How to Install Sage Attention in ComfyUI: A Complete Setup Guide

ComfyUI has become one of the most flexible frontends for running Stable Diffusion and related image generation models locally. Sage Attention is a memory-efficient attention mechanism that can significantly speed up inference — but installing it alongside ComfyUI requires a few deliberate steps that differ depending on your hardware and environment. Here's what you need to know.

What Is Sage Attention?

Sage Attention is an optimized attention kernel designed to reduce VRAM usage and improve throughput during diffusion model inference. It works by replacing the standard scaled dot-product attention with a more efficient implementation — particularly useful when running large models like FLUX, SD3, or Wan 2.1 on consumer GPUs.

In practical terms, Sage Attention can:

• Reduce peak VRAM consumption during generation
• Speed up steps-per-second on compatible hardware
• Allow larger batch sizes or higher resolutions on mid-range GPUs

It's not a ComfyUI plugin in the traditional sense — it's a Python library that ComfyUI nodes and workflows call upon when the option is enabled in supported custom nodes.

Prerequisites Before You Begin

Before installing Sage Attention, confirm your environment meets the baseline requirements:

AMD GPUs and CPU-only setups are generally not compatible with Sage Attention as of current releases, since the library relies on CUDA-specific Triton kernels.

Step 1 — Set Up Your Python Environment

If you're using the ComfyUI portable package, it ships with its own embedded Python interpreter. If you installed ComfyUI manually in a virtual environment (venv or conda), you'll work within that environment instead.

For portable ComfyUI users, open the embedded terminal or use the python_embeded folder path when calling pip.

For manual/venv users, activate your environment first: