Kristian Muñiz

Kristian Muñiz

@krismuniz.com

Software developer, designer, and open source enthusiast. 🇵🇷 Technologist turned software engineer by necessity. Building software products https://krismuniz.com

151 followers492 following105 posts

At this point I'm convinced that 4o image generation is not purely autoregressive. My guess is 4o generates image tokens or latent representations in sequential patches which are processed by a tighly integrated diffusion model.

At this point I'm convinced that 4o image generation is not purely autoregressive. My guess is 4o generates image tokens or latent representations in sequential patches which are processed by a tighly integrated diffusion model.

Increasingly, large multimodal models are becoming more and more powerful and one of the first ways we can optimize them is by simplifying their I/O and writing powerful, thick encoders/decoders.

Should an omni-model that is purely autoregressive be able to pass through an image in a semi-lossless way? I understand that it depends, to some extent, on post-training and the non-stochastic nature of sampling, but I'm having trouble with consistency using 4o's image generation feature.

Should an omni-model that is purely autoregressive be able to pass through an image in a semi-lossless way? I understand that it depends, to some extent, on post-training and the non-stochastic nature of sampling, but I'm having trouble with consistency using 4o's image generation feature.

And it's not structural or semantic consistency, but some information gets lost in the process. Perhaps it's safety mechanisms preventing certain behaviors like using people's likeness.

I want to understand the training and inference economics of autoregressive image generation. There's obviously latency implications but in my opinion, at least anecdotally, it makes for up for it in output quality.

What I know so far, autoregressive models are more expensive to run than diffusion models – of course slower too, latency correlated with cost. I'm still surprised that resolution is so good. It's almost too good. Could it be a hybrid Transformer + Diffusion approach?

Could that be a plausible solution? Using GPT-4o to generate initial image representations and passing these representations to a diffusion model component that specializes in creating high-quality, high-resolution visual outputs?

Optimist: The cup is half full Pessimist: The cup is half empty Software Engineer: That's a flaky test

Jordan DeVries@jordandevries.com

Optimist: The cup is half full Pessimist: The cup is half empty UI: People are having a hard time understanding which part of the cup icon is full and which part is empty

Here it is for the Python functionality. Much more limited and kinda janky, but pretty cool. Matplotlib, numpy and other packages available. All happens in-browser as well.

Kristian Muñiz reposted

Hey folks - I’m on the job market! Need product help with conversation ai and understanding the power of humans in the loop? Let me know!

This has been LLM Concern # 1

The Washington Post@washingtonpost.com

Artificial intelligence is automating the creation of fake news, spurring an explosion of web content mimicking factual articles that instead disseminate false information about elections, wars and natural disasters.

I've always found it very hard to see today's technology as permanent. Every digital medium has gone through so much change that it makes me think that it's all ephemeral and flimsy.

Reasons: cheaper flights to PR, closer to family, better cost of living, we needed a bit of distance from the hustle and bustle of the Bay Area, among other reasons...

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