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433 lines
13 KiB
433 lines
13 KiB
import { api } from "./api.js"; |
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export function getPngMetadata(file) { |
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return new Promise((r) => { |
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const reader = new FileReader(); |
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reader.onload = (event) => { |
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// Get the PNG data as a Uint8Array |
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const pngData = new Uint8Array(event.target.result); |
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const dataView = new DataView(pngData.buffer); |
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// Check that the PNG signature is present |
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if (dataView.getUint32(0) !== 0x89504e47) { |
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console.error("Not a valid PNG file"); |
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r(); |
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return; |
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} |
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// Start searching for chunks after the PNG signature |
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let offset = 8; |
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let txt_chunks = {}; |
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// Loop through the chunks in the PNG file |
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while (offset < pngData.length) { |
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// Get the length of the chunk |
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const length = dataView.getUint32(offset); |
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// Get the chunk type |
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const type = String.fromCharCode(...pngData.slice(offset + 4, offset + 8)); |
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if (type === "tEXt" || type == "comf" || type === "iTXt") { |
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// Get the keyword |
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let keyword_end = offset + 8; |
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while (pngData[keyword_end] !== 0) { |
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keyword_end++; |
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} |
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const keyword = String.fromCharCode(...pngData.slice(offset + 8, keyword_end)); |
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// Get the text |
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const contentArraySegment = pngData.slice(keyword_end + 1, offset + 8 + length); |
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const contentJson = new TextDecoder("utf-8").decode(contentArraySegment); |
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txt_chunks[keyword] = contentJson; |
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} |
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offset += 12 + length; |
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} |
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r(txt_chunks); |
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}; |
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reader.readAsArrayBuffer(file); |
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}); |
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} |
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function parseExifData(exifData) { |
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// Check for the correct TIFF header (0x4949 for little-endian or 0x4D4D for big-endian) |
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const isLittleEndian = new Uint16Array(exifData.slice(0, 2))[0] === 0x4949; |
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// Function to read 16-bit and 32-bit integers from binary data |
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function readInt(offset, isLittleEndian, length) { |
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let arr = exifData.slice(offset, offset + length) |
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if (length === 2) { |
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return new DataView(arr.buffer, arr.byteOffset, arr.byteLength).getUint16(0, isLittleEndian); |
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} else if (length === 4) { |
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return new DataView(arr.buffer, arr.byteOffset, arr.byteLength).getUint32(0, isLittleEndian); |
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} |
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} |
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// Read the offset to the first IFD (Image File Directory) |
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const ifdOffset = readInt(4, isLittleEndian, 4); |
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function parseIFD(offset) { |
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const numEntries = readInt(offset, isLittleEndian, 2); |
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const result = {}; |
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for (let i = 0; i < numEntries; i++) { |
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const entryOffset = offset + 2 + i * 12; |
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const tag = readInt(entryOffset, isLittleEndian, 2); |
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const type = readInt(entryOffset + 2, isLittleEndian, 2); |
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const numValues = readInt(entryOffset + 4, isLittleEndian, 4); |
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const valueOffset = readInt(entryOffset + 8, isLittleEndian, 4); |
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// Read the value(s) based on the data type |
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let value; |
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if (type === 2) { |
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// ASCII string |
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value = String.fromCharCode(...exifData.slice(valueOffset, valueOffset + numValues - 1)); |
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} |
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result[tag] = value; |
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} |
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return result; |
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} |
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// Parse the first IFD |
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const ifdData = parseIFD(ifdOffset); |
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return ifdData; |
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} |
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function splitValues(input) { |
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var output = {}; |
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for (var key in input) { |
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var value = input[key]; |
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var splitValues = value.split(':', 2); |
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output[splitValues[0]] = splitValues[1]; |
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} |
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return output; |
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} |
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export function getWebpMetadata(file) { |
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return new Promise((r) => { |
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const reader = new FileReader(); |
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reader.onload = (event) => { |
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const webp = new Uint8Array(event.target.result); |
