@0xDELUXA if possible can you try this PR branch on comfyui ?

@0xDELUXA if possible can you try this PR branch on comfyui ?

Sure! I’m AFK right now, but I’ll try it later. Looking great so far!

I am using this implementation of SageAttention with the Z-Image Turbo model on a 9070XT GPU.

Issue:
There is a noticeable drop in precision. Specifically, the output images suffer from "color fading" — the saturation is much lower than expected, and the overall image looks pale.

I am using this implementation of SageAttention with the Z-Image Turbo model on a 9070XT GPU.

Issue: There is a noticeable drop in precision. Specifically, the output images suffer from "color fading" — the saturation is much lower than expected, and the overall image looks pale.

Thanks for checking! does this happen with sageattention v1 too? or is it specific to this PR?

@trfmk123 just pushed a fix. Can you try again?

@trfmk123 just pushed a fix. Can you try again?

Just tested it, and the image outputs for Z-Image Turbo are completely normal now.
Thanks a lot for the fix and your hard work!

@trfmk123 just pushed a fix. Can you try again?

Just tested it, and the image outputs for Z-Image Turbo are completely normal now. Thanks a lot for the fix and your hard work!

Thanks! Perf has regressed a bit due to the fix, but getting the quality right is the main thing. I'll try to do a perf pass.

@trfmk123 just pushed a fix. Can you try again?

Just tested it, and the image outputs for Z-Image Turbo are completely normal now. Thanks a lot for the fix and your hard work!

Thanks! Perf has regressed a bit due to the fix, but getting the quality right is the main thing. I'll try to do a perf pass.

I suggest implementing v_scale scaling within the kernel. This would be a great way to fix the desaturation/precision issues without sacrificing any performance.

@trfmk123 just pushed a fix. Can you try again?

Just tested it, and the image outputs for Z-Image Turbo are completely normal now. Thanks a lot for the fix and your hard work!

Thanks! Perf has regressed a bit due to the fix, but getting the quality right is the main thing. I'll try to do a perf pass.

I suggest implementing v_scale scaling within the kernel. This would be a great way to fix the desaturation/precision issues without sacrificing any performance.

Good point. The gfx12 code currently takes raw fp8 weights without scaling which isn't desirable.

I'm using ROCm 7.13.0a20260504, and rocm-sdk init didn't populate _rocm_sdk_devel\lib\llvm\lib\clang\ (might be a local issue), so I had to set HIPCC_APPEND_FLAGS to point to _rocm_sdk_core\lib\llvm\lib\clang\23\include, otherwise the build fails with fatal error: '__clang_hip_runtime_wrapper.h' file not found.

I'm using ROCm 7.13.0a20260504, and rocm-sdk init didn't populate _rocm_sdk_devel\lib\llvm\lib\clang\ (might be a local issue), so I had to set HIPCC_APPEND_FLAGS to point to _rocm_sdk_core\lib\llvm\lib\clang\23\include, otherwise the build fails with fatal error: '__clang_hip_runtime_wrapper.h' file not found.

Did you install the "rocm-sdk-devel" pip wheel? it's part of the "rocm[libraries,devel]" when you to a pip install.
If that didn't help, try again on powershell/cmd in admin mode

Did you install the "rocm-sdk-devel" pip wheel? it's part of the "rocm[libraries,devel]" when you to a pip install. If that didn't help, try again on powershell/cmd in admin mode

I used this command to install: python -m pip install --pre --index-url https://rocm.nightlies.amd.com/v2-staging/gfx120X-all/ torch torchvision torchaudio rocm[devel,libraries]

Did you install the "rocm-sdk-devel" pip wheel? it's part of the "rocm[libraries,devel]" when you to a pip install. If that didn't help, try again on powershell/cmd in admin mode

I used this command to install: python -m pip install --pre --index-url https://rocm.nightlies.amd.com/v2-staging/gfx120X-all/ torch torchvision torchaudio rocm[devel,libraries]

Can you try pip install --index-url https://rocm.nightlies.amd.com/v2/gfx120X-all/ torch torchvision torchaudio rocm[libraries,devel] in a fresh venv?

