-
Notifications
You must be signed in to change notification settings - Fork 10
Expand file tree
/
Copy pathindex.html
More file actions
275 lines (232 loc) · 12 KB
/
Copy pathindex.html
File metadata and controls
275 lines (232 loc) · 12 KB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8">
<!-- Meta tags for social media banners, these should be filled in appropriatly as they are your "business card" -->
<!-- Replace the content tag with appropriate information -->
<meta name="description" content="DESCRIPTION META TAG">
<meta property="og:title" content="SOCIAL MEDIA TITLE TAG"/>
<meta property="og:description" content="SOCIAL MEDIA DESCRIPTION TAG TAG"/>
<meta property="og:url" content="URL OF THE WEBSITE"/>
<!-- Path to banner image, should be in the path listed below. Optimal dimenssions are 1200X630-->
<meta property="og:image" content="static/image/your_banner_image.png" />
<meta property="og:image:width" content="1200"/>
<meta property="og:image:height" content="630"/>
<meta name="twitter:title" content="TWITTER BANNER TITLE META TAG">
<meta name="twitter:description" content="TWITTER BANNER DESCRIPTION META TAG">
<!-- Path to banner image, should be in the path listed below. Optimal dimenssions are 1200X600-->
<meta name="twitter:image" content="static/images/your_twitter_banner_image.png">
<meta name="twitter:card" content="summary_large_image">
<!-- Keywords for your paper to be indexed by-->
<meta name="keywords" content="KEYWORDS SHOULD BE PLACED HERE">
<meta name="viewport" content="width=device-width, initial-scale=1">
<title>DistributedDiffusion</title>
<link rel="icon" type="image/x-icon" href="static/images/favico.ico">
<link href="https://fonts.googleapis.com/css?family=Google+Sans|Noto+Sans|Castoro"
rel="stylesheet">
<link rel="stylesheet" href="static/css/bulma.min.css">
<link rel="stylesheet" href="static/css/bulma-carousel.min.css">
<link rel="stylesheet" href="static/css/bulma-slider.min.css">
<link rel="stylesheet" href="static/css/fontawesome.all.min.css">
<link rel="stylesheet"
href="https://cdn.jsdelivr.net/gh/jpswalsh/academicons@1/css/academicons.min.css">
<link rel="stylesheet" href="static/css/index.css">
<script src="https://ajax.googleapis.com/ajax/libs/jquery/3.5.1/jquery.min.js"></script>
<script src="https://documentcloud.adobe.com/view-sdk/main.js"></script>
<script defer src="static/js/fontawesome.all.min.js"></script>
<script src="static/js/bulma-carousel.min.js"></script>
<script src="static/js/bulma-slider.min.js"></script>
<script src="static/js/index.js"></script>
<script type="text/javascript" async
src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.7/MathJax.js?config=TeX-MML-AM_CHTML">
</script>
<script type="text/x-mathjax-config">
MathJax.Hub.Config({
tex2jax: {
inlineMath: [['$','$'], ['\\(','\\)']],
processEscapes: true
}
});
</script>
</head>
<body>
<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="title is-1 publication-title">Collaborative Distributed Diffusion-Based AI-Generated Content (AIGC)</h1>
<div class="is-size-5 publication-authors">
<!-- Paper authors -->
<span class="author-block">
Hongyang Du, Ruichen Zhang, Dusit Niyato, Jiawen Kang, Zehui Xiong, Shuguang Cui, Xuemin Shen, Dong In Kim</span>
</div>
<div class="is-size-5 publication-authors">
<span class="author-block">Nanyang Technological University</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/2311.11094.pdf" target="_blank"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="fas fa-file-pdf"></i>
</span>
<span>Journal Paper</span>
</a>
</span>
<!-- Supplementary PDF link -->
<span class="link-block">
<a href="https://arxiv.org/pdf/2304.03446.pdf" target="_blank"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="fas fa-file-pdf"></i>
</span>
<span>Magazine Paper</span>
</a>
</span>
<!-- Github link -->
<span class="link-block">
<a href="https://github.com/HongyangDu/DistributedDiffusion" target="_blank"
class="external-link button is-normal is-rounded is-dark">
<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/2311.11094" target="_blank"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="ai ai-arxiv"></i>
</span>
<span>arXiv</span>
</a>
</span>
</div>
</div>
</div>
</div>
</div>
</div>
</section>
<!-- 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>
Driven by advances in generative artificial intelligence (AI) techniques and algorithms, the widespread adoption of AI-generated content (AIGC) has emerged, allowing for the generation of diverse and high-quality content. Especially, the <b>diffusion model-based AIGC</b> technique has been widely used to generate content in a variety of modalities. However, the real-world implementation of AIGC models, particularly on resource-constrained devices such as mobile phones, introduces significant challenges related to energy consumption and privacy concerns. To further promote the realization of ubiquitous AIGC services, we propose a novel collaborative distributed diffusion-based AIGC framework. By capitalizing on collaboration among devices in wireless networks, the proposed framework facilitates the efficient execution of AIGC tasks, optimizing edge computation resource utilization. Furthermore, we examine the practical implementation of the denoising steps on mobile phones, the impact of the proposed approach on the wireless network-aided AIGC landscape, and the future opportunities associated with its real-world integration. The contributions of this paper not only offer a promising solution to the existing limitations of AIGC services but also pave the way for future research in device collaboration, resource optimization, and the seamless delivery of AIGC services across various devices.
