { throw new TypeError("Super expression must either be null or a function, not " + typeof superClass); } subClass.prototype = Object.create(superClass && superClass.prototype, { constructor: { value: subClass, enumerable: false, writable: true, configurable: true } }); if (superClass) Object.setPrototypeOf ? Object.setPrototypeOf(subClass, superClass) : subClass.__proto__ = superClass; }
+
+
+
+var isString = function isString(unknown) {
+  return typeof unknown === 'string' || !!unknown && (typeof unknown === 'undefined' ? 'undefined' : _typeof(unknown)) === 'object' && Object.prototype.toString.call(unknown) === '[object String]';
+};
+
+var bulmaSlider = function (_EventEmitter) {
+  _inherits(bulmaSlider, _EventEmitter);
+
+  function bulmaSlider(selector) {
+    var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {};
+
+    _classCallCheck(this, bulmaSlider);
+
+    var _this = _possibleConstructorReturn(this, (bulmaSlider.__proto__ || Object.getPrototypeOf(bulmaSlider)).call(this));
+
+    _this.element = typeof selector === 'string' ? document.querySelector(selector) : selector;
+    // An invalid selector or non-DOM node has been provided.
+    if (!_this.element) {
+      throw new Error('An invalid selector or non-DOM node has been provided.');
+    }
+
+    _this._clickEvents = ['click'];
+    /// Set default options and merge with instance defined
+    _this.options = _extends({}, options);
+
+    _this.onSliderInput = _this.onSliderInput.bind(_this);
+
+    _this.init();
+    return _this;
+  }
+
+  /**
+   * Initiate all DOM element containing selector
+   * @method
+   * @return {Array} Array of all slider instances
+   */
+
+
+  _createClass(bulmaSlider, [{
+    key: 'init',
+
+
+    /**
+     * Initiate plugin
+     * @method init
+     * @return {void}
+     */
+    value: function init() {
+      this._id = 'bulmaSlider' + new Date().getTime() + Math.floor(Math.random() * Math.floor(9999));
+      this.output = this._findOutputForSlider();
+
+      this._bindEvents();
+
+      if (this.output) {
+        if (this.element.classList.contains('has-output-tooltip')) {
+          // Get new output position
+          var newPosition = this._getSliderOutputPosition();
+
+          // Set output position
+          this.output.style['left'] = newPosition.position;
+        }
+      }
+
+      this.emit('bulmaslider:ready', this.element.value);
+    }
+  }, {
+    key: '_findOutputForSlider',
+    value: function _findOutputForSlider() {
+      var _this2 = this;
+
+      var result = null;
+      var outputs = document.getElementsByTagName('output') || [];
+
+      Array.from(outputs).forEach(function (output) {
+        if (output.htmlFor == _this2.element.getAttribute('id')) {
+          result = output;
+          return true;
+        }
+      });
+      return result;
+    }
+  }, {
+    key: '_getSliderOutputPosition',
+    value: function _getSliderOutputPosition() {
+      // Update output position
+      var newPlace, minValue;
+
+      var style = window.getComputedStyle(this.element, null);
+      // Measure width of range input
+      var sliderWidth = parseInt(style.getPropertyValue('width'), 10);
+
+      // Figure out placement percentage between left and right of input
+      if (!this.element.getAttribute('min')) {
+        minValue = 0;
+      } else {
+        minValue = this.element.getAttribute('min');
+      }
+      var newPoint = (this.element.value - minValue) / (this.element.getAttribute('max') - minValue);
+
+      // Prevent bubble from going beyond left or right (unsupported browsers)
+      if (newPoint < 0) {
+        newPlace = 0;
+      } else if (newPoint > 1) {
+        newPlace = sliderWidth;
+      } else {
+        newPlace = sliderWidth * newPoint;
+      }
+
+      return {
+        'position': newPlace + 'px'
+      };
+    }
+
+    /**
+     * Bind all events
+     * @method _bindEvents
+     * @return {void}
+     */
+
+  }, {
+    key: '_bindEvents',
+    value: function _bindEvents() {
+      if (this.output) {
+        // Add event listener to update output when slider value change
+        this.element.addEventListener('input', this.onSliderInput, false);
+      }
+    }
+  }, {
+    key: 'onSliderInput',
+    value: function onSliderInput(e) {
+      e.preventDefault();
+
+      if (this.element.classList.contains('has-output-tooltip')) {
+        // Get new output position
+        var newPosition = this._getSliderOutputPosition();
+
+        // Set output position
+        this.output.style['left'] = newPosition.position;
+      }
+
+      // Check for prefix and postfix
+      var prefix = this.output.hasAttribute('data-prefix') ? this.output.getAttribute('data-prefix') : '';
+      var postfix = this.output.hasAttribute('data-postfix') ? this.output.getAttribute('data-postfix') : '';
+
+      // Update output with slider value
+      this.output.value = prefix + this.element.value + postfix;
+
+      this.emit('bulmaslider:ready', this.element.value);
+    }
+  }], [{
+    key: 'attach',
+    value: function attach() {
+      var _this3 = this;
+
+      var selector = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : 'input[type="range"].slider';
+      var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {};
+
+      var instances = new Array();
+
+      var elements = isString(selector) ? document.querySelectorAll(selector) : Array.isArray(selector) ? selector : [selector];
+      elements.forEach(function (element) {
+        if (typeof element[_this3.constructor.name] === 'undefined') {
+          var instance = new bulmaSlider(element, options);
+          element[_this3.constructor.name] = instance;
+          instances.push(instance);
+        } else {
+          instances.push(element[_this3.constructor.name]);
+        }
+      });
+
+      return instances;
+    }
+  }]);
+
+  return bulmaSlider;
+}(__WEBPACK_IMPORTED_MODULE_0__events__["a" /* default */]);
+
+/* harmony default export */ __webpack_exports__["default"] = (bulmaSlider);
+
+/***/ }),
+/* 1 */
+/***/ (function(module, __webpack_exports__, __webpack_require__) {
+
+"use strict";
+var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }();
+
+function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } }
+
+var EventEmitter = function () {
+  function EventEmitter() {
+    var listeners = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : [];
+
+    _classCallCheck(this, EventEmitter);
+
+    this._listeners = new Map(listeners);
+    this._middlewares = new Map();
+  }
+
+  _createClass(EventEmitter, [{
+    key: "listenerCount",
+    value: function listenerCount(eventName) {
+      if (!this._listeners.has(eventName)) {
+        return 0;
+      }
+
+      var eventListeners = this._listeners.get(eventName);
+      return eventListeners.length;
+    }
+  }, {
+    key: "removeListeners",
+    value: function removeListeners() {
+      var _this = this;
+
+      var eventName = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : null;
+      var middleware = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : false;
+
+      if (eventName !== null) {
+        if (Array.isArray(eventName)) {
+          name.forEach(function (e) {
+            return _this.removeListeners(e, middleware);
+          });
+        } else {
+          this._listeners.delete(eventName);
+
+          if (middleware) {
+            this.removeMiddleware(eventName);
+          }
+        }
+      } else {
+        this._listeners = new Map();
+      }
+    }
+  }, {
+    key: "middleware",
+    value: function middleware(eventName, fn) {
+      var _this2 = this;
+
+      if (Array.isArray(eventName)) {
+        name.forEach(function (e) {
+          return _this2.middleware(e, fn);
+        });
+      } else {
+        if (!Array.isArray(this._middlewares.get(eventName))) {
+          this._middlewares.set(eventName, []);
+        }
+
+        this._middlewares.get(eventName).push(fn);
+      }
+    }
+  }, {
+    key: "removeMiddleware",
+    value: function removeMiddleware() {
+      var _this3 = this;
+
+      var eventName = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : null;
+
+      if (eventName !== null) {
+        if (Array.isArray(eventName)) {
+          name.forEach(function (e) {
+            return _this3.removeMiddleware(e);
+          });
+        } else {
+          this._middlewares.delete(eventName);
+        }
+      } else {
+        this._middlewares = new Map();
+      }
+    }
+  }, {
+    key: "on",
+    value: function on(name, callback) {
+      var _this4 = this;
+
+      var once = arguments.length > 2 && arguments[2] !== undefined ? arguments[2] : false;
+
+      if (Array.isArray(name)) {
+        name.forEach(function (e) {
+          return _this4.on(e, callback);
+        });
+      } else {
+        name = name.toString();
+        var split = name.split(/,|, | /);
+
+        if (split.length > 1) {
+          split.forEach(function (e) {
+            return _this4.on(e, callback);
+          });
+        } else {
+          if (!Array.isArray(this._listeners.get(name))) {
+            this._listeners.set(name, []);
+          }
+
+          this._listeners.get(name).push({ once: once, callback: callback });
+        }
+      }
+    }
+  }, {
+    key: "once",
+    value: function once(name, callback) {
+      this.on(name, callback, true);
+    }
+  }, {
+    key: "emit",
+    value: function emit(name, data) {
+      var _this5 = this;
+
+      var silent = arguments.length > 2 && arguments[2] !== undefined ? arguments[2] : false;
+
+      name = name.toString();
+      var listeners = this._listeners.get(name);
+      var middlewares = null;
+      var doneCount = 0;
+      var execute = silent;
+
+      if (Array.isArray(listeners)) {
+        listeners.forEach(function (listener, index) {
+          // Start Middleware checks unless we're doing a silent emit
+          if (!silent) {
+            middlewares = _this5._middlewares.get(name);
+            // Check and execute Middleware
+            if (Array.isArray(middlewares)) {
+              middlewares.forEach(function (middleware) {
+                middleware(data, function () {
+                  var newData = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : null;
+
+                  if (newData !== null) {
+                    data = newData;
+                  }
+                  doneCount++;
+                }, name);
+              });
+
+              if (doneCount >= middlewares.length) {
+                execute = true;
+              }
+            } else {
+              execute = true;
+            }
+          }
+
+          // If Middleware checks have been passed, execute
+          if (execute) {
+            if (listener.once) {
+              listeners[index] = null;
+            }
+            listener.callback(data);
+          }
+        });
+
+        // Dirty way of removing used Events
+        while (listeners.indexOf(null) !== -1) {
+          listeners.splice(listeners.indexOf(null), 1);
+        }
+      }
+    }
+  }]);
+
+  return EventEmitter;
+}();
+
+/* harmony default export */ __webpack_exports__["a"] = (EventEmitter);
+
+/***/ })
+/******/ ])["default"];
+});

videoretalking/docs/static/js/bulma-slider.min.js

@@ -0,0 +1 @@
+!function(t,e){"object"==typeof exports&&"object"==typeof module?module.exports=e():"function"==typeof define&&define.amd?define([],e):"object"==typeof exports?exports.bulmaSlider=e():t.bulmaSlider=e()}("undefined"!=typeof self?self:this,function(){return function(n){var r={};function i(t){if(r[t])return r[t].exports;var e=r[t]={i:t,l:!1,exports:{}};return n[t].call(e.exports,e,e.exports,i),e.l=!0,e.exports}return i.m=n,i.c=r,i.d=function(t,e,n){i.o(t,e)||Object.defineProperty(t,e,{configurable:!1,enumerable:!0,get:n})},i.n=function(t){var e=t&&t.__esModule?function(){return t.default}:function(){return t};return i.d(e,"a",e),e},i.o=function(t,e){return Object.prototype.hasOwnProperty.call(t,e)},i.p="",i(i.s=0)}([function(t,e,n){"use strict";Object.defineProperty(e,"__esModule",{value:!0}),n.d(e,"isString",function(){return l});var r=n(1),i=Object.assign||function(t){for(var e=1;e<arguments.length;e++){var n=arguments[e];for(var r in n)Object.prototype.hasOwnProperty.call(n,r)&&(t[r]=n[r])}return t},u=function(){function r(t,e){for(var n=0;n<e.length;n++){var r=e[n];r.enumerable=r.enumerable||!1,r.configurable=!0,"value"in r&&(r.writable=!0),Object.defineProperty(t,r.key,r)}}return function(t,e,n){return e&&r(t.prototype,e),n&&r(t,n),t}}(),o="function"==typeof Symbol&&"symbol"==typeof Symbol.iterator?function(t){return typeof t}:function(t){return t&&"function"==typeof Symbol&&t.constructor===Symbol&&t!==Symbol.prototype?"symbol":typeof t};var l=function(t){return"string"==typeof t||!!t&&"object"===(void 0===t?"undefined":o(t))&&"[object String]"===Object.prototype.toString.call(t)},a=function(t){function o(t){var e=1<arguments.length&&void 0!==arguments[1]?arguments[1]:{};!function(t,e){if(!(t instanceof e))throw new TypeError("Cannot call a class as a function")}(this,o);var n=function(t,e){if(!t)throw new ReferenceError("this hasn't been initialised - super() hasn't been called");return!e||"object"!=typeof e&&"function"!=typeof e?t:e}(this,(o.__proto__||Object.getPrototypeOf(o)).call(this));if(n.element="string"==typeof t?document.querySelector(t):t,!n.element)throw new Error("An invalid selector or non-DOM node has been provided.");return n._clickEvents=["click"],n.options=i({},e),n.onSliderInput=n.onSliderInput.bind(n),n.init(),n}return function(t,e){if("function"!=typeof e&&null!==e)throw new TypeError("Super expression must either be null or a function, not "+typeof e);t.prototype=Object.create(e&&e.prototype,{constructor:{value:t,enumerable:!1,writable:!0,configurable:!0}}),e&&(Object.setPrototypeOf?Object.setPrototypeOf(t,e):t.__proto__=e)}(o,r["a"]),u(o,[{key:"init",value:function(){if(this._id="bulmaSlider"+(new Date).getTime()+Math.floor(Math.random()*Math.floor(9999)),this.output=this._findOutputForSlider(),this._bindEvents(),this.output&&this.element.classList.contains("has-output-tooltip")){var t=this._getSliderOutputPosition();this.output.style.left=t.position}this.emit("bulmaslider:ready",this.element.value)}},{key:"_findOutputForSlider",value:function(){var e=this,n=null,t=document.getElementsByTagName("output")||[];return Array.from(t).forEach(function(t){if(t.htmlFor==e.element.getAttribute("id"))return n=t,!0}),n}},{key:"_getSliderOutputPosition",value:function(){var t,e=window.getComputedStyle(this.element,null),n=parseInt(e.getPropertyValue("width"),10);t=this.element.getAttribute("min")?this.element.getAttribute("min"):0;var r=(this.element.value-t)/(this.element.getAttribute("max")-t);return{position:(r<0?0:1<r?n:n*r)+"px"}}},{key:"_bindEvents",value:function(){this.output&&this.element.addEventListener("input",this.onSliderInput,!1)}},{key:"onSliderInput",value:function(t){if(t.preventDefault(),this.element.classList.contains("has-output-tooltip")){var e=this._getSliderOutputPosition();this.output.style.left=e.position}var 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0!==arguments[0]?arguments[0]:null;null!==t?Array.isArray(t)?name.forEach(function(t){return e.removeMiddleware(t)}):this._middlewares.delete(t):this._middlewares=new Map}},{key:"on",value:function(t,e){var n=this,r=2<arguments.length&&void 0!==arguments[2]&&arguments[2];if(Array.isArray(t))t.forEach(function(t){return n.on(t,e)});else{var i=(t=t.toString()).split(/,|, | /);1<i.length?i.forEach(function(t){return n.on(t,e)}):(Array.isArray(this._listeners.get(t))||this._listeners.set(t,[]),this._listeners.get(t).push({once:r,callback:e}))}}},{key:"once",value:function(t,e){this.on(t,e,!0)}},{key:"emit",value:function(n,r){var i=this,o=2<arguments.length&&void 0!==arguments[2]&&arguments[2];n=n.toString();var u=this._listeners.get(n),l=null,a=0,s=o;if(Array.isArray(u))for(u.forEach(function(t,e){o||(l=i._middlewares.get(n),Array.isArray(l)?(l.forEach(function(t){t(r,function(){var t=0<arguments.length&&void 0!==arguments[0]?arguments[0]:null;null!==t&&(r=t),a++},n)}),a>=l.length&&(s=!0)):s=!0),s&&(t.once&&(u[e]=null),t.callback(r))});-1!==u.indexOf(null);)u.splice(u.indexOf(null),1)}}]),e}();e.a=i}]).default});

videoretalking/docs/static/js/index.js

@@ -0,0 +1,21 @@
+window.HELP_IMPROVE_VIDEOJS = false;
+
+
+$(document).ready(function() {
+    // Check for click events on the navbar burger icon
+
+    var options = {
+			slidesToScroll: 1,
+			slidesToShow: 1,
+			loop: true,
+			infinite: true,
+			autoplay: true,
+			autoplaySpeed: 5000,
+    }
+
+		// Initialize all div with carousel class
+    var carousels = bulmaCarousel.attach('.carousel', options);
+	
+    bulmaSlider.attach();
+
+})

videoretalking/docs/static/videos/Ablation.mp4

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c0417e7e2588c5436ae97d2d78fcdcfc55daea463b0bd0ac401bd2ec5af4701f
+size 3528171

videoretalking/docs/static/videos/Comparison.mp4

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9edbf9ec83a58ed25a4683597d4b7c982387139858344990b42239af80c69467
+size 22920622

videoretalking/docs/static/videos/Results_in_the_wild.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4e4623130898fe79bd95602c55062d434654d4e1f259bf84b114c3310b32d2c2
+size 8960716

