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	<id>http://stablediffusionwiki.com/index.php?action=history&amp;feed=atom&amp;title=Canny</id>
	<title>Canny - Revision history</title>
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	<updated>2026-04-16T20:32:19Z</updated>
	<subtitle>Revision history for this page on the wiki</subtitle>
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	<entry>
		<id>http://stablediffusionwiki.com/index.php?title=Canny&amp;diff=162&amp;oldid=prev</id>
		<title>StableTiger3 at 16:24, 22 August 2023</title>
		<link rel="alternate" type="text/html" href="http://stablediffusionwiki.com/index.php?title=Canny&amp;diff=162&amp;oldid=prev"/>
		<updated>2023-08-22T16:24:38Z</updated>

		<summary type="html">&lt;p&gt;&lt;/p&gt;
&lt;table style=&quot;background-color: #fff; color: #202122;&quot; data-mw=&quot;interface&quot;&gt;
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				&lt;td colspan=&quot;2&quot; style=&quot;background-color: #fff; color: #202122; text-align: center;&quot;&gt;← Older revision&lt;/td&gt;
				&lt;td colspan=&quot;2&quot; style=&quot;background-color: #fff; color: #202122; text-align: center;&quot;&gt;Revision as of 12:24, 22 August 2023&lt;/td&gt;
				&lt;/tr&gt;&lt;tr&gt;&lt;td colspan=&quot;2&quot; class=&quot;diff-lineno&quot; id=&quot;mw-diff-left-l1&quot;&gt;Line 1:&lt;/td&gt;
&lt;td colspan=&quot;2&quot; class=&quot;diff-lineno&quot;&gt;Line 1:&lt;/td&gt;&lt;/tr&gt;
&lt;tr&gt;&lt;td class=&quot;diff-marker&quot; data-marker=&quot;−&quot;&gt;&lt;/td&gt;&lt;td style=&quot;color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;div&gt;&lt;del style=&quot;font-weight: bold; text-decoration: none;&quot;&gt;A powerful computer program like &amp;#039;&amp;#039;&amp;#039;Stable Diffusion&amp;#039;&amp;#039;&amp;#039; that can create pictures from descriptions &lt;/del&gt;is &lt;del style=&quot;font-weight: bold; text-decoration: none;&quot;&gt;already pretty amazing. But someone created &lt;/del&gt;a &lt;del style=&quot;font-weight: bold; text-decoration: none;&quot;&gt;new tool called &amp;#039;&amp;#039;&amp;#039;&lt;/del&gt;ControlNet&lt;del style=&quot;font-weight: bold; text-decoration: none;&quot;&gt;&amp;#039;&amp;#039;&amp;#039; that makes it even better&lt;/del&gt;.&lt;/div&gt;&lt;/td&gt;&lt;td class=&quot;diff-marker&quot; data-marker=&quot;+&quot;&gt;&lt;/td&gt;&lt;td style=&quot;color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;div&gt;&lt;ins style=&quot;font-weight: bold; text-decoration: none;&quot;&gt;Canny Edge &lt;/ins&gt;is a &lt;ins style=&quot;font-weight: bold; text-decoration: none;&quot;&gt;control type of the &lt;/ins&gt;ControlNet &lt;ins style=&quot;font-weight: bold; text-decoration: none;&quot;&gt;extension.  The edge detector obtains 3M edge-imagecaption pairs from the internet. The model is trained with 600 GPU-hours with Nvidia A100 80G&lt;/ins&gt;.&lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;
&lt;tr&gt;&lt;td class=&quot;diff-marker&quot; data-marker=&quot;−&quot;&gt;&lt;/td&gt;&lt;td style=&quot;color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;div&gt; &lt;/div&gt;&lt;/td&gt;&lt;td class=&quot;diff-marker&quot; data-marker=&quot;+&quot;&gt;&lt;/td&gt;&lt;td style=&quot;color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;div&gt;&lt;ins style=&quot;font-weight: bold; text-decoration: none;&quot;&gt;[[File:CannyEdgeDetection.png|center|thumb|470x470px|Figure 1: Control &lt;/ins&gt;Stable Diffusion &lt;ins style=&quot;font-weight: bold; text-decoration: none;&quot;&gt;with Canny edge map. The canny edge map is input&lt;/ins&gt;, &lt;ins style=&quot;font-weight: bold; text-decoration: none;&quot;&gt;and the source image is not used when we generate the &lt;/ins&gt;images &lt;ins style=&quot;font-weight: bold; text-decoration: none;&quot;&gt;on the right&lt;/ins&gt;. &lt;ins style=&quot;font-weight: bold; text-decoration: none;&quot;&gt;The outputs are achieved with &lt;/ins&gt;a &lt;ins style=&quot;font-weight: bold; text-decoration: none;&quot;&gt;default prompt “a high&lt;/ins&gt;-&lt;ins style=&quot;font-weight: bold; text-decoration: none;&quot;&gt;quality&lt;/ins&gt;, &lt;ins style=&quot;font-weight: bold; text-decoration: none;&quot;&gt;detailed&lt;/ins&gt;, and &lt;ins