Did you install the "rocm-sdk-devel" pip wheel? it's part of the "rocm[libraries,devel]" when you to a pip install. If that didn't help, try again on powershell/cmd in admin mode

I used this command to install: python -m pip install --pre --index-url https://rocm.nightlies.amd.com/v2-staging/gfx120X-all/ torch torchvision torchaudio rocm[devel,libraries]

Can you try pip install --index-url https://rocm.nightlies.amd.com/v2/gfx120X-all/ torch torchvision torchaudio rocm[libraries,devel] in a fresh venv?

Actually, this one goes into an endless loop, so don't try that. Do pip install --index-url https://rocm.nightlies.amd.com/v2/gfx120X-all/ rocm[libraries,devel] first, then pip install --index-url https://rocm.nightlies.amd.com/v2/gfx120X-all/ torch torchvision torchaudio

Can you try pip install --index-url https://rocm.nightlies.amd.com/v2/gfx120X-all/ torch torchvision torchaudio rocm[libraries,devel] in a fresh venv?

This installs ROCm 7.13.0a20260416, and after running rocm-sdk init, the _rocm_sdk_devel/lib/llvm/lib/clang/23/include folder is actually created.

Can you try pip install --index-url https://rocm.nightlies.amd.com/v2/gfx120X-all/ torch torchvision torchaudio rocm[libraries,devel] in a fresh venv?

This installs ROCm 7.13.0a20260416, and after running rocm-sdk init, the _rocm_sdk_devel/lib/llvm/lib/clang/23/include folder is actually created.

Yeah that should work. I use the same wheels.

No errors when building now. But these wheels are close to a month old. Don’t we have any newer ones that are “stable”?

They're in the index but not being picked up for some reason. These ones are from may 11
pip install --index-url https://rocm.nightlies.amd.com/v2/gfx120X-all/ "rocm[libraries,devel]==7.13.0a20260511" "torch==2.10.0+rocm7.13.0a20260511" "torchaudio==2.10.0+rocm7.13.0a20260511" "torchvision==0.25.0+rocm7.13.0a20260511"

I don't really understand what you meant by:

With `--use-sage-attention`, ComfyUI reported `Using xformers attention in VAE`.

in #368 (comment).

AFAIK, if xformers isn't installed as a standalone package, ComfyUI won't print that message. For me, regardless of the attention backend used (SDPA, Sage, Flash), it always prints Using split attention in VAE unless I install xformers separately. This is partly why I created this node.

Could this be happening on your end because you're also working on ROCm/xformers#87?

Out of curiosity, is there a way to verify that the SA v2 path is actually used?

According to the PR description, it prioritizes this implementation over Triton on RDNA4:

• automatic gfx12 runtime dispatch from the public sageattn API

@trfmk123 Which FlashAttention version are you using? I assume it isn't the latest one from aiter. Are you using v2.8.3? Also, the script shouldn't print "FlashAttention-2 CK" there at all.

my setup is running Flash Attention 2.8.4 via the composable_kernel (ck) backend.

Quick script I used to bench this native Sage 2.2.0 implementation against Flash 2.8.4 (aiter triton) on Windows.

Results:

(venv) PS C:\> python bench_sage2_gfx12_vs_fa2.py

Device : AMD Radeon RX 9060 XT  (gfx1200)
Config : dtype=fp16  causal=False  B=1  Hq=32  Hkv=32  warmup=50  iters=200

Loading implementations …
  [sage] using sageattn_qk_int8_pv_gfx12_native (direct)
C:\ComfyUI\venv\Lib\site-packages\flash_attn\flash_attn_interface.py:17: UserWarning: flash_attn_2_cuda (which has ROCm/HIP kernels) not found, falling back to Triton implementation
  warnings.warn("flash_attn_2_cuda (which has ROCm/HIP kernels) not found, falling back to Triton implementation")
[aiter] Windows: CK and HIP ops are not available. Triton ops only.
  [fa2]  using flash_attn.flash_attn_func

