The page needs a brush-up. Bugs referenced have been fixed or worked around. Still might be useful info.
As of now Torch 2.11.0 remains too new. Workarounds in Comfy/Wrapper code are not aware of it yet. oom on decode has been reported with 3x memory consumption.
Which is a good Numpy version? I updated your wrapper and it installed 2.2.6 which broke multiple other nodes; Not sure about that, some older stuff just won’t work on any numpy 2 version; seedvr2 seems to be impacted by this, guess i’ll have to manually downgrade
Sage 3 really doesn’t seem all that great. for now, Sage 2.1/2.2 are still the mainstream options.
Sage3 quality loss on 2.1 was way too high to be useful; fp8 fast works far better with 2.2 and is even usable, while it never was for 2.1
B200 and onward is so good is because they have a giant L2 cache so cache issues are lowered
would you recommend migrating to 2.9.1 ? yes
Comfy comes with pytorch 2.9.1 … now
Unfortunately it looks like the fix bringing fp8e4 support to RTX30xx on Windows has not been implemented on Triton mainline; issue.
Sam Hodge suggested a script to install Sage Attention on Ubuntu 24.04 with an RTX 5090 in the following manner
ENV TORCH_CUDA_ARCH_LIST='8.0,8.0+PTX,8.6,8.6+PTX,8.9,8.9+PTX'
RUN git clone https://github.com/thu-ml/SageAttention.git && \
cd SageAttention && \
git reset --hard eb615cf6cf4d221338033340ee2de1c37fbdba4a && \
# sed -i "/compute_capabilities = set()/a compute_capabilities = {\"$TORCH_CUDA_ARCH_LIST\"}" setup.py && \
EXT_PARALLEL=4 NVCC_APPEND_FLAGS="--threads 8" \
MAX_JOBS=32 uv pip install -e . --no-build-isolation --break-system-packages
Python 3.12 is probably a good idea
Possible startup arguments: comfy –here launch – –reserve-vram 5 –max-upload-size 500 –use-sage-attention –disable-pinned-memory
Note: –async-offload can cause OOM-s
Around 16-17 Oct 2025 issues were reported with latest version of Comfy and other packages. Workflows started consuming more VRAM than previously. Among workarounds suggested were
One advice was to use fp32 version of Wan 2.1 VAE safetensors file; possibly a command line option might be needed as well.
Workaround has been commited to ComfyUI.
Kijai 17 Oct 2025 evening:
torch 2.9.0 has a bug that makes some conv3d operations (when using half precision) use 3x more VRAM, including the Wan VAE; it affects 2.10 too currently; both native and wrapper has workaround for the bug already anyway; they are different workarounds
Another source of higher VRAM usage was traced to triton compilation. It seems one particular reason was tritop upon seeing too many errors gave up on compiling. The other was that triton was recompiling too often. Suggestions were
force_parameter_static_shapes to false in TorchCompileModelWanVideoV2comfy/model_patcher.py file adding @torch.compiler.disable() one line above class LowVramPatch:run_every_op() from ops.py - this will undo “fast cancellation” changeKijai 18 Oct 2025:
The workarounds for the cancellation call and the torch compile disable on the problematic bit of the code are merged to comfyUI already btw
pytorch 2.8.0 was problematic so sticking with 2.7.0 was fine
2.9.0 has one problematic bit that needed workarounds for Wan VAE,
so that needs latest ComfyUI version to work
I’m on 2.10.0 dev and seems to work too
Triton 3.5 is what should have the e5 compile fix;
For Windows it’s triton-windows 3.5.0.post21 or later.
pytorch 2.8.0 works fine in Linux
and then if you happen to decide to update nvidia drivers on Linux you lose a week of your life
Q: ubuntu has nvidia drivers, doesnt it?
A: I mean latest drivers; the prebuilt stuff is fine, but usually very outdated
from distros with nvidia drivers built in I liked PopOs myself, but then I wanted some newer stuff and went with Debian-testing
SageAttenion 2.2.0 latest highly recommended, gets rid of all graph breaks so torch compile works better
https://github.com/woct0rdho/triton-windows - read instructions on the page, not simply pip install sageattention -U
https://github.com/woct0rdho/SageAttention
Youtube tutorial on installing SageAttention 2.2 on Windows - unconfirmed, advice from community: 9APXcBMpbgU.
How does Sage Attention compare against Flash Attention and Sdpa Attention? sage »»»»»»»> flash > sdpa, slight exaggeration
they all degrade quality? only Sage
“degrade quality” in this context means everything you do to differ from the reference code, like reducing steps etc. the quality loss from sage is so small in most cases that you can more than offset it from the speed gain
Flash Attention is something you have to install if you want to use the actual flash attention, the sdpa flash is different thing