Diffusion Models Beat GANs on Image Synthesis
The biggest downside with diffusion models is that GANs can be rendered in much less than half a second (sometimes 10fps or higher) on one core of a standard device you probably have. (Many of you have an nVidia GPU, and any ol’ nVidia GPU will render stylegan quickly.)<p>With diffusion models, you need to do &gt;25 forward passes to achieve a result. It’s kind of like an O(1) algorithm vs O(N): stylegan has one pass, diffusion models have N. And N is currently 25 or more, which means it tends to be 25x slower than stylegan at a minimum. (In our experience it was often many seconds to a full minute before we saw results, but we didn’t try very hard to make it fast, and this paper shows advances in speed since then.)<p>The flipside: this paper has the most beautiful photorealistic complex samples I’ve ever seen. I don’t really care if they’re cherry picked; it’s hard to pick cherries on a rotten tree. I know.<p>The last thing I want to say is that I am disappointed the model is, yet again, not released. The hoarding of ML models needs to stop. These research models aren’t commercially useful, but they have incredible benefits for people outside of ML. I myself got into ML thanks to being able to play with GPT-2 and Sfylegan. I would leap at the chance to play with AlphaZero or OpenAI’s old Dota 2 models; no reason to keep those locked up. I also don’t care about the excuses for not releasing: none of them hold water. In my experience, no model has ever had a society-threatening impact, and it’s usually a euphemism for “we’re worried there’s a small chance we might look bad.” Releasing your model is as simple as scp’ing to your server; just do it and quit worrying so much.<p>Love the work. Diffusion models are a really interesting way of thinking about generative approaches in ML.