image-server-wheels

Prebuilt Python 3.11 wheels.

Contents

File	OS	CUDA	Torch	Source	Notes
ace_step-1.6.0-py3-none-any.whl	any	—	—	built by us	Pure-Python, cross-platform
qwen_tts-0.1.1+deapi3-py3-none-any.whl	any	—	—	built by us	Pure-Python, patched fork (+deapi3)
image_server_kernels-0.4.2+cu128torch2.8-cp311-cp311-win_amd64.whl	Windows x64	12.8	2.8	built by us	Current. Adds Sol-Attn sparse attention, W4A8 linear, and in-tree llama.cpp GGUF CUDA kernels (_C_gguf), on top of: FP8 dense + grouped GEMM, NVFP4 W4A4, INT8 W8A8 + ConvRot, fused QK-norm+RoPE. SM89 + SM120
image_server_kernels-0.3.0-cp311-cp311-win_amd64.whl	Windows x64	12.8	2.8	built by us	Previous build, kept as a fallback. No Sol-Attn, no W4A8, no bundled GGUF kernels
image_server_kernels-0.3.0+cu129torch2.8-cp311-cp311-linux_x86_64.whl	Linux x86_64	12.9	2.8	built by us	Linux build is still at 0.3.0 — there is no Linux 0.4.2 yet
block_sparse_attn-0.0.2-cp311-cp311-win_amd64.whl	Windows x64	12.8	2.8	built by us	Used by video pipeline
block_sparse_attn-0.0.2-cp311-cp311-linux_x86_64.whl	Linux x86_64	12.8	2.8	built by us	Used by video pipeline
q8_kernels-0.0.5-cp311-cp311-win_amd64.whl	Windows x64	12.8	2.8	built by us	Used by LTX video
q8_kernels-0.0.5-cp311-cp311-linux_x86_64.whl	Linux x86_64	12.8	2.8	built by us	Used by LTX video
sageattention-2.2.0+cu128torch2.8.0-cp311-cp311-win_amd64.whl	Windows x64	12.8	2.8	woct0rdho/SageAttention	Mirror of upstream release
sageattention-2.2.0+cu129torch2.8-cp311-cp311-linux_x86_64.whl	Linux x86_64	12.9	2.8	provenance unconfirmed — see Credits
flash_attn-2.8.2+cu128torch2.8-cp311-cp311-win_amd64.whl	Windows x64	12.8	2.8	mjun0812/flash-attention-prebuild-wheels	Mirror of upstream release
flash_attn-2.8.3+cu130torch2.10-cp311-cp311-win_amd64.whl	Windows x64	13.0	2.10	mjun0812/flash-attention-prebuild-wheels	Mirror of upstream release
flash_attn-2.8.3+cu128torch2.8-cp311-cp311-linux_x86_64.whl	Linux x86_64	12.8	2.8	mjun0812/flash-attention-prebuild-wheels	Mirror of upstream release

Direct install

BASE=https://huggingface.co/deAPI-ai/image-server-wheels/resolve/main

# Windows
pip install $BASE/image_server_kernels-0.4.2+cu128torch2.8-cp311-cp311-win_amd64.whl
pip install $BASE/q8_kernels-0.0.5-cp311-cp311-win_amd64.whl
pip install $BASE/block_sparse_attn-0.0.2-cp311-cp311-win_amd64.whl
pip install $BASE/flash_attn-2.8.2+cu128torch2.8-cp311-cp311-win_amd64.whl
pip install $BASE/sageattention-2.2.0+cu128torch2.8.0-cp311-cp311-win_amd64.whl
pip install --no-deps $BASE/ace_step-1.6.0-py3-none-any.whl
pip install --no-deps $BASE/qwen_tts-0.1.1+deapi3-py3-none-any.whl

# Linux
pip install $BASE/image_server_kernels-0.3.0+cu129torch2.8-cp311-cp311-linux_x86_64.whl
pip install $BASE/q8_kernels-0.0.5-cp311-cp311-linux_x86_64.whl
pip install $BASE/block_sparse_attn-0.0.2-cp311-cp311-linux_x86_64.whl
pip install $BASE/sageattention-2.2.0+cu129torch2.8-cp311-cp311-linux_x86_64.whl
pip install --no-deps $BASE/flash_attn-2.8.3+cu128torch2.8-cp311-cp311-linux_x86_64.whl

sageattention is required by the MiniMax-H3 video model, which imports it unconditionally. Every other model that can use it falls back to SDPA when it is absent, so on a box that does not serve H3 the wheel is optional.

image_server_kernels is at 0.4.2 on Windows but only 0.3.0 on Linux — the Linux build of 0.4.2 has not been made yet. Sol-Attn, W4A8 and the bundled GGUF kernels are therefore Windows-only for now.

What is verified in image_server_kernels-0.4.2

Built and tested on Windows 11 / RTX 5090 Laptop (SM120), CUDA 12.8, torch 2.8.0+cu128.

• scripts/smoke_test_rebuild.py — ALL PASS: fp8_dense_gemm (per-tensor cos 0.99965, per-channel+bias 0.99972), dynamic_per_token_scaled_fp8_quant 0.99965, fp8_dense_gemm_pre_quantized 1.00000, fp8_dense_gemm_sm120 1.00000 via native CUTLASS 3.x, rmsnorm_forward 0.999999.
• scripts/validate_gguf_kernels.py against real GGUF tensors — ALL PASS for Q4_K, Q5_K, Q6_K, Q5_0, Q5_1, Q8_0 (cos 0.99984–1.00000), which covers the bf16 K-quant dequant path the in-tree patch exists for.
• sol_attn on real MiniMax-H3 activations (S=15,479, 5 capture points): 2.22x vs SageAttention2 at cos 0.9805 at tau=1.0. Both speed and accuracy rise with sequence length (4k → 1.68x/0.965, 15.5k → 2.90x/0.980 in one run), so longer clips do better than this figure.

Not exercised on this box: sol_attn on Ada SM89 (compiles and links, but no Ada GPU here), w4a8_linear, nvfp4_*, fp8_grouped_gemm, w8a16_dense_gemm.

Credits

flash_attn wheels are mirrored from mjun0812/flash-attention-prebuild-wheels — all credit for those builds goes to the upstream author.

sageattention is a mirror, not our build. The library is thu-ml/SageAttention (Apache-2.0); the Windows wheel is built and published by woct0rdho, and we found it through wildminder/AI-windows-whl, which indexes Windows CUDA wheels. All credit for the library and the build goes to them. The Windows file here is byte-identical to the upstream release (sha256:4379951403809dfcd5b1e10d35e287abf42afafb7b27615c09d6062cfbdf230a).

The Linux sageattention-2.2.0+cu129torch2.8 wheel's provenance is not recorded — we have not confirmed whether it is our own build or a mirror. Fill this in before relying on the attribution.

We mirror third-party wheels so the install scripts have a single source of truth and do not break if upstream release URLs change.

ace_step, qwen_tts, image_server_kernels, block_sparse_attn and q8_kernels were built in-house.

Sol-Attn in image_server_kernels wraps CUDA kernels from comfy-kitchen (Apache-2.0); see third_party/comfy_kitchen_sol/PROVENANCE.md in the image-server-kernels repo. The bundled GGUF kernels are llama.cpp's ggml-cuda (MIT), vendored per third_party/llamacpp_gguf/PROVENANCE.md.