As a result, the off-the-shelf LLM is able to perform both Optimized for both discriminativeness and reconstruction during the tokenizer Semantics consistent with the degree of semantic abstraction in words, and be (2) Image tokens should capture high-level Intrinsic interdependence that aligns with the left-to-right autoregressive Positions and instead be produced with a 1D causal dependency, exhibiting (1) Image tokens should be independent of 2D physical patch In this study, we identify two crucial principles for theĪrchitecture and training of SEED that effectively ease subsequent alignment ![]() Textual representations, facilitating scalable multimodal training with LLM's Limitations, we remain confident in its natural capacity to unify visual and ![]() Performance and convergence in multimodal comprehension (compared to BLIP-2,Įtc.) or generation (compared to Stable Diffusion, etc.). Research on image tokenizers has previously reached an impasse, as frameworksĮmploying quantized visual tokens have lost prominence due to subpar Models (LLMs) with the emergent ability to SEE and Draw at the same time. Download a PDF of the paper titled Planting a SEED of Vision in Large Language Model, by Yuying Ge and 3 other authors Download PDF Abstract: We present SEED, an elaborate image tokenizer that empowers Large Language
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