</p>
</div>
</div>
</div>
</div>
</section>
<!-- End paper abstract -->
<!-- Image carousel -->
<section class="hero is-small">
<div class="hero-body">
<div class="container">
<div id="results-carousel" class="carousel results-carousel">
<div class="item">
<!-- Your image here -->
<div align="center">
<img src="readme/img0.png" width="70%" alt="System Model"/>
</div>
<h2 class="subtitle has-text-centered">
System Model.
</h2>
</div>
</div>
</div>
</div>
</section>
<!-- End image carousel -->
<section class="section hero">
<div class="hero-body">
<div class="container is-max-desktop content">
<h2 class="title">🔧 Environment Setup</h2>
<p>To create a new conda environment, run the following command:</p>
<pre><code>conda create --name disdiff python==3.9</code></pre>
<h2 class="title">⚡ Activate Environment</h2>
<p>Activate the created environment with:</p>
<pre><code>conda activate disdiff</code></pre>
<h2 class="title">📦 Install Required Packages</h2>
<p>You need to install the following packages using pip:</p>
<pre><code>pip install diffusers==0.13.1
pip install torch==2.0.1
pip install transformers==4.29.2
pip install accelerate==0.20.0
</code></pre>
<h2 class="title">🔍 Locate StableDiffusionPipeline</h2>
<p>Open <code>offloading.py</code> in your code editor. Use <code>ctrl</code> (Windows) or <code>command</code> (Mac) and click <code>StableDiffusionPipeline</code> to navigate to <code>pipeline_stable_diffusion.py</code>.</p>
<img src="readme/img1.png" alt="Location of StableDiffusionPipeline">
<p>To find this file in your directory, right-click the filename and select 'open in' -> 'finder'.</p>
<img src="readme/img2.png" alt="File in Directory">
<h2 class="title">🔄 Replace with Project File</h2>
<p>Replace <code>pipeline_stable_diffusion.py</code> with the file of the same name from this repository.</p>
<img src="readme/img3.png" alt="Replace `pipeline_stable_diffusion.py`">
<h2 class="title">🏃♀️ Run the Program</h2>
<p>Finally, run <code>offloading.py</code> to start the program. The model will be downloaded automatically if you are running this code for the first time.</p>
<img src="readme/img4.png" alt="Download Automatically">
<h2 class="title">🔍 Check the results</h2>
<p>The parameter "tt" is the offloading processing point, and the parameter "ss" is the total denoising steps. For more details, please check the <code>offloading.py</code>.</p>
<img src="readme/img5.png" alt="Results">
</div>
</div>
</section>
<!--BibTex citation -->
<section class="section" id="BibTeX">
<div class="container is-max-desktop content">
<h2 class="title">BibTeX</h2>
<pre><code>@article{du2023exploring,
title={Exploring collaborative distributed diffusion-based AI-generated content (AIGC) in wireless networks},
author={Du, Hongyang and Zhang, Ruichen and Niyato, Dusit and Kang, Jiawen and Xiong, Zehui and Kim, Dong In and Shen, Xuemin Sherman and Poor, H Vincent},
journal={IEEE Network},
number={99},
pages={1--8},
year={2023},
publisher={IEEE}
}</code></pre>
</div>
</section>
<!--End BibTex citation -->
<!--BibTex citation -->
<section class="section" id="BibTeX">
<div class="container is-max-desktop content">
<h2 class="title">BibTeX</h2>
<pre><code>@article{du2023user,
title={User-Centric Interactive AI for Distributed Diffusion Model-based AI-Generated Content},
author={Du, Hongyang and Zhang, Ruichen and Niyato, Dusit and Kang, Jiawen and Xiong, Zehui and Cui, Shuguang and Shen, Xuemin and Kim, Dong In},
journal={arXiv preprint arXiv:2311.11094},
year={2023}
}</code></pre>
</div>
</section>
<!--End BibTex citation -->
<footer class="footer">
<div class="container">
<div class="columns is-centered">
<div class="column is-8">
<div class="content">
<p>
This page was built using the <a href="https://github.com/eliahuhorwitz/Academic-project-page-template" target="_blank">Academic Project Page Template</a> which was adopted from the <a href="https://nerfies.github.io" target="_blank">Nerfies</a> project page.
You are free to borrow the of this website, we just ask that you link back to this page in the footer. <br> This website is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/" target="_blank">Creative
Commons Attribution-ShareAlike 4.0 International License</a>.
</p>
</div>
</div>
</div>
</div>
</footer>
<!-- Statcounter tracking code -->
<!-- You can add a tracker to track page visits by creating an account at statcounter.com -->
<!-- End of Statcounter Code -->
</body>
</html>