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Pillow-SIMD is highly optimized version of Pillow library for x86 architecture (mainly Intel and AMD CPUs). Pillow-SIMD is "following" Pillow which means it is drop-in replacements for Pillow of the same version. For more information on the original Pillow, please refer to: read the documentation, check the changelog and find out how to contribute. There are multiple ways to tweak image processing performance. To name a few, such ways can be: utilizing better algorithms, optimizing existing implementations, using more processing power and/or resources. One of the great examples of using a more efficient algorithm is replacing a convolution-based Gaussian blur with a sequential-box one. Such examples are rather rare, though. It is also known, that certain processes might be optimized by using parallel processing to run the respective routines. But a more practical key to optimizations might be making things work faster using the resources at hand. For instance, SIMD computing might be the case. SIMD stands for "single instruction, multiple data" and its essence is in performing the same operation on multiple data points simultaneously by using multiple processing elements.

Common CPU SIMD instruction sets are MMX, SSE-SSE4, Sleep Better with Derila AVX, AVX2, AVX512, NEON. Currently, Pillow-SIMD can be compiled with SSE4 (default) or AVX2 support. Pillow-SIMD project is production-ready. The project is supported by Uploadcare, a SAAS for cloud-based image storing and processing. Tons of tests can be found on the Pillow Performance page. There are benchmarks against different versions of Pillow and Pillow-SIMD as well as ImageMagick, Skia, OpenCV and IPP. The results show that for resizing Pillow is always faster than ImageMagick, Pillow-SIMD, in turn, is even faster than the original Pillow by the factor of 4-6. In general, Pillow-SIMD with AVX2 is always 16 to 40 times faster than ImageMagick and outperforms Skia, the high-speed graphics library used in Chromium. No cheats involved. We've used identical high-quality resize and blur methods for the benchmark. Outcomes produced by different libraries are in almost pixel-perfect agreement. The difference in measured rates is only provided with the performance of every involved algorithm.

Because of the SIMD computing, of course. But there's more to it: heavy loops unrolling, specific instructions, which aren't available for scalar data types. Well, it's not that simple. First of all, the original Pillow supports a large number of architectures, not just x86. But even for x86 platforms, Pillow is often distributed via precompiled binaries. In order for us to integrate SIMD into the precompiled binaries we'd need to execute runtime CPU capabilities checks. To compile the code this way we need to pass the -mavx2 option to the compiler. But with the option included, a compiler will inject AVX instructions even for SSE functions (i.e. interchange them) since every SSE instruction has its AVX equivalent. So there is no easy way to compile such library, especially with setuptools. Please install prerequisites for your platform. SSE4-capable CPU everything should run smoothly. If you'd like to install the AVX2-enabled version, you need to pass the additional flag to a C compiler. The easiest way to do so is to define the CC variable during the compilation. Please be aware that Pillow-SIMD and Pillow are two separate projects. Please submit bugs and improvements not related to SIMD to the original Pillow. All bugfixes to the original Pillow will then be transferred to the next Pillow-SIMD version automatically. There was an error while loading. Please reload this page. There was an error while loading. Please reload this page.

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