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.gitattributes

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fashion-gan/.gitattributes

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+*.ipynb linguist-vendored

fashion-gan/.gitignore

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+app.ipynb
+Explore_latent_space.ipynb
+Prepare Data and Train.ipynb
+app/templates/.ipynb_checkpoints
+model/*
+stylegan-ada-pytorch

fashion-gan/.gradio/certificate.pem

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+-----END CERTIFICATE-----

fashion-gan/.idea/.gitignore

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+# Default ignored files
+/shelf/
+/workspace.xml

fashion-gan/.idea/fashion-gan.iml

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+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$" />
+    <orderEntry type="inheritedJdk" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+  <component name="PyDocumentationSettings">
+    <option name="format" value="GOOGLE" />
+    <option name="myDocStringFormat" value="Google" />
+  </component>
+</module>

fashion-gan/.idea/inspectionProfiles/profiles_settings.xml

@@ -0,0 +1,6 @@
+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
+  </settings>
+</component>

fashion-gan/.idea/misc.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectRootManager" version="2" project-jdk-name="$USER_HOME$/anaconda3" project-jdk-type="Python SDK" />
+</project>

fashion-gan/.idea/modules.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/fashion-gan.iml" filepath="$PROJECT_DIR$/.idea/fashion-gan.iml" />
+    </modules>
+  </component>
+</project>

fashion-gan/.idea/vcs.xml

@@ -0,0 +1,7 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="VcsDirectoryMappings">
+    <mapping directory="" vcs="Git" />
+    <mapping directory="$PROJECT_DIR$/stylegan-ada-pytorch" vcs="Git" />
+  </component>
+</project>

fashion-gan/Dockerfile

@@ -0,0 +1,22 @@
+# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
+#
+# NVIDIA CORPORATION and its licensors retain all intellectual property
+# and proprietary rights in and to this software, related documentation
+# and any modifications thereto.  Any use, reproduction, disclosure or
+# distribution of this software and related documentation without an express
+# license agreement from NVIDIA CORPORATION is strictly prohibited.
+
+FROM nvcr.io/nvidia/pytorch:20.12-py3
+
+ENV PYTHONDONTWRITEBYTECODE 1
+ENV PYTHONUNBUFFERED 1
+
+RUN pip install imageio-ffmpeg==0.4.3 pyspng==0.1.0
+
+WORKDIR /workspace
+
+# Unset TORCH_CUDA_ARCH_LIST and exec.  This makes pytorch run-time
+# extension builds significantly faster as we only compile for the
+# currently active GPU configuration.
+RUN (printf '#!/bin/bash\nunset TORCH_CUDA_ARCH_LIST\nexec \"$@\"\n' >> /entry.sh) && chmod a+x /entry.sh
+ENTRYPOINT ["/entry.sh"]

