<story><title>Stable Video Diffusion</title><url>https://stability.ai/news/stable-video-diffusion-open-ai-video-model</url></story><parent_chain><item><author>alberth</author><text>What was the big “unlock” that allowed so much progress this past year?&lt;p&gt;I ask as a noob in this area.</text></item><item><author>valine</author><text>The rate of progress in ML this past year has been breath taking.&lt;p&gt;I can’t wait to see what people do with this once controlnet is properly adapted to video. Generating videos from scratch is cool, but the real utility of this will be the temporal consistency. Getting stable video out of stable diffusion typically involves lots of manual post processing to remove flicker.</text></item></parent_chain><comment><author>4death4</author><text>I think these are the main drivers behind the progress:&lt;p&gt;- Unsupervised learning techniques, e.g. transformers and diffusion models. You need unsupervised techniques in order to utilize enough data. There have been other unsupervised techniques in the past, e.g. GANs, but they don&amp;#x27;t work as well.&lt;p&gt;- Massive amounts of training data.&lt;p&gt;- The belief that training these models will produce something valuable. It costs between hundreds of thousands to millions of dollars to train these models. The people doing the training need to believe they&amp;#x27;re going to get something interesting out at the end. More and more people and teams are starting to see training a large model as something worth pursuing.&lt;p&gt;- Better GPUs, which enables training larger models.&lt;p&gt;- Honestly the fall of crypto probably also contributed, because miners were eating a lot of GPU time.</text></comment>
<story><title>Stable Video Diffusion</title><url>https://stability.ai/news/stable-video-diffusion-open-ai-video-model</url></story><parent_chain><item><author>alberth</author><text>What was the big “unlock” that allowed so much progress this past year?&lt;p&gt;I ask as a noob in this area.</text></item><item><author>valine</author><text>The rate of progress in ML this past year has been breath taking.&lt;p&gt;I can’t wait to see what people do with this once controlnet is properly adapted to video. Generating videos from scratch is cool, but the real utility of this will be the temporal consistency. Getting stable video out of stable diffusion typically involves lots of manual post processing to remove flicker.</text></item></parent_chain><comment><author>Cyphase</author><text>One factor is that Stable Diffusion and ChatGPT were released within 3 months of each other – August 22, 2022 and November 3, 2022, respectively. That brought a lot of attention and excitement to the field. More excitement, more people, more work being done, more progress.&lt;p&gt;Of course those two releases didn&amp;#x27;t fall out of the sky.</text></comment>