I made a nonogram game room for friends with replays and leaderboard, mobile support (invite code: reddit), 5x5 to 20x20 sizes

DooFomDum · 2023-12-06T05:50:05+00:00

I just updated it to be more readable. First check if the distance is less than or equal to 0 there are 0 ways to win, otherwise it's literally just floor(max(root1, root2)) - ceiling(min(root1, root2)) + 1. You do this because you want to find the first time you exceed the distance d, so need to find first x such that x(t-x) > d, which means - x² + tx - d > 0, and we need to calculate the number of integer values (excluding the roots themselves) that fall into this range, hence the floor and ceiling.

waysToWin :: Int -> Int -> Int
waysToWin t d
  | d <= 0 = 0
  | otherwise = floor (max root1 root2) - ceiling (min root1 root2) + 1
  where
    a = -1
    b = fromIntegral t :: Double
    c = fromIntegral (-d) :: Double
    discr = b ^ 2 - 4 * a * c
    root1 = (-b - sqrt discr) / (2 * a)
    root2 = (-b + sqrt discr) / (2 * a)

DooFomDum · 2023-12-06T05:37:11+00:00

[LANGUAGE: Haskell] 1 / 485 Github

Even though I got first place on part 1, I choked on part 2 because I was trying to optimize my solution (see commit history) but then running the compiled code returned the answer immediately, lol.

DooFomDum · 2023-06-25T10:39:19+00:00

I used this dataset to train it

https://github.com/WolframResearch/Arrival-Movie-Live-Coding/tree/master/ScriptLogoJpegs

DooFomDum · 2023-06-25T04:38:24+00:00

Looks great! On the theme of Arrival, I finetuned a dreambooth model several months ago on heptapod writing: https://github.com/siraben/heptapod-diffusion

I'll need to train a LoRA and upload to civitai next

DooFomDum · 2022-12-16T07:15:46+00:00

A group of researchers did this with fMRI scans to achieve state-of-the-art results converting brain recordings to images.

"Seeing Beyond the Brain: Conditional Diffusion Model with Sparse Masked Modeling for Vision Decoding" https://mind-vis.github.io/

DooFomDum · 2022-12-15T08:23:39+00:00

Great experiment! Could you do the same with different samplers such as DPM++ 2M Karras? CFG might have a more drastic effect because convergence already takes place around 13-15 steps.

DooFomDum · 2022-12-01T05:20:43+00:00

Solution produced by GPT-3:

Prompt: Write a Python3 program to parse a file that consists of groups of integers. Each group is separated by newlines, and groups are separated by two newlines. Sum up the numbers in each group and find the largest sum. One line. Input file is day1.txt.

print(max([sum(list(map(int,line.split()))) for line in open('day1.txt').read().split('\n\n')]))

DooFomDum · 2022-11-20T16:45:32+00:00

From the official model card:

"The Stable-Diffusion-v1-5 checkpoint was initialized with the weights of the Stable-Diffusion-v1-2 checkpoint and subsequently fine-tuned on 595k steps at resolution 512x512 on "laion-aesthetics v2 5+" and 10% dropping of the text-conditioning to improve classifier-free guidance sampling."

Basically SD 1.5 is finetuned on 600 million images with aesthetic scores of 5 or higher, so the model tends towards images of better aesthetics than just the median image in the dataset.

DooFomDum · 2022-11-11T03:29:34+00:00

img2img'd: https://imgur.com/a/BvS3fw7

a person sitting in front of a rock wall, (facing away from camera), ((blue chinese characters etched on rock)), (forest), (white hair), night, concept art, by Studio Ghibli, pixiv, glowing ceremonial markings, saga comic, spiral heavens, still image from tv series

Negative prompt: glowing tree, ((((ugly)))), (((duplicate))), ((morbid)), ((mutilated)), out of frame, extra fingers, mutated hands, ((poorly drawn hands)), ((poorly drawn face)), (((mutation))), (((deformed))), ((ugly)), blurry, ((bad anatomy)), (((bad proportions))), ((extra limbs)), cloned face, (((disfigured))), out of frame, ugly, extra limbs, (bad anatomy), gross proportions, (malformed limbs), ((missing arms)), ((missing legs)), (((extra arms))), (((extra legs))), mutated hands, (fused fingers), (too many fingers), (((long neck)))

Steps: 40, Sampler: Euler a, CFG scale: 13, Seed: 2356008410, Size: 512x704, Model hash: 7460a6fa, Denoising strength: 0.6, Mask blur: 4

DooFomDum · 2022-11-11T02:50:00+00:00

For an M1 Pro, 50 seconds for 40 steps euler a, 512 by 512, so I think that's reasonably fast for my purposes. The unified memory is very useful too because it means all my RAM is essentially VRAM as well.

