camenduru
welcome #muse 🥳 https://t.co/WwpmnuRFsv
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Robert Scoble
RT @Jaesen_Moreaux: #midjourney #midjourneyai #aiartcommunity #AIart #AIArtwork #TheBeastsofAI #midjourneyartwork https://t.co/gSNXkOKRX4
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Samanyou Garg
RT @osakasaul: @TheMacSweaty All over chatsonic. It just wrote https://t.co/nnGCW6X4Kd and provided all the data from which I created the table in the article. Just amazing! https://t.co/ohuQwddXSU
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Riley Goodside
I’m not really an LLM *researcher*, I’m more of a… https://t.co/zOFkb5nmi7
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Grades 🗨
One thing I wish I'd done when starting building niche websites:

Document all the questions I had.

I must've typed hundreds of problem solving questions into Google over the last 3 years.

It would be great to tweet about my learnings now.

Lesson: document the process.
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Zohaib Ahmed
Expect to see a lot of alternatives to diffusion modeling in 2023.

“Compared to pixel-space diffusion models, such as Imagen and DALL-E 2, Muse is significantly more efficient due to the use of discrete tokens and requiring fewer sampling iterations”
https://t.co/GHeFnqrFOg https://t.co/NY0xnt9lU8
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pharmapsychotic
RT @arankomatsuzaki: Muse: Text-To-Image Generation via Masked Generative Transformers

Presents Muse, a text-to-image Transformer model that achieves SotA image generation perf while being far more efficient than diffusion or AR models.

proj: https://t.co/JljH4pGZX1
abs: https://t.co/52bc9HuT7o https://t.co/r5n8KJNgFU
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