comfy-kitchen
Fast Kernel Library for ComfyUI with multiple compute backends
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python >= 3.10.
- HIP backend on AMD GPUs requires ROCm toolchain; CUDA backend requires NVIDIA GPU and CUDA runtime.
- Building from source requires CMake >= 3.26 and Ninja.
License · maintenance · safety
Apache-2.0 (permissive) — Apache-2.0 is permissive and poses no restrictions on commercial or proprietary use; you may use, modify, and distribute this package freely provided you retain the license notice.
last release 2026-08-13 (1 days) · last repo commit 2026-08-13 · 156 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 3,022,578 downloads/mo, #2,785 on PyPI
Alternatives
Verify before relying
pip install comfy-kitchen
import comfy_kitchen
# Use quantization functions like quantize_per_tensor_fp8, apply_rope, adaln, etc.
# Backend selected automatically based on available hardware (CUDA/HIP/Triton/eager)- Whether QuantizedTensor subclass and its PyTorch operation interception are production-ready or still experimental.
- Performance benchmarks comparing backends (CUDA vs. Triton vs. HIP) for common diffusion workloads.
- Compatibility matrix with specific PyTorch versions and ROCm/CUDA versions.
- Whether the library is actively used in production ComfyUI deployments or primarily in development.
What it is and 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.
The 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.
Use it for
- 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.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
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.
Install
comfy-kitchen on PyPI
Before you install
Actively maintained with a recent release (1 day old) and no runtime dependencies, making installation straightforward. Alpha status signals the API may evolve, but the project shows active development with 156 repository stars.
Requires Python >= 3.10. HIP backend on AMD GPUs requires ROCm toolchain; CUDA backend requires NVIDIA GPU and CUDA runtime. Building from source requires CMake >= 3.26 and Ninja.
License in practice
Apache-2.0 is permissive and poses no restrictions on commercial or proprietary use; you may use, modify, and distribute this package freely provided you retain the license notice.
Quickstart
pip install comfy-kitchen
import comfy_kitchen
# Use quantization functions like quantize_per_tensor_fp8, apply_rope, adaln, etc.
# Backend selected automatically based on available hardware (CUDA/HIP/Triton/eager)Verify before relying
- Whether QuantizedTensor subclass and its PyTorch operation interception are production-ready or still experimental.
- Performance benchmarks comparing backends (CUDA vs. Triton vs. HIP) for common diffusion workloads.
- Compatibility matrix with specific PyTorch versions and ROCm/CUDA versions.
- Whether the library is actively used in production ComfyUI deployments or primarily in development.
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | None |
| Maintenance | Actively maintained 1 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 3,022,578 / month, #2,785 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 3 - AlphaLicense :: OSI Approved :: Apache Software LicenseProgramming Language :: C++Programming Language :: Python :: 3 |
Evidence: comfy_kitchen-0.2.31-py3-none-any.whl
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