git:20260822.039e7e3 to git:20260825.ea35391

110 added, 109 removed. Audit B to B.

---

name: triton-sageattention

- description: Install Triton + SageAttention to accelerate ComfyUI (the sageattn attention_mode and inductor torch.compile used by WanVideoWrapper / many video graphs) — Windows-first (triton-windows + woct0rdho prebuilt SageAttention wheels matched to torch/CUDA/python into the RIGHT python), plus Linux (official triton + build) and Mac (N/A → sdpa/MPS). Critically also covers the SAFE sdpa / no-compile fallback so an example that assumes sageattn + torch.compile still runs when these aren't installed (video-extend TRAP 5). Use when a loader crashes with "No module named 'sageattention'" or reports triton unavailable, when asked to speed up Wan/video workflows, or when deciding whether to install acceleration vs. fall back.

+ description: Install Triton + SageAttention to accelerate ComfyUI (the sageattn attention_mode and inductor torch.compile used by WanVideoWrapper / many video graphs). Windows-first (triton-windows + woct0rdho prebuilt SageAttention wheels matched to torch/CUDA/python into the RIGHT python), plus Linux (official triton + build) and Mac (N/A → sdpa/MPS). Also covers the SAFE sdpa / no-compile fallback so an example that assumes sageattn + torch.compile still runs when these aren't installed (video-extend TRAP 5). Use when a loader crashes with "No module named 'sageattention'" or reports triton unavailable, when asked to speed up Wan/video workflows, or when deciding whether to install acceleration vs. fall back.

globs:

- "**/*.json"

- "**/packs/**"

---

# Triton + SageAttention (ComfyUI acceleration)

> See also [`comfyui-launch-flags`](../comfyui-launch-flags/SKILL.md) for the full

- > attention / VRAM / cache flag matrix. Note the **Z-Image exception**: Z-Image is

- > broken under `--use-sage-attention` → launch it with

+ > attention / VRAM / cache flag matrix. Note the Z-Image exception: Z-Image is

+ > broken under `--use-sage-attention`, so launch it with

> `--use-pytorch-cross-attention` instead.

## Prefer kitchen INT8 attention when it is available

If `kitchen` action:"status" (or `panel_kitchen`) reports kitchen present and

`int8_attention_is_available` on this GPU, launch with **`--use-ck-attention`**

and **skip the sageattention wheel dance**. Kitchen INT8 attention is a ComfyUI

flag; it does not need a version-matched `sageattention` wheel. Restart

required, consent-gated like every restart.

Only fall through to the Triton + SageAttention install below when kitchen INT8

is **unknown** or **not available**. A failed kitchen probe is unknown, not a no.

## Overview

- Two **optional accelerators** that many modern video graphs (especially kijai's

- **ComfyUI-WanVideoWrapper**) reference by default:

+ Two optional accelerators that many modern video graphs (especially kijai's

+ ComfyUI-WanVideoWrapper) reference by default:

- - **SageAttention** (`import sageattention`) — a quantized attention kernel.

+ - SageAttention (`import sageattention`), a quantized attention kernel.

Selected via a node's `attention_mode = sageattn` (WanVideoWrapper) or ComfyUI's

- `--use-sage-attention` startup flag. ~20–40% faster sampling on supported NVIDIA

- GPUs.

- - **Triton** — the GPU kernel compiler that **inductor `torch.compile`** needs.

+ `--use-sage-attention` startup flag. ~20 to 40% faster sampling on supported

+ NVIDIA GPUs.

+ - Triton, the GPU kernel compiler that inductor `torch.compile` needs.

WanVideoWrapper's `WanVideoTorchCompileSettings` (and any `torch.compile`/

inductor node) compiles the model through Triton for another speedup.

- > ⚠️ **The risk.** Both are **version-locked to your exact torch + CUDA + python**.

- > A wrong wheel doesn't just fail to install — it can **break the torch install**

- > (mismatched CUDA DLLs, `ImportError`, or silent NaNs). And the *failure mode of

- > not having them* is a **hard crash before any sampling**:

+ > **The risk.** Both are version-locked to your exact torch + CUDA + python.