videoretalking/inference - Copy.py

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+import numpy as np
+import cv2, os, sys, subprocess, platform, torch
+from tqdm import tqdm
+from PIL import Image
+from scipy.io import loadmat
+
+sys.path.insert(0, 'third_part')
+sys.path.insert(0, 'third_part/GPEN')
+# sys.path.insert(0, 'third_part/GFPGAN')
+
+# 3dmm extraction
+from third_part.face3d.util.preprocess import align_img
+from third_part.face3d.util.load_mats import load_lm3d
+from third_part.face3d.extract_kp_videos import KeypointExtractor
+# face enhancement
+from third_part.GPEN.gpen_face_enhancer import FaceEnhancement
+# from third_part.GFPGAN.gfpgan import GFPGANer
+# expression control
+from third_part.ganimation_replicate.model.ganimation import GANimationModel
+
+from utils import audio
+from utils.ffhq_preprocess import Croper
+from utils.alignment_stit import crop_faces, calc_alignment_coefficients, paste_image
+from utils.inference_utils import Laplacian_Pyramid_Blending_with_mask, face_detect, load_model, options, split_coeff, \
+                                  trans_image, transform_semantic, find_crop_norm_ratio, load_face3d_net, exp_aus_dict
+import warnings
+warnings.filterwarnings("ignore")
+
+args = options()
+
+def main():    
+    device = 'cuda' if torch.cuda.is_available() else 'cpu'
+    print('[Info] Using {} for inference.'.format(device))
+    os.makedirs(os.path.join('temp', args.tmp_dir), exist_ok=True)
+
+    enhancer = FaceEnhancement(base_dir='checkpoints', size=512, model='GPEN-BFR-512', use_sr=False, \
+                               sr_model='rrdb_realesrnet_psnr', channel_multiplier=2, narrow=1, device=device)
+    # restorer = GFPGANer(model_path='checkpoints/GFPGANv1.3.pth', upscale=1, arch='clean', \
+                        # channel_multiplier=2, bg_upsampler=None)
+
+    base_name = args.face.split('/')[-1]
+    if os.path.isfile(args.face) and args.face.split('.')[1] in ['jpg', 'png', 'jpeg']:
+        args.static = True
+    if not os.path.isfile(args.face):
+        raise ValueError('--face argument must be a valid path to video/image file')
+    elif args.face.split('.')[1] in ['jpg', 'png', 'jpeg']:
+        full_frames = [cv2.imread(args.face)]
+        fps = args.fps
+    else:
+        video_stream = cv2.VideoCapture(args.face)
+        fps = video_stream.get(cv2.CAP_PROP_FPS)
+
+        full_frames = []
+        while True:
+            still_reading, frame = video_stream.read()
+            if not still_reading:
+                video_stream.release()
+                break
+            y1, y2, x1, x2 = args.crop
+            if x2 == -1: x2 = frame.shape[1]
+            if y2 == -1: y2 = frame.shape[0]
+            frame = frame[y1:y2, x1:x2]
+            full_frames.append(frame)
+
+    print ("[Step 0] Number of frames available for inference: "+str(len(full_frames)))
+    # face detection & cropping, cropping the first frame as the style of FFHQ
+    croper = Croper('checkpoints/shape_predictor_68_face_landmarks.dat')
+    full_frames_RGB = [cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) for frame in full_frames]
+    full_frames_RGB, crop, quad = croper.crop(full_frames_RGB, xsize=512)
+
+    clx, cly, crx, cry = crop
+    lx, ly, rx, ry = quad
+    lx, ly, rx, ry = int(lx), int(ly), int(rx), int(ry)
+    oy1, oy2, ox1, ox2 = cly+ly, min(cly+ry, full_frames[0].shape[0]), clx+lx, min(clx+rx, full_frames[0].shape[1])
+    # original_size = (ox2 - ox1, oy2 - oy1)
+    frames_pil = [Image.fromarray(cv2.resize(frame,(256,256))) for frame in full_frames_RGB]
+
+    # get the landmark according to the detected face.
+    if not os.path.isfile('temp/'+base_name+'_landmarks.txt') or args.re_preprocess:
+        print('[Step 1] Landmarks Extraction in Video.')
+        kp_extractor = KeypointExtractor()
+        lm = kp_extractor.extract_keypoint(frames_pil, './temp/'+base_name+'_landmarks.txt')
+    else:
+        print('[Step 1] Using saved landmarks.')
+        lm = np.loadtxt('temp/'+base_name+'_landmarks.txt').astype(np.float32)
+        lm = lm.reshape([len(full_frames), -1, 2])
+       
+    if not os.path.isfile('temp/'+base_name+'_coeffs.npy') or args.exp_img is not None or args.re_preprocess:
+        net_recon = load_face3d_net(args.face3d_net_path, device)
+        lm3d_std = load_lm3d('checkpoints/BFM')
+
+        video_coeffs = []
+        for idx in tqdm(range(len(frames_pil)), desc="[Step 2] 3DMM Extraction In Video:"):
+            frame = frames_pil[idx]
+            W, H = frame.size
+            lm_idx = lm[idx].reshape([-1, 2])
+            if np.mean(lm_idx) == -1:
+                lm_idx = (lm3d_std[:, :2]+1) / 2.
+                lm_idx = np.concatenate([lm_idx[:, :1] * W, lm_idx[:, 1:2] * H], 1)
+            else:
+                lm_idx[:, -1] = H - 1 - lm_idx[:, -1]
+
+            trans_params, im_idx, lm_idx, _ = align_img(frame, lm_idx, lm3d_std)
+            trans_params = np.array([float(item) for item in np.hsplit(trans_params, 5)]).astype(np.float32)
+            im_idx_tensor = torch.tensor(np.array(im_idx)/255., dtype=torch.float32).permute(2, 0, 1).to(device).unsqueeze(0) 
+            with torch.no_grad():
+                coeffs = split_coeff(net_recon(im_idx_tensor))
+
+            pred_coeff = {key:coeffs[key].cpu().numpy() for key in coeffs}
+            pred_coeff = np.concatenate([pred_coeff['id'], pred_coeff['exp'], pred_coeff['tex'], pred_coeff['angle'],\
+                                         pred_coeff['gamma'], pred_coeff['trans'], trans_params[None]], 1)
+            video_coeffs.append(pred_coeff)
+        semantic_npy = np.array(video_coeffs)[:,0]
+        np.save('temp/'+base_name+'_coeffs.npy', semantic_npy)
+    else:
+        print('[Step 2] Using saved coeffs.')
+        semantic_npy = np.load('temp/'+base_name+'_coeffs.npy').astype(np.float32)
+
+    # generate the 3dmm coeff from a single image
+    if args.exp_img is not None and ('.png' in args.exp_img or '.jpg' in args.exp_img):
+        print('extract the exp from',args.exp_img)
+        exp_pil = Image.open(args.exp_img).convert('RGB')
+        lm3d_std = load_lm3d('third_part/face3d/BFM')
+        
+        W, H = exp_pil.size
+        kp_extractor = KeypointExtractor()
+        lm_exp = kp_extractor.extract_keypoint([exp_pil], 'temp/'+base_name+'_temp.txt')[0]
+        if np.mean(lm_exp) == -1:
+            lm_exp = (lm3d_std[:, :2] + 1) / 2.
+            lm_exp = np.concatenate(
+                [lm_exp[:, :1] * W, lm_exp[:, 1:2] * H], 1)
+        else:
+            lm_exp[:, -1] = H - 1 - lm_exp[:, -1]
+
+        trans_params, im_exp, lm_exp, _ = align_img(exp_pil, lm_exp, lm3d_std)
+        trans_params = np.array([float(item) for item in np.hsplit(trans_params, 5)]).astype(np.float32)
+        im_exp_tensor = torch.tensor(np.array(im_exp)/255., dtype=torch.float32).permute(2, 0, 1).to(device).unsqueeze(0)
+        with torch.no_grad():
+            expression = split_coeff(net_recon(im_exp_tensor))['exp'][0]
+        del net_recon
+    elif args.exp_img == 'smile':
+        expression = torch.tensor(loadmat('checkpoints/expression.mat')['expression_mouth'])[0]
+    else:
+        print('using expression center')
+        expression = torch.tensor(loadmat('checkpoints/expression.mat')['expression_center'])[0]
+
+    # load DNet, model(LNet and ENet)
+    D_Net, model = load_model(args, device)
+
+    if not os.path.isfile('temp/'+base_name+'_stablized.npy') or args.re_preprocess:
+        imgs = []
+        for idx in tqdm(range(len(frames_pil)), desc="[Step 3] Stabilize the expression In Video:"):
+            if args.one_shot:
+                source_img = trans_image(frames_pil[0]).unsqueeze(0).to(device)
+                semantic_source_numpy = semantic_npy[0:1]
+            else:
+                source_img = trans_image(frames_pil[idx]).unsqueeze(0).to(device)
+                semantic_source_numpy = semantic_npy[idx:idx+1]
+            ratio = find_crop_norm_ratio(semantic_source_numpy, semantic_npy)
+            coeff = transform_semantic(semantic_npy, idx, ratio).unsqueeze(0).to(device)
+        
+            # hacking the new expression
+            coeff[:, :64, :] = expression[None, :64, None].to(device) 
+            with torch.no_grad():
+                output = D_Net(source_img, coeff)
+            img_stablized = np.uint8((output['fake_image'].squeeze(0).permute(1,2,0).cpu().clamp_(-1, 1).numpy() + 1 )/2. * 255)
+            imgs.append(cv2.cvtColor(img_stablized,cv2.COLOR_RGB2BGR)) 
+        np.save('temp/'+base_name+'_stablized.npy',imgs)
+        del D_Net
+    else:
+        print('[Step 3] Using saved stabilized video.')
+        imgs = np.load('temp/'+base_name+'_stablized.npy')
+    torch.cuda.empty_cache()
+
+    if not args.audio.endswith('.wav'):
+        command = 'ffmpeg -loglevel error -y -i {} -strict -2 {}'.format(args.audio, 'temp/{}/temp.wav'.format(args.tmp_dir))
+        subprocess.call(command, shell=True)
+        args.audio = 'temp/{}/temp.wav'.format(args.tmp_dir)
+    wav = audio.load_wav(args.audio, 16000)
+    mel = audio.melspectrogram(wav)
+    if np.isnan(mel.reshape(-1)).sum() > 0:
+        raise ValueError('Mel contains nan! Using a TTS voice? Add a small epsilon noise to the wav file and try again')
+
+    mel_step_size, mel_idx_multiplier, i, mel_chunks = 16, 80./fps, 0, []
+    while True:
+        start_idx = int(i * mel_idx_multiplier)
+        if start_idx + mel_step_size > len(mel[0]):
+            mel_chunks.append(mel[:, len(mel[0]) - mel_step_size:])
+            break
+        mel_chunks.append(mel[:, start_idx : start_idx + mel_step_size])
+        i += 1
+
+    print("[Step 4] Load audio; Length of mel chunks: {}".format(len(mel_chunks)))
+    imgs = imgs[:len(mel_chunks)]
+    full_frames = full_frames[:len(mel_chunks)]  
+    lm = lm[:len(mel_chunks)]
+    
+    imgs_enhanced = []
+    for idx in tqdm(range(len(imgs)), desc='[Step 5] Reference Enhancement'):
+        img = imgs[idx]
+        pred, _, _ = enhancer.process(img, img, face_enhance=True, possion_blending=False)
+        imgs_enhanced.append(pred)
+    gen = datagen(imgs_enhanced.copy(), mel_chunks, full_frames, None, (oy1,oy2,ox1,ox2))
+
+    frame_h, frame_w = full_frames[0].shape[:-1]
+    out = cv2.VideoWriter('temp/{}/result.mp4'.format(args.tmp_dir), cv2.VideoWriter_fourcc(*'mp4v'), fps, (frame_w, frame_h))
+    
+    if args.up_face != 'original':
+        instance = GANimationModel()
+        instance.initialize()
+        instance.setup()
+
+    kp_extractor = KeypointExtractor()
+    for i, (img_batch, mel_batch, frames, coords, img_original, f_frames) in enumerate(tqdm(gen, desc='[Step 6] Lip Synthesis:', total=int(np.ceil(float(len(mel_chunks)) / args.LNet_batch_size)))):
+        img_batch = torch.FloatTensor(np.transpose(img_batch, (0, 3, 1, 2))).to(device)
+        mel_batch = torch.FloatTensor(np.transpose(mel_batch, (0, 3, 1, 2))).to(device)
+        img_original = torch.FloatTensor(np.transpose(img_original, (0, 3, 1, 2))).to(device)/255. # BGR -> RGB
+        
+        with torch.no_grad():
+            incomplete, reference = torch.split(img_batch, 3, dim=1) 
+            pred, low_res = model(mel_batch, img_batch, reference)
+            pred = torch.clamp(pred, 0, 1)
+
+            if args.up_face in ['sad', 'angry', 'surprise']:
+                tar_aus = exp_aus_dict[args.up_face]
+            else:
+                pass
+            
+            if args.up_face == 'original':
+                cur_gen_faces = img_original
+            else:
+                test_batch = {'src_img': torch.nn.functional.interpolate((img_original * 2 - 1), size=(128, 128), mode='bilinear'), 
+                              'tar_aus': tar_aus.repeat(len(incomplete), 1)}
+                instance.feed_batch(test_batch)
+                instance.forward()
+                cur_gen_faces = torch.nn.functional.interpolate(instance.fake_img / 2. + 0.5, size=(384, 384), mode='bilinear')
+                
+            if args.without_rl1 is not False:
+                incomplete, reference = torch.split(img_batch, 3, dim=1)
+                mask = torch.where(incomplete==0, torch.ones_like(incomplete), torch.zeros_like(incomplete)) 
+                pred = pred * mask + cur_gen_faces * (1 - mask) 
+        
+        pred = pred.cpu().numpy().transpose(0, 2, 3, 1) * 255.
+
+        torch.cuda.empty_cache()
+        for p, f, xf, c in zip(pred, frames, f_frames, coords):
+            y1, y2, x1, x2 = c
+            p = cv2.resize(p.astype(np.uint8), (x2 - x1, y2 - y1))
+            
+            ff = xf.copy() 
+            ff[y1:y2, x1:x2] = p
+            
+            # month region enhancement by GFPGAN
+            # cropped_faces, restored_faces, restored_img = restorer.enhance(
+            #     ff, has_aligned=False, only_center_face=True, paste_back=True)
+            restored_img = ff
+            mm = [0,   0,   0,   0,   0,   0,   0,   0,   0,  0, 255, 255, 255, 0, 0, 0, 0, 0, 0]
+            mouse_mask = np.zeros_like(restored_img)
+            tmp_mask = enhancer.faceparser.process(restored_img[y1:y2, x1:x2], mm)[0]
+            mouse_mask[y1:y2, x1:x2]= cv2.resize(tmp_mask, (x2 - x1, y2 - y1))[:, :, np.newaxis] / 255.
+
+            height, width = ff.shape[:2]
+            restored_img, ff, full_mask = [cv2.resize(x, (512, 512)) for x in (restored_img, ff, np.float32(mouse_mask))]
+            img = Laplacian_Pyramid_Blending_with_mask(restored_img, ff, full_mask[:, :, 0], 10)
+            pp = np.uint8(cv2.resize(np.clip(img, 0 ,255), (width, height)))
+
+            pp, orig_faces, enhanced_faces = enhancer.process(pp, xf, bbox=c, face_enhance=False, possion_blending=True)
+            out.write(pp)
+    out.release()
+    
+    if not os.path.isdir(os.path.dirname(args.outfile)):
+        os.makedirs(os.path.dirname(args.outfile), exist_ok=True)
+    command = 'ffmpeg -loglevel error -y -i {} -i {} -strict -2 -q:v 1 {}'.format(args.audio, 'temp/{}/result.mp4'.format(args.tmp_dir), args.outfile)
+    subprocess.call(command, shell=platform.system() != 'Windows')
+    print('outfile:', args.outfile)
+
+
+# frames:256x256, full_frames: original size
+def datagen(frames, mels, full_frames, frames_pil, cox):
+    img_batch, mel_batch, frame_batch, coords_batch, ref_batch, full_frame_batch = [], [], [], [], [], []
+    base_name = args.face.split('/')[-1]
+    refs = []
+    image_size = 256 
+
+    # original frames
+    kp_extractor = KeypointExtractor()
+    fr_pil = [Image.fromarray(frame) for frame in frames]
+    lms = kp_extractor.extract_keypoint(fr_pil, 'temp/'+base_name+'x12_landmarks.txt')
+    frames_pil = [ (lm, frame) for frame,lm in zip(fr_pil, lms)] # frames is the croped version of modified face
+    crops, orig_images, quads  = crop_faces(image_size, frames_pil, scale=1.0, use_fa=True)
+    inverse_transforms = [calc_alignment_coefficients(quad + 0.5, [[0, 0], [0, image_size], [image_size, image_size], [image_size, 0]]) for quad in quads]
+    del kp_extractor.detector
+
+    oy1,oy2,ox1,ox2 = cox
+    face_det_results = face_detect(full_frames, args, jaw_correction=True)
+
+    for inverse_transform, crop, full_frame, face_det in zip(inverse_transforms, crops, full_frames, face_det_results):
+        imc_pil = paste_image(inverse_transform, crop, Image.fromarray(
+            cv2.resize(full_frame[int(oy1):int(oy2), int(ox1):int(ox2)], (256, 256))))
+
+        ff = full_frame.copy()
+        ff[int(oy1):int(oy2), int(ox1):int(ox2)] = cv2.resize(np.array(imc_pil.convert('RGB')), (ox2 - ox1, oy2 - oy1))
+        oface, coords = face_det
+        y1, y2, x1, x2 = coords
+        refs.append(ff[y1: y2, x1:x2])
+
+    for i, m in enumerate(mels):
+        idx = 0 if args.static else i % len(frames)
+        frame_to_save = frames[idx].copy()
+        face = refs[idx]
+        oface, coords = face_det_results[idx].copy()
+
+        face = cv2.resize(face, (args.img_size, args.img_size))
+        oface = cv2.resize(oface, (args.img_size, args.img_size))
+
+        img_batch.append(oface)
+        ref_batch.append(face) 
+        mel_batch.append(m)
+        coords_batch.append(coords)
+        frame_batch.append(frame_to_save)
+        full_frame_batch.append(full_frames[idx].copy())
+
+        if len(img_batch) >= args.LNet_batch_size:
+            img_batch, mel_batch, ref_batch = np.asarray(img_batch), np.asarray(mel_batch), np.asarray(ref_batch)
+            img_masked = img_batch.copy()
+            img_original = img_batch.copy()
+            img_masked[:, args.img_size//2:] = 0
+            img_batch = np.concatenate((img_masked, ref_batch), axis=3) / 255.
+            mel_batch = np.reshape(mel_batch, [len(mel_batch), mel_batch.shape[1], mel_batch.shape[2], 1])
+
+            yield img_batch, mel_batch, frame_batch, coords_batch, img_original, full_frame_batch
+            img_batch, mel_batch, frame_batch, coords_batch, img_original, full_frame_batch, ref_batch  = [], [], [], [], [], [], []
+
+    if len(img_batch) > 0:
+        img_batch, mel_batch, ref_batch = np.asarray(img_batch), np.asarray(mel_batch), np.asarray(ref_batch)
+        img_masked = img_batch.copy()
+        img_original = img_batch.copy()
+        img_masked[:, args.img_size//2:] = 0
+        img_batch = np.concatenate((img_masked, ref_batch), axis=3) / 255.
+        mel_batch = np.reshape(mel_batch, [len(mel_batch), mel_batch.shape[1], mel_batch.shape[2], 1])
+        yield img_batch, mel_batch, frame_batch, coords_batch, img_original, full_frame_batch
+
+
+if __name__ == '__main__':
+    main()

