Pillow-AllSIMD
Pillow, 6 times as fast on x86-64 and 3 times as fast on arm64.
Pillow-AllSIMD is a drop-in replacement for Pillow 13.0.0, the friendly PIL fork: the same PIL package and the same API, with SIMD kernels for x86-64 (SSE4, AVX2, AVX-VNNI) and arm64 (NEON). It is a fork of Pillow-SIMD, which brought SIMD to Pillow on x86.
Fast: as the geometric mean of 111 operations on one thread, 6.1 times as fast as Pillow 13.0.0 on x86-64 with AVX2 and 3.1 times on arm64 with NEON. That is 2.9 times as fast as Pillow-SIMD 12.1.1 with AVX2, Pillow-SIMD’s last release. Of the 104 operations that OpenCV 5.0 also has, Pillow-AllSIMD is faster in 78 on x86-64 and in 68 on arm64.
Drop-in: your code doesn’t change. Pillow’s test suite passes on every build.
Exact when you ask: in fast mode (the default) a few operations give results that differ from Pillow’s by a few levels at most, the same on every build and CPU. Exact mode follows Pillow’s arithmetic and is still 5.4 times as fast on x86-64 and 2.5 times on arm64.
arm64 too: NEON kernels for resizes, blurs, filters, conversions, transforms and more, tested on an NVIDIA Jetson Orin, in wheels for Linux (and macOS, as they are added to this release). Other CPUs build portable C that compilers vectorize.
Benchmarks
Selected operations, one thread, a 2560×1600 RGB image. Each cell shows Pillow 13.0.0’s time, then Pillow-AllSIMD’s, then the speed-up:
Operation |
x86-64, AVX2 |
arm64, NEON |
|---|---|---|
Resize to 320×200, bicubic |
22.3 → 1.41 ms, 15.8× |
30.2 → 5.02 ms, 6.0× |
Resize RGBA to 2048×1280, Lanczos |
119 → 9.95 ms, 12.0× |
145 → 40.7 ms, 3.6× |
Enlarge to 5478×3424, bicubic |
226 → 27.7 ms, 8.2× |
260 → 87.3 ms, 3.0× |
Gaussian blur, radius 1 |
121 → 5.59 ms, 21.6× |
162 → 16.7 ms, 9.7× |
Gaussian blur, radius 10 |
121 → 25.9 ms, 4.7× |
163 → 91.2 ms, 1.8× |
Unsharp mask |
197 → 15.8 ms, 12.5× |
236 → 40.1 ms, 5.9× |
SHARPEN filter |
52.9 → 4.21 ms, 12.6× |
99.0 → 27.1 ms, 3.7× |
Median filter, 3×3 |
1183 → 2.79 ms, 425× |
1262 → 8.20 ms, 154× |
RGB to HSV |
59.7 → 2.99 ms, 20.0× |
81.4 → 10.6 ms, 7.7× |
Unpremultiply alpha |
26.6 → 2.20 ms, 12.1× |
38.8 → 7.21 ms, 5.4× |
Rotate 30°, bicubic |
185 → 29.3 ms, 6.3× |
291 → 79.3 ms, 3.7× |
Alpha composite |
19.6 → 2.90 ms, 6.8× |
22.1 → 18.0 ms, 1.2× |
Draw 100 lines of text |
56.5 → 3.23 ms, 17.5× |
113 → 5.72 ms, 19.7× |
Decode JPEG |
16.2 → 15.5 ms, 1.0× |
28.3 → 28.4 ms, 1.0× |
Encode PNG |
789 → 509 ms, 1.6× |
1015 → 711 ms, 1.4× |
JPEG load, rotate, resize, blur, save |
86.1 → 28.6 ms, 3.0× |
112 → 66.4 ms, 1.7× |
Codecs gain little: decoding and encoding are mostly libjpeg’s, libpng’s and libwebp’s own work. The machines are an Intel Core Ultra 9 285H laptop (with turbo off) and an NVIDIA Jetson Orin (Cortex-A78AE). Each time is the best of 3 interleaved rounds, in Docker with Python 3.12 and GCC 14, measured on 2026-10-31 on the same x86-64 and arm64 code as this release. The benchmark page has all 111 operations, in exact and fast mode. It also compares Pillow-SIMD, OpenCV, libvips and ImageMagick, with notes where another library’s operation differs.
