<instructions>Your goal is to analyze the following comment and estimate how highly it will be upvoted by the Hacker News community.</instructions><comment><author>wombat_trouble</author><text>Oh come on. I&amp;#x27;m excited about new technologies and I think that image generation can be a net positive for the society, but can we not do that? First, we had people confidently asserting that stuff of this sort absolutely can&amp;#x27;t happen. Now, we&amp;#x27;re moving the goalposts to &amp;quot;well, not a legitimate criticism because it doesn&amp;#x27;t happen often&amp;quot;.&lt;p&gt;The point is that basically &lt;i&gt;all&lt;/i&gt; Stable Diffusion &amp;#x2F; DALL-E &amp;#x2F; MidJourney output is some shade of this; the only new data is that contrary to prior assertions, in some cases, it goes all the way to a verbatim copy.&lt;p&gt;I think there are some defensible stances one can take. One is to reject the idea of intellectual property. Another is to advocate for some specific legal or technical bar that the models would have to pass for it to qualify as &amp;quot;not stealing&amp;quot;. Yet another is to argue it&amp;#x27;s a morally-agnostic technology like VHS or a photocopier, and the burden of using it in a socially acceptable way rests with the user.</text><parent_chain><item><author>FeepingCreature</author><text>100 images out of 350,000 that they looked at were memorized.&lt;p&gt;This seems to mostly happen when an image appears frequently (more than 100 times) in the training data, and&amp;#x2F;or the dataset is small relative to the model.</text></item></parent_chain></comment><story><title>Extracting training data from diffusion models</title><url>https://arxiv.org/abs/2301.13188</url></story>
<instructions>Your goal is to analyze the following comment and estimate how highly it will be upvoted by the Hacker News community.</instructions><comment><author>seanhunter</author><text>Seems like it would be very simple to prevent this from happening by just adding a perceptual hash check[1] for collisions vs the training set before emitting an image.  I&amp;#x27;m sure someone would be smart enough to make a &amp;quot;perceptual cuckoo hash&amp;quot; type thing so it would be very fast at the expense of sometimes erring on the side of caution and not emitting an image that actually isn&amp;#x27;t the same as the training data.&lt;p&gt;I don&amp;#x27;t know why they didn&amp;#x27;t do this tbh.&lt;p&gt;[1] &lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;en.wikipedia.org&amp;#x2F;wiki&amp;#x2F;Perceptual_hashing&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;en.wikipedia.org&amp;#x2F;wiki&amp;#x2F;Perceptual_hashing&lt;/a&gt;</text><parent_chain><item><author>FeepingCreature</author><text>100 images out of 350,000 that they looked at were memorized.&lt;p&gt;This seems to mostly happen when an image appears frequently (more than 100 times) in the training data, and&amp;#x2F;or the dataset is small relative to the model.</text></item></parent_chain></comment><story><title>Extracting training data from diffusion models</title><url>https://arxiv.org/abs/2301.13188</url></story>