… SlimSpec achieves 4-5times acceleration over the standard LM-head architecture while maintaining a competitive acceptance length, surpassing existing methods by up to 8-9% of the end-to-end speedup . Our method requires minimal adjustments of training and inference pipelines. …
… View arXiv page View PDF Add to collection Community seeing how much the gains hinge on draft initialization and keeping drafts short, it looks like the method pays off most when the speculative drafts stay close to the current rollout distribution. my question is: how does speculative decoding beh… …
… Diffusers Library Highlights: The post details how the Diffusers library supports video generation through pretrained models, pipelines, and noise schedulers. …
… We formalize rollout pipelines with unified notation and introduce Generate-Filter-Control-Replay GFCR , a lifecycle taxonomy that decomposes rollout pipelines into four modular stages: Generate proposes candidate trajectories and topologies; Filter constructs intermediate signals via verifiers , j… …
Papers arxiv:2605.04569 Lightning Unified Video Editing via In-Context Sparse Attention Published on May 6 Submitted by taesiri on May 7 Authors: , , , , , , Abstract In-context sparse attention framework enables efficient video editing with reduced computational costs while maintaining visual qual… …
… The paper decouples the lightweight Distiller’s training/inference from the main LLM generation through an asynchronous pipeline, and the open-source tLLM implementation reports about 98.8% of the optimized vLLM baseline throughput in the aligned benchmark. …
… Along this trajectory we systematically review the capability shifts emerging in frontier systems such as Nano Banana and GPT-Image-2, and we distill the training recipes that are converging across recent technical reports — Qwen-Image, Z-Image, Seedream, HunyuanImage, LongCat, Wan-Image, and other… …