Showing 4 open source projects for "track"

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  • 1
    ACE-Step 1.5

    ACE-Step 1.5

    The most powerful local music generation model

    ACE-Step 1.5 is an advanced open-source foundation model for AI-driven music generation that pushes beyond traditional limitations in speed, musical coherence, and controllability by innovating in architecture and training design. It integrates cutting-edge generative techniques—such as diffusion-based synthesis combined with compressed autoencoders and lightweight transformer elements—to produce high-quality full-length music tracks with rapid inference times, capable of generating a...
    Downloads: 76 This Week
    Last Update:
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  • 2
    Gorden Super PPT Skills

    Gorden Super PPT Skills

    AI PPT Track Terminator, the strongest PPT Skill ever

    Gorden Super PPT Skills is an AI presentation skill package for generating high-density visual presentations and converting them into editable PowerPoint files. The workflow is split into three skills that can be used separately or together. One skill generates image-based presentation pages from a topic or content brief. Another skill reconstructs image slides into editable PPTX files by separating backgrounds, layout structures, icons, decorations, and text. The orchestration skill chains...
    Downloads: 8 This Week
    Last Update:
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  • 3
    Tracking Any Point (TAP)

    Tracking Any Point (TAP)

    DeepMind model for tracking arbitrary points across videos & robotics

    ...RoboTAP demonstrates how TAPIR-style tracks can drive real-world robot manipulation via efficient imitation, and ships with a dataset of annotated robotics videos. The repo provides JAX and PyTorch checkpoints, Colab demos, and a real-time live demo that runs on a GPU to let you select and track points interactively.
    Downloads: 0 This Week
    Last Update:
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  • 4
    Apple Neural Engine (ANE) Transformers

    Apple Neural Engine (ANE) Transformers

    Reference implementation of the Transformer architecture optimized

    ...The repository targets practitioners who want to keep familiar PyTorch modeling while preparing models for Core ML/ANE execution paths. Documentation highlights reported improvements in throughput and memory residency, while releases track incremental fixes and packaging updates. The project sits alongside related Apple ML repos that focus on deploying attention-based models efficiently to ANE-equipped hardware. In short, it’s a practical blueprint for adapting Transformers to Apple’s dedicated ML accelerator without rewriting entire model stacks.
    Downloads: 0 This Week
    Last Update:
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