| import { api } from "./api.js"; |
|
|
| export function getPngMetadata(file) { |
| return new Promise((r) => { |
| const reader = new FileReader(); |
| reader.onload = (event) => { |
| |
| const pngData = new Uint8Array(event.target.result); |
| const dataView = new DataView(pngData.buffer); |
|
|
| |
| if (dataView.getUint32(0) !== 0x89504e47) { |
| console.error("Not a valid PNG file"); |
| r(); |
| return; |
| } |
|
|
| |
| let offset = 8; |
| let txt_chunks = {}; |
| |
| while (offset < pngData.length) { |
| |
| const length = dataView.getUint32(offset); |
| |
| const type = String.fromCharCode(...pngData.slice(offset + 4, offset + 8)); |
| if (type === "tEXt") { |
| |
| let keyword_end = offset + 8; |
| while (pngData[keyword_end] !== 0) { |
| keyword_end++; |
| } |
| const keyword = String.fromCharCode(...pngData.slice(offset + 8, keyword_end)); |
| |
| const contentArraySegment = pngData.slice(keyword_end + 1, offset + 8 + length); |
| const contentJson = Array.from(contentArraySegment).map(s=>String.fromCharCode(s)).join('') |
| txt_chunks[keyword] = contentJson; |
| } |
|
|
| offset += 12 + length; |
| } |
|
|
| r(txt_chunks); |
| }; |
|
|
| reader.readAsArrayBuffer(file); |
| }); |
| } |
|
|
| export function getLatentMetadata(file) { |
| return new Promise((r) => { |
| const reader = new FileReader(); |
| reader.onload = (event) => { |
| const safetensorsData = new Uint8Array(event.target.result); |
| const dataView = new DataView(safetensorsData.buffer); |
| let header_size = dataView.getUint32(0, true); |
| let offset = 8; |
| let header = JSON.parse(new TextDecoder().decode(safetensorsData.slice(offset, offset + header_size))); |
| r(header.__metadata__); |
| }; |
|
|
| var slice = file.slice(0, 1024 * 1024 * 4); |
| reader.readAsArrayBuffer(slice); |
| }); |
| } |
|
|
| export async function importA1111(graph, parameters) { |
| const p = parameters.lastIndexOf("\nSteps:"); |
| if (p > -1) { |
| const embeddings = await api.getEmbeddings(); |
| const opts = parameters |
| .substr(p) |
| .split("\n")[1] |
| .split(",") |
| .reduce((p, n) => { |
| const s = n.split(":"); |
| p[s[0].trim().toLowerCase()] = s[1].trim(); |
| return p; |
| }, {}); |
| const p2 = parameters.lastIndexOf("\nNegative prompt:", p); |
| if (p2 > -1) { |
| let positive = parameters.substr(0, p2).trim(); |
| let negative = parameters.substring(p2 + 18, p).trim(); |
|
|
| const ckptNode = LiteGraph.createNode("CheckpointLoaderSimple"); |
| const clipSkipNode = LiteGraph.createNode("CLIPSetLastLayer"); |
| const positiveNode = LiteGraph.createNode("CLIPTextEncode"); |
| const negativeNode = LiteGraph.createNode("CLIPTextEncode"); |
| const samplerNode = LiteGraph.createNode("KSampler"); |
| const imageNode = LiteGraph.createNode("EmptyLatentImage"); |
| const vaeNode = LiteGraph.createNode("VAEDecode"); |
| const vaeLoaderNode = LiteGraph.createNode("VAELoader"); |
| const saveNode = LiteGraph.createNode("SaveImage"); |
| let hrSamplerNode = null; |
|
|
| const ceil64 = (v) => Math.ceil(v / 64) * 64; |
|
|
| function getWidget(node, name) { |
| return node.widgets.find((w) => w.name === name); |
| } |
|
|
| function setWidgetValue(node, name, value, isOptionPrefix) { |
| const w = getWidget(node, name); |
| if (isOptionPrefix) { |
| const o = w.options.values.find((w) => w.startsWith(value)); |
| if (o) { |
| w.value = o; |
