Showing 8 open source projects for "batch ai"

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  • 1
    LTX-Video

    LTX-Video

    Official repository for LTX-Video

    LTX-Video is a sophisticated multimedia processing framework from Lightricks designed to handle high-quality video editing, compositing, and transformation tasks with performance and scalability. It provides runtime components that efficiently decode, encode, and manipulate video streams, frame buffers, and audio tracks while exposing a rich API for building customized editing features like transitions, effects, color grading, and keyframe automation. The toolkit is built with both real-time...
    Downloads: 7 This Week
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  • 2
    FramePack

    FramePack

    Lets make video diffusion practical

    FramePack explores compact representations for sequences of image frames, targeting tasks where many near-duplicate frames carry redundant information. 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...
    Downloads: 20 This Week
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  • 3
    DeepSeek-OCR

    DeepSeek-OCR

    Contexts Optical Compression

    DeepSeek-OCR is an open-source optical character recognition solution built as part of the broader DeepSeek AI vision-language ecosystem. It is designed to extract text from images, PDFs, and scanned documents, and integrates with multimodal capabilities that understand layout, context, and visual elements beyond raw character recognition. The system treats OCR not simply as “read the text” but as “understand what the text is doing in the image”—for example distinguishing captions from body...
    Downloads: 6 This Week
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  • 4
    Stable Diffusion WebUI Forge

    Stable Diffusion WebUI Forge

    Stable Diffusion WebUI Forge is a platform on top of Stable Diffusion

    Stable Diffusion WebUI Forge is a performance- and feature-oriented fork of the popular AUTOMATIC1111 interface that experiments with new backends, memory optimizations, and UX improvements. It targets heavy users and researchers who push large models, control nets, and high-resolution pipelines where default settings can become bottlenecks. The fork typically introduces toggles for scheduler behavior, attention implementations, caching, and precision modes to reach better speed or quality...
    Downloads: 0 This Week
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  • 5
    Warlock-Studio

    Warlock-Studio

    Suite with Real-ESRGAN, BSRGAN , RealESRNet, IRCNN, GFPGAN & RIFE.

    v5.1.1. Warlock-Studio is a Windows application that uses Real-ESRGAN, BSRGAN, IRCNN, GFPGAN, RealESRNet, RealESRAnime and RIFE Artificial Intelligence models to upscale, restore faces, interpolate frames and reduce noise in images and videos. the application supports GPU acceleration (including multi-GPU setups) and offers batch processing for large workloads. It includes drag-and-drop handling for single or multiple files, optional pre-resize functions, and an automatic tiling system...
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    Downloads: 19 This Week
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  • 6
    Demucs

    Demucs

    Code for the paper Hybrid Spectrogram and Waveform Source Separation

    Demucs (Deep Extractor for Music Sources) is a deep-learning framework for music source separation—extracting individual instrument or vocal tracks from a mixed audio file. The system is based on a U-Net-like convolutional architecture combined with recurrent and transformer elements to capture both short-term and long-term temporal structure. It processes raw waveforms directly rather than spectrograms, allowing for higher-quality reconstruction and fewer artifacts in separated tracks. The...
    Downloads: 53 This Week
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  • 7
    Denoiser

    Denoiser

    Real Time Speech Enhancement in the Waveform Domain (Interspeech 2020)

    Denoiser is a real-time speech enhancement model operating directly on raw waveforms, designed to clean noisy audio while running efficiently on CPU. It uses a causal encoder-decoder architecture with skip connections, optimized with losses defined both in the time domain and frequency domain to better suppress noise while preserving speech. Unlike models that operate on spectrograms alone, this design enables lower latency and coherent waveform output. The implementation includes data...
    Downloads: 0 This Week
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  • 8
    FixRes

    FixRes

    Reproduces results of "Fixing the train-test resolution discrepancy"

    FixRes is a lightweight yet powerful training methodology for convolutional neural networks (CNNs) that addresses the common train-test resolution discrepancy problem in image classification. Developed by Facebook Research, FixRes improves model generalization by adjusting training and evaluation procedures to better align input resolutions used during different phases. The approach is simple but highly effective, requiring no architectural modifications and working across diverse CNN...
    Downloads: 0 This Week
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