After upgrading to 5060ti I found out that prebuilt xformers for torch 2.7.0 doesn't support cuda, so I publish this .whl package for interested people.

For first version during building I included ONLY 12.0 compute capacity support, so first version supports BLACKWELL arch only.

Xformers is not actual after torch 2.7.0, since built-in optimizations offer same speed.

If you use higher python versions and cuda-13 consider using Sage-attention instead, it offers even bigger speed compared to xformers.

Xformers has small effect on computations' precision, this may slightly alter resulting image, keep this in mind.

Xformers at my setup offers around 50% speed boost compared to doggetx.

Installation:

1. open cmd in stable diffustion venv/scripts folder

2. type activate.bat

3. type pip uninstall xformers

4. put unpacked .whl file from zip archive to venv/scripts folder

5. type pip install xformers- and hit tab to automaticall fill filename (If you want to use this version with higher python version you can try adding --ignore-requires-python flag after filename).

6. launch webui-user.bat with --xformers argument

xFormers 0.0.30+4cf69f09.d20260302
memory_efficient_attention.ckF:                    unavailable
memory_efficient_attention.ckB:                    unavailable
memory_efficient_attention.ck_decoderF:            unavailable
memory_efficient_attention.ck_splitKF:             unavailable
memory_efficient_attention.cutlassF-pt:            available
memory_efficient_attention.cutlassB-pt:            available
[email protected]:             available
[email protected]:             available
[email protected]:             unavailable
[email protected]:             unavailable
memory_efficient_attention.triton_splitKF:         available
indexing.scaled_index_addF:                        unavailable
indexing.scaled_index_addB:                        unavailable
indexing.index_select:                             unavailable
sp24.sparse24_sparsify_both_ways:                  available
sp24.sparse24_apply:                               available
sp24.sparse24_apply_dense_output:                  available
sp24._sparse24_gemm:                               available
[email protected]:                 available
[email protected]:                        available
swiglu.dual_gemm_silu:                             available
swiglu.gemm_fused_operand_sum:                     available
swiglu.fused.p.cpp:                                available
is_triton_available:                               False
pytorch.version:                                   2.7.0+cu128
pytorch.cuda:                                      available
gpu.compute_capability:                            12.0
gpu.name:                                          NVIDIA GeForce RTX 5060 Ti
dcgm_profiler:                                     unavailable
build.info:                                        available
build.cuda_version:                                1208
build.hip_version:                                 None
build.python_version:                              3.10.9
build.torch_version:                               2.7.0+cu128
build.env.TORCH_CUDA_ARCH_LIST:                    12.0
build.env.PYTORCH_ROCM_ARCH:                       None
build.env.XFORMERS_BUILD_TYPE:                     None
build.env.XFORMERS_ENABLE_DEBUG_ASSERTIONS:        None
build.env.NVCC_FLAGS:                              None
build.env.XFORMERS_PACKAGE_FROM:                   None
build.nvcc_version:                                12.8.61
source.privacy:                                    open source