Reflected Diffusion Models

It’s funny, the original paper (DALL-E maybe?) used it as an example of something unlikely to exist in the training data, and therefore a good test of the model's compositional ability. Now that there’s so many successful generative models producing examples of horse-astronauts, producing them isn’t as obviously convincing.

What about the Obama on a scale picture for image understanding?

I have been using The quick brown fox jumps over the lazy dog

Something about bridging transportation technologies.

I wouldn't be so harsh. The author of the blog post is also the author of the paper. If I had to guess, the author decided (or was advised) that writing a blog post about the paper would increase its visibility (which turned out to be true, considering it's on HN). So they wrote a blog post summarizing the paper. But because they are really deep in the weeds with the technical content of the paper, might not have done a great job at making it more accessible to a wider audience. Doing that well takes some skill, and is not usually something graduate students get much, if any, training on. (Edit: on reading the blog post, I do have to agree that the presentation is pretty abstruse to the point that the paper is a better source. And yes, I am a "Phd level AI researcher").

they didn’t submit it here - I’m not sure they bear a responsibility to make sure their work is accessible to HN or any other target audience. If you don’t find an otherwise respectful and substantive blog post useful, perhaps best to leave it be.

I find these posts to be great. It lets you absorb the main idea of the paper without even reading it. I wish there were more like it. And btw, “responsibility to your readers”? It’s a blog post on the internet. Nobody owes anybody anything.

Are you a PHD level AI researcher? If not, I don't think you are the intended audience.

I'm not, so as a reader here, I'm just happy that I didn't have to pay to get exposed to new challenging ideas. A lot of other science is sadly paywalled inside of journals.

Give it a few weeks for the fast followers to dumb it down for us.

This already exists, check out the Spatial Palette (Color) Adapter here: https://github.com/TencentARC/T2I-Adapter

It works with the Mikubill ControlNet plugin for A1111.

A lot of the times equations are meant to be general and can apply to a vast number of scenerios.

For example x̄ (x-bar) is used in statistics to represent the sample mean of a distribution. It doesn't matter what your distribution is, you'd likely call it x̄.

Whenever you see an equation mentioning "n", this will very often be the number of items in a set/list/array, not matter what that set is counting.

Usually these equations are very general, and they don't care what "n" actually is, and this way it's easy to see where you should plug that number into. If you look at the equation for x̄ (averaging) then you'll see "n" in there, and you already know what it's for, and it's incredibly concise.

If you're familiar with them they actually are named relatively well, and generally they're references that you then implement, they aren't meant to be the implementation themselves.

A lot of times mathematical notation use domain specific naming conventions for they variables and each the tradition is a leftover from a time when you had to write the whole line (by hand) for every step you wrote down and only a few name variables. Take a look code at the other extreme can you imagine writing down verbose Java code line by line using quill and ink? I don't even want to read such a low entropy highly redundant "notation" on a 34" 8k screen. The only way to navigate it is through load bearing IDEs.

You often do math on the whiteboard or on paper, and it involves repeated references to the same things, which might take forever to write out at each step. Additionally, since it's a 2d notation, the "shape" of the formulas is informative, which is an aspect you lose with bigger names.

You should try it with long names and without and let's see what is easy to understand.

e^pii = -1

or

theNatural....Constant... = theCircleConstant... theSqureRootOfMinusOne = minusOn

or maybe you replace One with theNeutralElementForMultiplication

but maybe someone is too lazy and you need to give a good clear name for what multiplication is,

Sorry, but I could not help it, I seen this kind of comments multiple time, some people want to jump to the middle or end of a book/course and skip the hard work.

Obviously, you're not a golfer...

This topic is complex. For a more thoughtful take, start with:

https://www.jsoftware.com/papers/tot.htm

It has always been tolerated in math. The more unpronouncable the Greek letter, the better. If you want to sound really badass, you start using Hebrew letters as well. The pinnacle of badassness is multiple capital Pi and capital Sigma with some set notation below instead of indexes laid out clearly like a for loop. Or "Einstein notation" where you stop writing the summation symbol because a small group of people think it is implied, which helps gatekeep others from the field of research because they can't make sense of your equations anymore. At that point, people give up on reviewing your paper and just publish the damn thing because you're from Google, Stanford, or wherever.

If mathematicians worked like software engineers they'd label things like "density[x, y]" instead of "\rho_{xy}" or at least stop using PDFs and use HTML with a tooltip over each variable name like we have in pretty much every modern editor, but we all know they hate readability.

My favourite is when CS professors rewrite algorithms in latex and use single letter variables only. So much student time could be saved if they just wrote it in python.

Historically paper was costly, and there is no autocomplete on paper too.

As is so often the case in neural network design, preventing numbers from blowing up is the cause of so many necessary, but poorly understood math hacks. It seems these researchers have found a potentially better way to control the sampling process so that diffusion models can generate outputs that are more correct.

I often wonder if Midjourney, behind the scenes, has already discovered something like this in their v5 release, which is substantially better at modelling things like hands.

I hate to ask but is this ChatGPT?

Is there a way to run this locally?