     S     D    Sage ms     FA2 ms   Sage TF/s    FA2 TF/s   Speedup
────────────────────────────────────────────────────────────────────
   512    64      0.240      0.320        8.96        6.71    1.335x
  1024    64      0.391      0.567       21.95       15.15    1.449x
  2048    64      0.710      1.284       48.37       26.75    1.808x
  4096    64      2.110      4.218       65.14       32.58    1.999x
  8192    64      7.314     16.832       75.16       32.66    2.301x
   512   128      0.290      0.398       14.81       10.80    1.371x
  1024   128      0.603      0.797       28.47       21.55    1.322x
  2048   128      1.444      2.117       47.59       32.46    1.466x
  4096   128      4.216      8.041       65.20       34.19    1.907x
  8192   128     14.962     31.508       73.49       34.90    2.106x
  [sage] S=1024 D=256 FAILED: gfx12 fp8 value path currently supports head_dim 16, 64, or 128.
  1024   256      n/a      1.654      n/a       20.77      n/a
  [sage] S=2048 D=256 FAILED: gfx12 fp8 value path currently supports head_dim 16, 64, or 128.
  2048   256      n/a      4.876      n/a       28.19      n/a
  [sage] S=4096 D=256 FAILED: gfx12 fp8 value path currently supports head_dim 16, 64, or 128.
  4096   256      n/a     16.888      n/a       32.55      n/a
────────────────────────────────────────────────────────────────────

SageAttn gfx12 wins 10/10 configs  |  avg speedup vs FA2: 1.706x
→ SageAttention gfx12 native is faster on average.

── Numerical sanity check (S=1024, D=64) ──
  max |sage - fa2| : 0.02026
  mean|sage - fa2| : 0.001528
  ✓ outputs are numerically close (expected for INT8 quant)

Quick chart:

Great results overall! 🚀

@jammm Is there a limitation preventing support for head_dim 256?

my setup is running Flash Attention 2.8.4 via the composable_kernel (ck) backend.

Oh, I thought you're on Windows - never mind then. My point was that if someone doesn't have FA-2 CK, it automatically uses Triton, but the script still prints CK, which is a bit misleading.

@jammm Is there a limitation preventing support for head_dim 256?

There is. I'm working on bringing parity vs the CUDA path.
Having said that, the CUDA path also rejects head_dim > 128, so this is not specific to gfx12. It's the blackwell specific code (sageattention v3) which supports 256. The scope of this PR is sageattention v2 though, since sageattention v3 needs fp4 support, which RDNA4 doesn't have.

@jammm Is there a limitation preventing support for head_dim 256?

There is. I'm working on bringing parity vs the CUDA path.
Having said that, the CUDA path also rejects head_dim > 128, so this is not specific to gfx12. It's the blackwell specific code (sageattention v3) which supports 256. The scope of this PR is sageattention v2 though, since sageattention v3 needs fp4 support, which RDNA4 doesn't have.

I see.

Again, huge respect for the work you put into this.

At first, we only had SDPA Math on RDNA4 (Windows).
After that, you helped make AOTriton a thing.
Later, your contributions to triton-windows made Sage1 and FA2 possible to use.
And now here we are with Sage2.

Oh, and it all started with your PyTorch wheels on Windows. 👀

@0xDELUXA

Oh, I thought you're on Windows - never mind then. My point was that if someone doesn't have FA-2 CK, it automatically uses Triton, but the script still prints CK, which is a bit misleading.

My OS is Windows 11. Below are my test results.

My OS is Windows 11. Below are my test results.

So you’ve built FA2 CK on Windows? Not many people have done this...

Based on your results, FA2 CK outperforms Sage2 at smaller batch sizes (which wasn't the case for FA2 Triton), while Sage2 takes the lead at larger ones.

@0xDELUXA

So you’ve built FA2 CK on Windows? Not many people have done this...

Based on your results, FA2 CK outperforms Sage2 at smaller batch sizes (which wasn't the case for FA2 Triton), while Sage2 takes the lead at larger ones.

I referred to this Japanese article to compile Flash Attention 2.8.4 (CK backend) on Windows:
https://note.com/lpp/n/nf24da8645c3c

I referred to this Japanese article to compile Flash Attention 2.8.4 (CK backend) on Windows: https://note.com/lpp/n/nf24da8645c3c

It's kind of all over the place with questions like "Can fa4-v4.0.0.beta be built?" The answer is no - it can't. We can build FA3, though. It also has FP8 support on RDNA4, for example. Also, that setup.py patch isn't really needed after Dao-AILab/flash-attention#2517.

Finally able to compile Windows multi arch

@jammm I had to remove the include dirs

I was hitting duplicate headers (sent you a Discord message about that). Not sure if related to torch, multiarch or what (fresh venv)

Compared against FA2 CK backend. Similar results to @trfmk123

Using the Torchmark script

Is there a limitation preventing support for head_dim 256?