videoretalking/inference.py

@@ -0,0 +1,345 @@
+import numpy as np
+import cv2, os, sys, subprocess, platform, torch
+from tqdm import tqdm
+from PIL import Image
+from scipy.io import loadmat
+
+sys.path.insert(0, 'third_part')
+sys.path.insert(0, 'third_part/GPEN')
+# sys.path.insert(0, 'third_part/GFPGAN')
+
+# 3dmm extraction
+from third_part.face3d.util.preprocess import align_img
+from third_part.face3d.util.load_mats import load_lm3d
+from third_part.face3d.extract_kp_videos import KeypointExtractor
+# face enhancement
+from third_part.GPEN.gpen_face_enhancer import FaceEnhancement
+# from third_part.GFPGAN.gfpgan import GFPGANer
+# expression control
+from third_part.ganimation_replicate.model.ganimation import GANimationModel
+
+from utils import audio
+from utils.ffhq_preprocess import Croper
+from utils.alignment_stit import crop_faces, calc_alignment_coefficients, paste_image
+from utils.inference_utils import Laplacian_Pyramid_Blending_with_mask, face_detect, load_model, options, split_coeff, \
+                                  trans_image, transform_semantic, find_crop_norm_ratio, load_face3d_net, exp_aus_dict
+import warnings
+warnings.filterwarnings("ignore")
+
+args = options()
+
+def main():    
+    device = 'cuda' if torch.cuda.is_available() else 'cpu'
+    print('[Info] Using {} for inference.'.format(device))
+    os.makedirs(os.path.join('temp', args.tmp_dir), exist_ok=True)
+
+    enhancer = FaceEnhancement(base_dir='checkpoints', size=512, model='GPEN-BFR-512', use_sr=False, \
+                               sr_model='rrdb_realesrnet_psnr', channel_multiplier=2, narrow=1, device=device)
+    # restorer = GFPGANer(model_path='checkpoints/GFPGANv1.3.pth', upscale=1, arch='clean', \
+                        # channel_multiplier=2, bg_upsampler=None)
+
+    base_name = args.face.split('/')[-1]
+    if os.path.isfile(args.face) and args.face.split('.')[1] in ['jpg', 'png', 'jpeg']:
+        args.static = True
+    if not os.path.isfile(args.face):
+        raise ValueError('--face argument must be a valid path to video/image file')
+    elif args.face.split('.')[1] in ['jpg', 'png', 'jpeg']:
+        full_frames = [cv2.imread(args.face)]
+        fps = args.fps
+    else:
+        video_stream = cv2.VideoCapture(args.face)
+        fps = video_stream.get(cv2.CAP_PROP_FPS)
+
+        full_frames = []
+        while True:
+            still_reading, frame = video_stream.read()
+            if not still_reading:
+                video_stream.release()
+                break
+            y1, y2, x1, x2 = args.crop
+            if x2 == -1: x2 = frame.shape[1]
+            if y2 == -1: y2 = frame.shape[0]
+            frame = frame[y1:y2, x1:x2]
+            full_frames.append(frame)
+
+    print ("[Step 0] Number of frames available for inference: "+str(len(full_frames)))
+    # face detection & cropping, cropping the first frame as the style of FFHQ
+    croper = Croper('checkpoints/shape_predictor_68_face_landmarks.dat')
+    full_frames_RGB = [cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) for frame in full_frames]
+    full_frames_RGB, crop, quad = croper.crop(full_frames_RGB, xsize=512)
+
+    clx, cly, crx, cry = crop
+    lx, ly, rx, ry = quad
+    lx, ly, rx, ry = int(lx), int(ly), int(rx), int(ry)
+    oy1, oy2, ox1, ox2 = cly+ly, min(cly+ry, full_frames[0].shape[0]), clx+lx, min(clx+rx, full_frames[0].shape[1])
+    # original_size = (ox2 - ox1, oy2 - oy1)
+    frames_pil = [Image.fromarray(cv2.resize(frame,(256,256))) for frame in full_frames_RGB]
+
+    # get the landmark according to the detected face.
+    if not os.path.isfile('temp/'+base_name+'_landmarks.txt') or args.re_preprocess:
+        print('[Step 1] Landmarks Extraction in Video.')
+        kp_extractor = KeypointExtractor()
+        lm = kp_extractor.extract_keypoint(frames_pil, './temp/'+base_name+'_landmarks.txt')
+    else:
+        print('[Step 1] Using saved landmarks.')
+        lm = np.loadtxt('temp/'+base_name+'_landmarks.txt').astype(np.float32)
+        lm = lm.reshape([len(full_frames), -1, 2])
+       
+    if not os.path.isfile('temp/'+base_name+'_coeffs.npy') or args.exp_img is not None or args.re_preprocess:
+        net_recon = load_face3d_net(args.face3d_net_path, device)
+        lm3d_std = load_lm3d('checkpoints/BFM')
+
+        video_coeffs = []
+        for idx in tqdm(range(len(frames_pil)), desc="[Step 2] 3DMM Extraction In Video:"):
+            frame = frames_pil[idx]
+            W, H = frame.size
+            lm_idx = lm[idx].reshape([-1, 2])
+            if np.mean(lm_idx) == -1:
+                lm_idx = (lm3d_std[:, :2]+1) / 2.
+                lm_idx = np.concatenate([lm_idx[:, :1] * W, lm_idx[:, 1:2] * H], 1)
+            else:
+                lm_idx[:, -1] = H - 1 - lm_idx[:, -1]
+
+            trans_params, im_idx, lm_idx, _ = align_img(frame, lm_idx, lm3d_std)
+            trans_params = np.array([float(item) for item in np.hsplit(trans_params, 5)]).astype(np.float32)
+            im_idx_tensor = torch.tensor(np.array(im_idx)/255., dtype=torch.float32).permute(2, 0, 1).to(device).unsqueeze(0) 
+            with torch.no_grad():
+                coeffs = split_coeff(net_recon(im_idx_tensor))
+
+            pred_coeff = {key:coeffs[key].cpu().numpy() for key in coeffs}
+            pred_coeff = np.concatenate([pred_coeff['id'], pred_coeff['exp'], pred_coeff['tex'], pred_coeff['angle'],\
+                                         pred_coeff['gamma'], pred_coeff['trans'], trans_params[None]], 1)
+            video_coeffs.append(pred_coeff)
+        semantic_npy = np.array(video_coeffs)[:,0]
+        np.save('temp/'+base_name+'_coeffs.npy', semantic_npy)
+    else:
+        print('[Step 2] Using saved coeffs.')
+        semantic_npy = np.load('temp/'+base_name+'_coeffs.npy').astype(np.float32)
+
+    # generate the 3dmm coeff from a single image
+    if args.exp_img is not None and ('.png' in args.exp_img or '.jpg' in args.exp_img):
+        print('extract the exp from',args.exp_img)
+        exp_pil = Image.open(args.exp_img).convert('RGB')
+        lm3d_std = load_lm3d('third_part/face3d/BFM')
+        
+        W, H = exp_pil.size
+        kp_extractor = KeypointExtractor()
+        lm_exp = kp_extractor.extract_keypoint([exp_pil], 'temp/'+base_name+'_temp.txt')[0]
+        if np.mean(lm_exp) == -1:
+            lm_exp = (lm3d_std[:, :2] + 1) / 2.
+            lm_exp = np.concatenate(
+                [lm_exp[:, :1] * W, lm_exp[:, 1:2] * H], 1)
+        else:
+            lm_exp[:, -1] = H - 1 - lm_exp[:, -1]
+
+        trans_params, im_exp, lm_exp, _ = align_img(exp_pil, lm_exp, lm3d_std)
+        trans_params = np.array([float(item) for item in np.hsplit(trans_params, 5)]).astype(np.float32)
+        im_exp_tensor = torch.tensor(np.array(im_exp)/255., dtype=torch.float32).permute(2, 0, 1).to(device).unsqueeze(0)
+        with torch.no_grad():
+            expression = split_coeff(net_recon(im_exp_tensor))['exp'][0]
+        del net_recon
+    elif args.exp_img == 'smile':
+        expression = torch.tensor(loadmat('checkpoints/expression.mat')['expression_mouth'])[0]
+    else:
+        print('using expression center')
+        expression = torch.tensor(loadmat('checkpoints/expression.mat')['expression_center'])[0]
+
+    # load DNet, model(LNet and ENet)
+    D_Net, model = load_model(args, device)
+
+    if not os.path.isfile('temp/'+base_name+'_stablized.npy') or args.re_preprocess:
+        imgs = []
+        for idx in tqdm(range(len(frames_pil)), desc="[Step 3] Stabilize the expression In Video:"):
+            if args.one_shot:
+                source_img = trans_image(frames_pil[0]).unsqueeze(0).to(device)
+                semantic_source_numpy = semantic_npy[0:1]
+            else:
+                source_img = trans_image(frames_pil[idx]).unsqueeze(0).to(device)
+                semantic_source_numpy = semantic_npy[idx:idx+1]
+            ratio = find_crop_norm_ratio(semantic_source_numpy, semantic_npy)
+            coeff = transform_semantic(semantic_npy, idx, ratio).unsqueeze(0).to(device)
+        
+            # hacking the new expression
+            coeff[:, :64, :] = expression[None, :64, None].to(device) 
+            with torch.no_grad():
+                output = D_Net(source_img, coeff)
+            img_stablized = np.uint8((output['fake_image'].squeeze(0).permute(1,2,0).cpu().clamp_(-1, 1).numpy() + 1 )/2. * 255)
+            imgs.append(cv2.cvtColor(img_stablized,cv2.COLOR_RGB2BGR)) 
+        np.save('temp/'+base_name+'_stablized.npy',imgs)
+        del D_Net
+    else:
+        print('[Step 3] Using saved stabilized video.')
+        imgs = np.load('temp/'+base_name+'_stablized.npy')
+    torch.cuda.empty_cache()
+
+    if not args.audio.endswith('.wav'):
+        command = 'ffmpeg -loglevel error -y -i {} -strict -2 {}'.format(args.audio, 'temp/{}/temp.wav'.format(args.tmp_dir))
+        subprocess.call(command, shell=True)
+        args.audio = 'temp/{}/temp.wav'.format(args.tmp_dir)
+    wav = audio.load_wav(args.audio, 16000)
+    mel = audio.melspectrogram(wav)
+    if np.isnan(mel.reshape(-1)).sum() > 0:
+        raise ValueError('Mel contains nan! Using a TTS voice? Add a small epsilon noise to the wav file and try again')
+
+    mel_step_size, mel_idx_multiplier, i, mel_chunks = 16, 80./fps, 0, []
+    while True:
+        start_idx = int(i * mel_idx_multiplier)
+        if start_idx + mel_step_size > len(mel[0]):
+            mel_chunks.append(mel[:, len(mel[0]) - mel_step_size:])
+            break
+        mel_chunks.append(mel[:, start_idx : start_idx + mel_step_size])
+        i += 1
+
+    print("[Step 4] Load audio; Length of mel chunks: {}".format(len(mel_chunks)))
+    imgs = imgs[:len(mel_chunks)]
+    full_frames = full_frames[:len(mel_chunks)]  
+    lm = lm[:len(mel_chunks)]
+    
+    imgs_enhanced = []
+    for idx in tqdm(range(len(imgs)), desc='[Step 5] Reference Enhancement'):
+        img = imgs[idx]
+        pred, _, _ = enhancer.process(img, img, face_enhance=True, possion_blending=False)
+        imgs_enhanced.append(pred)
+    gen = datagen(imgs_enhanced.copy(), mel_chunks, full_frames, None, (oy1,oy2,ox1,ox2))
+
+    frame_h, frame_w = full_frames[0].shape[:-1]
+    out = cv2.VideoWriter('temp/{}/result.mp4'.format(args.tmp_dir), cv2.VideoWriter_fourcc(*'mp4v'), fps, (frame_w, frame_h))
+    
+    if args.up_face != 'original':
+        instance = GANimationModel()
+        instance.initialize()
+        instance.setup()
+
+    kp_extractor = KeypointExtractor()
+    for i, (img_batch, mel_batch, frames, coords, img_original, f_frames) in enumerate(tqdm(gen, desc='[Step 6] Lip Synthesis:', total=int(np.ceil(float(len(mel_chunks)) / args.LNet_batch_size)))):
+        img_batch = torch.FloatTensor(np.transpose(img_batch, (0, 3, 1, 2))).to(device)
+        mel_batch = torch.FloatTensor(np.transpose(mel_batch, (0, 3, 1, 2))).to(device)
+        img_original = torch.FloatTensor(np.transpose(img_original, (0, 3, 1, 2))).to(device)/255. # BGR -> RGB
+        
+        with torch.no_grad():
+            incomplete, reference = torch.split(img_batch, 3, dim=1) 
+            pred, low_res = model(mel_batch, img_batch, reference)
+            pred = torch.clamp(pred, 0, 1)
+
+            if args.up_face in ['sad', 'angry', 'surprise']:
+                tar_aus = exp_aus_dict[args.up_face]
+            else:
+                pass
+            
+            if args.up_face == 'original':
+                cur_gen_faces = img_original
+            else:
+                test_batch = {'src_img': torch.nn.functional.interpolate((img_original * 2 - 1), size=(128, 128), mode='bilinear'), 
+                              'tar_aus': tar_aus.repeat(len(incomplete), 1)}
+                instance.feed_batch(test_batch)
+                instance.forward()
+                cur_gen_faces = torch.nn.functional.interpolate(instance.fake_img / 2. + 0.5, size=(384, 384), mode='bilinear')
+                
+            if args.without_rl1 is not False:
+                incomplete, reference = torch.split(img_batch, 3, dim=1)
+                mask = torch.where(incomplete==0, torch.ones_like(incomplete), torch.zeros_like(incomplete)) 
+                pred = pred * mask + cur_gen_faces * (1 - mask) 
+        
+        pred = pred.cpu().numpy().transpose(0, 2, 3, 1) * 255.
+
+        torch.cuda.empty_cache()
+        for p, f, xf, c in zip(pred, frames, f_frames, coords):
+            y1, y2, x1, x2 = c
+            p = cv2.resize(p.astype(np.uint8), (x2 - x1, y2 - y1))
+            
+            ff = xf.copy() 
+            ff[y1:y2, x1:x2] = p
+            
+            # month region enhancement by GFPGAN
+            # cropped_faces, restored_faces, restored_img = restorer.enhance(
+            #     ff, has_aligned=False, only_center_face=True, paste_back=True)
+            restored_img = ff
+            mm = [0,   0,   0,   0,   0,   0,   0,   0,   0,  0, 255, 255, 255, 0, 0, 0, 0, 0, 0]
+            mouse_mask = np.zeros_like(restored_img)
+            tmp_mask = enhancer.faceparser.process(restored_img[y1:y2, x1:x2], mm)[0]
+            mouse_mask[y1:y2, x1:x2]= cv2.resize(tmp_mask, (x2 - x1, y2 - y1))[:, :, np.newaxis] / 255.
+
+            height, width = ff.shape[:2]
+            restored_img, ff, full_mask = [cv2.resize(x, (512, 512)) for x in (restored_img, ff, np.float32(mouse_mask))]
+            img = Laplacian_Pyramid_Blending_with_mask(restored_img, ff, full_mask[:, :, 0], 10)
+            pp = np.uint8(cv2.resize(np.clip(img, 0 ,255), (width, height)))
+
+            pp, orig_faces, enhanced_faces = enhancer.process(pp, xf, bbox=c, face_enhance=False, possion_blending=True)
+            out.write(pp)
+    out.release()
+    
+    if not os.path.isdir(os.path.dirname(args.outfile)):
+        os.makedirs(os.path.dirname(args.outfile), exist_ok=True)
+    command = 'ffmpeg -loglevel error -y -i {} -i {} -strict -2 -q:v 1 {}'.format(args.audio, 'temp/{}/result.mp4'.format(args.tmp_dir), args.outfile)
+    subprocess.call(command, shell=platform.system() != 'Windows')
+    print('outfile:', args.outfile)
+
+
+# frames:256x256, full_frames: original size
+def datagen(frames, mels, full_frames, frames_pil, cox):
+    img_batch, mel_batch, frame_batch, coords_batch, ref_batch, full_frame_batch = [], [], [], [], [], []
+    base_name = args.face.split('/')[-1]
+    refs = []
+    image_size = 256 
+
+    # original frames
+    kp_extractor = KeypointExtractor()
+    fr_pil = [Image.fromarray(frame) for frame in frames]
+    lms = kp_extractor.extract_keypoint(fr_pil, 'temp/'+base_name+'x12_landmarks.txt')
+    frames_pil = [ (lm, frame) for frame,lm in zip(fr_pil, lms)] # frames is the croped version of modified face
+    crops, orig_images, quads  = crop_faces(image_size, frames_pil, scale=1.0, use_fa=True)
+    inverse_transforms = [calc_alignment_coefficients(quad + 0.5, [[0, 0], [0, image_size], [image_size, image_size], [image_size, 0]]) for quad in quads]
+    del kp_extractor.detector
+
+    oy1,oy2,ox1,ox2 = cox
+    face_det_results = face_detect(full_frames, args, jaw_correction=True)
+
+    for inverse_transform, crop, full_frame, face_det in zip(inverse_transforms, crops, full_frames, face_det_results):
+        imc_pil = paste_image(inverse_transform, crop, Image.fromarray(
+            cv2.resize(full_frame[int(oy1):int(oy2), int(ox1):int(ox2)], (256, 256))))
+
+        ff = full_frame.copy()
+        ff[int(oy1):int(oy2), int(ox1):int(ox2)] = cv2.resize(np.array(imc_pil.convert('RGB')), (ox2 - ox1, oy2 - oy1))
+        oface, coords = face_det
+        y1, y2, x1, x2 = coords
+        refs.append(ff[y1: y2, x1:x2])
+
+    for i, m in enumerate(mels):
+        idx = 0 if args.static else i % len(frames)
+        frame_to_save = frames[idx].copy()
+        face = refs[idx]
+        oface, coords = face_det_results[idx].copy()
+
+        face = cv2.resize(face, (args.img_size, args.img_size))
+        oface = cv2.resize(oface, (args.img_size, args.img_size))
+
+        img_batch.append(oface)
+        ref_batch.append(face) 
+        mel_batch.append(m)
+        coords_batch.append(coords)
+        frame_batch.append(frame_to_save)
+        full_frame_batch.append(full_frames[idx].copy())
+
+        if len(img_batch) >= args.LNet_batch_size:
+            img_batch, mel_batch, ref_batch = np.asarray(img_batch), np.asarray(mel_batch), np.asarray(ref_batch)
+            img_masked = img_batch.copy()
+            img_original = img_batch.copy()
+            img_masked[:, args.img_size//2:] = 0
+            img_batch = np.concatenate((img_masked, ref_batch), axis=3) / 255.
+            mel_batch = np.reshape(mel_batch, [len(mel_batch), mel_batch.shape[1], mel_batch.shape[2], 1])
+
+            yield img_batch, mel_batch, frame_batch, coords_batch, img_original, full_frame_batch
+            img_batch, mel_batch, frame_batch, coords_batch, img_original, full_frame_batch, ref_batch  = [], [], [], [], [], [], []
+
+    if len(img_batch) > 0:
+        img_batch, mel_batch, ref_batch = np.asarray(img_batch), np.asarray(mel_batch), np.asarray(ref_batch)
+        img_masked = img_batch.copy()
+        img_original = img_batch.copy()
+        img_masked[:, args.img_size//2:] = 0
+        img_batch = np.concatenate((img_masked, ref_batch), axis=3) / 255.
+        mel_batch = np.reshape(mel_batch, [len(mel_batch), mel_batch.shape[1], mel_batch.shape[2], 1])
+        yield img_batch, mel_batch, frame_batch, coords_batch, img_original, full_frame_batch
+
+
+if __name__ == '__main__':
+    main()