+ > A wrong wheel does worse than fail to install. It can break the torch install

+ > (mismatched CUDA DLLs, `ImportError`, or silent NaNs). And the failure mode of

+ > not having them is a hard crash before any sampling:

> `ValueError: Can't import SageAttention: No module named 'sageattention'`, or

> compile errors / `triton: unavailable` in the startup log. This is exactly the

- > [`video-extend`](../video-extend/SKILL.md) **TRAP 5**.

+ > [`video-extend`](../video-extend/SKILL.md) TRAP 5.

- > ✅ **Therefore the default is: get a working render FIRST with the

+ > **Therefore the default is to get a working render FIRST with the

> [sdpa / no-compile fallback](#the-safe-sdpa--no-compile-fallback-do-this-first),

- > then OFFER to install acceleration for speed.** Never silently run a

- > torch-breaking install to "fix" a workflow — fall back, render, *then* ask.

+ > then OFFER to install acceleration for speed.** Never run a torch-breaking

+ > install unannounced to "fix" a workflow. Fall back, render, then ask.

- > ⚠️ **Verification note (June 2026).** Wheel sources, the triton↔torch table, and

+ > **Verification note (June 2026).** Wheel sources, the triton↔torch table, and

> the live `attention_mode` enum below were verified against

> `woct0rdho/triton-windows`, `woct0rdho/SageAttention` releases, and

- > WanVideoWrapper's nodes (see [Sources](#sources)). Versions move fast — **always

- > re-read the live torch/CUDA/python first** (commands below) and pick the wheel

- > that matches; flag anything you can't confirm rather than guessing.

+ > WanVideoWrapper's nodes (see [Sources](#sources)). Versions move fast, so always

+ > re-read the live torch/CUDA/python first (commands below) and pick the wheel

+ > that matches. Flag anything you can't confirm rather than guessing.

---

## Decide first: do you even need them?

```

Workflow crashes "No module named 'sageattention'" ──┐

or "triton: unavailable" / torch.compile error ─┤

▼

1. APPLY THE SDPA / NO-COMPILE FALLBACK → render works now

▼

2. OFFER acceleration, in this order:

a. If kitchen INT8 attention is available:

"Want --use-ck-attention? No sageattention wheel."

b. Else:

"Want me to install Triton + SageAttention for ~20–40%

faster sampling? It's a version-matched install that

touches your torch env — I'll verify torch/CUDA/python

first and can roll back."

▼

3. Only on YES → install per-OS below → verify → re-enable

sageattn + torch.compile in the workflow.

```

- Mac (no CUDA): **skip the install entirely**, the answer is always sdpa/MPS.

+ Mac (no CUDA): skip the install entirely. The answer is always sdpa/MPS.

---

## The safe sdpa / no-compile fallback (DO THIS FIRST)

- When Triton/SageAttention aren't installed, make the workflow run **unaccelerated

- but correct** by switching attention to **sdpa** (PyTorch's built-in scaled

- dot-product attention — always available, no extra deps) and removing the

+ When Triton/SageAttention aren't installed, make the workflow run unaccelerated

+ but correct by switching attention to sdpa (PyTorch's built-in scaled

+ dot-product attention, always available, no extra deps) and removing the

`torch.compile`/inductor wiring.

- **WanVideoWrapper (the common case):**

+ WanVideoWrapper (the common case):

- 1. On **every** `WanVideoModelLoader` set `attention_mode` → **`sdpa`**.

+ 1. On every `WanVideoModelLoader` set `attention_mode` to `sdpa`.

- Confirmed enum values: `sdpa`, `flash_attn_2`, `flash_attn_3`, `sageattn`,

`sparse_sage_attention`. The examples ship with `sageattn`; `sdpa` is the

universal safe one.

- 2. **Disconnect `WanVideoTorchCompileSettings`** from each loader's `compile_args`

+ 2. Disconnect `WanVideoTorchCompileSettings` from each loader's `compile_args`

input (or delete/bypass the node). No compile = no Triton needed.

3. (If present) bypass any `WanVideoSetRadialAttention` /

- `sparse_sage_attention` node — those also route through SageAttention.

+ `sparse_sage_attention` node. Those also route through SageAttention.

- **Generic ComfyUI:** don't launch with `--use-sage-attention`; bypass any

+ Generic ComfyUI: don't launch with `--use-sage-attention`; bypass any

`TorchCompileModel` / inductor node.

This costs you speed, not quality. Use `create_workflow (action:"modify")` / the panel's

strip-and-re-point flow to flip the widget and drop the link, then enqueue. Once

it renders, offer the install.

