Do you mean model’s policy as it works on a query, or learning as it works on a dataset? Or something specific to stable diffusion? What is the sample space here, and what are the actions that decisions choose between?
You’re talking about the score function, right? Which is the derivative of the log probability density function. I dunno how to get from there to a utility function interpretation. Like, we don’t produce samples from the model by globally maximizing over the PDF (at worst, trying that might produce an adversarial example, and at best, that would sample the “most modal” image).
Lots of things “have a utility function” in the colloquial sense that they can be usefully modeled as having consistent preferences. But sure, I’ll be somewhat skeptical if you want to continue “taking the utility-function perspective on stable diffusion is in some way useful for thinking about its alignment properties.”
Ah, you’re talking about guidance? That makes sense, but you could also take the perspective that guidance isn’t really playing the role of a utility function, it’s just nudging around this big dynamical system by small amounts.
no, I’m talking about the basic diffusion model underneath. It models the derivative of the probability density function, which seems reasonable to call a utility function to me. see my other comment for link
component of why I’m not sure I agree with this: I claim stable diffusion has a utility function. does anyone disagree with this subclaim?
Do you mean model’s policy as it works on a query, or learning as it works on a dataset? Or something specific to stable diffusion? What is the sample space here, and what are the actions that decisions choose between?
score based models, such as diffusion, work by modeling the derivative of the utility function (density function) over examples, I believe?
see, eg, https://lilianweng.github.io/posts/2021-07-11-diffusion-models/ or any of the other recommended posts at the top.
actions are denoising steps. sample space is output space, ie image space for stable diffusion.
You’re talking about the score function, right? Which is the derivative of the log probability density function. I dunno how to get from there to a utility function interpretation. Like, we don’t produce samples from the model by globally maximizing over the PDF (at worst, trying that might produce an adversarial example, and at best, that would sample the “most modal” image).
ah, okay. yup, you’re right, that’s what I was referring to. I am now convinced I was wrong in my original comment!
Lots of things “have a utility function” in the colloquial sense that they can be usefully modeled as having consistent preferences. But sure, I’ll be somewhat skeptical if you want to continue “taking the utility-function perspective on stable diffusion is in some way useful for thinking about its alignment properties.”
but diffusion specifically works by modeling the derivative of the utility function, yeah?
Ah, you’re talking about guidance? That makes sense, but you could also take the perspective that guidance isn’t really playing the role of a utility function, it’s just nudging around this big dynamical system by small amounts.
no, I’m talking about the basic diffusion model underneath. It models the derivative of the probability density function, which seems reasonable to call a utility function to me. see my other comment for link