videoretalking/inference1.py

@@ -0,0 +1,347 @@
+import numpy as np
+import cv2, os, sys, subprocess, platform, torch
+from tqdm import tqdm
+from PIL import Image
+from scipy.io import loadmat
+
+sys.path.insert(0, 'third_part')
+sys.path.insert(0, 'third_part/GPEN')
+sys.path.insert(0, 'third_part/GFPGAN')
+
+# 3dmm extraction
+from third_part.face3d.util.preprocess import align_img
+from third_part.face3d.util.load_mats import load_lm3d
+from third_part.face3d.extract_kp_videos import KeypointExtractor
+# face enhancement
+from third_part.GPEN.gpen_face_enhancer import FaceEnhancement
+from third_part.GFPGAN.gfpgan import GFPGANer
+# expression control
+from third_part.ganimation_replicate.model.ganimation import GANimationModel
+
+from utils import audio
+from utils.ffhq_preprocess import Croper
+from utils.alignment_stit import crop_faces, calc_alignment_coefficients, paste_image
+from utils.inference_utils import Laplacian_Pyramid_Blending_with_mask, face_detect, load_model, options, split_coeff, \
+                                  trans_image, transform_semantic, find_crop_norm_ratio, load_face3d_net, exp_aus_dict
+import warnings
+warnings.filterwarnings("ignore")
+
+args = options()
+
+def main():    
+    device = 'cuda' if torch.cuda.is_available() else 'cpu'
+    print('[Info] Using {} for inference.'.format(device))
+    os.makedirs(os.path.join('temp', args.tmp_dir), exist_ok=True)
+
+    enhancer = FaceEnhancement(base_dir='checkpoints', size=1024, model='GPEN-BFR-1024', use_sr=False, \
+                               sr_model='rrdb_realesrnet_psnr', channel_multiplier=2, narrow=1, device=device)
+    restorer = GFPGANer(model_path='checkpoints/GFPGANv1.3.pth', upscale=1, arch='clean', \
+                        channel_multiplier=2, bg_upsampler=None)
+
+    base_name = args.face.split('/')[-1]
+    if os.path.isfile(args.face) and args.face.split('.')[1] in ['jpg', 'png', 'jpeg']:
+        args.static = True
+    if not os.path.isfile(args.face):
+        raise ValueError('--face argument must be a valid path to video/image file')
+    elif args.face.split('.')[1] in ['jpg', 'png', 'jpeg']:
+        full_frames = [cv2.imread(args.face)]
+        fps = args.fps
+    else:
+        video_stream = cv2.VideoCapture(args.face)
+        fps = video_stream.get(cv2.CAP_PROP_FPS)
+
+        full_frames = []
+        while True:
+            still_reading, frame = video_stream.read()
+            if not still_reading:
+                video_stream.release()
+                break
+            y1, y2, x1, x2 = args.crop
+            if x2 == -1: x2 = frame.shape[1]
+            if y2 == -1: y2 = frame.shape[0]
+            frame = frame[y1:y2, x1:x2]
+            full_frames.append(frame)
+
+    print ("[Step 0] Number of frames available for inference: "+str(len(full_frames)))
+    # face detection & cropping, cropping the first frame as the style of FFHQ
+    croper = Croper('checkpoints/shape_predictor_68_face_landmarks.dat')
+    full_frames_RGB = [cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) for frame in full_frames]
+    full_frames_RGB, crop, quad = croper.crop(full_frames_RGB, xsize=512)
+
+    clx, cly, crx, cry = crop
+    lx, ly, rx, ry = quad
+    lx, ly, rx, ry = int(lx), int(ly), int(rx), int(ry)
+    oy1, oy2, ox1, ox2 = cly+ly, min(cly+ry, full_frames[0].shape[0]), clx+lx, min(clx+rx, full_frames[0].shape[1])
+    # original_size = (ox2 - ox1, oy2 - oy1)
+    frames_pil = [Image.fromarray(cv2.resize(frame,(256,256))) for frame in full_frames_RGB]
+
+    # get the landmark according to the detected face.
+    if not os.path.isfile('temp/'+base_name+'_landmarks.txt') or args.re_preprocess:
+        print('[Step 1] Landmarks Extraction in Video.')
+        kp_extractor = KeypointExtractor()
+        lm = kp_extractor.extract_keypoint(frames_pil, './temp/'+base_name+'_landmarks.txt')
+    else:
+        print('[Step 1] Using saved landmarks.')
+        lm = np.loadtxt('temp/'+base_name+'_landmarks.txt').astype(np.float32)
+        lm = lm.reshape([len(full_frames), -1, 2])
+       
+    if not os.path.isfile('temp/'+base_name+'_coeffs.npy') or args.exp_img is not None or args.re_preprocess:
+        net_recon = load_face3d_net(args.face3d_net_path, device)
+        lm3d_std = load_lm3d('checkpoints/BFM')
+
+        video_coeffs = []
+        for idx in tqdm(range(len(frames_pil)), desc="[Step 2] 3DMM Extraction In Video:"):
+            frame = frames_pil[idx]
+            W, H = frame.size
+            lm_idx = lm[idx].reshape([-1, 2])
+            if np.mean(lm_idx) == -1:
+                lm_idx = (lm3d_std[:, :2]+1) / 2.
+                lm_idx = np.concatenate([lm_idx[:, :1] * W, lm_idx[:, 1:2] * H], 1)
+            else:
+                lm_idx[:, -1] = H - 1 - lm_idx[:, -1]
+
+            trans_params, im_idx, lm_idx, _ = align_img(frame, lm_idx, lm3d_std)
+            trans_params = np.array([float(item) for item in np.hsplit(trans_params, 5)]).astype(np.float32)
+            im_idx_tensor = torch.tensor(np.array(im_idx)/255., dtype=torch.float32).permute(2, 0, 1).to(device).unsqueeze(0) 
+            with torch.no_grad():
+                coeffs = split_coeff(net_recon(im_idx_tensor))
+
+            pred_coeff = {key:coeffs[key].cpu().numpy() for key in coeffs}
+            pred_coeff = np.concatenate([pred_coeff['id'], pred_coeff['exp'], pred_coeff['tex'], pred_coeff['angle'],\
+                                         pred_coeff['gamma'], pred_coeff['trans'], trans_params[None]], 1)
+            video_coeffs.append(pred_coeff)
+        semantic_npy = np.array(video_coeffs)[:,0]
+        np.save('temp/'+base_name+'_coeffs.npy', semantic_npy)
+    else:
+        print('[Step 2] Using saved coeffs.')
+        semantic_npy = np.load('temp/'+base_name+'_coeffs.npy').astype(np.float32)
+
+    # generate the 3dmm coeff from a single image
+    if args.exp_img is not None and ('.png' in args.exp_img or '.jpg' in args.exp_img):
+        print('extract the exp from',args.exp_img)
+        exp_pil = Image.open(args.exp_img).convert('RGB')
+        lm3d_std = load_lm3d('third_part/face3d/BFM')
+        
+        W, H = exp_pil.size
+        kp_extractor = KeypointExtractor()
+        lm_exp = kp_extractor.extract_keypoint([exp_pil], 'temp/'+base_name+'_temp.txt')[0]
+        if np.mean(lm_exp) == -1:
+            lm_exp = (lm3d_std[:, :2] + 1) / 2.
+            lm_exp = np.concatenate(
+                [lm_exp[:, :1] * W, lm_exp[:, 1:2] * H], 1)
+        else:
+            lm_exp[:, -1] = H - 1 - lm_exp[:, -1]
+
+        trans_params, im_exp, lm_exp, _ = align_img(exp_pil, lm_exp, lm3d_std)
+        trans_params = np.array([float(item) for item in np.hsplit(trans_params, 5)]).astype(np.float32)
+        im_exp_tensor = torch.tensor(np.array(im_exp)/255., dtype=torch.float32).permute(2, 0, 1).to(device).unsqueeze(0)
+        with torch.no_grad():
+            expression = split_coeff(net_recon(im_exp_tensor))['exp'][0]
+        del net_recon
+    elif args.exp_img == 'smile':
+        expression = torch.tensor(loadmat('checkpoints/expression.mat')['expression_mouth'])[0]
+    else:
+        print('using expression center')
+        expression = torch.tensor(loadmat('checkpoints/expression.mat')['expression_center'])[0]
+
+    # load DNet, model(LNet and ENet)
+    D_Net, model = load_model(args, device)
+
+    if not os.path.isfile('temp/'+base_name+'_stablized.npy') or args.re_preprocess:
+        imgs = []
+        for idx in tqdm(range(len(frames_pil)), desc="[Step 3] Stabilize the expression In Video:"):
+            if args.one_shot:
+                source_img = trans_image(frames_pil[0]).unsqueeze(0).to(device)
+                semantic_source_numpy = semantic_npy[0:1]
+            else:
+                source_img = trans_image(frames_pil[idx]).unsqueeze(0).to(device)
+                semantic_source_numpy = semantic_npy[idx:idx+1]
+            ratio = find_crop_norm_ratio(semantic_source_numpy, semantic_npy)
+            coeff = transform_semantic(semantic_npy, idx, ratio).unsqueeze(0).to(device)
+        
+            # hacking the new expression
+            coeff[:, :64, :] = expression[None, :64, None].to(device) 
+            with torch.no_grad():
+                output = D_Net(source_img, coeff)
+            img_stablized = np.uint8((output['fake_image'].squeeze(0).permute(1,2,0).cpu().clamp_(-1, 1).numpy() + 1 )/2. * 255)
+            imgs.append(cv2.cvtColor(img_stablized,cv2.COLOR_RGB2BGR)) 
+        np.save('temp/'+base_name+'_stablized.npy',imgs)
+        del D_Net
+    else:
+        print('[Step 3] Using saved stabilized video.')
+        imgs = np.load('temp/'+base_name+'_stablized.npy')
+    torch.cuda.empty_cache()
+
+    if not args.audio.endswith('.wav'):
+        command = 'ffmpeg -loglevel error -y -i {} -strict -2 {}'.format(args.audio, 'temp/{}/temp.wav'.format(args.tmp_dir))
+        subprocess.call(command, shell=True)
+        args.audio = 'temp/{}/temp.wav'.format(args.tmp_dir)
+    wav = audio.load_wav(args.audio, 16000)
+    mel = audio.melspectrogram(wav)
+    if np.isnan(mel.reshape(-1)).sum() > 0:
+        raise ValueError('Mel contains nan! Using a TTS voice? Add a small epsilon noise to the wav file and try again')
+
+    mel_step_size, mel_idx_multiplier, i, mel_chunks = 16, 80./fps, 0, []
+    while True:
+        start_idx = int(i * mel_idx_multiplier)
+        if start_idx + mel_step_size > len(mel[0]):
+            mel_chunks.append(mel[:, len(mel[0]) - mel_step_size:])
+            break
+        mel_chunks.append(mel[:, start_idx : start_idx + mel_step_size])
+        i += 1
+
+    print("[Step 4] Load audio; Length of mel chunks: {}".format(len(mel_chunks)))
+    imgs = imgs[:len(mel_chunks)]
+    full_frames = full_frames[:len(mel_chunks)]  
+    lm = lm[:len(mel_chunks)]
+    
+    imgs_enhanced = []
+    for idx in tqdm(range(len(imgs)), desc='[Step 5] Reference Enhancement'):
+        img = imgs[idx]
+        pred, _, _ = enhancer.process(img, img, face_enhance=True, possion_blending=False)
+        imgs_enhanced.append(pred)
+    gen = datagen(imgs_enhanced.copy(), mel_chunks, full_frames, None, (oy1,oy2,ox1,ox2))
+
+    frame_h, frame_w = full_frames[0].shape[:-1]
+    out = cv2.VideoWriter('temp/{}/result.mp4'.format(args.tmp_dir), cv2.VideoWriter_fourcc(*'mp4v'), fps, (frame_w, frame_h))
+    
+    if args.up_face != 'original':
+        instance = GANimationModel()
+        instance.initialize()
+        instance.setup()
+
+    kp_extractor = KeypointExtractor()
+    for i, (img_batch, mel_batch, frames, coords, img_original, f_frames) in enumerate(tqdm(gen, desc='[Step 6] Lip Synthesis:', total=int(np.ceil(float(len(mel_chunks)) / args.LNet_batch_size)))):
+        img_batch = torch.FloatTensor(np.transpose(img_batch, (0, 3, 1, 2))).to(device)
+        mel_batch = torch.FloatTensor(np.transpose(mel_batch, (0, 3, 1, 2))).to(device)
+        img_original = torch.FloatTensor(np.transpose(img_original, (0, 3, 1, 2))).to(device)/255. # BGR -> RGB
+        
+        with torch.no_grad():
+            incomplete, reference = torch.split(img_batch, 3, dim=1) 
+            pred, low_res = model(mel_batch, img_batch, reference)
+            pred = torch.clamp(pred, 0, 1)
+
+            if args.up_face in ['sad', 'angry', 'surprise']:
+                tar_aus = exp_aus_dict[args.up_face]
+            else:
+                pass
+            
+            if args.up_face == 'original':
+                cur_gen_faces = img_original
+            else:
+                test_batch = {'src_img': torch.nn.functional.interpolate((img_original * 2 - 1), size=(128, 128), mode='bilinear'), 
+                              'tar_aus': tar_aus.repeat(len(incomplete), 1)}
+                instance.feed_batch(test_batch)
+                instance.forward()
+                cur_gen_faces = torch.nn.functional.interpolate(instance.fake_img / 2. + 0.5, size=(384, 384), mode='bilinear')
+                
+            if args.without_rl1 is not False:
+                incomplete, reference = torch.split(img_batch, 3, dim=1)
+                mask = torch.where(incomplete==0, torch.ones_like(incomplete), torch.zeros_like(incomplete)) 
+                pred = pred * mask + cur_gen_faces * (1 - mask) 
+        
+        pred = pred.cpu().numpy().transpose(0, 2, 3, 1) * 255.
+
+        torch.cuda.empty_cache()
+        for p, f, xf, c in zip(pred, frames, f_frames, coords):
+            y1, y2, x1, x2 = c
+            p = cv2.resize(p.astype(np.uint8), (x2 - x1, y2 - y1))
+            
+            ff = xf.copy() 
+            ff[y1:y2, x1:x2] = p
+            height, width = ff.shape[:2]
+            pp = np.uint8(cv2.resize(np.clip(ff, 0 ,512), (width, height)))
+
+            pp, orig_faces, enhanced_faces = enhancer.process(pp, xf, bbox=c, face_enhance=True, possion_blending=False)
+            # month region enhancement by GFPGAN
+            cropped_faces, restored_faces, restored_img = restorer.enhance(
+                pp, has_aligned=False, only_center_face=True, paste_back=True)
+                # 0,   1,   2,   3,   4,   5,   6,   7,   8,  9, 10,  11,  12,
+            mm = [0,   0,   0,   0,   0,   0,   0,   0,   0,  0, 255, 255, 255, 0, 0, 0, 0, 0, 0]
+            #mm = [0, 255, 255, 255, 255, 255, 255, 255, 0, 0, 255, 255, 255, 0, 0, 0, 0, 0, 0]
+            mouse_mask = np.zeros_like(restored_img)
+            tmp_mask = enhancer.faceparser.process(restored_img[y1:y2, x1:x2], mm)[0]
+            mouse_mask[y1:y2, x1:x2]= cv2.resize(tmp_mask, (x2 - x1, y2 - y1))[:, :, np.newaxis] / 255.
+
+           
+            restored_img, ff, full_mask = [cv2.resize(x, (1024, 1024)) for x in (restored_img, ff, np.float32(mouse_mask))]
+            img = Laplacian_Pyramid_Blending_with_mask(restored_img, ff, full_mask[:, :, 0], 10)
+            pp = np.uint8(cv2.resize(np.clip(img, 0 ,1024), (width, height)))
+            out.write(pp)
+    out.release()
+    
+    if not os.path.isdir(os.path.dirname(args.outfile)):
+        os.makedirs(os.path.dirname(args.outfile), exist_ok=True)
+    command = 'ffmpeg -loglevel error -y -i {} -i {} -strict -2 -q:v 1 {}'.format(args.audio, 'temp/{}/result.mp4'.format(args.tmp_dir), args.outfile)
+    subprocess.call(command, shell=platform.system() != 'Windows')
+    print('outfile:', args.outfile)
+
+
+# frames:256x256, full_frames: original size
+def datagen(frames, mels, full_frames, frames_pil, cox):
+    img_batch, mel_batch, frame_batch, coords_batch, ref_batch, full_frame_batch = [], [], [], [], [], []
+    base_name = args.face.split('/')[-1]
+    refs = []
+    image_size = 256 
+
+    # original frames
+    kp_extractor = KeypointExtractor()
+    fr_pil = [Image.fromarray(frame) for frame in frames]
+    lms = kp_extractor.extract_keypoint(fr_pil, 'temp/'+base_name+'x12_landmarks.txt')
+    frames_pil = [ (lm, frame) for frame,lm in zip(fr_pil, lms)] # frames is the croped version of modified face
+    crops, orig_images, quads  = crop_faces(image_size, frames_pil, scale=1.0, use_fa=True)
+    inverse_transforms = [calc_alignment_coefficients(quad + 0.5, [[0, 0], [0, image_size], [image_size, image_size], [image_size, 0]]) for quad in quads]
+    del kp_extractor.detector
+
+    oy1,oy2,ox1,ox2 = cox
+    face_det_results = face_detect(full_frames, args, jaw_correction=True)
+
+    for inverse_transform, crop, full_frame, face_det in zip(inverse_transforms, crops, full_frames, face_det_results):
+        imc_pil = paste_image(inverse_transform, crop, Image.fromarray(
+            cv2.resize(full_frame[int(oy1):int(oy2), int(ox1):int(ox2)], (256, 256))))
+
+        ff = full_frame.copy()
+        ff[int(oy1):int(oy2), int(ox1):int(ox2)] = cv2.resize(np.array(imc_pil.convert('RGB')), (ox2 - ox1, oy2 - oy1))
+        oface, coords = face_det
+        y1, y2, x1, x2 = coords
+        refs.append(ff[y1: y2, x1:x2])
+
+    for i, m in enumerate(mels):
+        idx = 0 if args.static else i % len(frames)
+        frame_to_save = frames[idx].copy()
+        face = refs[idx]
+        oface, coords = face_det_results[idx].copy()
+
+        face = cv2.resize(face, (args.img_size, args.img_size))
+        oface = cv2.resize(oface, (args.img_size, args.img_size))
+
+        img_batch.append(oface)
+        ref_batch.append(face) 
+        mel_batch.append(m)
+        coords_batch.append(coords)
+        frame_batch.append(frame_to_save)
+        full_frame_batch.append(full_frames[idx].copy())
+
+        if len(img_batch) >= args.LNet_batch_size:
+            img_batch, mel_batch, ref_batch = np.asarray(img_batch), np.asarray(mel_batch), np.asarray(ref_batch)
+            img_masked = img_batch.copy()
+            img_original = img_batch.copy()
+            img_masked[:, args.img_size//2:] = 0
+            img_batch = np.concatenate((img_masked, ref_batch), axis=3) / 255.
+            mel_batch = np.reshape(mel_batch, [len(mel_batch), mel_batch.shape[1], mel_batch.shape[2], 1])
+
+            yield img_batch, mel_batch, frame_batch, coords_batch, img_original, full_frame_batch
+            img_batch, mel_batch, frame_batch, coords_batch, img_original, full_frame_batch, ref_batch  = [], [], [], [], [], [], []
+
+    if len(img_batch) > 0:
+        img_batch, mel_batch, ref_batch = np.asarray(img_batch), np.asarray(mel_batch), np.asarray(ref_batch)
+        img_masked = img_batch.copy()
+        img_original = img_batch.copy()
+        img_masked[:, args.img_size//2:] = 0
+        img_batch = np.concatenate((img_masked, ref_batch), axis=3) / 255.
+        mel_batch = np.reshape(mel_batch, [len(mel_batch), mel_batch.shape[1], mel_batch.shape[2], 1])
+        yield img_batch, mel_batch, frame_batch, coords_batch, img_original, full_frame_batch
+
+
+if __name__ == '__main__':
+    main()