Installing
On arm64 Linux (glibc 2.28 or later, or musl), pip installs a wheel with the NEON kernels and the image libraries bundled, as Pillow’s wheels have them; wheels for macOS 11 or later on Apple silicon are added to this release as GitHub’s macOS runners build them, and until then macOS builds from source, with the NEON kernels too. Elsewhere, x86 included, Pillow-AllSIMD is a source package, so pip compiles it on installation: install the libraries that building Pillow from source needs first. It installs the PIL package, as Pillow does. Install your other packages first, then replace Pillow with it:
python3 -m pip uninstall -y pillow pillow-simd python3 -m pip install pillow-allsimd # arm64: a wheel python3 -m pip install -C simd=avx2 pillow-allsimd # x86: built with AVX2
Uninstall Pillow before installing, not after: both own the files in PIL. The -C simd= options below apply to source builds only; to build from source on arm64 anyway, add --no-binary pillow-allsimd. To rebuild with another simd option, add --no-cache-dir --force-reinstall. Otherwise pip reuses the wheel it built the first time. With -v, pip shows the line --- SIMD kernels: ... from the build, which names the kernels you got.
-C simd= |
Kernels |
|---|---|
auto (default) |
SSE4.1 on x86, NEON on arm64, portable C elsewhere |
sse4 |
SSE4.1 |
avx2 |
SSE4.1 and AVX2: x86 CPUs since Intel Haswell and AMD Excavator, except some Pentium, Celeron and Atom models |
avxvnni |
SSE4.1, AVX2 and AVX-VNNI, for faster integer filters and resize: Intel Alder Lake, Sapphire Rapids and newer, AMD Zen 5 |
native |
Everything the build machine’s CPU supports (-march=native); the build runs only on similar CPUs |
native512 |
Like native, preferring 512-bit vectors (x86) |
neon |
NEON (arm64) |
disable |
Portable C only |
Exact and fast modes
Fast mode is on by default. The Gaussian blur, RGB ↔ YCbCr, RGB → HSV, the affine, perspective and quad transforms, rotate(), blend(), the resize of RGB, RGBA and other 4-byte images and large downscales then use faster kernels. Their results differ from Pillow’s by a few levels at most and are the same on every build and architecture. Exact mode follows Pillow’s arithmetic for every operation. Use it to compare pixels with images made by Pillow:
PILLOW_ALLSIMD_FAST=0 python3 app.py
Or, in code:
from PIL import Image, ImageFilter
Image.set_fast(False) # every operation from now on
with Image.fast_mode(False): # one block
blurred = im.filter(ImageFilter.GaussianBlur(2))Fast mode in the documentation lists each operation and how far its results may differ.
Testing
Pillow’s test suite runs on every build, and in every wheel on its own platform. Consistency tests check each SIMD kernel against the scalar code, and per-kernel output hashes compare the builds with each other. The hardware tests run on x86-64 (SSE4, AVX2, AVX-VNNI) and on arm64 (NEON). The portable build also passes the suite on riscv64, ppc64le, s390x, armv7 and 32-bit x86, under QEMU. Status: beta.
Links
Pillow is by Jeffrey ‘Alex’ Clark and contributors, on PIL by Fredrik Lundh and Secret Labs AB. Pillow-SIMD is by Alexander Karpinsky and contributors, supported by Uploadcare. All are licensed under the MIT-CMU License, and so is Pillow-AllSIMD.
1 maintainer
Metadata
1 maintainer
Release files for pillow-allsimd 13.0.0.post2
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Transparency logRelease files / pillow_allsimd-13.0.0.post2-cp311-cp311-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl
| Download URL | pillow_allsimd-13.0.0.post2-cp311-cp311-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl |
|---|---|
| Size | 7.1 MB |
| Tags | CPython 3.11 Linux glibc 2.27+ ARM64 Linux glibc 2.28+ ARM64 |
|
SHA-256 checksum How to use checksums |
84c40e6e7e001d81777b9c81de1b5cee3cc6d8899d48d07cb14ba7533ea6e401
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a43052f88f7c4e980c744473413e23bed26386f53258b6d868e9150433b4aa52
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Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
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Transparency logSecurity for pillow-allsimd 13.0.0.post2
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