AI Research
SJD-SV: Speculative Jacobi Decoding with Semantics Verification for Autoregressive Image Generation
Bridging the gap between speed and accuracy in parallel autoregressive visual modeling.
Speculative Jacobi Decoding (SJD) is an important approach for accelerating autoregressive image generation. Although SJD has shown superior performance, recent studies point out that it usually suffers from a token ambiguity issue during token verification but its reason can not...
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ArXiv CS.CV
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2 min
ArXiv CS.CV
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This research addresses the inherent instability of parallel decoding in image synthesis by introducing semantic verification to ensure token accuracy. By solving the token ambiguity problem, SJD-SV paves the way for significantly faster, high-fidelity autoregressive image generation.
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