videoretalking/inference_function.py

@@ -0,0 +1,368 @@
+import numpy as np
+import cv2, os, sys, subprocess, platform, torch
+from tqdm import tqdm
+from PIL import Image
+from scipy.io import loadmat
+from moviepy.editor import AudioFileClip, VideoFileClip
+
+sys.path.insert(0, 'third_part')
+sys.path.insert(0, 'third_part/GPEN')
+
+# 3dmm extraction
+from third_part.face3d.util.preprocess import align_img
+from third_part.face3d.util.load_mats import load_lm3d
+from third_part.face3d.extract_kp_videos import KeypointExtractor
+# face enhancement
+from third_part.GPEN.gpen_face_enhancer import FaceEnhancement
+# expression control
+from third_part.ganimation_replicate.model.ganimation import GANimationModel
+
+from utils import audio
+from utils.ffhq_preprocess import Croper
+from utils.alignment_stit import crop_faces, calc_alignment_coefficients, paste_image
+from utils.inference_utils import Laplacian_Pyramid_Blending_with_mask, face_detect, load_model, options, split_coeff, \
+                                  trans_image, transform_semantic, find_crop_norm_ratio, load_face3d_net, exp_aus_dict
+import warnings
+warnings.filterwarnings("ignore")
+
+def video_lipsync_correctness(face, audio_path, outfile=None, tmp_dir="temp", crop=[0, -1, 0, -1], re_preprocess=False, exp_img="neutral", face3d_net_path="checkpoints/face3d_pretrain_epoch_20.pth", one_shot=False, up_face="original", LNet_batch_size=16, without_rl1=False, static=False):
+    device = 'cuda' if torch.cuda.is_available() else 'cpu'
+    print('[Info] Using {} for inference.'.format(device))
+    os.makedirs(os.path.join('temp', tmp_dir), exist_ok=True)
+
+    enhancer = FaceEnhancement(base_dir='checkpoints', size=512, model='GPEN-BFR-512', use_sr=False, \
+                               sr_model='rrdb_realesrnet_psnr', channel_multiplier=2, narrow=1, device=device)
+
+    base_name = face.split('/')[-1]
+    print('base_name',base_name)
+    if os.path.isfile(face) and face.split('.')[1] in ['jpg', 'png', 'jpeg']:
+        static = True
+    if not os.path.isfile(face):
+        raise ValueError('--face argument must be a valid path to video/image file')
+    elif face.split('.')[1] in ['jpg', 'png', 'jpeg']:
+        full_frames = [cv2.imread(face)]
+        fps = fps
+    else:
+        video_stream = cv2.VideoCapture(face)
+        fps = video_stream.get(cv2.CAP_PROP_FPS)
+
+        full_frames = []
+        while True:
+            still_reading, frame = video_stream.read()
+            if not still_reading:
+                video_stream.release()
+                break
+            y1, y2, x1, x2 = crop
+            if x2 == -1: x2 = frame.shape[1]
+            if y2 == -1: y2 = frame.shape[0]
+            frame = frame[y1:y2, x1:x2]
+            full_frames.append(frame)
+    
+    print ("[Step 0] Number of frames available for inference: "+str(len(full_frames)))
+    # face detection & cropping, cropping the first frame as the style of FFHQ
+    croper = Croper('checkpoints/shape_predictor_68_face_landmarks.dat')
+    full_frames_RGB = [cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) for frame in full_frames]
+    full_frames_RGB, crop, quad = croper.crop(full_frames_RGB, xsize=512)
+
+    clx, cly, crx, cry = crop
+    lx, ly, rx, ry = quad
+    lx, ly, rx, ry = int(lx), int(ly), int(rx), int(ry)
+    oy1, oy2, ox1, ox2 = cly+ly, min(cly+ry, full_frames[0].shape[0]), clx+lx, min(clx+rx, full_frames[0].shape[1])
+    # original_size = (ox2 - ox1, oy2 - oy1)
+    frames_pil = [Image.fromarray(cv2.resize(frame,(256,256))) for frame in full_frames_RGB]
+
+    # get the landmark according to the detected face.
+    if not os.path.isfile('temp/'+base_name+'_landmarks.txt') or re_preprocess:
+        print('[Step 1] Landmarks Extraction in Video.')
+        kp_extractor = KeypointExtractor()
+        lm = kp_extractor.extract_keypoint(frames_pil, 'temp/'+base_name+'_landmarks.txt')
+    else:
+        print('[Step 1] Using saved landmarks.')
+        lm = np.loadtxt('temp/'+base_name+'_landmarks.txt').astype(np.float32)
+        lm = lm.reshape([len(full_frames), -1, 2])
+       
+    if not os.path.isfile('temp/'+base_name+'_coeffs.npy') or exp_img is not None or re_preprocess:
+        net_recon = load_face3d_net(face3d_net_path, device)
+        lm3d_std = load_lm3d('checkpoints/BFM_Fitting')
+
+        video_coeffs = []
+        for idx in tqdm(range(len(frames_pil)), desc="[Step 2] 3DMM Extraction In Video:"):
+            frame = frames_pil[idx]
+            W, H = frame.size
+            lm_idx = lm[idx].reshape([-1, 2])
+            if np.mean(lm_idx) == -1:
+                lm_idx = (lm3d_std[:, :2]+1) / 2.
+                lm_idx = np.concatenate([lm_idx[:, :1] * W, lm_idx[:, 1:2] * H], 1)
+            else:
+                lm_idx[:, -1] = H - 1 - lm_idx[:, -1]
+
+            trans_params, im_idx, lm_idx, _ = align_img(frame, lm_idx, lm3d_std)
+            trans_params = np.array([float(item) for item in np.hsplit(trans_params, 5)]).astype(np.float32)
+            im_idx_tensor = torch.tensor(np.array(im_idx)/255., dtype=torch.float32).permute(2, 0, 1).to(device).unsqueeze(0) 
+            with torch.no_grad():
+                coeffs = split_coeff(net_recon(im_idx_tensor))
+
+            pred_coeff = {key:coeffs[key].cpu().numpy() for key in coeffs}
+            pred_coeff = np.concatenate([pred_coeff['id'], pred_coeff['exp'], pred_coeff['tex'], pred_coeff['angle'],\
+                                         pred_coeff['gamma'], pred_coeff['trans'], trans_params[None]], 1)
+            video_coeffs.append(pred_coeff)
+        semantic_npy = np.array(video_coeffs)[:,0]
+        np.save('temp/'+base_name+'_coeffs.npy', semantic_npy)
+    else:
+        print('[Step 2] Using saved coeffs.')
+        semantic_npy = np.load('temp/'+base_name+'_coeffs.npy').astype(np.float32)
+
+    # generate the 3dmm coeff from a single image
+    if exp_img is not None and ('.png' in exp_img or '.jpg' in exp_img):
+        print('extract the exp from',exp_img)
+        exp_pil = Image.open(exp_img).convert('RGB')
+        lm3d_std = load_lm3d('third_part/face3d/BFM')
+        
+        W, H = exp_pil.size
+        kp_extractor = KeypointExtractor()
+        lm_exp = kp_extractor.extract_keypoint([exp_pil], 'temp/'+base_name+'_temp.txt')[0]
+        if np.mean(lm_exp) == -1:
+            lm_exp = (lm3d_std[:, :2] + 1) / 2.
+            lm_exp = np.concatenate(
+                [lm_exp[:, :1] * W, lm_exp[:, 1:2] * H], 1)
+        else:
+            lm_exp[:, -1] = H - 1 - lm_exp[:, -1]
+
+        trans_params, im_exp, lm_exp, _ = align_img(exp_pil, lm_exp, lm3d_std)
+        trans_params = np.array([float(item) for item in np.hsplit(trans_params, 5)]).astype(np.float32)
+        im_exp_tensor = torch.tensor(np.array(im_exp)/255., dtype=torch.float32).permute(2, 0, 1).to(device).unsqueeze(0)
+        with torch.no_grad():
+            expression = split_coeff(net_recon(im_exp_tensor))['exp'][0]
+        del net_recon
+    elif exp_img == 'smile':
+        expression = torch.tensor(loadmat('checkpoints/expression.mat')['expression_mouth'])[0]
+    else:
+        print('using expression center')
+        expression = torch.tensor(loadmat('checkpoints/expression.mat')['expression_center'])[0]
+
+    # load DNet, model(LNet and ENet)
+    D_Net, model = load_model(device,DNet_path='checkpoints/DNet.pt',LNet_path='checkpoints/LNet.pth',ENet_path='checkpoints/ENet.pth')
+
+    if not os.path.isfile('temp/'+base_name+'_stablized.npy') or re_preprocess:
+        imgs = []
+        for idx in tqdm(range(len(frames_pil)), desc="[Step 3] Stabilize the expression In Video:"):
+            if one_shot:
+                source_img = trans_image(frames_pil[0]).unsqueeze(0).to(device)
+                semantic_source_numpy = semantic_npy[0:1]
+            else:
+                source_img = trans_image(frames_pil[idx]).unsqueeze(0).to(device)
+                semantic_source_numpy = semantic_npy[idx:idx+1]
+            ratio = find_crop_norm_ratio(semantic_source_numpy, semantic_npy)
+            coeff = transform_semantic(semantic_npy, idx, ratio).unsqueeze(0).to(device)
+        
+            # hacking the new expression
+            coeff[:, :64, :] = expression[None, :64, None].to(device) 
+            with torch.no_grad():
+                output = D_Net(source_img, coeff)
+            img_stablized = np.uint8((output['fake_image'].squeeze(0).permute(1,2,0).cpu().clamp_(-1, 1).numpy() + 1 )/2. * 255)
+            imgs.append(cv2.cvtColor(img_stablized,cv2.COLOR_RGB2BGR)) 
+        np.save('temp/'+base_name+'_stablized.npy',imgs)
+        del D_Net
+    else:
+        print('[Step 3] Using saved stabilized video.')
+        imgs = np.load('temp/'+base_name+'_stablized.npy')
+    torch.cuda.empty_cache()
+
+    if not audio_path.endswith('.wav'):
+        # command = 'ffmpeg -loglevel error -y -i {} -strict -2 {}'.format(audio_path, 'temp/{}/temp.wav'.format(tmp_dir))
+        # subprocess.call(command, shell=True)
+        converted_audio_path = os.path.join('temp', tmp_dir, 'temp.wav')
+        audio_clip = AudioFileClip(audio_path)
+        audio_clip.write_audiofile(converted_audio_path, codec='pcm_s16le')
+        audio_clip.close()
+        audio_path = converted_audio_path
+        # audio_path = 'temp/{}/temp.wav'.format(tmp_dir)
+    wav = audio.load_wav(audio_path, 16000)
+    mel = audio.melspectrogram(wav)
+    if np.isnan(mel.reshape(-1)).sum() > 0:
+        raise ValueError('Mel contains nan! Using a TTS voice? Add a small epsilon noise to the wav file and try again')
+
+    mel_step_size, mel_idx_multiplier, i, mel_chunks = 16, 80./fps, 0, []
+    while True:
+        start_idx = int(i * mel_idx_multiplier)
+        if start_idx + mel_step_size > len(mel[0]):
+            mel_chunks.append(mel[:, len(mel[0]) - mel_step_size:])
+            break
+        mel_chunks.append(mel[:, start_idx : start_idx + mel_step_size])
+        i += 1
+
+    print("[Step 4] Load audio; Length of mel chunks: {}".format(len(mel_chunks)))
+    imgs = imgs[:len(mel_chunks)]
+    full_frames = full_frames[:len(mel_chunks)]  
+    lm = lm[:len(mel_chunks)]
+
+    imgs_enhanced = []
+    for idx in tqdm(range(len(imgs)), desc='[Step 5] Reference Enhancement'):
+        img = imgs[idx]
+        pred, _, _ = enhancer.process(img, img, face_enhance=True, possion_blending=False)
+        imgs_enhanced.append(pred)
+    gen = datagen(imgs_enhanced.copy(), mel_chunks, full_frames, None, (oy1,oy2,ox1,ox2), face, static, LNet_batch_size, img_size=384)
+
+    frame_h, frame_w = full_frames[0].shape[:-1]
+    out = cv2.VideoWriter('temp/{}/result.mp4'.format(tmp_dir), cv2.VideoWriter_fourcc(*'mp4v'), fps, (frame_w, frame_h))
+    
+    if up_face != 'original':
+        instance = GANimationModel()
+        instance.initialize()
+        instance.setup()
+    
+    kp_extractor = KeypointExtractor()
+    for i, (img_batch, mel_batch, frames, coords, img_original, f_frames) in enumerate(tqdm(gen, desc='[Step 6] Lip Synthesis:', total=int(np.ceil(float(len(mel_chunks)) / LNet_batch_size)))):
+        img_batch = torch.FloatTensor(np.transpose(img_batch, (0, 3, 1, 2))).to(device)
+        mel_batch = torch.FloatTensor(np.transpose(mel_batch, (0, 3, 1, 2))).to(device)
+        img_original = torch.FloatTensor(np.transpose(img_original, (0, 3, 1, 2))).to(device)/255. # BGR -> RGB
+        
+        with torch.no_grad():
+            incomplete, reference = torch.split(img_batch, 3, dim=1) 
+            pred, low_res = model(mel_batch, img_batch, reference)
+            pred = torch.clamp(pred, 0, 1)
+
+            if up_face in ['sad', 'angry', 'surprise']:
+                tar_aus = exp_aus_dict[up_face]
+            else:
+                pass
+            
+            if up_face == 'original':
+                cur_gen_faces = img_original
+            else:
+                test_batch = {'src_img': torch.nn.functional.interpolate((img_original * 2 - 1), size=(128, 128), mode='bilinear'), 
+                              'tar_aus': tar_aus.repeat(len(incomplete), 1)}
+                instance.feed_batch(test_batch)
+                instance.forward()
+                cur_gen_faces = torch.nn.functional.interpolate(instance.fake_img / 2. + 0.5, size=(384, 384), mode='bilinear')
+                
+            if without_rl1 is not False:
+                incomplete, reference = torch.split(img_batch, 3, dim=1)
+                mask = torch.where(incomplete==0, torch.ones_like(incomplete), torch.zeros_like(incomplete)) 
+                pred = pred * mask + cur_gen_faces * (1 - mask) 
+        
+        pred = pred.cpu().numpy().transpose(0, 2, 3, 1) * 255.
+
+        torch.cuda.empty_cache()
+        for p, f, xf, c in zip(pred, frames, f_frames, coords):
+            y1, y2, x1, x2 = c
+            p = cv2.resize(p.astype(np.uint8), (x2 - x1, y2 - y1))
+            
+            ff = xf.copy() 
+            ff[y1:y2, x1:x2] = p
+            
+            restored_img = ff
+            mm = [0,   0,   0,   0,   0,   0,   0,   0,   0,  0, 255, 255, 255, 0, 0, 0, 0, 0, 0]
+            mouse_mask = np.zeros_like(restored_img)
+            tmp_mask = enhancer.faceparser.process(restored_img[y1:y2, x1:x2], mm)[0]
+            mouse_mask[y1:y2, x1:x2]= cv2.resize(tmp_mask, (x2 - x1, y2 - y1))[:, :, np.newaxis] / 255.
+
+            height, width = ff.shape[:2]
+            restored_img, ff, full_mask = [cv2.resize(x, (512, 512)) for x in (restored_img, ff, np.float32(mouse_mask))]
+            img = Laplacian_Pyramid_Blending_with_mask(restored_img, ff, full_mask[:, :, 0], 10)
+            pp = np.uint8(cv2.resize(np.clip(img, 0 ,255), (width, height)))
+
+            pp, orig_faces, enhanced_faces = enhancer.process(pp, xf, bbox=c, face_enhance=False, possion_blending=True)
+            out.write(pp)
+    out.release()
+
+    if not os.path.isdir(os.path.dirname(outfile)):
+        os.makedirs(os.path.dirname(outfile), exist_ok=True)
+    # command = 'ffmpeg -loglevel error -y -i {} -i {} -strict -2 -q:v 1 {}'.format(audio_path, 'temp/{}/result.mp4'.format(tmp_dir), outfile)
+    # subprocess.call(command, shell=platform.system() != 'Windows')
+    video_path = 'temp/{}/result.mp4'.format(tmp_dir)
+    audio_clip = AudioFileClip(audio_path)
+    video_clip = VideoFileClip(video_path)
+    video_clip = video_clip.set_audio(audio_clip)
+
+    # Write the result to the output file
+    video_clip.write_videofile(outfile, codec='libx264', audio_codec='aac')
+    print('outfile:', outfile)
+
+# frames:256x256, full_frames: original size
+def datagen(frames, mels, full_frames, frames_pil, cox, face, static, LNet_batch_size, img_size):
+    img_batch, mel_batch, frame_batch, coords_batch, ref_batch, full_frame_batch = [], [], [], [], [], []
+    base_name = face.split('/')[-1]
+    refs = []
+    image_size = 256 
+
+    # original frames
+    kp_extractor = KeypointExtractor()
+    fr_pil = [Image.fromarray(frame) for frame in frames]
+    lms = kp_extractor.extract_keypoint(fr_pil, 'temp/'+base_name+'x12_landmarks.txt')
+    frames_pil = [ (lm, frame) for frame,lm in zip(fr_pil, lms)] # frames is the croped version of modified face
+    crops, orig_images, quads  = crop_faces(image_size, frames_pil, scale=1.0, use_fa=True)
+    inverse_transforms = [calc_alignment_coefficients(quad + 0.5, [[0, 0], [0, image_size], [image_size, image_size], [image_size, 0]]) for quad in quads]
+    del kp_extractor.detector
+
+    oy1,oy2,ox1,ox2 = cox
+    face_det_results = face_detect(full_frames, face_det_batch_size=4, nosmooth=False, pads=[0, 20, 0, 0], jaw_correction=True, detector=None)
+
+    for inverse_transform, crop, full_frame, face_det in zip(inverse_transforms, crops, full_frames, face_det_results):
+        imc_pil = paste_image(inverse_transform, crop, Image.fromarray(
+            cv2.resize(full_frame[int(oy1):int(oy2), int(ox1):int(ox2)], (256, 256))))
+
+        ff = full_frame.copy()
+        ff[int(oy1):int(oy2), int(ox1):int(ox2)] = cv2.resize(np.array(imc_pil.convert('RGB')), (ox2 - ox1, oy2 - oy1))
+        oface, coords = face_det
+        y1, y2, x1, x2 = coords
+        refs.append(ff[y1: y2, x1:x2])
+    
+    for i, m in enumerate(mels):
+        idx = 0 if static else i % len(frames)
+        frame_to_save = frames[idx].copy()
+        face = refs[idx]
+        oface, coords = face_det_results[idx].copy()
+
+        face = cv2.resize(face, (img_size, img_size))
+        oface = cv2.resize(oface, (img_size, img_size))
+
+        img_batch.append(oface)
+        ref_batch.append(face) 
+        mel_batch.append(m)
+        coords_batch.append(coords)
+        frame_batch.append(frame_to_save)
+        full_frame_batch.append(full_frames[idx].copy())
+
+        if len(img_batch) >= LNet_batch_size:
+            img_batch, mel_batch, ref_batch = np.asarray(img_batch), np.asarray(mel_batch), np.asarray(ref_batch)
+            img_masked = img_batch.copy()
+            img_original = img_batch.copy()
+            img_masked[:, img_size//2:] = 0
+            img_batch = np.concatenate((img_masked, ref_batch), axis=3) / 255.
+            mel_batch = np.reshape(mel_batch, [len(mel_batch), mel_batch.shape[1], mel_batch.shape[2], 1])
+
+            yield img_batch, mel_batch, frame_batch, coords_batch, img_original, full_frame_batch
+            img_batch, mel_batch, frame_batch, coords_batch, img_original, full_frame_batch, ref_batch  = [], [], [], [], [], [], []
+
+    if len(img_batch) > 0:
+        img_batch, mel_batch, ref_batch = np.asarray(img_batch), np.asarray(mel_batch), np.asarray(ref_batch)
+        img_masked = img_batch.copy()
+        img_original = img_batch.copy()
+        img_masked[:, img_size//2:] = 0
+        img_batch = np.concatenate((img_masked, ref_batch), axis=3) / 255.
+        mel_batch = np.reshape(mel_batch, [len(mel_batch), mel_batch.shape[1], mel_batch.shape[2], 1])
+        yield img_batch, mel_batch, frame_batch, coords_batch, img_original, full_frame_batch
+
+
+
+if __name__ == "__main__":
+    face_path = "C:/Users/fd01076/Downloads/download_1.mp4"  # Replace with the path to your face image or video
+    audio_path = "C:/Users/fd01076/Downloads/audio_1.mp3"  # Replace with the path to your audio file
+    output_path = "C:/Users/fd01076/Downloads/result.mp4"  # Replace with the path for the output video
+
+    # Call the function
+    video_lipsync_correctness(
+        face=face_path,
+        audio_path=audio_path,
+        outfile=output_path,
+        tmp_dir="temp",
+        crop=[0, -1, 0, -1],
+        re_preprocess=True,  # Set to True if you want to reprocess; False otherwise
+        exp_img="neutral",  # Can be 'smile', 'neutral', or path to an expression image
+        face3d_net_path="checkpoints/face3d_pretrain_epoch_20.pth",
+        one_shot=False,
+        up_face="original",  # Options: 'original', 'sad', 'angry', 'surprise'
+        LNet_batch_size=16,
+        without_rl1=False
+    )