fashion-gan/README.MD

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+# Fashion Generation through Controllable StyleGAN
+<img src="images/PCA_result_example.jpg" alt="pca"/>
+GANs for All: Supporting Fun and Intuitive Exploration of GAN Latent Spaces  
+
+Author: Wei Jiang, Richard Lee Davis, Kevin Gonyop Kim, Pierre Dillenbourg  
+
+https://proceedings.mlr.press/v176/jiang22a.html  
+
+Abstract: 
+_We have developed a new tool that makes it possible for people with zero programming experience to intentionally and meaningfully explore the latent space of a GAN. 
+We combine a number of methods from the literature into a single system that includes multiple functionalities: 
+uploading and locating images in the latent space, image generation with text, visual style mixing, 
+and intentional and intuitive latent space exploration. 
+This tool was developed to provide a means for designers to explore the "design space" of their domains. 
+Our goal was to create a system to support novices in gaining a more complete, expert understanding of their domain's 
+design space by lowering the barrier of entry to using deep generative models in creative practice._
+
+## Dataset
+We use [Zalando dataset](https://github.com/zalandoresearch/feidegger)
+which can also be downloaded from [Zalando images](https://drive.google.com/file/d/1vmBHGiqoICdaLaUqFUy6YiXMBLPuhTCy/view?usp=drive_link) and [Zalando Text Image Pairs](https://drive.google.com/drive/folders/1-i9y8dhKjRX36152dBTEPmL_7ers2mr4?usp=drive_link).
+The dataset itself consists of 8732 high-resolution images, 
+each depicting a dress from the available on the Zalando shop against a white-background.
+## Models
+#### Train StyleGAN Model from scratch
+```commandline
+!python train.py --outdir "training_runs" --snap 20 --metrics "none" --data "data/square_256_imgs.zip"
+```
+If the resume the model from a checkpoint, we can --resume
+```commandline
+!python train.py --outdir "training_runs" --snap 20 --metrics "none" --data "data/square_256_imgs.zip" --resume "training_runs/00015-square_256_imgs-auto1-resumecustom/network-snapshot-000400.pkl"
+```
+
+#### Finetune DALL-E Model
+```commandline
+!python "DALLE-pytorch/train_dalle.py" --vae_path "DALLE-pytorch/wandb/vae-final.pt" --image_text_folder "data/text_images"
+```
+
+Download the models in the following links and save them in your Google Drive.
+
+|         Model          |                                                      Download                                                       |
+|:----------------------:|:-------------------------------------------------------------------------------------------------------------------:|
+| Pretrained fashion GAN | [fashion-gan-pretrained.pkl](https://drive.google.com/file/d/1wjMtDejgj1GZmf0AqKY6LzKld3Rirbuo/view?usp=drive_link) |
+| Finetuned DALL-E model |    [DALLE-finetuend.pkl](https://drive.google.com/file/d/1yhtBeFMc-kTW3nOsxYgwJb6CffFe-V_e/view?usp=drive_link)     |
+
+## Explore Latent Space
+We applied PCA analysis to identify the semantically meaningful directions in latent space. 
+By exploring the first 10 principle components, we found sleeve, pattern, etc.
+
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://drive.google.com/file/d/1S-iiGj6f4PgWsQJVfFduLv1XUskxniZT/view?usp=sharing)
+
+To project the image into latent space, we employ SGD with perceptual loss + pixel-by-pixel MSE loss between two images. 
+This loss noticeably improved our tool’s ability to embed out-of-sample examples in the latent space of the GAN.
+
+$$ w^{*} = \min_{w} L(w) =  \min_{w} \lVert f(G(w)) - f(I) \rVert_2^2 + \lambda_{pix} \lVert G(w) - I \rVert_2^2 $$
+
+[//]: # (Examples of interpolating images simultaneously along two meaningful directions)
+
+[//]: # (in the latent space &#40;sleeve and pattern&#41; found using PCA. The image in the green)
+
+[//]: # (box shows the original image with 0 magnitude.)
+
+[//]: # (<img src="images/pca.png" alt="pca"/>)
+
+## Text-to-image Generation
+We implemented two
+methods to locate the design. The first method was to randomly sample images from the
+latent space, then to pass these along with the text description through a CLIP. model to find a small number of images which most closely matched the
+text. The second method was to fine-tune a DALL-E model on the
+Feidegger dataset, and then to pass the text descriptions to DALL-E and let it generate
+designs. We compare it with other models: 
+- FahionGAN: realistic, diverse but low resolution. 
+- DALLE: diverse, creative but less accurate.
+- Stable Diffusion:  accurate, high resolution but not diverse (when given specific text with only changing background and models).
+
+<img src="images/text-to-image.png" alt="text-to-image"/>
+
+## WebApp
+We have built a website for user testing: generarive.fashion
+
+https://generative.fashion
+
+[![YouTube Demo](https://img.shields.io/badge/YouTube-%23FF0000.svg?style=for-the-badge&logo=YouTube&logoColor=white)](https://www.youtube.com/watch?v=dcC7G2zBuL8&t=42s)
+
+To run it in Google Colab: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1VY3-9zvnXqjYNxNzGqZgF0pRfafdGpJm#scrollTo=JFgsJTee-EnO)
+
+The interface of our neural design space exploration tool. Users can upload images
+in the workplace on the left or generate random image through random button.
+Also, they can generate examples via text descriptions using the text box. Users
+can drag these examples to the style-mixing region or save them in the workplace. Users can selectively combine elements from three designs using the visual
+style-mixing panel. The output image is shown in the center of the canvas on the
+right. The 2D-dimensional canvas represents the design space for two attributes
+in the horizontal and vertical axes, and these attributes can be changed by using
+a drop-down menu for each axis. Dragging the image within the canvas is equivalent to moving through the latent space of the GAN in semantically meaningful
+directions.
+<table><tr>
+<td> <img src="images/interface_part1.png" alt="interface_part1" /> </td>
+<td> <img src="images/interface_part2.png" alt="interface_part2" /> </td>
+</tr></table>
+
+## Citation
+```
+@InProceedings{pmlr-v176-jiang22a,
+  title =     {GANs for All: Supporting Fun and Intuitive Exploration of GAN Latent Spaces},
+  author =    {Jiang, Wei and Davis, Richard Lee and Kim, Kevin Gonyop and Dillenbourg, Pierre},
+  booktitle = {Proceedings of the NeurIPS 2021 Competitions and Demonstrations Track},
+  pages =     {292--296},
+  year =      {2022}
+}
+```
+
+## Acknowledgements
+This project and application is a semester project at [EPFL CHILI Lab](https://www.epfl.ch/labs/chili/)
+
+