> Cross-ref: [`video-extend`](../video-extend/SKILL.md) documents this exact fix

> as TRAP 5 for the Pusa extension graph (both `WanVideoModelLoader`s →

> `attention_mode=sdpa`, disconnect `WanVideoTorchCompileSettings`).

---

## Windows install (the priority)

- Windows has **no official Triton or SageAttention build**. You use community

- prebuilt wheels, and **they must match torch + CUDA + python exactly**. The panel

- agent has a shell (Bash for Claude / `exec` for Codex) — use it to run these in

- the **correct python**, never the system `python`.

+ Windows has no official Triton or SageAttention build. You use community

+ prebuilt wheels, and they must match torch + CUDA + python exactly. The panel

+ agent has a shell (Bash for Claude / `exec` for Codex). Use it to run these in

+ the correct python, never the system `python`.

### Step 1 — find the RIGHT python (NOT system python)

ComfyUI on Windows comes in three flavors; each has its own python whose `pip` you

must target:

| Variant | Where its python lives | How to invoke pip |

|---|---|---|

| **Desktop (standalone)** | a `standalone-env\` (or `venv`) beside the install, e.g. `C:\Users\<you>\ComfyUI-Installs\ComfyUI\standalone-env\python.exe` | `"<install>\standalone-env\python.exe" -m pip ...` |

| **Portable** | `ComfyUI_windows_portable\python_embeded\python.exe` | `"<...>\python_embeded\python.exe" -m pip ...` |

| **Manual venv** | the venv you created (`venv\Scripts\python.exe`) | activate it, then `python -m pip ...` |

- Detect it from the **live server** — the surest way to hit the same python ComfyUI

+ Detect it from the live server, the surest way to hit the same python ComfyUI

runs on:

- `install_comfyui (action:"environment")` / `get_system_stats` report `embedded_python` (true →

Portable), the python version and the `pytorch_version` (e.g. `2.10.0+cu130`).

- - Inspect the running process's `argv` (from `get_system_stats`) — the path to

+ - Inspect the running process's `argv` (from `get_system_stats`). The path to

`main.py` reveals the install root; its sibling `standalone-env` / `python_embeded`

holds the python.

- Last resort, ask the user for their ComfyUI folder.

- > ⚠️ Installing into the wrong python (e.g. a global `pip install`) is the #1

- > Windows mistake: the package lands somewhere ComfyUI never imports from, so the

+ > Installing into the wrong python (e.g. a global `pip install`) is the #1

+ > Windows mistake. The package lands somewhere ComfyUI never imports from, so the

> loader still crashes "No module named 'sageattention'". Always use

> `"<that python>" -m pip`.

### Step 2 — read the installed torch + CUDA + python

- Run with the python you just found:

+ Run with the python you found:

```bash

"<python>" -c "import sys, torch; print(sys.version.split()[0], torch.__version__, torch.version.cuda)"

```

- Example live output on this machine: `3.13.12 2.10.0+cu130 13.0` →

- **python 3.13, torch 2.10, CUDA line cu130**. You'll pick wheels for that triple.

+ Example live output on this machine: `3.13.12 2.10.0+cu130 13.0`, meaning

+ python 3.13, torch 2.10, CUDA line cu130. You'll pick wheels for that triple.

### Step 3 — install **triton-windows** (matched to torch)

- Source: **`woct0rdho/triton-windows`** (the canonical Windows Triton fork; also on

- PyPI as `triton-windows`). The pin is just an upper bound — pip resolves the right

+ Source: `woct0rdho/triton-windows` (the canonical Windows Triton fork; also on

+ PyPI as `triton-windows`). The pin is an upper bound. pip resolves the right

build for your torch:

```bash

"<python>" -m pip install -U "triton-windows<3.7"

```

- **Why `<3.7`:** each torch minor pins a Triton minor. Verified table:

+ Why `<3.7`: each torch minor pins a Triton minor. Verified table:

| PyTorch | triton-windows | constraint to use |

|---|---|---|

| 2.7 | 3.3 | `"triton-windows<3.4"` |

| 2.8 | 3.4 | `"triton-windows<3.5"` |

| 2.9 | 3.5 | `"triton-windows<3.6"` |

| **2.10** | **3.6** | **`"triton-windows<3.7"`** |

- (torch 2.6 or older → triton 3.2 or earlier.) Pick the row for *your* torch.