style=&quot;font-weight: bold; text-decoration: none;&quot;&gt;professional image”&lt;/ins&gt;. This &lt;ins style=&quot;font-weight: bold; text-decoration: none;&quot;&gt;prompt &lt;/ins&gt;is &lt;ins style=&quot;font-weight: bold; text-decoration: none;&quot;&gt;used in this paper as &lt;/ins&gt;a &lt;ins style=&quot;font-weight: bold; text-decoration: none;&quot;&gt;default prompt that does not mention anything about the image contents &lt;/ins&gt;and &lt;ins style=&quot;font-weight: bold; text-decoration: none;&quot;&gt;object names&lt;/ins&gt;. &lt;ins style=&quot;font-weight: bold; text-decoration: none;&quot;&gt;Most of figures &lt;/ins&gt;in &lt;ins style=&quot;font-weight: bold; text-decoration: none;&quot;&gt;this paper are high&lt;/ins&gt;-&lt;ins style=&quot;font-weight: bold; text-decoration: none;&quot;&gt;resolution images &lt;/ins&gt;and &lt;ins style=&quot;font-weight: bold; text-decoration: none;&quot;&gt;best viewed when zoomed in. arXiv:2302&lt;/ins&gt;.&lt;ins style=&quot;font-weight: bold; text-decoration: none;&quot;&gt;05543v1 [cs&lt;/ins&gt;.&lt;ins style=&quot;font-weight: bold; text-decoration: none;&quot;&gt;CV] 10 Feb 2023]]&lt;/ins&gt;&lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;
&lt;tr&gt;&lt;td class=&quot;diff-marker&quot; data-marker=&quot;−&quot;&gt;&lt;/td&gt;&lt;td style=&quot;color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;div&gt;&lt;del style=&quot;font-weight: bold; text-decoration: none;&quot;&gt;Think of a neural network, like &amp;#039;&amp;#039;&amp;#039;&lt;/del&gt;Stable Diffusion&lt;del style=&quot;font-weight: bold; text-decoration: none;&quot;&gt;&amp;#039;&amp;#039;&amp;#039;&lt;/del&gt;, &lt;del style=&quot;font-weight: bold; text-decoration: none;&quot;&gt;that can turn text into &lt;/del&gt;images. &lt;del style=&quot;font-weight: bold; text-decoration: none;&quot;&gt;&amp;#039;&amp;#039;&amp;#039;ControlNet&amp;#039;&amp;#039;&amp;#039; is &lt;/del&gt;a &lt;del style=&quot;font-weight: bold; text-decoration: none;&quot;&gt;new system designed to fine&lt;/del&gt;-&lt;del style=&quot;font-weight: bold; text-decoration: none;&quot;&gt;tune this process, making it more flexible to different tasks. It can work with small amounts of data&lt;/del&gt;, &lt;del style=&quot;font-weight: bold; text-decoration: none;&quot;&gt;be trained quickly&lt;/del&gt;, and &lt;del style=&quot;font-weight: bold; text-decoration: none;&quot;&gt;doesn&amp;#039;t require huge computers to run&lt;/del&gt;. This &lt;del style=&quot;font-weight: bold; text-decoration: none;&quot;&gt;system can be tailored to various image-related tasks, making it more efficient and useful.&lt;/del&gt;&lt;/div&gt;&lt;/td&gt;&lt;td colspan=&quot;2&quot; class=&quot;diff-side-added&quot;&gt;&lt;/td&gt;&lt;/tr&gt;
&lt;tr&gt;&lt;td class=&quot;diff-marker&quot; data-marker=&quot;−&quot;&gt;&lt;/td&gt;&lt;td style=&quot;color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;div&gt; &lt;/div&gt;&lt;/td&gt;&lt;td colspan=&quot;2&quot; class=&quot;diff-side-added&quot;&gt;&lt;/td&gt;&lt;/tr&gt;
&lt;tr&gt;&lt;td class=&quot;diff-marker&quot; data-marker=&quot;−&quot;&gt;&lt;/td&gt;&lt;td style=&quot;color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;div&gt;&lt;del style=&quot;font-weight: bold; text-decoration: none;&quot;&gt;&amp;#039;&amp;#039;&amp;#039;ControlNet&amp;#039;&amp;#039;&amp;#039; &lt;/del&gt;is a &lt;del style=&quot;font-weight: bold; text-decoration: none;&quot;&gt;neural network architecture designed to control pre-trained large diffusion models, enabling them to support additional input conditions &lt;/del&gt;and &lt;del style=&quot;font-weight: bold; text-decoration: none;&quot;&gt;tasks&lt;/del&gt;. &lt;del style=&quot;font-weight: bold; text-decoration: none;&quot;&gt;This end-to-end learning approach ensures robustness, even with small training datasets. Training a &amp;#039;&amp;#039;&amp;#039;ControlNet&amp;#039;&amp;#039;&amp;#039; is comparable &lt;/del&gt;in &lt;del style=&quot;font-weight: bold; text-decoration: none;&quot;&gt;speed to fine&lt;/del&gt;-&lt;del style=&quot;font-weight: bold; text-decoration: none;&quot;&gt;tuning a diffusion model, &lt;/del&gt;and &lt;del style=&quot;font-weight: bold; text-decoration: none;&quot;&gt;it can be done on personal devices or scaled up if powerful computation clusters are available&lt;/del&gt;. &lt;del style=&quot;font-weight: bold; text-decoration: none;&quot;&gt;This flexibility makes &amp;#039;&amp;#039;&amp;#039;ControlNet&amp;#039;&amp;#039;&amp;#039; an effective tool for augmenting large diffusion models like &amp;#039;&amp;#039;&amp;#039;Stable Diffusion&amp;#039;&amp;#039;&amp;#039;, allowing for conditional inputs and facilitating diverse applications&lt;/del&gt;.&lt;/div&gt;&lt;/td&gt;&lt;td colspan=&quot;2&quot; class=&quot;diff-side-added&quot;&gt;&lt;/td&gt;&lt;/tr&gt;
