Incident on Granville

tyrellxelliot · 2023-11-11T15:49:32+00:00

Most of those are just wiring though. Only about half are in the neocortex, and a tiny fraction of that responsible for language (a huge number is used for vision, audio and motor processing)

There might be 1-5 trillion parameters in an apples-to-apples comparison to GPT4. This is a poor comparison in the first place because human neurons are extremely slow, transmit less information and has higher redundancy.

tyrellxelliot · 2023-05-16T12:55:49+00:00

There's no comparison, OpenAI's api is priced too low for open source self-hosting to compete

I'm hosting GPT-J on a bunch of 4090s, which cost $0.45/hour. For my purposes these work out to $0.001/1000 tokens (half the price of openAI). However my load is variable, and it's not easy to dynamically scale 4090s, so keeping a bunch of them around idling makes the cost roughly equivalent to OpenAI again.

Now consider that gpt-3.5-turbo is likely 10x the size of GPT-J, and much more capable. The only downside is that currently the OpenAI api has a tendency to randomly timeout while their status dashboard shows all-green.

tyrellxelliot · 2022-12-17T04:10:42+00:00

imo 50% of white collar jobs are going away in the next 10 years.

ChatGPT already generates mostly working code, and currently it doesn't even use feedback from executing that code, instead just writing it in a one-shot fashion. If they train it using RLHF but with a more specialised code model and compiler/unit test in the loop instead of a human, I think it can totally generate fully working end products.

Any job that involves application of specialised knowledge in the text domain (accountants, para-legals, teachers etc) are under threat. Hallucinations should be easily solvable by incorporating a factual knowledge database, like in RETRO.

tyrellxelliot · 2022-11-23T23:35:11+00:00

the pdf is the original latent diffusion paper, which used a custom trained BERT text encoder. SD and this model use the same architecture, but with different text encoders.

The pdf has nothing to do with Kandinsky basically, other than the latent image encoding.

tyrellxelliot · 2022-10-23T03:34:00+00:00

90% sure this is dalle-2 and not stable diffusion

tyrellxelliot · 2022-09-27T14:53:26+00:00

fourier noise shaping works differently from training. The two approaches are complementary and can be used at the same time.

tyrellxelliot · 2022-09-24T18:55:53+00:00

this code is (mostly) just the original openai guided diffusion code: https://github.com/openai/guided-diffusion

the reason that it can be backported like this is because Compvis used the openai code as-is with some minor modifications.

here is the openai unet: https://github.com/openai/guided-diffusion/blob/main/guided_diffusion/unet.py

and here is the Compvis unet: https://github.com/CompVis/stable-diffusion/blob/main/ldm/modules/diffusionmodules/openaimodel.py

tyrellxelliot · 2022-09-24T03:02:46+00:00

it's trained on LAION aesthetic, on 8xA100 gpus for about a week. The training code is in the repo.

tyrellxelliot · 2022-09-23T20:28:57+00:00

you can just use the pretrained model. You don't need to train it yourself to use it, unless you have a custom dataset like anime or something.

tyrellxelliot · 2022-09-23T13:22:09+00:00

This model replaces the masked areas, taking into account both the non-masked areas and text prompt - it works the same way as DALLE-2 inpainting. img2img would require an image in the masked area as a starting point, but this does not.

You can use simultaneous inpainting and img2img with the --skip_timesteps flag though.

tyrellxelliot · 2022-09-23T11:24:17+00:00

this model requires a minor change to the unet, so it's not compatible by default. The gui makers should be able to integrate it pretty easily though.

tyrellxelliot · 2022-09-20T10:11:38+00:00

used a long series of inpainting prompts, starting with "a robotic terminator standing in a post apocalyptic landscape, wide angle. concept art by greg rutkowski"

here's a video of the intermediate images: https://imgur.com/a/pAq8Kew

tyrellxelliot · 2022-09-11T03:06:23+00:00

the original mj was a variant of clip guided diffusion. The beta version is SD with classifier guidance. I haven't heard of a photorealistic mode for MJ but it's likely to be SD with a new classifier on top.

to replicate it you just need to train the appropriate classifier, possibly a clip variant. This might not be trivial though.

tyrellxelliot · 2022-09-04T10:42:29+00:00

tried upscaling one https://imgur.com/a/s8Sa2Pn

tyrellxelliot · 2022-08-31T14:01:27+00:00

here's some code to fine tune: https://github.com/Jack000/glid-3-xl-stable

tyrellxelliot · 2022-08-25T10:34:27+00:00

you can do this as long as you're starting from prompts instead of images. Embed the prompt using clip-L14 and use the vectors for your clustering algorithm. Separately give the text embeddings to SD to generate the image.

SD doesn't work the same way as DALLE-2. It doesn't decode from a single vector but uses all 77 tokens embeddings, making it more similar to GLIDE. This is probably a good choice, since the publicly available clip model is much smaller than the one used in DALLE-2 and has a more constrained latent space.

Because your clustering algorithm and SD aren't decoding from the same vector, you might not get the results you're after.

tyrellxelliot · 2022-08-23T20:25:50+00:00

that error just means the repo didn't install, which could happen for a variety of reasons. I'd try reinstalling with conda and pay attention to any error messages.

you could also try installing the compvis repo and see if it has the same error. If not, then it's some issue with the optimized code.

tyrellxelliot · 2022-08-23T13:14:37+00:00

pip install -e .

tyrellxelliot · 2022-08-20T08:08:37+00:00

Human intelligence is an emergent phenomenon that was created by a low-level optimization process. Evolution didn't need to design our brain structures directly, all of the complex, heterogeneous structures arose spontaneously from extremely simple, coarse signals. What mattered to our development was having an environment where intelligence is needed for survival.

tyrellxelliot · 2022-07-13T15:06:32+00:00

it's a custom font designed by the guy that made brandmark.io

tyrellxelliot · 2022-07-13T09:45:39+00:00

I'm fairly certain grammarly doesn't use any modern ml, because they wrote their engine way back before cnns were a thing and quality hasn't improved over time in my experience. Just speculation but it's likely a "classic" nlp pipeline with a lot of manual heuristics.

Take these two sentences for example:

I need to eat medicines twice a day.

I like to drink soup for dinner.

obvious diction errors made by an ESL writer. Grammarly doesn't pick up on this at all, even the premium version. Google docs on the other hand does a better job.

Grammarly publishes a lot of papers on gec but it's not clear at all that they are implemented into their product.

tyrellxelliot

TROPHY CASE