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Autocorrect hates me, I am sorry.

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Joined 1 year ago
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Cake day: July 20th, 2025

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  • The original source is clearer:

    The launch version of PSSR framed almost the entire reconstruction pipeline as a single Color-Predicting Network. This gave the model a wide surface area for optimization, but it also implicitly asked the model to solve a number of disparate problems that already had efficient closed-form solutions. The talk will show how that generality became a cost, and how the upgrade walked much of it back: restoring explicit closed-form solutions where possible and focusing the model on the pattern recognition it is best suited for. The result improved visual quality and temporal stability while reducing runtime, memory, and training cost by giving the model less to do, not more.

    So basically, they treated the whole render process as frame-in frame-out predictions. But by using traditional rendering techniques instead of pure ML the whole system gets faster and better (no surprise to me on that).

    Games expect 3x upscaling now to meet performance targets, and ML requires exponentially more data to train a geberal model and test PSSR on a game — that’s the GPU month thing, if I had 30 GPUs it would take 1 day to train 1GPU month (ish — depends on gpu/etc).

    The 540p thing is the training goes 4K -> downsample to 1080p -> downscale to 540p as an autencoder (model that tries to predict original input from progressively downscaled/bottle necked layers, with the idea it learns the best representation this way — not actually true though, not relevant here though).

    The new model does the same ML but finishes with traditional compositing. That is to say, do you need an ML model to blend 50% blue onto yellow? No, you can use simple math and your ML can focus on the harder bits. So this all works faster and better with HDR. So now less work is done on that and more work is spent on upscaling.

    The new model is also more deterministic, so it won’t hallucinate artifacts or turn your main character into a blond instagram model like NVidia DLSS.

    Tl;dr, by replacing ML with traditional rendering you do less work, it’s faster to train and cheaper, and it does a better job overall.


  • Just scrape all the seeds into a mortar and pestle, grind them, throw the beans in some alcohol (rum, bourbon style Canadian whiskey, sherry), soak then leave them in a sugar bowl and save the sugar.

    Now take the vanilla bean paste from your pestle and put it directly in my mouth.










  • I dumped my phone company over their AI answering system.

    It was totally broken:

    • you can’t know when the AI is lying or will do something it says it will for you
    • it’s not designed to be helpful, it’s designed to keep you away from humans
    • this particular system required you to say a correct and available time you want a callback, and if you were incorrect it would hang up on you.

    I’ll bet the cost metrics looked great, 100% of calls being handled without a callback! Oh, the customers leaving is unrelated, talk to the retention department.

    Just utter fucking stupidity. No, not stupidity, complete disregard for the customer to serve some department heads self interests. There needs to be a word stronger than greed…




  • I cannot for the life of me understand AI mandates.

    Who doesn’t want to be able to get their work done easier? Nobody.

    Then it follows you shouldn’t have to tell your employees to use a tool if it makes them however much as productive as CEOs think that tech demo should make them.

    I think it’s pretty obvious that at the exec level AI is the ultimate hoodwinking machine. It’s made 90% of them look like the out of touch morons they are. The 10% or whatever that aren’t trying to force this down their staff’s throats in the hopes they can replace said staff are coming out smelling like roses.

    Don’t fit a square peg in a round hole.


  • My idempotent claim was too broad and wrong, up to aliasing that is reversible.

    I think I’ve been talking about this differently than you, when you get 8bit into, 255 in/out is correct.

    When generating data I do it in the natural [0,1] space, then map to whatever domain I’m quantizing for.

    In that case, for N bins you divide by N+1 (and round however you want via correction) so 256. I was probably wrong earlier around this, 256 is correct for quantizing that way.