@0xDELUXA FYI, head dim 256 is impossible on Nvidia sm90 (without some splitting) due to constraint of WGMMA size, and it's possible on Nvidia consumer architectures. I've tried it in #329 . I haven't tried how to do it on AMD GPU.

Windows ROCm Build - Image Corruption Bug

Hi @jammm, I successfully built SageAttention on Windows with ROCm 7.13 + gfx1201 (RX 9070 XT), but the generated images come out as pure noise.

Environment

• OS: Windows 11
• GPU: AMD RX 9070 XT (gfx1201)
• ROCm: 7.13.0a20260511
• PyTorch: 2.10.0+rocm7.13
• Python: 3.12

Build Issues & Workarounds

The code couldn't be compiled as-is on Windows due to math function resolution issues. The following functions were not recognized by the HIP compiler on Windows:

• fmaxf, fabsf, fminf, nearbyintf — no matching function for call
• max in reduction_utils_hip.cuh — use of undeclared identifier

Workaround used to get it to compile:

• Replaced fmaxf( → __builtin_fmaxf( etc. in .cu source files
• Replaced val = max(...) with ternary operator in reduction_utils_hip.cuh

Problem

With the above workaround, the build succeeds and SageAttention loads, but all generated images are corrupted (pure noise). Without --use-sage-attention, images generate correctly.

I also tried replacing with ::fmaxf(, __ocml_fmax_f32( etc. but the same corruption occurs.

This suggests the math function substitution is producing incorrect results in the GPU kernel, likely affecting the quantization scale calculation in qk_int_sv_gfx12_native.

Question

Is there a correct way to resolve fmaxf/fabsf on Windows HIP compiler? Or is there a known fix for this?

Speed improvement without the bug would be significant — the workflow went from 34s → 23s in testing with ROCm 7.2 triton fallback.

Thanks for the great work on this PR!

Update:

I tried again from a clean checkout and found that the pure-noise output was likely caused by my previous workaround using __builtin_fmaxf, __builtin_fabsf, etc.

A less aggressive workaround now builds and produces correct images on my setup.

Additional workaround that worked for me:

• Removed explicit include_dirs=include_dirs from the ROCm CUDAExtension entries in setup.py
• Added simple device helper functions for fabsf, fmaxf, fminf instead of replacing them with __builtin_*
• Reused the existing v_cvt_i32_f32 inline asm rounding path instead of nearbyintf
• Added local max / min helpers in reduction_utils.cuh
• Replaced a few host-side std::max(q_heads, kv_heads) / ::max(...) grid dimension expressions with explicit ternaries, because hipify/Windows HIP produced ::max errors

After this, SageAttention imports and ComfyUI generates correct images.

Current result:

• Baseline ComfyUI: ~34s
• --use-sage-attention: ~28s
• About 17.6% lower wall time / ~1.21x speedup

Environment:

• Windows 11
• RX 9070 XT / gfx1201
• Python 3.12
• PyTorch 2.10.0+rocm7.13.0a20260511
• ROCm HIP 7.13.26176

One more Windows-specific issue: triton is not available for this environment via the AMD gfx120X wheel index, so importing sageattention failed at first because core.py imports the Triton backend unconditionally. I patched core.py locally to allow import without Triton and use only the gfx12 native backend.

So the current status is: the gfx12 native backend can work on Windows/gfx1201, but Windows HIP needs a few compile/import fixes. The previous pure-noise result should probably be treated as caused by my bad math-function workaround, not necessarily a kernel bug.

@qweqweewqe7-create This looks like an environment-specific issue on your side rather than a universal problem. Also, triton-windows needs to be installed separately in the venv - not specifically for this PR, but in general.

@qweqweewqe7-create over the past days I have compiled it several times using nightlies of 20260416 which has a newer torch not currently available with newer nightly builds, and I had no issues with compiling nor running it. It might be the specific nightly you're trying to build against is (partially) broken?

Been busy I see @jammm. Curious about all of the changes and whether it adds further improvements to the performance.

