Showing 7 open source projects for "visual\"

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  • 1
    Langflow

    Langflow

    Low-code app builder for RAG and multi-agent AI applications

    Langflow is a low-code app builder for RAG and multi-agent AI applications. It’s Python-based and agnostic to any model, API, or database.
    Downloads: 13 This Week
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  • 2
    InvokeAI

    InvokeAI

    InvokeAI is a leading creative engine for Stable Diffusion models

    ...It runs on Windows, Mac and Linux machines, and runs on GPU cards with as little as 4 GB or RAM. InvokeAI is a leading creative engine built to empower professionals and enthusiasts alike. Generate and create stunning visual media using the latest AI-driven technologies. InvokeAI offers an industry leading Web Interface, interactive Command Line Interface, and also serves as the foundation for multiple commercial products. This fork is supported across Linux, Windows and Macintosh. Linux users can use either an Nvidia-based card (with CUDA support) or an AMD card (using the ROCm driver). ...
    Downloads: 15 This Week
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  • 3
    Coframe

    Coframe

    Coframe brings your UX to life with AI-powered optimization

    Bring your UX to life with AI-powered optimization and personalization. Coframe brings the content of your app or website to life through AI-powered optimization, personalization, and overall self-improvement. It takes minutes to integrate, and the ROI is clear to measure. Your website or app gains self-enhancing abilities with Coframe, learning from real-world performance. It's A/B testing, but with a serious upgrade. Coframe uses the latest in AI to generate copy that is tailored to your...
    Downloads: 0 This Week
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  • 4
    Dalai

    Dalai

    The simplest way to run LLaMA on your local machine

    Run LLaMA and Alpaca on your computer. Dalai runs on all of the following operating systems, Linux, Mac, and Windows. Runs on most modern computers. Unless your computer is very very old, it should work.
    Downloads: 0 This Week
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  • 5
    GANformer

    GANformer

    Generative Adversarial Transformers

    ...The network employs a bipartite structure that enables long-range interactions across the image, while maintaining computation of linearly efficiency, that can readily scale to high-resolution synthesis. The model iteratively propagates information from a set of latent variables to the evolving visual features and vice versa, to support the refinement of each in light of the other and encourage the emergence of compositional representations of objects and scenes. In contrast to the classic transformer architecture, it utilizes multiplicative integration that allows flexible region-based modulation and can thus be seen as a generalization of the successful StyleGAN network. ...
    Downloads: 0 This Week
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  • 6
    Deep Feature Rotation Multimodal Image

    Deep Feature Rotation Multimodal Image

    Implementation of Deep Feature Rotation for Multimodal Image

    ...Our approach is a representative of the many ways of augmentation for intermediate feature embedding without consuming too much computational expense. Prepare your content image and style image. I provide some in the data/content and data/style and you can try to use them easily. We provide a visual comparison between other rotation angles that do not appear in the paper. The rotation angles will produce a very diverse number of outputs. This has proven the effectiveness of our method with other methods.
    Downloads: 0 This Week
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  • 7
    FID score for PyTorch

    FID score for PyTorch

    Compute FID scores with PyTorch

    This is a port of the official implementation of Fréchet Inception Distance to PyTorch. FID is a measure of similarity between two datasets of images. It was shown to correlate well with human judgement of visual quality and is most often used to evaluate the quality of samples of Generative Adversarial Networks. FID is calculated by computing the Fréchet distance between two Gaussians fitted to feature representations of the Inception network. The weights and the model are exactly the same as in the official Tensorflow implementation, and were tested to give very similar results (e.g. .08 absolute error and 0.0009 relative error on LSUN, using ProGAN generated images). ...
    Downloads: 0 This Week
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