+ (torch 2.6 or older → triton 3.2 or earlier.) Pick the row for your torch.

- - **CUDA toolkit:** since `triton-windows 3.2.0.post11` a minimal CUDA toolchain

- is **bundled in the wheel** — you do NOT need a separate CUDA Toolkit install for

- Triton itself. (Triton 3.3–3.6 bundle the CUDA 12.8 line; works against cu12x/

- cu13x torch.)

- - **MSVC / vcredist:** Triton compiles C++ at runtime, so it needs the **MSVC

- toolchain + "Visual C++ Redistributable 2015–2022"** present. A TinyCC is

+ - CUDA toolkit: since `triton-windows 3.2.0.post11` a minimal CUDA toolchain

+ is bundled in the wheel, so you do NOT need a separate CUDA Toolkit install for

+ Triton itself. (Triton 3.3 through 3.6 bundle the CUDA 12.8 line; works against

+ cu12x/cu13x torch.)

+ - MSVC / vcredist: Triton compiles C++ at runtime, so it needs the MSVC

+ toolchain and "Visual C++ Redistributable 2015-2022" present. A TinyCC is

bundled (since 3.2.0.post13) which covers many cases, but installing the

- **Visual Studio Build Tools (C++ workload)** + latest vcredist is the reliable

+ Visual Studio Build Tools (C++ workload) plus the latest vcredist is the reliable

fix if you hit compiler errors (see Traps).

- - **Embedded/Portable python only:** the embedded distro ships without C headers,

+ - Embedded/Portable python only: the embedded distro ships without C headers,

so Triton can't compile. Download the matching `python_<ver>_include_libs.zip`

- from the triton-windows releases and copy its **`include`** and **`libs`**

+ from the triton-windows releases and copy its `include` and `libs`

(note: `libs`, not `lib`) folders into `python_embeded\`. The Desktop

`standalone-env` usually already has these.

### Step 4 — install **SageAttention** (prebuilt wheel, matched to torch+CUDA)

- **Strongly prefer the prebuilt wheel** — building from source needs the full CUDA

- Toolkit (`nvcc`) + MSVC and frequently fails on Windows. Source:

- **`woct0rdho/SageAttention` releases** (Windows wheels; v2 = SageAttention 2.x).

+ Prefer the prebuilt wheel. Building from source needs the full CUDA

+ Toolkit (`nvcc`) plus MSVC and often fails on Windows. Source:

+ `woct0rdho/SageAttention` releases (Windows wheels; v2 = SageAttention 2.x).

- Latest verified tag: **`v2.2.0-windows.post5`**, with these four wheels (all

- `cp310-abi3` → work on **python 3.10 through 3.13+** via the stable ABI; one wheel

- covers all those pythons):

+ Latest verified tag: `v2.2.0-windows.post5`, with these four wheels (all

+ `cp310-abi3`, so they work on python 3.10 through 3.13+ via the stable ABI; one

+ wheel covers all those pythons):

| Wheel filename | For |

|---|---|

| `sageattention-2.2.0+cu128torch2.9.1.post5-cp310-abi3-win_amd64.whl` | CUDA 12.8 line, torch 2.9.x |

| `sageattention-2.2.0+cu128torch2.10.0andhigher.post5-cp310-abi3-win_amd64.whl` | CUDA 12.8 line, torch ≥2.10 |

| `sageattention-2.2.0+cu130torch2.9.1.post5-cp310-abi3-win_amd64.whl` | CUDA 13.0 line, torch 2.9.x |

| `sageattention-2.2.0+cu130torch2.10.0andhigher.post5-cp310-abi3-win_amd64.whl` | **CUDA 13.0 line, torch ≥2.10** |

- Pick by your **CUDA line** (`cu128` vs `cu130` — from `torch.version.cuda`: `12.8`

- → cu128, `13.0` → cu130) and **torch minor**. For the live machine above

- (torch 2.10.0+cu130, py3.13) → the **last** wheel. Install by full URL:

+ Pick by your CUDA line (`cu128` vs `cu130`, from `torch.version.cuda`: `12.8`

+ → cu128, `13.0` → cu130) and torch minor. For the live machine above

+ (torch 2.10.0+cu130, py3.13) that is the last wheel. Install by full URL:

```bash

"<python>" -m pip install "https://github.com/woct0rdho/SageAttention/releases/download/v2.2.0-windows.post5/sageattention-2.2.0+cu130torch2.10.0andhigher.post5-cp310-abi3-win_amd64.whl"

```

- - The `cpXXX-abi3` tag means **one wheel works across python ≥ its base** (3.10+),

- so py3.13 is covered even though there's no `cp313`-specific wheel — this is

+ - The `cpXXX-abi3` tag means one wheel works across python ≥ its base (3.10+),

+ so py3.13 is covered even though there's no `cp313`-specific wheel. This is

expected, not a mismatch.

- - **Always check the releases page for a newer tag** than `.post5` and newer torch

- variants — the filename pattern is stable (`+cu<line>torch<minor>...abi3`).

- - **Don't build from source** unless no wheel matches your torch/CUDA at all (then

- you need CUDA Toolkit + MSVC; flag the cost to the user first).

+ - Always check the releases page for a newer tag than `.post5` and newer torch

+ variants. The filename pattern is stable (`+cu<line>torch<minor>...abi3`).

+ - Don't build from source unless no wheel matches your torch/CUDA at all (then

+ you need CUDA Toolkit plus MSVC; flag the cost to the user first).

### Step 5 — verify (Windows)

```bash

"<python>" -c "import triton; print('triton', triton.__version__)"

"<python>" -c "import sageattention; print('sageattention OK')"

"<python>" -c "import torch; print('torch still ok', torch.__version__, torch.cuda.is_available())"

```

- All three must succeed **and torch must still import with CUDA** — if the third

- line now fails, the install clobbered torch (see Traps → roll back). Then restart

+ All three must succeed and torch must still import with CUDA. If the third

+ line now fails, the install clobbered torch (see Traps, roll back). Then restart

ComfyUI and confirm the startup log no longer prints `Could not load

sageattention` / `triton: unavailable`. Finally re-enable in the workflow:

`WanVideoModelLoader.attention_mode = sageattn` and reconnect

`WanVideoTorchCompileSettings`, enqueue, and confirm it samples (a torch.compile

- node will spend extra time on the **first** run compiling — that's normal).

+ node will spend extra time on the first run compiling, which is normal).

---

## Linux install

- Official builds exist here — much simpler:

+ Official builds exist here, so this is much simpler:

```bash

# Triton: official, pip-installable; torch usually already pulls a matching triton.

pip install -U triton # or let torch's pinned triton stand; match torch minor

# SageAttention: pip, or build from source for your GPU arch

pip install sageattention # if a matching wheel exists for your torch/CUDA

```

- - **Use the python that runs ComfyUI** (its venv/conda env) — same rule as Windows.

- - **Version matching still applies:** torch pins a triton minor (e.g. torch 2.9.x

+ - Use the python that runs ComfyUI (its venv/conda env), the same rule as Windows.

+ - Version matching still applies. torch pins a triton minor (e.g. torch 2.9.x

↔ triton 3.5.x, torch 2.10 ↔ 3.6); patch versions within a minor are

interchangeable. Don't `pip install triton` blindly if it would upgrade past

what your torch pins.

- - **Build deps (if building SageAttention from source):** the **CUDA Toolkit with

- `nvcc`** (matching your torch CUDA line), `gcc/g++`, and the torch headers. If

+ - Build deps (if building SageAttention from source): the CUDA Toolkit with

+ `nvcc` (matching your torch CUDA line), `gcc/g++`, and the torch headers. If

CUDA is in a nonstandard path, `export PATH=/usr/local/cuda-<ver>/bin:$PATH` so

- the right `nvcc` is found. Building is GPU-arch specific and slow — prefer a

+ the right `nvcc` is found. Building is GPU-arch specific and slow, so prefer a

matching prebuilt wheel when one exists.

- Verify exactly as in Windows Step 5 (`import triton`, `import sageattention`,

torch still imports with CUDA).

