luboslenco
123 lines
3.3 KiB
TypeScript
123 lines
3.3 KiB
TypeScript
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// @:keep
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class VarianceNode extends LogicNode {
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static temp: Image = null;
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static image: Image = null;
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static inst: VarianceNode = null;
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static prompt = "";
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constructor() {
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super();
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inst = this;
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init();
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}
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static init = () => {
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if (temp == null) {
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temp = Image.createRenderTarget(512, 512);
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}
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}
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static buttons = (ui: zui.Zui, nodes: zui.Zui.Nodes, node: zui.Zui.TNode) => {
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prompt = ui.textArea(zui.Zui.handle("variancenode_0"), true, tr("prompt"), true);
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node.buttons[0].height = prompt.split("\n").length;
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}
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override getAsImage = (from: i32, done: (img: Image)=>void) => {
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let strength = inst.inputs[1].node.value;
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inst.inputs[0].getAsImage((source: Image) => {
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temp.g2.begin(false);
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temp.g2.drawScaledImage(source, 0, 0, 512, 512);
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temp.g2.end();
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let bytes_img = temp.getPixels().b.buffer;
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let u8a = new Uint8Array(bytes_img);
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let f32a = new Float32Array(3 * 512 * 512);
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for (let i = 0; i < (512 * 512); ++i) {
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f32a[i ] = (u8a[i * 4 ] / 255) * 2.0 - 1.0;
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f32a[i + 512 * 512 ] = (u8a[i * 4 + 1] / 255) * 2.0 - 1.0;
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f32a[i + 512 * 512 * 2] = (u8a[i * 4 + 2] / 255) * 2.0 - 1.0;
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}
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Console.progress(tr("Processing") + " - " + tr("Variance"));
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Base.notifyOnNextFrame(() => {
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Data.getBlob("models/sd_vae_encoder.quant.onnx", (vae_encoder_blob: ArrayBuffer) => {
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let latents_buf = Krom.mlInference(vae_encoder_blob, [f32a.buffer], [[1, 3, 512, 512]], [1, 4, 64, 64], Config.raw.gpu_inference);
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let latents = new Float32Array(latents_buf);
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for (let i = 0; i < latents.length; ++i) {
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latents[i] = 0.18215 * latents[i];
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}
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let noise = new Float32Array(latents.length);
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for (let i = 0; i < noise.length; ++i) noise[i] = Math.cos(2.0 * 3.14 * RandomNode.getFloat()) * Math.sqrt(-2.0 * Math.log(RandomNode.getFloat()));
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let num_inference_steps = 50;
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let init_timestep = Math.floor(num_inference_steps * strength);
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let timesteps = TextToPhotoNode.timesteps[num_inference_steps - init_timestep];
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let alphas_cumprod = TextToPhotoNode.alphas_cumprod;
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let sqrt_alpha_prod = Math.pow(alphas_cumprod[timesteps], 0.5);
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let sqrt_one_minus_alpha_prod = Math.pow(1.0 - alphas_cumprod[timesteps], 0.5);
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for (let i = 0; i < latents.length; ++i) {
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latents[i] = sqrt_alpha_prod * latents[i] + sqrt_one_minus_alpha_prod * noise[i];
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}
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let t_start = num_inference_steps - init_timestep;
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TextToPhotoNode.stableDiffusion(prompt, (_image: Image) => {
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image = _image;
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done(image);
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}, latents, t_start);
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});
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});
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});
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}
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override getCachedImage = (): Image => {
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return image;
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}
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static def: zui.Zui.TNode = {
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id: 0,
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name: _tr("Variance"),
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type: "VarianceNode",
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x: 0,
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y: 0,
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color: 0xff4982a0,
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inputs: [
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{
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id: 0,
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node_id: 0,
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name: _tr("Color"),
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type: "RGBA",
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color: 0xffc7c729,
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default_value: array_f32([0.0, 0.0, 0.0, 1.0])
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},
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{
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id: 0,
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node_id: 0,
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name: _tr("Strength"),
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type: "VALUE",
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color: 0xffa1a1a1,
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default_value: 0.5
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}
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],
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outputs: [
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{
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id: 0,
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node_id: 0,
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name: _tr("Color"),
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type: "RGBA",
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color: 0xffc7c729,
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default_value: array_f32([0.0, 0.0, 0.0, 1.0])
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}
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],
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buttons: [
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{
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name: "arm.nodes.VarianceNode.buttons",
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type: "CUSTOM",
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height: 1
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}
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]
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};
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}
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