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const dataView = new DataView(webp.buffer); |
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// Check that the WEBP signature is present |
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if (dataView.getUint32(0) !== 0x52494646 || dataView.getUint32(8) !== 0x57454250) { |
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console.error("Not a valid WEBP file"); |
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r(); |
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return; |
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} |
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// Start searching for chunks after the WEBP signature |
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let offset = 12; |
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let txt_chunks = {}; |
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// Loop through the chunks in the WEBP file |
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while (offset < webp.length) { |
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const chunk_length = dataView.getUint32(offset + 4, true); |
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const chunk_type = String.fromCharCode(...webp.slice(offset, offset + 4)); |
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if (chunk_type === "EXIF") { |
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if (String.fromCharCode(...webp.slice(offset + 8, offset + 8 + 6)) == "Exif\0\0") { |
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offset += 6; |
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} |
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let data = parseExifData(webp.slice(offset + 8, offset + 8 + chunk_length)); |
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for (var key in data) { |
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var value = data[key]; |
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let index = value.indexOf(':'); |
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txt_chunks[value.slice(0, index)] = value.slice(index + 1); |
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} |
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} |
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offset += 8 + chunk_length; |
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} |
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r(txt_chunks); |
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}; |
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reader.readAsArrayBuffer(file); |
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}); |
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} |
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export function getLatentMetadata(file) { |
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return new Promise((r) => { |
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const reader = new FileReader(); |
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reader.onload = (event) => { |
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const safetensorsData = new Uint8Array(event.target.result); |
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const dataView = new DataView(safetensorsData.buffer); |
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let header_size = dataView.getUint32(0, true); |
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let offset = 8; |
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let header = JSON.parse(new TextDecoder().decode(safetensorsData.slice(offset, offset + header_size))); |
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r(header.__metadata__); |
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}; |
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var slice = file.slice(0, 1024 * 1024 * 4); |
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reader.readAsArrayBuffer(slice); |
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}); |
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} |
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export async function importA1111(graph, parameters) { |
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const p = parameters.lastIndexOf("\nSteps:"); |
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if (p > -1) { |
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const embeddings = await api.getEmbeddings(); |
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const opts = parameters |
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.substr(p) |
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.split("\n")[1] |
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.match(new RegExp("\\s*([^:]+:\\s*([^\"\\{].*?|\".*?\"|\\{.*?\\}))\\s*(,|$)", "g")) |
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.reduce((p, n) => { |
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const s = n.split(":"); |
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if (s[1].endsWith(',')) { |
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s[1] = s[1].substr(0, s[1].length -1); |
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} |
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p[s[0].trim().toLowerCase()] = s[1].trim(); |
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return p; |
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}, {}); |
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const p2 = parameters.lastIndexOf("\nNegative prompt:", p); |
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if (p2 > -1) { |
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let positive = parameters.substr(0, p2).trim(); |
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let negative = parameters.substring(p2 + 18, p).trim(); |
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const ckptNode = LiteGraph.createNode("CheckpointLoaderSimple"); |
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const clipSkipNode = LiteGraph.createNode("CLIPSetLastLayer"); |
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const positiveNode = LiteGraph.createNode("CLIPTextEncode"); |
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const negativeNode = LiteGraph.createNode("CLIPTextEncode"); |
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const samplerNode = LiteGraph.createNode("KSampler"); |
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const imageNode = LiteGraph.createNode("EmptyLatentImage"); |
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const vaeNode = LiteGraph.createNode("VAEDecode"); |
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const vaeLoaderNode = LiteGraph.createNode("VAELoader"); |
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const saveNode = LiteGraph.createNode("SaveImage"); |
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let hrSamplerNode = null; |
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let hrSteps = null; |
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const ceil64 = (v) => Math.ceil(v / 64) * 64; |
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function getWidget(node, name) { |
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return node.widgets.find((w) => w.name === name); |
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} |
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function setWidgetValue(node, name, value, isOptionPrefix) { |
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const w = getWidget(node, name); |
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if (isOptionPrefix) { |
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const o = w.options.values.find((w) => w.startsWith(value)); |
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if (o) { |
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w.value = o; |
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} else { |
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console.warn(`Unknown value '${value}' for widget '${name}'`, node); |
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w.value = value; |
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} |