videoretalking/inference_videoretalking.sh

@@ -0,0 +1,4 @@
+python3 inference.py \
+  --face ./examples/face/1.mp4 \
+  --audio ./examples/audio/1.wav \
+  --outfile results/1_1.mp4

videoretalking/models/DNet.py

@@ -0,0 +1,118 @@
+# TODO
+import functools
+import numpy as np
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from utils import flow_util
+from models.base_blocks import LayerNorm2d, ADAINHourglass, FineEncoder, FineDecoder
+
+# DNet
+class DNet(nn.Module):
+    def __init__(self):  
+        super(DNet, self).__init__()
+        self.mapping_net = MappingNet()
+        self.warpping_net = WarpingNet()
+        self.editing_net = EditingNet()
+ 
+    def forward(self, input_image, driving_source, stage=None):
+        if stage == 'warp':
+            descriptor = self.mapping_net(driving_source)
+            output = self.warpping_net(input_image, descriptor)
+        else:
+            descriptor = self.mapping_net(driving_source)
+            output = self.warpping_net(input_image, descriptor)
+            output['fake_image'] = self.editing_net(input_image, output['warp_image'], descriptor)
+        return output
+
+class MappingNet(nn.Module):
+    def __init__(self, coeff_nc=73, descriptor_nc=256, layer=3):
+        super( MappingNet, self).__init__()
+
+        self.layer = layer
+        nonlinearity = nn.LeakyReLU(0.1)
+
+        self.first = nn.Sequential(
+            torch.nn.Conv1d(coeff_nc, descriptor_nc, kernel_size=7, padding=0, bias=True))
+
+        for i in range(layer):
+            net = nn.Sequential(nonlinearity,
+                torch.nn.Conv1d(descriptor_nc, descriptor_nc, kernel_size=3, padding=0, dilation=3))
+            setattr(self, 'encoder' + str(i), net)   
+
+        self.pooling = nn.AdaptiveAvgPool1d(1)
+        self.output_nc = descriptor_nc
+
+    def forward(self, input_3dmm):
+        out = self.first(input_3dmm)
+        for i in range(self.layer):
+            model = getattr(self, 'encoder' + str(i))
+            out = model(out) + out[:,:,3:-3]
+        out = self.pooling(out)
+        return out   
+
+class WarpingNet(nn.Module):
+    def __init__(
+        self, 
+        image_nc=3, 
+        descriptor_nc=256, 
+        base_nc=32, 
+        max_nc=256, 
+        encoder_layer=5, 
+        decoder_layer=3, 
+        use_spect=False
+        ):
+        super( WarpingNet, self).__init__()
+
+        nonlinearity = nn.LeakyReLU(0.1)
+        norm_layer = functools.partial(LayerNorm2d, affine=True) 
+        kwargs = {'nonlinearity':nonlinearity, 'use_spect':use_spect}
+
+        self.descriptor_nc = descriptor_nc 
+        self.hourglass = ADAINHourglass(image_nc, self.descriptor_nc, base_nc,
+                                       max_nc, encoder_layer, decoder_layer, **kwargs)
+
+        self.flow_out = nn.Sequential(norm_layer(self.hourglass.output_nc), 
+                                      nonlinearity,
+                                      nn.Conv2d(self.hourglass.output_nc, 2, kernel_size=7, stride=1, padding=3))
+
+        self.pool = nn.AdaptiveAvgPool2d(1)
+
+    def forward(self, input_image, descriptor):
+        final_output={}
+        output = self.hourglass(input_image, descriptor)
+        final_output['flow_field'] = self.flow_out(output)
+
+        deformation = flow_util.convert_flow_to_deformation(final_output['flow_field'])
+        final_output['warp_image'] = flow_util.warp_image(input_image, deformation)
+        return final_output
+
+
+class EditingNet(nn.Module):
+    def __init__(
+        self, 
+        image_nc=3, 
+        descriptor_nc=256, 
+        layer=3, 
+        base_nc=64, 
+        max_nc=256, 
+        num_res_blocks=2, 
+        use_spect=False):  
+        super(EditingNet, self).__init__()
+
+        nonlinearity = nn.LeakyReLU(0.1)
+        norm_layer = functools.partial(LayerNorm2d, affine=True) 
+        kwargs = {'norm_layer':norm_layer, 'nonlinearity':nonlinearity, 'use_spect':use_spect}
+        self.descriptor_nc = descriptor_nc
+
+        # encoder part
+        self.encoder = FineEncoder(image_nc*2, base_nc, max_nc, layer, **kwargs)
+        self.decoder = FineDecoder(image_nc, self.descriptor_nc, base_nc, max_nc, layer, num_res_blocks, **kwargs)
+
+    def forward(self, input_image, warp_image, descriptor):
+        x = torch.cat([input_image, warp_image], 1)
+        x = self.encoder(x)
+        gen_image = self.decoder(x, descriptor)
+        return gen_image

videoretalking/models/ENet.py

@@ -0,0 +1,139 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from models.base_blocks import ResBlock, StyleConv, ToRGB
+
+
+class ENet(nn.Module):
+    def __init__(
+        self, 
+        num_style_feat=512,
+        lnet=None,
+        concat=False
+        ):  
+        super(ENet, self).__init__()
+
+        self.low_res = lnet
+        for param in self.low_res.parameters():
+            param.requires_grad = False
+
+        channel_multiplier, narrow = 2, 1
+        channels = {
+            '4': int(512 * narrow),
+            '8': int(512 * narrow),
+            '16': int(512 * narrow),
+            '32': int(512 * narrow),
+            '64': int(256 * channel_multiplier * narrow),
+            '128': int(128 * channel_multiplier * narrow),
+            '256': int(64 * channel_multiplier * narrow),
+            '512': int(32 * channel_multiplier * narrow),
+            '1024': int(16 * channel_multiplier * narrow)
+        }
+
+        self.log_size = 8
+        first_out_size = 128
+        self.conv_body_first = nn.Conv2d(3, channels[f'{first_out_size}'], 1) # 256 -> 128
+
+        # downsample
+        in_channels = channels[f'{first_out_size}']
+        self.conv_body_down = nn.ModuleList()
+        for i in range(8, 2, -1):
+            out_channels = channels[f'{2**(i - 1)}']
+            self.conv_body_down.append(ResBlock(in_channels, out_channels, mode='down'))
+            in_channels = out_channels
+
+        self.num_style_feat = num_style_feat
+        linear_out_channel = num_style_feat
+        self.final_linear = nn.Linear(channels['4'] * 4 * 4, linear_out_channel)
+        self.final_conv = nn.Conv2d(in_channels, channels['4'], 3, 1, 1)
+
+        self.style_convs = nn.ModuleList()
+        self.to_rgbs = nn.ModuleList()
+        self.noises = nn.Module()
+        
+        self.concat = concat
+        if concat:
+            in_channels = 3 + 32 # channels['64']
+        else:
+            in_channels = 3
+
+        for i in range(7, 9):  # 128, 256
+            out_channels = channels[f'{2**i}'] # 
+            self.style_convs.append(
+                StyleConv(
+                    in_channels,
+                    out_channels,
+                    kernel_size=3,
+                    num_style_feat=num_style_feat,
+                    demodulate=True,
+                    sample_mode='upsample'))
+            self.style_convs.append(
+                StyleConv(
+                    out_channels,
+                    out_channels,
+                    kernel_size=3,
+                    num_style_feat=num_style_feat,
+                    demodulate=True,
+                    sample_mode=None))
+            self.to_rgbs.append(ToRGB(out_channels, num_style_feat, upsample=True))
+            in_channels = out_channels
+
+    def forward(self, audio_sequences, face_sequences, gt_sequences):
+        B = audio_sequences.size(0)
+        input_dim_size = len(face_sequences.size())
+        inp, ref = torch.split(face_sequences,3,dim=1)
+
+        if input_dim_size > 4:
+            audio_sequences = torch.cat([audio_sequences[:, i] for i in range(audio_sequences.size(1))], dim=0)
+            inp = torch.cat([inp[:, :, i] for i in range(inp.size(2))], dim=0)
+            ref = torch.cat([ref[:, :, i] for i in range(ref.size(2))], dim=0)
+            gt_sequences = torch.cat([gt_sequences[:, :, i] for i in range(gt_sequences.size(2))], dim=0)
+        
+        # get the global style
+        feat = F.leaky_relu_(self.conv_body_first(F.interpolate(ref, size=(256,256), mode='bilinear')), negative_slope=0.2)
+        for i in range(self.log_size - 2):
+            feat = self.conv_body_down[i](feat)
+        feat = F.leaky_relu_(self.final_conv(feat), negative_slope=0.2)
+
+        # style code
+        style_code = self.final_linear(feat.reshape(feat.size(0), -1))
+        style_code = style_code.reshape(style_code.size(0), -1, self.num_style_feat)
+        
+        LNet_input = torch.cat([inp, gt_sequences], dim=1)
+        LNet_input = F.interpolate(LNet_input, size=(96,96), mode='bilinear')
+        
+        if self.concat:
+            low_res_img, low_res_feat = self.low_res(audio_sequences, LNet_input)
+            low_res_img.detach()
+            low_res_feat.detach()
+            out = torch.cat([low_res_img, low_res_feat], dim=1) 
+
+        else:
+            low_res_img = self.low_res(audio_sequences, LNet_input)
+            low_res_img.detach()
+            # 96 x 96
+            out = low_res_img 
+        
+        p2d = (2,2,2,2)
+        out = F.pad(out, p2d, "reflect", 0)
+        skip = out
+
+        for conv1, conv2, to_rgb in zip(self.style_convs[::2], self.style_convs[1::2], self.to_rgbs):
+            out = conv1(out, style_code)  # 96, 192, 384
+            out = conv2(out, style_code)
+            skip = to_rgb(out, style_code, skip)
+        _outputs = skip
+
+        # remove padding
+        _outputs = _outputs[:,:,8:-8,8:-8]
+
+        if input_dim_size > 4:
+            _outputs = torch.split(_outputs, B, dim=0)
+            outputs = torch.stack(_outputs, dim=2)
+            low_res_img = F.interpolate(low_res_img, outputs.size()[3:])
+            low_res_img = torch.split(low_res_img, B, dim=0) 
+            low_res_img = torch.stack(low_res_img, dim=2)
+        else:
+            outputs = _outputs
+        return outputs, low_res_img

