@upscalerjs/esrgan-medium/dist/umd/models/esrgan-medium/src/x2/index.js

ESRGAN Medium Model for UpscalerJS. Upscale images and increase image resolution with AI using Javascript

(function (global, factory) {
    typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() :
    typeof define === 'function' && define.amd ? define(factory) :
    (global = typeof globalThis !== 'undefined' ? globalThis : global || self, global.ESRGANMedium2x = factory());
})(this, (function () { 'use strict';

    const isTensorArray = (inputs) => {
        return Array.isArray(inputs);
    };
    const getInput = (inputs) => {
        if (isTensorArray(inputs)) {
            return inputs[0];
        }
        return inputs;
    };
    const getESRGANModelDefinition = ({ scale, name, version, meta: { architecture, ...meta }, path: modelPath, }) => {
        const path = modelPath || `models/x${scale}/model.json`;
        if (architecture === 'rdn') {
            return {
                scale,
                modelType: 'layers',
                _internals: {
                    path,
                    name,
                    version,
                },
                meta: {
                    architecture,
                    ...meta,
                },
                inputRange: [0, 255,],
                outputRange: [0, 255,],
            };
        }
        const setup = (tf) => {
            const Layer = tf.layers.Layer;
            const BETA = 0.2;
            class MultiplyBeta extends Layer {
                beta;
                constructor() {
                    super({});
                    this.beta = BETA;
                }
                call(inputs) {
                    return tf.mul(getInput(inputs), this.beta);
                }
                static className = 'MultiplyBeta';
            }
            const getPixelShuffle = (_scale) => {
                class PixelShuffle extends Layer {
                    scale = _scale;
                    constructor() {
                        super({});
                    }
                    computeOutputShape(inputShape) {
                        return [inputShape[0], inputShape[1], inputShape[2], 3,];
                    }
                    call(inputs) {
                        return tf.depthToSpace(getInput(inputs), this.scale, 'NHWC');
                    }
                    static className = `PixelShuffle${scale}x`;
                }
                return PixelShuffle;
            };
            [
                MultiplyBeta,
                getPixelShuffle(scale),
            ].forEach((layer) => {
                tf.serialization.registerClass(layer);
            });
        };
        return {
            setup,
            scale,
            modelType: 'layers',
            _internals: {
                path,
                name,
                version,
            },
            meta: {
                architecture,
                ...meta,
            },
            inputRange: [0, 1,],
            outputRange: [0, 1,],
        };
    };

    const NAME = "@upscalerjs/esrgan-medium";
    const VERSION = "1.0.0-beta.13";

    const getModelDefinition = (scale, modelFileName) => getESRGANModelDefinition({
        scale,
        path: `models/x${scale}/model.json`,
        name: NAME,
        version: VERSION,
        meta: {
            C: 1,
            D: 10,
            G: 64,
            G0: 64,
            T: 10,
            architecture: "rdn",
            patchSize: scale === 3 ? 129 : 128,
            size: 'medium',
            artifactReducing: false,
            sharpening: false,
            dataset: 'div2k',
            modelFileName,
        },
    });

    var index = getModelDefinition(2, 'rdn-C1-D10-G64-G064-T10-x2-patchsize128-compress100-sharpen0-datadiv2k-vary_cFalse_best-val_generator_PSNR_Y_epoch478');

    return index;

}));