交互式扩散模型在消费级显卡上实现实时生成
The Weight Is Over - Interactive Diffusion on Consumer GPUs
这是关于如何让AI绘画在普通电脑上流畅运行的技术，作者做了三件事：一是用嵌入翻译器减少模型大小，二是提供了一套优化流程，三是做出了一个能秒级出图的编辑器，和之前需要专业显卡的方案不同。
这篇论文提出了一种嵌入翻译器，将小文本编码器映射到大编码器空间，以减少权重和延迟。他们还提供了一套可复现的扫描食谱，用于在扩散管道中平衡速度、质量和内存三角关系。这项工作实现了在最新消费级显卡上亚秒级的TTFI（首次生成时间）的交互式图像生成编辑器。
The Weight Is Over - Interactive Diffusion on Consumer GPUs
On-device inference is booming, but the momentum is almost all in language models. Diffusion pipelines are memory hungry, latency-sensitive, and require orchestrating an embedder, a transformer, a decoder, and often further postprocessing that is not as standardized as LLM inference loops are. We navigate the trade-off between performance, quality, and model footprint to reach as many client devices in the wild as possible. We make three contributions: an embedding translator that maps a small text encoder into a large encoder space to cut weight and latency; a reproducible sweep recipe for navigating the speed/quality/memory triangle in diffusion pipelines; and an interactive on-device image generation editor achieving sub-second TTFI on recent GPUs.