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} else { |
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w.value = value; |
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} |
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} |
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function createLoraNodes(clipNode, text, prevClip, prevModel) { |
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const loras = []; |
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text = text.replace(/<lora:([^:]+:[^>]+)>/g, function (m, c) { |
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const s = c.split(":"); |
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const weight = parseFloat(s[1]); |
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if (isNaN(weight)) { |
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console.warn("Invalid LORA", m); |
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} else { |
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loras.push({ name: s[0], weight }); |
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} |
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return ""; |
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}); |
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for (const l of loras) { |
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const loraNode = LiteGraph.createNode("LoraLoader"); |
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graph.add(loraNode); |
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setWidgetValue(loraNode, "lora_name", l.name, true); |
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setWidgetValue(loraNode, "strength_model", l.weight); |
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setWidgetValue(loraNode, "strength_clip", l.weight); |
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prevModel.node.connect(prevModel.index, loraNode, 0); |
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prevClip.node.connect(prevClip.index, loraNode, 1); |
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prevModel = { node: loraNode, index: 0 }; |
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prevClip = { node: loraNode, index: 1 }; |
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} |
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prevClip.node.connect(1, clipNode, 0); |
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prevModel.node.connect(0, samplerNode, 0); |
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if (hrSamplerNode) { |
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prevModel.node.connect(0, hrSamplerNode, 0); |
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} |
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return { text, prevModel, prevClip }; |
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} |
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function replaceEmbeddings(text) { |
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if(!embeddings.length) return text; |
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return text.replaceAll( |
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new RegExp( |
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"\\b(" + embeddings.map((e) => e.replace(/[.*+?^${}()|[\]\\]/g, "\\$&")).join("\\b|\\b") + ")\\b", |
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"ig" |
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), |
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"embedding:$1" |
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); |
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} |
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function popOpt(name) { |
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const v = opts[name]; |
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delete opts[name]; |
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return v; |
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} |
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graph.clear(); |
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graph.add(ckptNode); |
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graph.add(clipSkipNode); |
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graph.add(positiveNode); |
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graph.add(negativeNode); |
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graph.add(samplerNode); |
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graph.add(imageNode); |
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graph.add(vaeNode); |
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graph.add(vaeLoaderNode); |
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graph.add(saveNode); |
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ckptNode.connect(1, clipSkipNode, 0); |
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clipSkipNode.connect(0, positiveNode, 0); |
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clipSkipNode.connect(0, negativeNode, 0); |
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ckptNode.connect(0, samplerNode, 0); |
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positiveNode.connect(0, samplerNode, 1); |
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negativeNode.connect(0, samplerNode, 2); |
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imageNode.connect(0, samplerNode, 3); |
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vaeNode.connect(0, saveNode, 0); |
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samplerNode.connect(0, vaeNode, 0); |
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vaeLoaderNode.connect(0, vaeNode, 1); |
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const handlers = { |
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model(v) { |
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setWidgetValue(ckptNode, "ckpt_name", v, true); |
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}, |
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"vae"(v) { |
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setWidgetValue(vaeLoaderNode, "vae_name", v, true); |
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}, |
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"cfg scale"(v) { |
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setWidgetValue(samplerNode, "cfg", +v); |
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}, |
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"clip skip"(v) { |
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setWidgetValue(clipSkipNode, "stop_at_clip_layer", -v); |
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}, |
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sampler(v) { |
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let name = v.toLowerCase().replace("++", "pp").replaceAll(" ", "_"); |
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if (name.includes("karras")) { |
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name = name.replace("karras", "").replace(/_+$/, ""); |
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setWidgetValue(samplerNode, "scheduler", "karras"); |
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} else { |
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setWidgetValue(samplerNode, "scheduler", "normal"); |
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} |
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const w = getWidget(samplerNode, "sampler_name"); |
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const o = w.options.values.find((w) => w === name || w === "sample_" + name); |
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if (o) { |
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setWidgetValue(samplerNode, "sampler_name", o); |
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} |
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}, |
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size(v) { |
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const wxh = v.split("x"); |
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const w = ceil64(+wxh[0]); |
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const h = ceil64(+wxh[1]); |
