This weekly roundup thread is intended for all culture war posts. 'Culture war' is vaguely defined, but it basically means controversial issues that fall along set tribal lines. Arguments over culture war issues generate a lot of heat and little light, and few deeply entrenched people ever change their minds. This thread is for voicing opinions and analyzing the state of the discussion while trying to optimize for light over heat.
Optimistically, we think that engaging with people you disagree with is worth your time, and so is being nice! Pessimistically, there are many dynamics that can lead discussions on Culture War topics to become unproductive. There's a human tendency to divide along tribal lines, praising your ingroup and vilifying your outgroup - and if you think you find it easy to criticize your ingroup, then it may be that your outgroup is not who you think it is. Extremists with opposing positions can feed off each other, highlighting each other's worst points to justify their own angry rhetoric, which becomes in turn a new example of bad behavior for the other side to highlight.
We would like to avoid these negative dynamics. Accordingly, we ask that you do not use this thread for waging the Culture War. Examples of waging the Culture War:
-
Shaming.
-
Attempting to 'build consensus' or enforce ideological conformity.
-
Making sweeping generalizations to vilify a group you dislike.
-
Recruiting for a cause.
-
Posting links that could be summarized as 'Boo outgroup!' Basically, if your content is 'Can you believe what Those People did this week?' then you should either refrain from posting, or do some very patient work to contextualize and/or steel-man the relevant viewpoint.
In general, you should argue to understand, not to win. This thread is not territory to be claimed by one group or another; indeed, the aim is to have many different viewpoints represented here. Thus, we also ask that you follow some guidelines:
-
Speak plainly. Avoid sarcasm and mockery. When disagreeing with someone, state your objections explicitly.
-
Be as precise and charitable as you can. Don't paraphrase unflatteringly.
-
Don't imply that someone said something they did not say, even if you think it follows from what they said.
-
Write like everyone is reading and you want them to be included in the discussion.
On an ad hoc basis, the mods will try to compile a list of the best posts/comments from the previous week, posted in Quality Contribution threads and archived at /r/TheThread. You may nominate a comment for this list by clicking on 'report' at the bottom of the post and typing 'Actually a quality contribution' as the report reason.
Jump in the discussion.
No email address required.
Notes -
The codebook on this model must be absolutely tiny. They are probably trying to avoid bloating up the model vocabulary, but the quality is awful. Latent diffusion models don't use a quantizing vae but instead a continuous latent space, so they have an advantage there. In a sense the diffusion vae isn't compressing the image so much as reducing its dimensionality, but the VQ-VAE is doing some crazy compression.
The saying is that a picture is worth a thousand words, but the ratio is actually much more. I'm sure they'll get it right eventually, but it will be tricky to strike a balance between allocating model capacity to images vs text.
Anyways the problem will probably be solved through scaling up, just like how sora as impressive capabilities but micro-video models are next to worthless. (Of course sora is also useless but it's cool)
Image tokens should have no impact at all on the vocabulary size. I guess that they are doing the same as other multimodal models (input image is compressed, as you say probably using a VAE, but classically using a pretrained vision transformer), and let the ouput image tokens just be free. No need to quantize anything.
More options
Context Copy link
More options
Context Copy link