comfyui-launch-flags

ComfyUI launch/performance flags

Overview

CLI flags passed to main.py control ComfyUI's runtime behavior (e.g. python main.py --reserve-vram 2 --use-sage-attention). The three that matter most for making a graph run rather than OOM or crawl are the VRAM strategy, the attention backend, and the cache mode. This skill is the decision matrix for choosing them.

⚠️ Verification note (August 2026). Every flag below was checked against upstream comfy/cli_args.py on current master. ComfyUI adds/renames flags often — when in doubt run python main.py --help in the target install and prefer that over this list. --enable-triton-backend / --disable-triton-backend ARE ComfyUI main.py flags on master (they used to be documented as SwarmUI-only; that is stale). --use-ck-attention is kitchen INT8 attention — no sageattention wheel. A June ComfyUI checkout still pins comfy-kitchen 0.2.10 and lacks --use-ck-attention; kitchen action:"status" reports ComfyUI-side flag support, not only the kitchen version. Use kitchen / panel_kitchen to see what this GPU can actually run.

How to apply today. The MCP's restart_comfyui (with action: "start") currently replays the exact argv of the previous run. It does not compose fresh flags. So set these when you launch ComfyUI yourself (the python main.py … line, a run.bat/shell alias, or the SwarmUI backend args box), and the tool will preserve them on restart. Injecting flags through the tool is a tracked follow-up.

Decide first: which flag do you need?

Symptom                                             ▶ Flag(s) to try
─────────────────────────────────────────────────────────────────────────────
CUDA out of memory, long video (LTX 2 / WAN)        ▶ --novram  (+ --cache-none)
OOM, still want models resident when they fit       ▶ --reserve-vram N  then --disable-smart-memory
GPU slows to a crawl, spills into "shared GPU        ▶ --reserve-vram 2..4
  memory" (Windows WDDM) mid-run
RAM blows up switching between models, or a huge     ▶ --cache-none
  text encoder (FLUX 2 / Mistral) won't unload
Plenty of VRAM (48GB+), want max throughput         ▶ --gpu-only  or  --highvram
Want faster sampling on NVIDIA                       ▶ --use-ck-attention if kitchen INT8 is available (skip the sage wheel); else --use-sage-attention
Z-Image produces BLACK / wrong output               ▶ --use-pytorch-cross-attention (NOT sage)
Sage gives black output on some models              ▶ --use-pytorch-cross-attention (or fix dtype)
ROCm, kitchen present, triton ≥ 3.7                  ▶ --enable-triton-backend