---

## Mac

- **Triton and SageAttention are N/A on Mac — there is no CUDA.** Do not attempt to

- install them. Use **PyTorch sdpa** attention (the fallback above is the permanent

- answer), which on Apple Silicon runs on the **MPS** backend. Set any

+ Triton and SageAttention are N/A on Mac. There is no CUDA. Do not attempt to

+ install them. Use PyTorch sdpa attention (the fallback above is the permanent

+ answer), which on Apple Silicon runs on the MPS backend. Set any

`attention_mode` to `sdpa`, never load `torch.compile`/inductor (Triton) nodes,

and run unaccelerated. If a workflow hard-requires `sageattn`, edit it to `sdpa`

rather than trying to satisfy the dependency.

---

## Verification checklist (any OS)

1. `import triton` succeeds and prints a version matching your torch (table above).

2. `import sageattention` succeeds.

- 3. **torch STILL imports** and `torch.cuda.is_available()` is `True` (the install

+ 3. torch STILL imports and `torch.cuda.is_available()` is `True` (the install

didn't break the env).

4. ComfyUI startup log: no `Could not load sageattention`, no `triton: unavailable`.

5. In the graph: `attention_mode = sageattn` loads without the `No module named

'sageattention'` ValueError; a `torch.compile`/`WanVideoTorchCompileSettings`

node completes its (slow) first-run compile and then samples.

6. A real render completes and looks correct (SageAttention can rarely introduce

- NaN/noise on some GPUs — if output degrades vs. sdpa, fall back to sdpa).

+ NaN/noise on some GPUs; if output degrades vs. sdpa, fall back to sdpa).

---

## Traps

- **Wrong python / global pip.** Installing into system python (or the wrong

- venv) means ComfyUI never imports it — the loader still crashes. Always

+ venv) means ComfyUI never imports it, so the loader still crashes. Always

`"<that exact python>" -m pip`; for Portable that's `python_embeded\python.exe`,

for Desktop the `standalone-env\python.exe`. Verify with `pip show sageattention`

- run by *that* python.

+ run by that python.

- **torch / CUDA / python wheel mismatch breaks torch.** Installing a `cu128` wheel

on a `cu130` torch (or a torch2.9 wheel on torch2.10) can drag in mismatched CUDA

- DLLs and break `import torch` itself, or surface as a runtime DLL error. Match

- `cu128`↔`12.x` / `cu130`↔`13.0` and the torch minor exactly. **Pin and verify:**

+ DLLs and break `import torch` itself, or show up as a runtime DLL error. Match

+ `cu128`↔`12.x` / `cu130`↔`13.0` and the torch minor exactly. Pin and verify:

before installing, record `pip freeze | grep -i torch`; after, confirm torch

- still imports with CUDA. If broken, **roll back** (`pip install

+ still imports with CUDA. If broken, roll back (`pip install

torch==<old>+cu<line> --index-url https://download.pytorch.org/whl/cu<line>`,

or uninstall the bad wheel) and re-apply the sdpa fallback.

- **Stale Triton cache after a torch/GPU/driver change.** Triton caches compiled

kernels in `~/.triton` (`%USERPROFILE%\.triton` on Windows). After upgrading torch,

swapping GPUs, a driver update, or a failed compile, that cache can go stale and

- cause `torch.compile`/SageAttention runs to fail *even though the install is

- correct* — recurring compile errors, `RuntimeError` in a Triton kernel, or a hang

- on the first sample. **Fix: clear the cache and re-run** (Triton recompiles fresh):

+ cause `torch.compile`/SageAttention runs to fail even though the install is

+ correct. Symptoms are recurring compile errors, `RuntimeError` in a Triton

+ kernel, or a hang on the first sample. Fix: clear the cache and re-run (Triton

+ recompiles fresh):

```

# Windows

rmdir /s /q "%USERPROFILE%\.triton"

# macOS / Linux

rm -rf ~/.triton

```

- Safe to delete — it's a pure cache. Do this BEFORE assuming the wheel is wrong

- (it's a much cheaper fix than a reinstall/roll-back). If it recurs every run, the

- install is genuinely mismatched (see the wheel-mismatch trap above).

+ Safe to delete; it's a pure cache. Do this BEFORE assuming the wheel is wrong

+ (it's a much cheaper fix than a reinstall or roll-back). If it recurs every run,

+ the install is mismatched (see the wheel-mismatch trap above).