fashion-gan/app/static/css/style.css

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+.upload{
+    width:900px;
+    float:left;
+    height = 250px;
+    padding:5px; 
+}
+#style-mixing-region{
+    position:absolute;
+    left:250px;
+    top:0px;
+    margin-left:30px; 
+    width:780px;
+}
+#result {
+    margin-left:20px;
+    width:780px;
+    float:left;
+    padding:5px; 
+}
+
+.imgshow{
+    width:180px;
+    height: 180px;
+    float:left;
+    border: 1px solid black;
+}
+.drop{
+    height:180px;
+    width:180px;
+    float:left;
+}
+.block{
+    float:left;
+    width:210px;
+    height:210px;
+    margin-right:15px;
+    margin-left:15px;
+}
+.center{
+    margin-left: 20px;
+	margin-right: 20px;
+}
+.title{
+    float:center;
+    padding-top:50px;
+    padding-left:150px;
+}
+
+#canvas-container{
+    position:absolute;
+    top:30px;
+    left:1050px;
+    width: 900px;
+    height: 900px;
+}
+#canvas{
+  podition:absolute;
+    background-color: #fff;
+    border-radius:20px;
+    box-shadow:0 0 10px rgba(0,0,0,0.05);
+  top:20px;
+  width: 800px;
+  height: 800px;
+}
+
+#xaxix{
+  position:absolute;
+  left:1050px;
+  top:0px;
+}
+#yaxis{
+  position:absolute;
+  left:580px;
+  top:830px;
+}
+
+
+.tag:first-of-type {
+      border-radius: 50px 0px 0px 50px;
+}
+ .tag:last-of-type {
+       border-radius: 0px 50px 50px 0px;
+}
+
+.tag:last-of-type>.slider-button {
+     display:none !important;
+}
+
+span{
+  font-family:sans-serif !important;
+}
+
+
+
+
+#drop-region {
+	background-color: #fff;
+	border-radius:20px;
+	box-shadow:0 0 35px rgba(0,0,0,0.05);
+	width:230px;
+    height:800px;
+    margin-top:50px;
+  
+	text-align: center;
+	cursor:pointer;
+	transition:.3s;
+}
+#drop-region:hover {
+	box-shadow:0 0 45px rgba(0,0,0,0.1);
+}
+
+#image-preview {
+	margin-top:20px;
+}
+#image-preview .image-view {
+    display: inline-block;
+	position:relative;
+	margin-right: 13px;
+	margin-bottom: 13px;
+}
+#image-preview .image-view img {
+    max-width: 100px;
+    max-height: 100px;
+}
+#image-preview .overlay {
+    position: absolute;
+    width: 100%;
+    height: 100%;
+    top: 0;
+    right: 0;
+    z-index: 2;
+    background: rgba(255,255,255,0.5);
+}
+
+.imggenerate {
+    float:left;
+    height:180px;
+    width:180px;
+}
+#textbox{
+    position:absolute;
+    left:300px;
+    top:450px;
+    margin-top:30px;
+    height:200px;
+    width:800px;
+}
+
+#image-region{
+    margin-top:10px;
+    float:left;
+    height:360;
+    width:640;
+}
+#clear{
+    margin-left:30px;
+}

fashion-gan/app/static/dic

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+    4: ["layers": [3, 9], "mag":[-5, 10], "effect": "pattern"]
+    10: ["layers":[0, 7], "mag": [-10, 10], effect": "collar, hemline, width"],
+    6: ["layers":[10, 14], "mag": [-20, 20], effect": "orange, blue"],
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+    7: ["layers":[10, 14], "mag":[-40, 30], "effect": "dark, light"],
+    9: ["layers":[0, 4], "mag":[-10, 10], "effect": "hemline, collar"],
+    8: ["layers":[3, 4], "mag":[-10, 20], "effect": "waistline"],
+}

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fashion-gan/app/static/output/w_4.pt

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fashion-gan/app/static/script.js

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+let imgShape = document.getElementById("imgShape");
+let imgPattern = document.getElementById("imgPattern");
+let resultImg = document.getElementById("resultImg");
+
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