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const hrUp = popOpt("hires upscale"); |
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const hrSz = popOpt("hires resize"); |
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hrSteps = popOpt("hires steps"); |
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let hrMethod = popOpt("hires upscaler"); |
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setWidgetValue(imageNode, "width", w); |
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setWidgetValue(imageNode, "height", h); |
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if (hrUp || hrSz) { |
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let uw, uh; |
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if (hrUp) { |
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uw = w * hrUp; |
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uh = h * hrUp; |
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} else { |
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const s = hrSz.split("x"); |
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uw = +s[0]; |
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uh = +s[1]; |
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} |
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let upscaleNode; |
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let latentNode; |
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if (hrMethod.startsWith("Latent")) { |
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latentNode = upscaleNode = LiteGraph.createNode("LatentUpscale"); |
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graph.add(upscaleNode); |
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samplerNode.connect(0, upscaleNode, 0); |
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switch (hrMethod) { |
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case "Latent (nearest-exact)": |
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hrMethod = "nearest-exact"; |
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break; |
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} |
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setWidgetValue(upscaleNode, "upscale_method", hrMethod, true); |
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} else { |
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const decode = LiteGraph.createNode("VAEDecodeTiled"); |
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graph.add(decode); |
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samplerNode.connect(0, decode, 0); |
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vaeLoaderNode.connect(0, decode, 1); |
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const upscaleLoaderNode = LiteGraph.createNode("UpscaleModelLoader"); |
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graph.add(upscaleLoaderNode); |
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setWidgetValue(upscaleLoaderNode, "model_name", hrMethod, true); |
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const modelUpscaleNode = LiteGraph.createNode("ImageUpscaleWithModel"); |
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graph.add(modelUpscaleNode); |
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decode.connect(0, modelUpscaleNode, 1); |
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upscaleLoaderNode.connect(0, modelUpscaleNode, 0); |
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upscaleNode = LiteGraph.createNode("ImageScale"); |
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graph.add(upscaleNode); |
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modelUpscaleNode.connect(0, upscaleNode, 0); |
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const vaeEncodeNode = (latentNode = LiteGraph.createNode("VAEEncodeTiled")); |
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graph.add(vaeEncodeNode); |
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upscaleNode.connect(0, vaeEncodeNode, 0); |
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vaeLoaderNode.connect(0, vaeEncodeNode, 1); |
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} |
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setWidgetValue(upscaleNode, "width", ceil64(uw)); |
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setWidgetValue(upscaleNode, "height", ceil64(uh)); |
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hrSamplerNode = LiteGraph.createNode("KSampler"); |
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graph.add(hrSamplerNode); |
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ckptNode.connect(0, hrSamplerNode, 0); |
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positiveNode.connect(0, hrSamplerNode, 1); |
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negativeNode.connect(0, hrSamplerNode, 2); |
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latentNode.connect(0, hrSamplerNode, 3); |
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hrSamplerNode.connect(0, vaeNode, 0); |
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} |
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}, |
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steps(v) { |
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setWidgetValue(samplerNode, "steps", +v); |
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}, |
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seed(v) { |
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setWidgetValue(samplerNode, "seed", +v); |
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}, |
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}; |
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for (const opt in opts) { |
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if (opt in handlers) { |
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handlers[opt](popOpt(opt)); |
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} |
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} |
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if (hrSamplerNode) { |
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setWidgetValue(hrSamplerNode, "steps", hrSteps? +hrSteps : getWidget(samplerNode, "steps").value); |
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setWidgetValue(hrSamplerNode, "cfg", getWidget(samplerNode, "cfg").value); |
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setWidgetValue(hrSamplerNode, "scheduler", getWidget(samplerNode, "scheduler").value); |
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setWidgetValue(hrSamplerNode, "sampler_name", getWidget(samplerNode, "sampler_name").value); |
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setWidgetValue(hrSamplerNode, "denoise", +(popOpt("denoising strength") || "1")); |
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} |
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let n = createLoraNodes(positiveNode, positive, { node: clipSkipNode, index: 0 }, { node: ckptNode, index: 0 }); |
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positive = n.text; |
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n = createLoraNodes(negativeNode, negative, n.prevClip, n.prevModel); |
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negative = n.text; |
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setWidgetValue(positiveNode, "text", replaceEmbeddings(positive)); |
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setWidgetValue(negativeNode, "text", replaceEmbeddings(negative)); |
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graph.arrange(); |
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for (const opt of ["model hash", "ensd", "version", "vae hash", "ti hashes", "lora hashes", "hashes"]) { |
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delete opts[opt]; |
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} |
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console.warn("Unhandled parameters:", opts); |
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} |
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} |
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}
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