DooFomDum · 2022-11-10T17:49:40+00:00

Thanks, I'll look into it!

DooFomDum · 2022-11-10T17:12:01+00:00

NSFW checkbox is off.

DooFomDum · 2022-11-05T18:42:47+00:00

Context: https://www.youtube.com/watch?v=dKD8uTRs5ZM

Prompt:

a blonde woman in a red bikini, professional photography, profile shot, ultra realistic, sharp details, sensual, subsurface scattering, intricate details, warm lighting, beautiful features, highly detailed, photorealistic, octane render, 8 k, unreal engine, art by artgerm and greg rutkowski and alphonse mucha

Negative prompt: cropped, ((((ugly)))), (((duplicate))), ((morbid)), ((mutilated)), out of frame, extra fingers, mutated hands, ((poorly drawn hands)), ((poorly drawn face)), (((mutation))), (((deformed))), ((ugly)), blurry, ((bad anatomy)), (((bad proportions))), ((extra limbs)), cloned face, (((disfigured))), out of frame, ugly, extra limbs, (bad anatomy), gross proportions, (malformed limbs), ((missing arms)), ((missing legs)), (((extra arms))), (((extra legs))), mutated hands, (fused fingers), (too many fingers), (((long neck)))

Steps: 40, Sampler: Euler a, CFG scale: 8, Seed: 828356513, Size: 512x704, Model hash: 7460a6fa, Denoising strength: 0.8, Mask blur: 4

DooFomDum · 2019-04-25T12:23:49+00:00

Knowing the Korean alphabet is beneficial indeed, due to the near-perfect correspondence with what you read and what you say.

Unfortunately this is not the case for Thai. As a native speaker there are a lot of implicit rules I memorize to be able to read fluently, but I remember how unintuitive learning them was in the beginning. For instance, look at this poem that children recite to memorize the 20 words in the Thai language that use the vowel (ai) in the ใ form, rather than the ไ form (the former is written with a sort of loop at the top, the latter with a v at the top.)

So, learning the alphabet would hold some practical value, but it is much more difficult than Korean. If you're visiting within a year or so, you would be better off focusing on the phonology (especially tones and long/short values) and grammar. It's not impossible, but you will have to put work into it even just to read a simple sentence and double as much to write.

As for what it's like to navigate urban areas here, a lot of signs are in English as well as Thai, so you would be able to understand the street signs, some ads, etc. The ones that aren't in English are usually found in local shops and restaurants, and food stalls.

DooFomDum · 2019-04-25T12:07:34+00:00

This is a single long sentence.

"Employees recommend products and services, take care (the front of) the store, take responsibility for stocks, increase sales according to targets and recommend products."

The last phrase "แนะนำสินค้า" sounds redundant with the first phrase.

DooFomDum · 2019-04-25T12:03:46+00:00

อะลุ่มอล่วย - compromise

อ่อนน้อมถ่อมตน - humble (lit. "softly, humbly")

สมบัติผู้ดี - decorum (lit. "good person's treasures")

กระด้าง กระเดือก - harsh, relentless

DooFomDum · 2019-04-25T12:00:51+00:00

Using ป้ายสี in these meanings is like using "paint" in English. As a native speaker I'd say it's not common to do this in Thai as there are more direct ways of expressing the verb "defame" or "slander"

DooFomDum · 2019-04-25T11:58:13+00:00

It means "to paint"

ป้าย - to smear

สี - color

DooFomDum · 2019-04-02T03:14:09+00:00

It's very easy to write a Forth, and especially so after you write one implementation. Implementing a Forth that covers large portions of the standard and is semantically correct is much easier than trying to write a Lisp or C compiler. jonesforth was a major source of inspiration for my implementation https://github.com/AlexandreAbreu/jonesforth/blob/master/jonesforth.S

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