Unverified50% confidenceFactExact time
Common inference acceleration techniques include consistency distillation, flow matching, and diffusion model step compression methods such as LCM and SDXL-Turbo.
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9/15/2026
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Unverified从30步CFG 4到12步CFG 1的加速本质上是模型蒸馏技术的应用成果，与Consistency Model、SDXL-Turbo、LCM共享相似底层思路80% similarUnverifiedTurbo version models are typically based on techniques such as consistency distillation or flow matching, compressing the tens of denoising inference steps of standard diffusion models to 4 to 8 steps or even fewer78% similarUnverifiedNormalizing flows can directly reuse the mature inference infrastructure of LLMs, including KV-cache management, tensor parallelism strategies, and inference acceleration techniques such as speculative decoding.75% similarUnverifiedEngineering approaches to compressing LLM inference latency and reducing costs include model distillation, speculative decoding, KV Cache optimization, and model quantization.74% similarUnverifiedTechnical approaches to improving LLM inference speed include model distillation, speculative decoding, quantization, and task-specific model architecture optimization71% similar
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