&lt;/table&gt;</summary>
		<author><name>StableTiger3</name></author>
	</entry>
	<entry>
		<id>http://stablediffusionwiki.com/index.php?title=Canny&amp;diff=160&amp;oldid=prev</id>
		<title>StableTiger3: Created page with &quot;A powerful computer program like &#039;&#039;&#039;Stable Diffusion&#039;&#039;&#039; that can create pictures from descriptions is already pretty amazing. But someone created a new tool called &#039;&#039;&#039;ControlNet&#039;&#039;&#039; that makes it even better.  Think of a neural network, like &#039;&#039;&#039;Stable Diffusion&#039;&#039;&#039;, that can turn text into images. &#039;&#039;&#039;ControlNet&#039;&#039;&#039; is a new system designed to fine-tune this process, making it more flexible to different tasks. It can work with small amounts of data, be trained quickly, and d...&quot;</title>
		<link rel="alternate" type="text/html" href="http://stablediffusionwiki.com/index.php?title=Canny&amp;diff=160&amp;oldid=prev"/>
		<updated>2023-08-22T16:15:30Z</updated>

		<summary type="html">&lt;p&gt;Created page with &amp;quot;A powerful computer program like &amp;#039;&amp;#039;&amp;#039;Stable Diffusion&amp;#039;&amp;#039;&amp;#039; that can create pictures from descriptions is already pretty amazing. But someone created a new tool called &amp;#039;&amp;#039;&amp;#039;ControlNet&amp;#039;&amp;#039;&amp;#039; that makes it even better.  Think of a neural network, like &amp;#039;&amp;#039;&amp;#039;Stable Diffusion&amp;#039;&amp;#039;&amp;#039;, that can turn text into images. &amp;#039;&amp;#039;&amp;#039;ControlNet&amp;#039;&amp;#039;&amp;#039; is a new system designed to fine-tune this process, making it more flexible to different tasks. It can work with small amounts of data, be trained quickly, and d...&amp;quot;&lt;/p&gt;
&lt;p&gt;&lt;b&gt;New page&lt;/b&gt;&lt;/p&gt;&lt;div&gt;A powerful computer program like &amp;#039;&amp;#039;&amp;#039;Stable Diffusion&amp;#039;&amp;#039;&amp;#039; that can create pictures from descriptions is already pretty amazing. But someone created a new tool called &amp;#039;&amp;#039;&amp;#039;ControlNet&amp;#039;&amp;#039;&amp;#039; that makes it even better.&lt;br /&gt;
&lt;br /&gt;
Think of a neural network, like &amp;#039;&amp;#039;&amp;#039;Stable Diffusion&amp;#039;&amp;#039;&amp;#039;, that can turn text into images. &amp;#039;&amp;#039;&amp;#039;ControlNet&amp;#039;&amp;#039;&amp;#039; is a new system designed to fine-tune this process, making it more flexible to different tasks. It can work with small amounts of data, be trained quickly, and doesn&amp;#039;t require huge computers to run. This system can be tailored to various image-related tasks, making it more efficient and useful.&lt;br /&gt;
&lt;br /&gt;
&amp;#039;&amp;#039;&amp;#039;ControlNet&amp;#039;&amp;#039;&amp;#039; is a neural network architecture designed to control pre-trained large diffusion models, enabling them to support additional input conditions and tasks. This end-to-end learning approach ensures robustness, even with small training datasets. Training a &amp;#039;&amp;#039;&amp;#039;ControlNet&amp;#039;&amp;#039;&amp;#039; is comparable in speed to fine-tuning a diffusion model, and it can be done on personal devices or scaled up if powerful computation clusters are available. This flexibility makes &amp;#039;&amp;#039;&amp;#039;ControlNet&amp;#039;&amp;#039;&amp;#039; an effective tool for augmenting large diffusion models like &amp;#039;&amp;#039;&amp;#039;Stable Diffusion&amp;#039;&amp;#039;&amp;#039;, allowing for conditional inputs and facilitating diverse applications.&lt;/div&gt;</summary>
		<author><name>StableTiger3</name></author>
	</entry>
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