luboslenco
122 lines
3.5 KiB
TypeScript
122 lines
3.5 KiB
TypeScript
|
|
class VarianceNode extends LogicNode {
|
|
|
|
static temp: image_t = null;
|
|
static image: image_t = null;
|
|
static inst: VarianceNode = null;
|
|
static prompt = "";
|
|
|
|
constructor() {
|
|
super();
|
|
VarianceNode.inst = this;
|
|
VarianceNode.init();
|
|
}
|
|
|
|
static init = () => {
|
|
if (VarianceNode.temp == null) {
|
|
VarianceNode.temp = image_create_render_target(512, 512);
|
|
}
|
|
}
|
|
|
|
static buttons = (ui: zui_t, nodes: zui_nodes_t, node: zui_node_t) => {
|
|
VarianceNode.prompt = zui_text_area(zui_handle("variancenode_0"), Align.Left, true, tr("prompt"), true);
|
|
node.buttons[0].height = VarianceNode.prompt.split("\n").length;
|
|
}
|
|
|
|
override getAsImage = (from: i32, done: (img: image_t)=>void) => {
|
|
let strength = (VarianceNode.inst.inputs[1].node as any).value;
|
|
|
|
VarianceNode.inst.inputs[0].getAsImage((source: image_t) => {
|
|
g2_begin(VarianceNode.temp, false);
|
|
g2_draw_scaled_image(source, 0, 0, 512, 512);
|
|
g2_end();
|
|
|
|
let bytes_img = image_get_pixels(VarianceNode.temp);
|
|
let u8a = new Uint8Array(bytes_img);
|
|
let f32a = new Float32Array(3 * 512 * 512);
|
|
for (let i = 0; i < (512 * 512); ++i) {
|
|
f32a[i ] = (u8a[i * 4 ] / 255) * 2.0 - 1.0;
|
|
f32a[i + 512 * 512 ] = (u8a[i * 4 + 1] / 255) * 2.0 - 1.0;
|
|
f32a[i + 512 * 512 * 2] = (u8a[i * 4 + 2] / 255) * 2.0 - 1.0;
|
|
}
|
|
|
|
Console.progress(tr("Processing") + " - " + tr("Variance"));
|
|
Base.notifyOnNextFrame(() => {
|
|
data_get_blob("models/sd_vae_encoder.quant.onnx", (vae_encoder_blob: ArrayBuffer) => {
|
|
let latents_buf = krom_ml_inference(vae_encoder_blob, [f32a.buffer], [[1, 3, 512, 512]], [1, 4, 64, 64], Config.raw.gpu_inference);
|
|
let latents = new Float32Array(latents_buf);
|
|
for (let i = 0; i < latents.length; ++i) {
|
|
latents[i] = 0.18215 * latents[i];
|
|
}
|
|
|
|
let noise = new Float32Array(latents.length);
|
|
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()));
|
|
let num_inference_steps = 50;
|
|
let init_timestep = Math.floor(num_inference_steps * strength);
|
|
let timesteps = TextToPhotoNode.timesteps[num_inference_steps - init_timestep];
|
|
let alphas_cumprod = TextToPhotoNode.alphas_cumprod;
|
|
let sqrt_alpha_prod = Math.pow(alphas_cumprod[timesteps], 0.5);
|
|
let sqrt_one_minus_alpha_prod = Math.pow(1.0 - alphas_cumprod[timesteps], 0.5);
|
|
for (let i = 0; i < latents.length; ++i) {
|
|
latents[i] = sqrt_alpha_prod * latents[i] + sqrt_one_minus_alpha_prod * noise[i];
|
|
}
|
|
let t_start = num_inference_steps - init_timestep;
|
|
|
|
TextToPhotoNode.stableDiffusion(VarianceNode.prompt, (_image: image_t) => {
|
|
VarianceNode.image = _image;
|
|
done(VarianceNode.image);
|
|
}, latents, t_start);
|
|
});
|
|
});
|
|
});
|
|
}
|
|
|
|
override getCachedImage = (): image_t => {
|
|
return VarianceNode.image;
|
|
}
|
|
|
|
static def: zui_node_t = {
|
|
id: 0,
|
|
name: _tr("Variance"),
|
|
type: "VarianceNode",
|
|
x: 0,
|
|
y: 0,
|
|
color: 0xff4982a0,
|
|
inputs: [
|
|
{
|
|
id: 0,
|
|
node_id: 0,
|
|
name: _tr("Color"),
|
|
type: "RGBA",
|
|
color: 0xffc7c729,
|
|
default_value: new Float32Array([0.0, 0.0, 0.0, 1.0])
|
|
},
|
|
{
|
|
id: 0,
|
|
node_id: 0,
|
|
name: _tr("Strength"),
|
|
type: "VALUE",
|
|
color: 0xffa1a1a1,
|
|
default_value: 0.5
|
|
}
|
|
],
|
|
outputs: [
|
|
{
|
|
id: 0,
|
|
node_id: 0,
|
|
name: _tr("Color"),
|
|
type: "RGBA",
|
|
color: 0xffc7c729,
|
|
default_value: new Float32Array([0.0, 0.0, 0.0, 1.0])
|
|
}
|
|
],
|
|
buttons: [
|
|
{
|
|
name: "VarianceNode.buttons",
|
|
type: "CUSTOM",
|
|
height: 1
|
|
}
|
|
]
|
|
};
|
|
}
|