--- license: other tags: - wheels - cuda - pytorch - windows - linux --- # 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](https://github.com/woct0rdho/SageAttention/releases/tag/v2.2.0-windows) | 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](https://github.com/mjun0812/flash-attention-prebuild-wheels/releases/tag/v0.4.10) | 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](https://github.com/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](https://github.com/mjun0812/flash-attention-prebuild-wheels/releases/tag/v0.7.16) | Mirror of upstream release | ## Direct install ```bash 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](https://github.com/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](https://github.com/thu-ml/SageAttention) (Apache-2.0); the Windows wheel is built and published by [woct0rdho](https://github.com/woct0rdho/SageAttention/releases), and we found it through [wildminder/AI-windows-whl](https://github.com/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`.