videoretalking/models/LNet.py

@@ -0,0 +1,139 @@
+import functools
+import torch
+import torch.nn as nn
+
+from models.transformer import RETURNX, Transformer
+from models.base_blocks import Conv2d, LayerNorm2d, FirstBlock2d, DownBlock2d, UpBlock2d, \
+                               FFCADAINResBlocks, Jump, FinalBlock2d
+
+
+class Visual_Encoder(nn.Module):
+    def __init__(self, image_nc, ngf, img_f, layers, norm_layer=nn.BatchNorm2d, nonlinearity=nn.LeakyReLU(), use_spect=False):
+        super(Visual_Encoder, self).__init__()
+        self.layers = layers
+        self.first_inp = FirstBlock2d(image_nc, ngf, norm_layer, nonlinearity, use_spect)
+        self.first_ref = FirstBlock2d(image_nc, ngf, norm_layer, nonlinearity, use_spect)
+        for i in range(layers):
+            in_channels = min(ngf*(2**i), img_f)
+            out_channels = min(ngf*(2**(i+1)), img_f)
+            model_ref = DownBlock2d(in_channels, out_channels, norm_layer, nonlinearity, use_spect)
+            model_inp = DownBlock2d(in_channels, out_channels, norm_layer, nonlinearity, use_spect)
+            if i < 2:
+                ca_layer = RETURNX()
+            else:
+                ca_layer = Transformer(2**(i+1) * ngf,2,4,ngf,ngf*4)
+            setattr(self, 'ca' + str(i), ca_layer)
+            setattr(self, 'ref_down' + str(i), model_ref)
+            setattr(self, 'inp_down' + str(i), model_inp)
+        self.output_nc = out_channels * 2
+
+    def forward(self, maskGT, ref):
+        x_maskGT, x_ref = self.first_inp(maskGT), self.first_ref(ref)
+        out=[x_maskGT]
+        for i in range(self.layers):
+            model_ref = getattr(self, 'ref_down'+str(i))
+            model_inp = getattr(self, 'inp_down'+str(i))
+            ca_layer = getattr(self, 'ca'+str(i))
+            x_maskGT, x_ref = model_inp(x_maskGT), model_ref(x_ref)
+            x_maskGT = ca_layer(x_maskGT, x_ref)
+            if i < self.layers - 1:
+                out.append(x_maskGT)
+            else:           
+                out.append(torch.cat([x_maskGT, x_ref], dim=1)) # concat ref features !
+        return out
+
+
+class Decoder(nn.Module):
+    def __init__(self, image_nc, feature_nc, ngf, img_f, layers, num_block, norm_layer=nn.BatchNorm2d, nonlinearity=nn.LeakyReLU(), use_spect=False):
+        super(Decoder, self).__init__()
+        self.layers = layers
+        for i in range(layers)[::-1]:
+            if  i == layers-1:
+                in_channels = ngf*(2**(i+1)) * 2
+            else:
+                in_channels = min(ngf*(2**(i+1)), img_f)
+            out_channels = min(ngf*(2**i), img_f)
+            up = UpBlock2d(in_channels, out_channels, norm_layer, nonlinearity, use_spect)
+            res = FFCADAINResBlocks(num_block, in_channels, feature_nc, norm_layer, nonlinearity, use_spect)
+            jump = Jump(out_channels, norm_layer, nonlinearity, use_spect)
+
+            setattr(self, 'up' + str(i), up)
+            setattr(self, 'res' + str(i), res)            
+            setattr(self, 'jump' + str(i), jump)
+
+        self.final = FinalBlock2d(out_channels, image_nc, use_spect, 'sigmoid')
+        self.output_nc = out_channels
+
+    def forward(self, x, z):
+        out = x.pop()
+        for i in range(self.layers)[::-1]:
+            res_model = getattr(self, 'res' + str(i))
+            up_model = getattr(self, 'up' + str(i))
+            jump_model = getattr(self, 'jump' + str(i))
+            out = res_model(out, z)
+            out = up_model(out)
+            out = jump_model(x.pop()) + out
+        out_image = self.final(out)
+        return out_image
+
+
+class LNet(nn.Module): 
+    def __init__(
+        self, 
+        image_nc=3, 
+        descriptor_nc=512, 
+        layer=3, 
+        base_nc=64, 
+        max_nc=512, 
+        num_res_blocks=9, 
+        use_spect=True,
+        encoder=Visual_Encoder,
+        decoder=Decoder
+        ):  
+        super(LNet, self).__init__()
+
+        nonlinearity = nn.LeakyReLU(0.1)
+        norm_layer = functools.partial(LayerNorm2d, affine=True) 
+        kwargs = {'norm_layer':norm_layer, 'nonlinearity':nonlinearity, 'use_spect':use_spect}
+        self.descriptor_nc = descriptor_nc
+
+        self.encoder = encoder(image_nc, base_nc, max_nc, layer, **kwargs)
+        self.decoder = decoder(image_nc, self.descriptor_nc, base_nc, max_nc, layer, num_res_blocks, **kwargs)
+        self.audio_encoder = nn.Sequential(
+            Conv2d(1, 32, kernel_size=3, stride=1, padding=1),
+            Conv2d(32, 32, kernel_size=3, stride=1, padding=1, residual=True),
+            Conv2d(32, 32, kernel_size=3, stride=1, padding=1, residual=True),
+
+            Conv2d(32, 64, kernel_size=3, stride=(3, 1), padding=1),
+            Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True),
+            Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True),
+
+            Conv2d(64, 128, kernel_size=3, stride=3, padding=1),
+            Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True),
+            Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True),
+
+            Conv2d(128, 256, kernel_size=3, stride=(3, 2), padding=1),
+            Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True),
+
+            Conv2d(256, 512, kernel_size=3, stride=1, padding=0),
+            Conv2d(512, descriptor_nc, kernel_size=1, stride=1, padding=0),
+            )
+
+    def forward(self, audio_sequences, face_sequences):
+        B = audio_sequences.size(0)
+        input_dim_size = len(face_sequences.size())
+        if input_dim_size > 4:
+            audio_sequences = torch.cat([audio_sequences[:, i] for i in range(audio_sequences.size(1))], dim=0)
+            face_sequences = torch.cat([face_sequences[:, :, i] for i in range(face_sequences.size(2))], dim=0)
+        cropped, ref = torch.split(face_sequences, 3, dim=1)
+
+        vis_feat = self.encoder(cropped, ref)
+        audio_feat = self.audio_encoder(audio_sequences) 
+        _outputs = self.decoder(vis_feat, audio_feat)
+
+        if input_dim_size > 4:
+            _outputs = torch.split(_outputs, B, dim=0)
+            outputs = torch.stack(_outputs, dim=2) 
+        else:
+            outputs = _outputs
+        return outputs

videoretalking/models/__init__.py

@@ -0,0 +1,37 @@
+import torch
+from models.DNet import DNet
+from models.LNet import LNet
+from models.ENet import ENet
+
+
+def _load(checkpoint_path):
+    map_location=None if torch.cuda.is_available() else torch.device('cpu')
+    checkpoint = torch.load(checkpoint_path, map_location=map_location)
+    return checkpoint
+
+def load_checkpoint(path, model):
+    print("Load checkpoint from: {}".format(path))
+    checkpoint = _load(path)
+    s = checkpoint["state_dict"] if 'arcface' not in path else checkpoint
+    new_s = {}
+    for k, v in s.items():
+        if 'low_res' in k:
+            continue
+        else:
+            new_s[k.replace('module.', '')] = v
+    model.load_state_dict(new_s, strict=False)
+    return model
+
+def load_network(LNet_path,ENet_path):
+    L_net = LNet()
+    L_net = load_checkpoint(LNet_path, L_net)
+    E_net = ENet(lnet=L_net)
+    model = load_checkpoint(ENet_path, E_net)
+    return model.eval()
+
+def load_DNet(DNet_path):
+    D_Net = DNet()
+    print("Load checkpoint from: {}".format(DNet_path))
+    checkpoint =  torch.load(DNet_path, map_location=lambda storage, loc: storage)
+    D_Net.load_state_dict(checkpoint['net_G_ema'], strict=False)
+    return D_Net.eval()