Hello there. Im trying to get this to work for the third day now and since im unable to i guess ill ask if there is someone willing to make a "for dummies" guide? as a side note: ive never build something and have no real idea why the build process fails.

this is my .bat

cd G:\ComfyUI
git clone https://github.com/jammm/SageAttention.git SageAttention-jammm
cd SageAttention-jammm
git checkout jam/gfx12

Activate Visual Studio environment

cmd /c '"C:\Program Files\Microsoft Visual Studio\2022\Community\VC\Auxiliary\Build\vcvars64.bat" >nul 2>&1 && set' | ForEach-Object { if ($_ -match '^([^=]+)=(.*)$') { [System.Environment]::SetEnvironmentVariable($matches[1], $matches[2], 'Process') } }

::set CUDA_HOME=C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.0
::set CUDA_PATH=%CUDA_HOME%
set PATH=%CUDA_HOME%\bin;%PATH%
set ROCM_HOME=C:\Program Files\AMD\ROCm\7.1
set HIP_PATH=C:\Program Files\AMD\ROCm\7.1
set INCLUDE=G:\ComfyUI\python_embeded\include;G:\ComfyUI\python_embeded\Include;%INCLUDE%
set CUDA_HOME=C:\Program Files\AMD\ROCm\7.1
set "ROCM_HOME=C:\Program Files\AMD\ROCm\7.1"
set INCLUDE=G:\ComfyUI\python_embeded\include;G:\ComfyUI\python_embeded\Include;%INCLUDE%

$env:PYTORCH_ROCM_ARCH = "gfx1201"

:: Move into your SageAttention source directory
cd G:\ComfyUI\SageAttention-jammm

python -m pip install --upgrade setuptools wheel
python setup.py sdist bdist_wheel

:: Force a clean install using ComfyUI's python
..\python_embeded\python.exe -m pip install . --force-reinstall

pause

and this is the error:
Note: including file: G:\ComfyUI\SageAttention-jammm\csrc\fused\fused.h
1 error generated.
ninja: build stopped: subcommand failed.
Traceback (most recent call last):
File "G:\ComfyUI\python_embeded\Lib\site-packages\torch\utils\cpp_extension.py", line 2597, in _run_ninja_build
subprocess.run(
File "subprocess.py", line 571, in run
subprocess.CalledProcessError: Command '['ninja', '-v', '-j', '32']' returned non-zero exit status 1.

  The above exception was the direct cause of the following exception:

  Traceback (most recent call last):
    File "G:\ComfyUI\python_embeded\Lib\site-packages\pip\_vendor\pyproject_hooks\_in_process\_in_process.py", line 389, in <module>
      main()
    File "G:\ComfyUI\python_embeded\Lib\site-packages\pip\_vendor\pyproject_hooks\_in_process\_in_process.py", line 373, in main
      json_out["return_val"] = hook(**hook_input["kwargs"])
                               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
    File "G:\ComfyUI\python_embeded\Lib\site-packages\pip\_vendor\pyproject_hooks\_in_process\_in_process.py", line 280, in build_wheel
      return _build_backend().build_wheel(
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
    File "G:\ComfyUI\python_embeded\Lib\site-packages\setuptools\build_meta.py", line 441, in build_wheel
      return _build(['bdist_wheel', '--dist-info-dir', str(metadata_directory)])
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
    File "G:\ComfyUI\python_embeded\Lib\site-packages\setuptools\build_meta.py", line 429, in _build
      return self._build_with_temp_dir(
             ^^^^^^^^^^^^^^^^^^^^^^^^^^
    File "G:\ComfyUI\python_embeded\Lib\site-packages\setuptools\build_meta.py", line 410, in _build_with_temp_dir
      self.run_setup()
    File "G:\ComfyUI\python_embeded\Lib\site-packages\setuptools\build_meta.py", line 317, in run_setup
      exec(code, locals())
    File "<string>", line 467, in <module>
    File "G:\ComfyUI\python_embeded\Lib\site-packages\setuptools\__init__.py", line 117, in setup
      return distutils.core.setup(**attrs)  # type: ignore[return-value]
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
    File "G:\ComfyUI\python_embeded\Lib\site-packages\setuptools\_distutils\core.py", line 186, in setup
      return run_commands(dist)
             ^^^^^^^^^^^^^^^^^^
    File "G:\ComfyUI\python_embeded\Lib\site-packages\setuptools\_distutils\core.py", line 202, in run_commands
      dist.run_commands()
    File "G:\ComfyUI\python_embeded\Lib\site-packages\setuptools\_distutils\dist.py", line 1000, in run_commands
      self.run_command(cmd)
    File "G:\ComfyUI\python_embeded\Lib\site-packages\setuptools\dist.py", line 1107, in run_command
      super().run_command(command)
    File "G:\ComfyUI\python_embeded\Lib\site-packages\setuptools\_distutils\dist.py", line 1019, in run_command
      cmd_obj.run()
    File "G:\ComfyUI\python_embeded\Lib\site-packages\setuptools\command\bdist_wheel.py", line 370, in run
      self.run_command("build")
    File "G:\ComfyUI\python_embeded\Lib\site-packages\setuptools\_distutils\cmd.py", line 341, in run_command
      self.distribution.run_command(command)
    File "G:\ComfyUI\python_embeded\Lib\site-packages\setuptools\dist.py", line 1107, in run_command
      super().run_command(command)
    File "G:\ComfyUI\python_embeded\Lib\site-packages\setuptools\_distutils\dist.py", line 1019, in run_command
      cmd_obj.run()
    File "G:\ComfyUI\python_embeded\Lib\site-packages\setuptools\_distutils\command\build.py", line 135, in run
      self.run_command(cmd_name)
    File "G:\ComfyUI\python_embeded\Lib\site-packages\setuptools\_distutils\cmd.py", line 341, in run_command
      self.distribution.run_command(command)
    File "G:\ComfyUI\python_embeded\Lib\site-packages\setuptools\dist.py", line 1107, in run_command
      super().run_command(command)
    File "G:\ComfyUI\python_embeded\Lib\site-packages\setuptools\_distutils\dist.py", line 1019, in run_command
      cmd_obj.run()
    File "G:\ComfyUI\python_embeded\Lib\site-packages\setuptools\command\build_ext.py", line 97, in run
      _build_ext.run(self)
    File "G:\ComfyUI\python_embeded\Lib\site-packages\setuptools\_distutils\command\build_ext.py", line 367, in run
      self.build_extensions()
    File "G:\ComfyUI\python_embeded\Lib\site-packages\torch\utils\cpp_extension.py", line 1082, in build_extensions
      build_ext.build_extensions(self)
    File "G:\ComfyUI\python_embeded\Lib\site-packages\setuptools\_distutils\command\build_ext.py", line 481, in build_extensions
      self._build_extensions_parallel()
    File "G:\ComfyUI\python_embeded\Lib\site-packages\setuptools\_distutils\command\build_ext.py", line 504, in _build_extensions_parallel
      fut.result()
    File "concurrent\futures\_base.py", line 456, in result
    File "concurrent\futures\_base.py", line 401, in __get_result
    File "concurrent\futures\thread.py", line 59, in run
    File "<string>", line 224, in build_extension
    File "G:\ComfyUI\python_embeded\Lib\site-packages\setuptools\command\build_ext.py", line 262, in build_extension
      _build_ext.build_extension(self, ext)
    File "G:\ComfyUI\python_embeded\Lib\site-packages\setuptools\_distutils\command\build_ext.py", line 564, in build_extension
      objects = self.compiler.compile(
                ^^^^^^^^^^^^^^^^^^^^^^
    File "<string>", line 215, in compile_new
    File "G:\ComfyUI\python_embeded\Lib\site-packages\torch\utils\cpp_extension.py", line 1051, in win_wrap_ninja_compile
      _write_ninja_file_and_compile_objects(
    File "G:\ComfyUI\python_embeded\Lib\site-packages\torch\utils\cpp_extension.py", line 2223, in _write_ninja_file_and_compile_objects
      _run_ninja_build(
    File "G:\ComfyUI\python_embeded\Lib\site-packages\torch\utils\cpp_extension.py", line 2614, in _run_ninja_build
      raise RuntimeError(message) from e
  RuntimeError: Error compiling objects for extension
  [end of output]

note: This error originates from a subprocess, and is likely not a problem with pip.
ERROR: Failed building wheel for sageattention
Failed to build sageattention
error: failed-wheel-build-for-install

× Failed to build installable wheels for some pyproject.toml based projects
╰─> sageattention

G:\ComfyUI\SageAttention-jammm>