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Donutanti

Donutanti
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Reacted to m-ric's post with ๐Ÿ”ฅ about 2 months ago
Emu3: Next-token prediction conquers multimodal tasks ๐Ÿ”ฅ This is the most important research in months: weโ€™re now very close to having a single architecture to handle all modalities. The folks at Beijing Academy of Artificial Intelligence (BAAI) just released Emu3, a single model that handles text, images, and videos all at once. ๐—ช๐—ต๐—ฎ๐˜'๐˜€ ๐˜๐—ต๐—ฒ ๐—ฏ๐—ถ๐—ด ๐—ฑ๐—ฒ๐—ฎ๐—น? ๐ŸŒŸ Emu3 is the first model to truly unify all these different types of data (text, images, video) using just one simple trick: predicting the next token. And itโ€™s only 8B, but really strong: ๐Ÿ–ผ๏ธ For image generation, it's matching the best specialized models out there, like SDXL. ๐Ÿ‘๏ธ In vision tasks, it's outperforming top models like LLaVA-1.6-7B, which is a big deal for a model that wasn't specifically designed for this. ๐ŸŽฌ It's the first to nail video generation without using complicated diffusion techniques. ๐—›๐—ผ๐˜„ ๐—ฑ๐—ผ๐—ฒ๐˜€ ๐—ถ๐˜ ๐˜„๐—ผ๐—ฟ๐—ธ? ๐Ÿงฉ Emu3 uses a special tokenizer (SBER-MoVQGAN) to turn images and video clips into sequences of 4,096 tokens. ๐Ÿ”— Then, it treats everything - text, images, and videos - as one long series of tokens to predict. ๐Ÿ”ฎ During training, it just tries to guess the next token, whether that's a word, part of an image, or a video frame. ๐—–๐—ฎ๐˜ƒ๐—ฒ๐—ฎ๐˜๐˜€ ๐—ผ๐—ป ๐˜๐—ต๐—ฒ ๐—ฟ๐—ฒ๐˜€๐˜‚๐—น๐˜๐˜€: ๐Ÿ‘‰ In image generation, Emu3 beats SDXL, but itโ€™s also much bigger (8B vs 3.5B). It would be more difficult to beat the real diffusion GOAT FLUX-dev. ๐Ÿ‘‰ In vision, authors also donโ€™t show a comparison against all the current SOTA models like Qwen-VL or Pixtral. This approach is exciting because it's simple (next token prediction) and scalable(handles all sorts of data)! Read the paper ๐Ÿ‘‰ https://huggingface.co/papers/2409.18869
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Introduction to 3D Gaussian Splatting

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