FLUX-Lightning Slashes Diffusion Inference to 4 Steps, Doubling Speed

FLUX-Lightning, introduced by PaddleMIX, combines phased consistency distillation, adversarial learning, distribution‑matching distillation, and reflow loss to reduce diffusion model inference to just four steps while preserving image quality, and leverages the CINN compiler to achieve over 30% speed gains on A800 GPUs, surpassing existing SOTA acceleration methods.

AI inferenceCINNDiffusion Models

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FLUX-Lightning Slashes Diffusion Inference to 4 Steps, Doubling Speed

Baidu Tech Salon

Aug 20, 2024 · Artificial Intelligence

PaddlePaddle Neural Network Compiler (CINN): Architecture, Optimization Techniques, and Performance

The PaddlePaddle Neural Network Compiler (CINN) combines a PIR‑based frontend and a hardware‑specific backend to apply graph‑level optimizations, operator fusion, schedule transformations and automatic tuning, delivering up to 4× faster kernels and 30‑60% overall speed‑ups for deep‑learning and scientific workloads.

CINNGPU OptimizationOperator fusion

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PaddlePaddle Neural Network Compiler (CINN): Architecture, Optimization Techniques, and Performance

Baidu Geek Talk

Aug 19, 2024 · Artificial Intelligence

PaddlePaddle Neural Network Compiler (CINN): Architecture, Optimization Techniques, and Performance Gains

The PaddlePaddle Neural Network Compiler (CINN) combines a PIR‑based frontend that performs graph‑level optimizations such as constant folding, dead‑code elimination and operator fusion with a backend that applies schedule transformations and auto‑tuning, delivering up to 4× faster RMSNorm kernels and 30‑60% overall speed‑ups for generative AI and scientific‑computing workloads.

CINNDeep LearningGPU

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PaddlePaddle Neural Network Compiler (CINN): Architecture, Optimization Techniques, and Performance Gains