Showing 4 open source projects for "bandwidth"

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  • 1
    FramePack

    FramePack

    Lets make video diffusion practical

    ...The idea is to “pack” frames by detecting shared structure and storing differences efficiently, which can accelerate training or inference on video-like data. By reducing I/O and memory bandwidth, datasets become lighter to load while models still see the essential temporal variation. The repository demonstrates both packing and unpacking steps, making it straightforward to integrate into preprocessing pipelines. It’s useful for diffusion and generative models that learn from sequential image datasets, as well as classical pipelines that batch many related frames. ...
    Downloads: 13 This Week
    Last Update:
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  • 2
    MCP Monitor

    MCP Monitor

    A system monitoring tool that exposes system metrics

    The MCP System Monitor is a tool that exposes system metrics via the Model Context Protocol (MCP), allowing Large Language Models (LLMs) to retrieve real-time system information through an MCP-compatible interface. ​
    Downloads: 0 This Week
    Last Update:
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  • 3
    how-to-optim-algorithm-in-cuda

    how-to-optim-algorithm-in-cuda

    How to optimize some algorithm in cuda

    how-to-optim-algorithm-in-cuda is an open educational repository focused on teaching developers how to optimize algorithms for high-performance execution on GPUs using CUDA. The project combines technical notes, code examples, and practical experiments that demonstrate how common computational kernels can be optimized to improve speed and memory efficiency. Instead of presenting only theoretical explanations, the repository includes hand-written CUDA implementations of fundamental operations...
    Downloads: 6 This Week
    Last Update:
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  • 4
    VoiceFixer

    VoiceFixer

    General Speech Restoration

    VoiceFixer is a machine-learning framework for “speech restoration”: given a degraded or distorted audio recording — with noise, clipping, low sampling rate, reverberation, or other artifacts — it attempts to recover high-fidelity, clean speech. The architecture works in two stages: first an analysis stage that tries to extract “clean” intermediate features from the noisy audio (e.g. removing noise, denoising, dereverberation, upsampling), and then a neural vocoder-based synthesis stage that...
    Downloads: 6 This Week
    Last Update:
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