GHOST is a geometry-hierarchical token eviction framework that halves the KV cache size in monocular video 3D reconstruction while maintaining quality and achieving 1.75x faster inference.
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A sparse set of massive activation channels in DiTs carries semantic information, proven critical by disruption tests, spatially aligned with image subjects via clustering, and transferable for semantic interpolation between prompts.
FairyFuse enables multiplication-free ternary LLM inference on CPUs via fused AVX-512 kernels, achieving 29.6x kernel speedup and 32.4 tokens/s on Xeon with near-lossless quality.
citing papers explorer
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GHOST: Geometry-Hierarchical Online Streaming Token Eviction for Efficient 3D Reconstruction
GHOST is a geometry-hierarchical token eviction framework that halves the KV cache size in monocular video 3D reconstruction while maintaining quality and achieving 1.75x faster inference.
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Few Channels Draw The Whole Picture: Revealing Massive Activations in Diffusion Transformers
A sparse set of massive activation channels in DiTs carries semantic information, proven critical by disruption tests, spatially aligned with image subjects via clustering, and transferable for semantic interpolation between prompts.