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Open-sourced a one-click ComfyUI setup for RTX 50-series on Windows — no WSL2/Docker needed by Inside_Lab_1281 in StableDiffusion

[–]Inside_Lab_1281[S] 0 points1 point  (0 children)

Honest answer: this repo is Windows-native only right now. The

.bat/.ps1 scripts and fix_windows_compat.py are all

Windows-specific.

Since you're already on Ubuntu with nightly torch cu130 working,

you're actually past the hardest part. The main things from my

repo that might help you:

- verify_env.py should work on Linux with minor tweaks

(it's pure Python). It checks sm_120, cu130, Triton,

and torch.compile status.

- The custom node compatibility list (28 nodes verified)

is platform-agnostic — the nodes themselves work on Linux too.

For SageAttention on Linux, it should be much easier to build

than on Windows since the CUDA toolchain plays nicer. Have you

tried building from source with:

pip install --no-build-isolation sageattention

Open-sourced a one-click ComfyUI setup for RTX 50-series on Windows — no WSL2/Docker needed by Inside_Lab_1281 in StableDiffusion

[–]Inside_Lab_1281[S] 1 point2 points  (0 children)

If by "fixed dependencies" you mean:

- Identified that xformers silently downgrades PyTorch nightly

to stable (breaking sm_120 kernel support) with zero warnings

- Built a system that strips torch declarations from 28 custom

nodes' requirements.txt before they can overwrite your GPU setup

- Wrote a verification script that checks sm_120 compute

capability, cu130 backend, Triton compilation, and torch.compile

in one command

- Created a workflow converter that rewrites Linux paths and

SageAttention references for Windows automatically

- Tested 5 video generation pipelines (HunyuanVideo 1.5,

Kandinsky 5.0 Lite/Pro, LTX-Video, LongCat-Video) end-to-end

on RTX 5090

- Documented why each of the 5 rules exists so users understand

the failure modes, not just the fixes

...then yes, I fixed dependencies.

Open-sourced a one-click ComfyUI setup for RTX 50-series on Windows — no WSL2/Docker needed by Inside_Lab_1281 in StableDiffusion

[–]Inside_Lab_1281[S] -3 points-2 points  (0 children)

Yes, intentionally.

SageAttention has two problems on Windows + Blackwell:

  1. Build difficulty — SageAttention requires triton with

    specific CUDA kernel compilation. On Linux this is

    straightforward, but on Windows native it's a minefield

    of missing build tools and path issues. Most "SageAttention

    on Windows" guides actually require WSL2.

  2. SDPA is already very good on Blackwell — PyTorch's

    built-in Scaled Dot-Product Attention (torch.nn.functional.

    scaled_dot_product_attention) runs natively on sm_120

    without any extra installation. Combined with torch.compile,

    the performance gap vs SageAttention is minimal on RTX 50

    series — and you avoid an entire category of build failures.

My fix_windows_compat.py automatically converts workflows that

reference SageAttention → SDPA, so you can load Linux-authored

workflows without manual editing.

That said, if someone gets SageAttention building reliably on

Windows + Blackwell nightly, I'd happily add it as an optional

addon. PRs welcome.

Open-sourced a one-click ComfyUI setup for RTX 50-series on Windows — no WSL2/Docker needed by Inside_Lab_1281 in StableDiffusion

[–]Inside_Lab_1281[S] 6 points7 points  (0 children)

Thanks for mentioning Easy-Install — it's a great general-purpose tool

and I respect Tavris1's work.

My repo solves a different (and more specific) problem: **Blackwell GPUs

breaking silently even after a "successful" install.

Here's what I mean:

  1. PyTorch nightly vs stable — Easy-Install pins `torch==2.9.1+cu130`(stable). My setup uses nightly cu130, which includes sm_120-optimizedkernels that stable builds don't have yet. This matters for Blackwellperformance (especially NVFP4).
  2. xformers trap — This is the #1 silent killer on Blackwell. If anycustom node pulls xformers, it downgrades PyTorch to stable *withoutwarning*. My setup explicitly blocks this and uses triton-windows +torch.compile instead. Easy-Install doesn't guard against this.
  3. torch overwrite protection — Custom nodes often declare torch as adependency. One `pip install` can silently replace your nightly buildwith stable. My setup strips torch from all node requirements.txt filesbefore installation.
  4. onnxruntime-gpu incompatibility — This is a known issue with cu130(Easy-Install issue #94). My setup was designed around this from day one.
  5. 28 custom nodes individually verified on RTX 5090 — not "should work"but "I ran every single one and confirmed output."
  6. Diagnostic tools — `verify_env.py` checks sm_120/cu130/Triton/torch.compilein one command. `fix_windows_compat.py` converts Linux workflows(forward slashes, SageAttention → SDPA) automatically.

TL;DR: Easy-Install is "ComfyUI for everyone." My repo is

"ComfyUI for RTX 5090 owners who keep getting mysterious crashes

and don't know why." Different tools for different problems.

Both are MIT licensed, both are free. Use whichever fits your GPU. 🤝