{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/2"}],"enrichment":{"capability":"Comfy Kitchen provides optimized GPU kernels for quantized tensor operations in diffusion inference, supporting multiple compute backends (eager, CUDA, Triton, HIP) with operations like FP8/INT8/INT4 quantization, RoPE, AdaLN, and fused attention.","skillfed_tags":["gpu-kernels","quantization","diffusion-models"],"use_cases":["Accelerate diffusion model inference on NVIDIA GPUs by using FP8 quantization with CUDA-optimized kernels.","Deploy quantized diffusion models on AMD RDNA GPUs using the HIP backend without external BLAS libraries.","Reduce memory bandwidth in transformer attention by applying fused RoPE and AdaLN operations.","Integrate low-precision quantization (INT4, INT8) into ComfyUI workflows for faster generation.","Benchmark quantization strategies across multiple backends (CUDA, Triton, HIP) on the same hardware."],"what_it_does":"Comfy Kitchen is a GPU kernel library designed to accelerate quantized tensor computations in diffusion model inference. It provides low-level implementations of quantization (FP8, INT8, INT4, NVFP4, MXFP8), rotary position embeddings (RoPE), normalization-fusion operations (AdaLN, RMS-AdaLN), and attention kernels, each with backend-specific optimizations for NVIDIA CUDA, AMD HIP, Triton, and CPU eager execution.\n\nThe library targets the ComfyUI ecosystem and is built to minimize memory bandwidth and latency by fusing operations and supporting in-place transformations. It includes a QuantizedTensor subclass that transparently routes PyTorch operations to optimized kernels. The HIP backend (for AMD RDNA2/3/3.5/4 GPUs) implements its own matrix-core GEMMs and quantization kernels without relying on hipBLAS, while RDNA2 (which lacks matrix cores) falls back to non-WMMA paths. The library is in alpha and actively maintained, with no external runtime dependencies beyond PyTorch.","worth_installing":"Yes, with conditions. Install if you are building or optimizing diffusion inference in ComfyUI and have compatible GPU hardware (NVIDIA CUDA, AMD RDNA2+, or CPU). The library is actively maintained, has no external dependencies, and offers significant kernel-level optimizations. However, alpha status means the API and QuantizedTensor behavior may change; verify compatibility with your PyTorch and CUDA/ROCm versions before production use. Not necessary if you are not using ComfyUI or quantized inference."},"id":"comfy-kitchen","links":{"html":"https://skillfed.io/packages/comfy-kitchen","md":"https://skillfed.io/packages/comfy-kitchen.md","pypi":"https://pypi.org/project/comfy-kitchen/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-13","license_spdx":null,"license_treatment":"permissive","name":"comfy-kitchen","python_support":"supports_current","summary":"Fast Kernel Library for ComfyUI with multiple compute backends"},"popularity":{"monthly_downloads":3022578,"position":2785,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"0.2.31"}