misc/py-comfy-kitchen 0.2.37 FreeBSD

Comfy Kitchen is a high-performance kernel library designed for Diffusion model inference. It provides optimized implementations for critical operations, including various quantization formats and Rotary Positional Embeddings (RoPE). The library features a flexible dispatch system that automatically selects the most efficient compute backend—CUDA, Triton, or eager PyTorch—based on available hardware and input constraints.

Key features include: * Optimized kernels specifically tuned for Diffusion inference workloads. * Support for multiple compute backends (CUDA C, Triton JIT, and pure PyTorch). * Transparent quantization via a QuantizedTensor subclass that intercepts PyTorch operations. * Support for advanced quantization formats including FP8, NVFP4, and MXFP8. * Automatic backend selection and constraint validation for hardware-specific optimizations. * Implementation of performance-critical functions like RoPE and scaled matrix multiplication.