videoretalking/models/base_blocks.py

@@ -0,0 +1,554 @@
+import math
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.nn.modules.batchnorm import BatchNorm2d
+from torch.nn.utils.spectral_norm import spectral_norm as SpectralNorm
+
+from models.ffc import FFC
+from basicsr.archs.arch_util import default_init_weights
+
+
+class Conv2d(nn.Module):
+    def __init__(self, cin, cout, kernel_size, stride, padding, residual=False, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.conv_block = nn.Sequential(
+                            nn.Conv2d(cin, cout, kernel_size, stride, padding),
+                            nn.BatchNorm2d(cout)
+                            )
+        self.act = nn.ReLU()
+        self.residual = residual
+
+    def forward(self, x):
+        out = self.conv_block(x)
+        if self.residual:
+            out += x
+        return self.act(out)
+
+
+class ResBlock(nn.Module):
+    def __init__(self, in_channels, out_channels, mode='down'):
+        super(ResBlock, self).__init__()
+        self.conv1 = nn.Conv2d(in_channels, in_channels, 3, 1, 1)
+        self.conv2 = nn.Conv2d(in_channels, out_channels, 3, 1, 1)
+        self.skip = nn.Conv2d(in_channels, out_channels, 1, bias=False)
+        if mode == 'down':
+            self.scale_factor = 0.5
+        elif mode == 'up':
+            self.scale_factor = 2
+
+    def forward(self, x):
+        out = F.leaky_relu_(self.conv1(x), negative_slope=0.2)
+        # upsample/downsample
+        out = F.interpolate(out, scale_factor=self.scale_factor, mode='bilinear', align_corners=False)
+        out = F.leaky_relu_(self.conv2(out), negative_slope=0.2)
+        # skip
+        x = F.interpolate(x, scale_factor=self.scale_factor, mode='bilinear', align_corners=False)
+        skip = self.skip(x)
+        out = out + skip
+        return out
+
+
+class LayerNorm2d(nn.Module):
+    def __init__(self, n_out, affine=True):
+        super(LayerNorm2d, self).__init__()
+        self.n_out = n_out
+        self.affine = affine
+
+        if self.affine:
+          self.weight = nn.Parameter(torch.ones(n_out, 1, 1))
+          self.bias = nn.Parameter(torch.zeros(n_out, 1, 1))
+
+    def forward(self, x):
+        normalized_shape = x.size()[1:]
+        if self.affine:
+          return F.layer_norm(x, normalized_shape, \
+              self.weight.expand(normalized_shape), 
+              self.bias.expand(normalized_shape))    
+        else:
+          return F.layer_norm(x, normalized_shape)  
+
+
+def spectral_norm(module, use_spect=True):
+    if use_spect:
+        return SpectralNorm(module)
+    else:
+        return module
+
+
+class FirstBlock2d(nn.Module):
+    def __init__(self, input_nc, output_nc, norm_layer=nn.BatchNorm2d, nonlinearity=nn.LeakyReLU(), use_spect=False):
+        super(FirstBlock2d, self).__init__()
+        kwargs = {'kernel_size': 7, 'stride': 1, 'padding': 3}
+        conv = spectral_norm(nn.Conv2d(input_nc, output_nc, **kwargs), use_spect)
+
+        if type(norm_layer) == type(None):
+            self.model = nn.Sequential(conv, nonlinearity)
+        else:
+            self.model = nn.Sequential(conv, norm_layer(output_nc), nonlinearity)
+
+    def forward(self, x):
+        out = self.model(x)
+        return out 
+
+
+class DownBlock2d(nn.Module):
+    def __init__(self, input_nc, output_nc, norm_layer=nn.BatchNorm2d, nonlinearity=nn.LeakyReLU(), use_spect=False):
+        super(DownBlock2d, self).__init__()
+        kwargs = {'kernel_size': 3, 'stride': 1, 'padding': 1}
+        conv = spectral_norm(nn.Conv2d(input_nc, output_nc, **kwargs), use_spect)
+        pool = nn.AvgPool2d(kernel_size=(2, 2))
+
+        if type(norm_layer) == type(None):
+            self.model = nn.Sequential(conv, nonlinearity, pool)
+        else:
+            self.model = nn.Sequential(conv, norm_layer(output_nc), nonlinearity, pool)
+
+    def forward(self, x):
+        out = self.model(x)
+        return out 
+
+
+class UpBlock2d(nn.Module):
+    def __init__(self, input_nc, output_nc, norm_layer=nn.BatchNorm2d, nonlinearity=nn.LeakyReLU(), use_spect=False):
+        super(UpBlock2d, self).__init__()
+        kwargs = {'kernel_size': 3, 'stride': 1, 'padding': 1}
+        conv = spectral_norm(nn.Conv2d(input_nc, output_nc, **kwargs), use_spect)
+        if type(norm_layer) == type(None):
+            self.model = nn.Sequential(conv, nonlinearity)
+        else:
+            self.model = nn.Sequential(conv, norm_layer(output_nc), nonlinearity)
+
+    def forward(self, x):
+        out = self.model(F.interpolate(x, scale_factor=2))
+        return out
+
+
+class ADAIN(nn.Module):
+    def __init__(self, norm_nc, feature_nc):
+        super().__init__()
+
+        self.param_free_norm = nn.InstanceNorm2d(norm_nc, affine=False)
+
+        nhidden = 128
+        use_bias=True
+
+        self.mlp_shared = nn.Sequential(
+            nn.Linear(feature_nc, nhidden, bias=use_bias),            
+            nn.ReLU()
+        )
+        self.mlp_gamma = nn.Linear(nhidden, norm_nc, bias=use_bias)    
+        self.mlp_beta = nn.Linear(nhidden, norm_nc, bias=use_bias)    
+
+    def forward(self, x, feature):
+
+        # Part 1. generate parameter-free normalized activations
+        normalized = self.param_free_norm(x)
+        # Part 2. produce scaling and bias conditioned on feature
+        feature = feature.view(feature.size(0), -1)
+        actv = self.mlp_shared(feature)
+        gamma = self.mlp_gamma(actv)
+        beta = self.mlp_beta(actv)
+
+        # apply scale and bias
+        gamma = gamma.view(*gamma.size()[:2], 1,1)
+        beta = beta.view(*beta.size()[:2], 1,1)
+        out = normalized * (1 + gamma) + beta
+        return out
+
+
+class FineADAINResBlock2d(nn.Module):
+    """
+    Define an Residual block for different types
+    """
+    def __init__(self, input_nc, feature_nc, norm_layer=nn.BatchNorm2d, nonlinearity=nn.LeakyReLU(), use_spect=False):
+        super(FineADAINResBlock2d, self).__init__()
+        kwargs = {'kernel_size': 3, 'stride': 1, 'padding': 1}
+        self.conv1 = spectral_norm(nn.Conv2d(input_nc, input_nc, **kwargs), use_spect)
+        self.conv2 = spectral_norm(nn.Conv2d(input_nc, input_nc, **kwargs), use_spect)
+        self.norm1 = ADAIN(input_nc, feature_nc)
+        self.norm2 = ADAIN(input_nc, feature_nc)
+        self.actvn = nonlinearity
+
+    def forward(self, x, z):
+        dx = self.actvn(self.norm1(self.conv1(x), z))
+        dx = self.norm2(self.conv2(x), z)
+        out = dx + x
+        return out  
+
+
+class FineADAINResBlocks(nn.Module):
+    def __init__(self, num_block, input_nc, feature_nc, norm_layer=nn.BatchNorm2d, nonlinearity=nn.LeakyReLU(), use_spect=False):
+        super(FineADAINResBlocks, self).__init__()                                
+        self.num_block = num_block
+        for i in range(num_block):
+            model = FineADAINResBlock2d(input_nc, feature_nc, norm_layer, nonlinearity, use_spect)
+            setattr(self, 'res'+str(i), model)
+
+    def forward(self, x, z):
+        for i in range(self.num_block):
+            model = getattr(self, 'res'+str(i))
+            x = model(x, z)
+        return x   
+
+
+class ADAINEncoderBlock(nn.Module):       
+    def __init__(self, input_nc, output_nc, feature_nc, nonlinearity=nn.LeakyReLU(), use_spect=False):
+        super(ADAINEncoderBlock, self).__init__()
+        kwargs_down = {'kernel_size': 4, 'stride': 2, 'padding': 1}
+        kwargs_fine = {'kernel_size': 3, 'stride': 1, 'padding': 1}
+
+        self.conv_0 = spectral_norm(nn.Conv2d(input_nc,  output_nc, **kwargs_down), use_spect)
+        self.conv_1 = spectral_norm(nn.Conv2d(output_nc, output_nc, **kwargs_fine), use_spect)
+
+
+        self.norm_0 = ADAIN(input_nc, feature_nc)
+        self.norm_1 = ADAIN(output_nc, feature_nc)
+        self.actvn = nonlinearity
+
+    def forward(self, x, z):
+        x = self.conv_0(self.actvn(self.norm_0(x, z)))
+        x = self.conv_1(self.actvn(self.norm_1(x, z)))
+        return x
+
+
+class ADAINDecoderBlock(nn.Module):
+    def __init__(self, input_nc, output_nc, hidden_nc, feature_nc, use_transpose=True, nonlinearity=nn.LeakyReLU(), use_spect=False):
+        super(ADAINDecoderBlock, self).__init__()        
+        # Attributes
+        self.actvn = nonlinearity
+        hidden_nc = min(input_nc, output_nc) if hidden_nc is None else hidden_nc
+
+        kwargs_fine = {'kernel_size':3, 'stride':1, 'padding':1}
+        if use_transpose:
+            kwargs_up = {'kernel_size':3, 'stride':2, 'padding':1, 'output_padding':1}
+        else:
+            kwargs_up = {'kernel_size':3, 'stride':1, 'padding':1}
+
+        # create conv layers
+        self.conv_0 = spectral_norm(nn.Conv2d(input_nc, hidden_nc, **kwargs_fine), use_spect)
+        if use_transpose:
+            self.conv_1 = spectral_norm(nn.ConvTranspose2d(hidden_nc, output_nc, **kwargs_up), use_spect)
+            self.conv_s = spectral_norm(nn.ConvTranspose2d(input_nc, output_nc, **kwargs_up), use_spect)
+        else:
+            self.conv_1 = nn.Sequential(spectral_norm(nn.Conv2d(hidden_nc, output_nc, **kwargs_up), use_spect),
+                                        nn.Upsample(scale_factor=2))
+            self.conv_s = nn.Sequential(spectral_norm(nn.Conv2d(input_nc, output_nc, **kwargs_up), use_spect),
+                                        nn.Upsample(scale_factor=2))
+        # define normalization layers
+        self.norm_0 = ADAIN(input_nc, feature_nc)
+        self.norm_1 = ADAIN(hidden_nc, feature_nc)
+        self.norm_s = ADAIN(input_nc, feature_nc)
+        
+    def forward(self, x, z):
+        x_s = self.shortcut(x, z)
+        dx = self.conv_0(self.actvn(self.norm_0(x, z)))
+        dx = self.conv_1(self.actvn(self.norm_1(dx, z)))
+        out = x_s + dx
+        return out
+
+    def shortcut(self, x, z):
+        x_s = self.conv_s(self.actvn(self.norm_s(x, z)))
+        return x_s   
+
+
+class FineEncoder(nn.Module):
+    """docstring for Encoder"""
+    def __init__(self, image_nc, ngf, img_f, layers, norm_layer=nn.BatchNorm2d, nonlinearity=nn.LeakyReLU(), use_spect=False):
+        super(FineEncoder, self).__init__()
+        self.layers = layers
+        self.first = FirstBlock2d(image_nc, ngf, norm_layer, nonlinearity, use_spect)
+        for i in range(layers):
+            in_channels = min(ngf*(2**i), img_f)
+            out_channels = min(ngf*(2**(i+1)), img_f)
+            model = DownBlock2d(in_channels, out_channels, norm_layer, nonlinearity, use_spect)
+            setattr(self, 'down' + str(i), model)
+        self.output_nc = out_channels
+
+    def forward(self, x):
+        x = self.first(x)
+        out=[x]
+        for i in range(self.layers):
+            model = getattr(self, 'down'+str(i))
+            x = model(x)
+            out.append(x)
+        return out
+
+
+class FineDecoder(nn.Module):
+    """docstring for FineDecoder"""
+    def __init__(self, image_nc, feature_nc, ngf, img_f, layers, num_block, norm_layer=nn.BatchNorm2d, nonlinearity=nn.LeakyReLU(), use_spect=False):
+        super(FineDecoder, self).__init__()
+        self.layers = layers
+        for i in range(layers)[::-1]:
+            in_channels = min(ngf*(2**(i+1)), img_f)
+            out_channels = min(ngf*(2**i), img_f)
+            up = UpBlock2d(in_channels, out_channels, norm_layer, nonlinearity, use_spect)
+            res = FineADAINResBlocks(num_block, in_channels, feature_nc, norm_layer, nonlinearity, use_spect)
+            jump = Jump(out_channels, norm_layer, nonlinearity, use_spect)
+            setattr(self, 'up' + str(i), up)
+            setattr(self, 'res' + str(i), res)            
+            setattr(self, 'jump' + str(i), jump)
+        self.final = FinalBlock2d(out_channels, image_nc, use_spect, 'tanh')
+        self.output_nc = out_channels
+
+    def forward(self, x, z):
+        out = x.pop()
+        for i in range(self.layers)[::-1]:
+            res_model = getattr(self, 'res' + str(i))
+            up_model = getattr(self, 'up' + str(i))
+            jump_model = getattr(self, 'jump' + str(i))
+            out = res_model(out, z)
+            out = up_model(out)
+            out = jump_model(x.pop()) + out
+        out_image = self.final(out)
+        return out_image
+
+
+class ADAINEncoder(nn.Module):
+    def __init__(self, image_nc, pose_nc, ngf, img_f, layers, nonlinearity=nn.LeakyReLU(), use_spect=False):
+        super(ADAINEncoder, self).__init__()
+        self.layers = layers
+        self.input_layer = nn.Conv2d(image_nc, ngf, kernel_size=7, stride=1, padding=3)
+        for i in range(layers):
+            in_channels = min(ngf * (2**i), img_f)
+            out_channels = min(ngf *(2**(i+1)), img_f)
+            model = ADAINEncoderBlock(in_channels, out_channels, pose_nc, nonlinearity, use_spect)
+            setattr(self, 'encoder' + str(i), model)
+        self.output_nc = out_channels
+        
+    def forward(self, x, z):
+        out = self.input_layer(x)
+        out_list = [out]
+        for i in range(self.layers):
+            model = getattr(self, 'encoder' + str(i))
+            out = model(out, z)
+            out_list.append(out)
+        return out_list
+        
+        
+class ADAINDecoder(nn.Module):
+    """docstring for ADAINDecoder"""
+    def __init__(self, pose_nc, ngf, img_f, encoder_layers, decoder_layers, skip_connect=True, 
+                 nonlinearity=nn.LeakyReLU(), use_spect=False):
+
+        super(ADAINDecoder, self).__init__()
+        self.encoder_layers = encoder_layers
+        self.decoder_layers = decoder_layers
+        self.skip_connect = skip_connect
+        use_transpose = True
+        for i in range(encoder_layers-decoder_layers, encoder_layers)[::-1]:
+            in_channels = min(ngf * (2**(i+1)), img_f)
+            in_channels = in_channels*2 if i != (encoder_layers-1) and self.skip_connect else in_channels
+            out_channels = min(ngf * (2**i), img_f)
+            model = ADAINDecoderBlock(in_channels, out_channels, out_channels, pose_nc, use_transpose, nonlinearity, use_spect)
+            setattr(self, 'decoder' + str(i), model)
+        self.output_nc = out_channels*2 if self.skip_connect else out_channels
+
+    def forward(self, x, z):
+        out = x.pop() if self.skip_connect else x
+        for i in range(self.encoder_layers-self.decoder_layers, self.encoder_layers)[::-1]:
+            model = getattr(self, 'decoder' + str(i))
+            out = model(out, z)
+            out = torch.cat([out, x.pop()], 1) if self.skip_connect else out
+        return out
+
+
+class ADAINHourglass(nn.Module):
+    def __init__(self, image_nc, pose_nc, ngf, img_f, encoder_layers, decoder_layers, nonlinearity, use_spect):
+        super(ADAINHourglass, self).__init__()
+        self.encoder = ADAINEncoder(image_nc, pose_nc, ngf, img_f, encoder_layers, nonlinearity, use_spect)
+        self.decoder = ADAINDecoder(pose_nc, ngf, img_f, encoder_layers, decoder_layers, True, nonlinearity, use_spect)
+        self.output_nc = self.decoder.output_nc
+
+    def forward(self, x, z):
+        return self.decoder(self.encoder(x, z), z)        
+
+
+class FineADAINLama(nn.Module):
+    def __init__(self, input_nc, feature_nc, norm_layer=nn.BatchNorm2d, nonlinearity=nn.LeakyReLU(), use_spect=False):
+        super(FineADAINLama, self).__init__()
+        kwargs = {'kernel_size': 3, 'stride': 1, 'padding': 1}
+        self.actvn = nonlinearity
+        ratio_gin = 0.75
+        ratio_gout = 0.75        
+        self.ffc = FFC(input_nc, input_nc, 3,
+                       ratio_gin, ratio_gout, 1, 1, 1,
+                       1, False, False, padding_type='reflect')
+        global_channels = int(input_nc * ratio_gout)
+        self.bn_l = ADAIN(input_nc - global_channels, feature_nc)
+        self.bn_g = ADAIN(global_channels, feature_nc)
+
+    def forward(self, x, z):
+        x_l, x_g = self.ffc(x)
+        x_l = self.actvn(self.bn_l(x_l,z))
+        x_g = self.actvn(self.bn_g(x_g,z))
+        return x_l, x_g
+
+
+class FFCResnetBlock(nn.Module):
+    def __init__(self, dim, feature_dim, padding_type='reflect', norm_layer=BatchNorm2d, activation_layer=nn.ReLU, dilation=1,
+                 spatial_transform_kwargs=None, inline=False, **conv_kwargs):
+        super().__init__()
+        self.conv1 = FineADAINLama(dim, feature_dim, **conv_kwargs)
+        self.conv2 = FineADAINLama(dim, feature_dim, **conv_kwargs)
+        self.inline = True
+
+    def forward(self, x, z):
+        if self.inline:
+            x_l, x_g = x[:, :-self.conv1.ffc.global_in_num], x[:, -self.conv1.ffc.global_in_num:]
+        else:
+            x_l, x_g = x if type(x) is tuple else (x, 0)
+
+        id_l, id_g = x_l, x_g
+        x_l, x_g = self.conv1((x_l, x_g), z)
+        x_l, x_g = self.conv2((x_l, x_g), z)
+
+        x_l, x_g = id_l + x_l, id_g + x_g
+        out = x_l, x_g
+        if self.inline:
+            out = torch.cat(out, dim=1)
+        return out
+
+
+class FFCADAINResBlocks(nn.Module):
+    def __init__(self, num_block, input_nc, feature_nc, norm_layer=nn.BatchNorm2d, nonlinearity=nn.LeakyReLU(), use_spect=False):
+        super(FFCADAINResBlocks, self).__init__()                                
+        self.num_block = num_block
+        for i in range(num_block):
+            model = FFCResnetBlock(input_nc, feature_nc, norm_layer, nonlinearity, use_spect)
+            setattr(self, 'res'+str(i), model)
+
+    def forward(self, x, z):
+        for i in range(self.num_block):
+            model = getattr(self, 'res'+str(i))
+            x = model(x, z)
+        return x 
+
+
+class Jump(nn.Module):
+    def __init__(self, input_nc, norm_layer=nn.BatchNorm2d, nonlinearity=nn.LeakyReLU(), use_spect=False):
+        super(Jump, self).__init__()
+        kwargs = {'kernel_size': 3, 'stride': 1, 'padding': 1}
+        conv = spectral_norm(nn.Conv2d(input_nc, input_nc, **kwargs), use_spect)
+        if type(norm_layer) == type(None):
+            self.model = nn.Sequential(conv, nonlinearity)
+        else:
+            self.model = nn.Sequential(conv, norm_layer(input_nc), nonlinearity)
+
+    def forward(self, x):
+        out = self.model(x)
+        return out   
+
+
+class FinalBlock2d(nn.Module):
+    def __init__(self, input_nc, output_nc, use_spect=False, tanh_or_sigmoid='tanh'):
+        super(FinalBlock2d, self).__init__()
+        kwargs = {'kernel_size': 7, 'stride': 1, 'padding':3}
+        conv = spectral_norm(nn.Conv2d(input_nc, output_nc, **kwargs), use_spect)
+        if tanh_or_sigmoid == 'sigmoid':
+            out_nonlinearity = nn.Sigmoid()
+        else:
+            out_nonlinearity = nn.Tanh()            
+        self.model = nn.Sequential(conv, out_nonlinearity)
+
+    def forward(self, x):
+        out = self.model(x)
+        return out    
+
+
+class ModulatedConv2d(nn.Module):
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 kernel_size,
+                 num_style_feat,
+                 demodulate=True,
+                 sample_mode=None,
+                 eps=1e-8):
+        super(ModulatedConv2d, self).__init__()
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.kernel_size = kernel_size
+        self.demodulate = demodulate
+        self.sample_mode = sample_mode
+        self.eps = eps
+
+        # modulation inside each modulated conv
+        self.modulation = nn.Linear(num_style_feat, in_channels, bias=True)
+        # initialization
+        default_init_weights(self.modulation, scale=1, bias_fill=1, a=0, mode='fan_in', nonlinearity='linear')
+
+        self.weight = nn.Parameter(
+            torch.randn(1, out_channels, in_channels, kernel_size, kernel_size) /
+            math.sqrt(in_channels * kernel_size**2))
+        self.padding = kernel_size // 2
+
+    def forward(self, x, style):
+        b, c, h, w = x.shape   
+        style = self.modulation(style).view(b, 1, c, 1, 1)
+        weight = self.weight * style  
+
+        if self.demodulate:
+            demod = torch.rsqrt(weight.pow(2).sum([2, 3, 4]) + self.eps)
+            weight = weight * demod.view(b, self.out_channels, 1, 1, 1)
+
+        weight = weight.view(b * self.out_channels, c, self.kernel_size, self.kernel_size)
+
+        # upsample or downsample if necessary
+        if self.sample_mode == 'upsample':
+            x = F.interpolate(x, scale_factor=2, mode='bilinear', align_corners=False)
+        elif self.sample_mode == 'downsample':
+            x = F.interpolate(x, scale_factor=0.5, mode='bilinear', align_corners=False)
+
+        b, c, h, w = x.shape
+        x = x.view(1, b * c, h, w)
+        out = F.conv2d(x, weight, padding=self.padding, groups=b)
+        out = out.view(b, self.out_channels, *out.shape[2:4])
+        return out
+
+    def __repr__(self):
+        return (f'{self.__class__.__name__}(in_channels={self.in_channels}, out_channels={self.out_channels}, '
+                f'kernel_size={self.kernel_size}, demodulate={self.demodulate}, sample_mode={self.sample_mode})')
+
+
+class StyleConv(nn.Module):
+    def __init__(self, in_channels, out_channels, kernel_size, num_style_feat, demodulate=True, sample_mode=None):
+        super(StyleConv, self).__init__()
+        self.modulated_conv = ModulatedConv2d(
+            in_channels, out_channels, kernel_size, num_style_feat, demodulate=demodulate, sample_mode=sample_mode)
+        self.weight = nn.Parameter(torch.zeros(1))  # for noise injection
+        self.bias = nn.Parameter(torch.zeros(1, out_channels, 1, 1))
+        self.activate = nn.LeakyReLU(negative_slope=0.2, inplace=True)
+
+    def forward(self, x, style, noise=None):
+        # modulate
+        out = self.modulated_conv(x, style) * 2**0.5  # for conversion
+        # noise injection
+        if noise is None:
+            b, _, h, w = out.shape
+            noise = out.new_empty(b, 1, h, w).normal_()
+        out = out + self.weight * noise
+        # add bias
+        out = out + self.bias
+        # activation
+        out = self.activate(out)
+        return out
+
+
+class ToRGB(nn.Module):
+    def __init__(self, in_channels, num_style_feat, upsample=True):
+        super(ToRGB, self).__init__()
+        self.upsample = upsample
+        self.modulated_conv = ModulatedConv2d(
+            in_channels, 3, kernel_size=1, num_style_feat=num_style_feat, demodulate=False, sample_mode=None)
+        self.bias = nn.Parameter(torch.zeros(1, 3, 1, 1))
+
+    def forward(self, x, style, skip=None):
+        out = self.modulated_conv(x, style)
+        out = out + self.bias
+        if skip is not None:
+            if self.upsample:
+                skip = F.interpolate(skip, scale_factor=2, mode='bilinear', align_corners=False)
+            out = out + skip
+        return out