Showing 4 open source projects for "abc-map"

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  • 1
    Satori

    Satori

    Enlightened library to convert HTML and CSS to SVG

    Enlightened library to convert HTML and CSS to SVG. Satori supports the JSX syntax, which makes it very straightforward to use. Satori will render the element into a 600×400 SVG, and return the SVG string. Under the hood, it handles layout calculation, font, typography and more, to generate a SVG that matches the exact same HTML and CSS in a browser. Satori only accepts JSX elements that are pure and stateless. You can use a subset of HTML elements (see section below), or custom React...
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  • 2
    Diffusers-Interpret

    Diffusers-Interpret

    Model explainability for Diffusers

    diffusers-interpret is a model explainability tool built on top of Diffusers. Model explainability for Diffusers. Get explanations for your generated images. Install directly from PyPI. It is possible to visualize pixel attributions of the input image as a saliency map. diffusers-interpret also computes these token/pixel attributions for generating a particular part of the image. To analyze how a token in the input prompt influenced the generation, you can study the token attribution scores...
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  • 3
    RQ-Transformer

    RQ-Transformer

    Implementation of RQ Transformer, autoregressive image generation

    Implementation of RQ Transformer, which proposes a more efficient way of training multi-dimensional sequences autoregressively. This repository will only contain the transformer for now. You can use this vector quantization library for the residual VQ. This type of axial autoregressive transformer should be compatible with memcodes, proposed in NWT. It would likely also work well with multi-headed VQ. I also think there is something deeper going on, and have generalized this to any number of...
    Downloads: 0 This Week
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  • 4
    Edward

    Edward

    A probabilistic programming language in TensorFlow

    .... It enables features such as computational graphs, distributed training, CPU/GPU integration, automatic differentiation, and visualization with TensorBoard. Expectation-Maximization, pseudo-marginal and ABC methods, and message passing algorithms.
    Downloads: 0 This Week
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