Showing 4 open source projects for "double"

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    Upscayl

    Upscayl

    Free and Open Source AI Image Upscaler for Linux, MacOS and Windows

    ...Upscayl does not work without a GPU, sorry. You'll need a Vulkan-compatible GPU to upscale images. CPU or iGPU won't work. You can also download the flatpak version and double-click the flatpak file to install via Store but wait for the full release, we'll be pushing it to Flathub for easy access. Upscayl uses AI models to enhance your images by guessing what the details could be. It uses Real-ESRGAN (and more in the future) model to achieve this. The CLI tool is called real-esrgan-ncnn-vulkan and it's available on the Real-ESRGAN repository.
    Downloads: 180 This Week
    Last Update:
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  • 2
    PNG Upscale

    PNG Upscale

    AI Super - Resolution

    ...To upscale an image you just need to choose a mode, load a picture and press start. Save button can be used to choose an output folder and filename before you start the process (either just name or .png) You can double-click the text box to change [Dark - Light] theme (disabled when upScaling) Use PNG images for best results. Download the executable corresponding with your operating system, and the Models folder. It's possible to download only some of the models if you want (It just won't let you use them inside the program) The Models folder needs to be in the same directory as the Jar/Exe to use them.
    Downloads: 1 This Week
    Last Update:
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  • 3

    waifu2x-cpu-torch-vks

    waifu2x fork without CUDA

    The original is here: https://github.com/nagadomi/waifu2x Differences: - CPU only, no CUDA needed; - double-precision version; - GPU and CPU-trained models (on the fly conversion in memory); - adapted for Lua v5.2 (works with Torch 7 on x32 Ubuntu). 31-12-2016: - removed unneeded data copies, left from CUDA processing; - removed large duplicate files (see "ReadMe" before use).
    Downloads: 1 This Week
    Last Update:
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  • 4

    blitz-image

    GPU-accelerated image processing for java

    ...The performance of a typical bicubic upscale operation is typically in the order of 10 times faster than via the standard AWT mechanism using RenderingHints.VALUE_INTERPOLATION_BICUBIC. The library depends on Aparapi (https://code.google.com/p/aparapi/). Any GPU supporting OpenCL should be supported (including low-end GPUs without double-precision fp support), and there is automatic fallback to pure java implementations for all operations in the event that there is no OpenCL support available. A high-level API allows for very easy use from AWT/Swing, whilst the low-level array-based API allows integration with any imaging framework (e.g. from Android or SWT).
    Downloads: 0 This Week
    Last Update:
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