- **MSVC missing (Windows Triton).** `torch.compile`/Triton errors like "Microsoft

Visual C++ ... required", `cl.exe not found`, or `PY_SSIZE_T_CLEAN`/DLL load

- failures usually mean no MSVC toolchain. Install **Visual Studio Build Tools (C++

- workload)** + the latest **"Visual C++ Redistributable 2015–2022"**; copying

+ failures usually mean no MSVC toolchain. Install Visual Studio Build Tools (C++

+ workload) plus the latest "Visual C++ Redistributable 2015-2022"; copying

`msvcp140.dll`/`vcruntime140*.dll` into the python folder is the documented

last-resort fix.

- **Embedded python has no headers.** Portable's `python_embeded` lacks

`include`/`libs`, so Triton can't compile and `torch.compile` fails. Copy the

- matching `python_<ver>_include_libs.zip` `include` + **`libs`** (not `lib`)

+ matching `python_<ver>_include_libs.zip` `include` and `libs` (not `lib`)

folders from the triton-windows releases into `python_embeded\`.

- - **py3.13 "no wheel" panic.** SageAttention's Windows wheels are `cp310-abi3` —

- one wheel covers **py3.10–3.13+**. The absence of a `cp313` filename is *normal*;

- do not conclude "no wheel for 3.13." (Source builds, by contrast, can genuinely

- lag on the newest python — another reason to use the abi3 wheel.) Triton-windows

+ - **py3.13 "no wheel" panic.** SageAttention's Windows wheels are `cp310-abi3`, so

+ one wheel covers py3.10 through 3.13+. The absence of a `cp313` filename is normal;

+ do not conclude "no wheel for 3.13." (Source builds, by contrast, can lag on the

+ newest python, another reason to use the abi3 wheel.) Triton-windows

does ship py3.13-specific builds.

- **CUDA line confusion.** `torch.version.cuda` is the source of truth: `12.8` →

- pick `cu128` wheels, `13.0` → `cu130`. Don't read the *system* CUDA driver

- version — match what **torch** was built against.

+ pick `cu128` wheels, `13.0` → `cu130`. Don't read the system CUDA driver

+ version. Match what torch was built against.

- **"Install can break torch."** Treat every acceleration install as risky to the

- env: get a working sdpa render first, capture the torch version, install,

+ env. Get a working sdpa render first, capture the torch version, install,

re-verify torch, and be ready to roll back. Never leave the user with a broken

torch and no render.

- **SageAttention numerical artifacts.** On some GPUs (reported on H100/Hopper)

`sageattn` produces noise that `sdpa` doesn't. If a render looks worse than the

- sdpa version, switch that workflow back to `sdpa` — correctness over speed.

+ sdpa version, switch that workflow back to `sdpa`. Correctness over speed.

- **First torch.compile run is slow.** Inductor compiles on the first sample

(tens of seconds to minutes); that's expected, not a hang. Subsequent runs are

fast. Don't "fix" it by ripping out compile unless it actually errors.

---

## See also

- - [`video-extend`](../video-extend/SKILL.md) — **TRAP 5** is the canonical

- example: the Pusa graph ships with `attention_mode=sageattn` +

+ - [`video-extend`](../video-extend/SKILL.md). TRAP 5 is the canonical

+ example. The Pusa graph ships with `attention_mode=sageattn` and

`WanVideoTorchCompileSettings`; this skill is how you either satisfy or safely

fall back from that. Read its TRAP 5 for the exact node-by-node sdpa fix.

- - [`troubleshooting`](../troubleshooting/SKILL.md) — "Torch / CUDA Version Errors"

+ - [`troubleshooting`](../troubleshooting/SKILL.md). "Torch / CUDA Version Errors"

and "Missing Nodes" sections for diagnosing a torch env that an install broke.

- - [`installer-packs`](../installer-packs/SKILL.md) — packs note SageAttention/

+ - [`installer-packs`](../installer-packs/SKILL.md). Packs note SageAttention/

Triton requirements in `pack.yaml` `notes`/`post_install`; acceleration is an

opt-in post-install step, never baked into a model download.

## Sources

- **Official:** triton-windows https://github.com/woct0rdho/triton-windows and SageAttention Windows wheels https://github.com/woct0rdho/SageAttention/releases; ComfyUI `--use-ck-attention` in `comfy/cli_args.py`; comfy-kitchen `int8_attention_is_available()` at https://github.com/Comfy-Org/comfy-kitchen

- **Empirical:** sdpa / no-compile fallback, wheel-matching recipes, and WanVideoWrapper attention_mode notes from observed loader crashes.