diffusion-kernel

Optimize diffusion model inference speed by integrating custom JIT CUDA and Triton kernels to replace slow PyTorch operations. This reduces generation latency by fusing operations like RMSNorm, AdaLN, and RoPE into highly efficient kernels.

Performance profiling identifies bottlenecks in diffusion pipelines by analyzing execution flows using torch.profiler, nsys, and ncu. These tools expose slow operations, enabling structured workflows for targeted kernel optimization and integration.

Yes, you can integrate custom Triton and CUDA kernels to execute fused operations like RMSNorm and AdaLN. This seamless integration accelerates high-throughput diffusion pipelines by replacing unoptimized operations with compiled kernels.

The best way to reduce image generation latency is replacing unoptimized PyTorch operations with fused JIT CUDA or Triton kernels. This structured kernel optimization directly targets and minimizes critical operation overhead during inference.

Yes, JIT compilation is needed to accelerate high-throughput diffusion pipelines. It enables the integration of custom CUDA and Triton kernels for fused operations, which significantly reduces inference latency compared to standard execution.

Use Triton kernels over standard PyTorch operations when optimizing critical diffusion model operations like RoPE. Triton facilitates kernel fusion and JIT compilation, delivering high-throughput inference that unoptimized operations cannot achieve.