| } else { |
| console.warn(`Unknown value '${value}' for widget '${name}'`, node); |
| w.value = value; |
| } |
| } else { |
| w.value = value; |
| } |
| } |
|
|
| function createLoraNodes(clipNode, text, prevClip, prevModel) { |
| const loras = []; |
| text = text.replace(/<lora:([^:]+:[^>]+)>/g, function (m, c) { |
| const s = c.split(":"); |
| const weight = parseFloat(s[1]); |
| if (isNaN(weight)) { |
| console.warn("Invalid LORA", m); |
| } else { |
| loras.push({ name: s[0], weight }); |
| } |
| return ""; |
| }); |
|
|
| for (const l of loras) { |
| const loraNode = LiteGraph.createNode("LoraLoader"); |
| graph.add(loraNode); |
| setWidgetValue(loraNode, "lora_name", l.name, true); |
| setWidgetValue(loraNode, "strength_model", l.weight); |
| setWidgetValue(loraNode, "strength_clip", l.weight); |
| prevModel.node.connect(prevModel.index, loraNode, 0); |
| prevClip.node.connect(prevClip.index, loraNode, 1); |
| prevModel = { node: loraNode, index: 0 }; |
| prevClip = { node: loraNode, index: 1 }; |
| } |
|
|
| prevClip.node.connect(1, clipNode, 0); |
| prevModel.node.connect(0, samplerNode, 0); |
| if (hrSamplerNode) { |
| prevModel.node.connect(0, hrSamplerNode, 0); |
| } |
|
|
| return { text, prevModel, prevClip }; |
| } |
|
|
| function replaceEmbeddings(text) { |
| if(!embeddings.length) return text; |
| return text.replaceAll( |
| new RegExp( |
| "\\b(" + embeddings.map((e) => e.replace(/[.*+?^${}()|[\]\\]/g, "\\$&")).join("\\b|\\b") + ")\\b", |
| "ig" |
| ), |
| "embedding:$1" |
| ); |
| } |
|
|
| function popOpt(name) { |
| const v = opts[name]; |
| delete opts[name]; |
| return v; |
| } |
|
|
| graph.clear(); |
| graph.add(ckptNode); |
| graph.add(clipSkipNode); |
| graph.add(positiveNode); |
| graph.add(negativeNode); |
| graph.add(samplerNode); |
| graph.add(imageNode); |
| graph.add(vaeNode); |
| graph.add(vaeLoaderNode); |
| graph.add(saveNode); |
|
|
| ckptNode.connect(1, clipSkipNode, 0); |
| clipSkipNode.connect(0, positiveNode, 0); |
| clipSkipNode.connect(0, negativeNode, 0); |
| ckptNode.connect(0, samplerNode, 0); |
| positiveNode.connect(0, samplerNode, 1); |
| negativeNode.connect(0, samplerNode, 2); |
| imageNode.connect(0, samplerNode, 3); |
| vaeNode.connect(0, saveNode, 0); |
| samplerNode.connect(0, vaeNode, 0); |
| vaeLoaderNode.connect(0, vaeNode, 1); |
|
|
| const handlers = { |
| model(v) { |
| setWidgetValue(ckptNode, "ckpt_name", v, true); |
| }, |
| "cfg scale"(v) { |
| setWidgetValue(samplerNode, "cfg", +v); |
| }, |
| "clip skip"(v) { |
| setWidgetValue(clipSkipNode, "stop_at_clip_layer", -v); |
| }, |
| sampler(v) { |
| let name = v.toLowerCase().replace("++", "pp").replaceAll(" ", "_"); |
| if (name.includes("karras")) { |
| name = name.replace("karras", "").replace(/_+$/, ""); |
| setWidgetValue(samplerNode, "scheduler", "karras"); |
| } else { |
| setWidgetValue(samplerNode, "scheduler", "normal"); |
| } |
| const w = getWidget(samplerNode, "sampler_name"); |
| const o = w.options.values.find((w) => w === name || w === "sample_" + name); |
| if (o) { |
| setWidgetValue(samplerNode, "sampler_name", o); |
| } |
| }, |
| size(v) { |
| const wxh = v.split("x"); |
| const w = ceil64(+wxh[0]); |
| const h = ceil64(+wxh[1]); |
| const hrUp = popOpt("hires upscale"); |
| const hrSz = popOpt("hires resize"); |
| let hrMethod = popOpt("hires upscaler"); |
|
|
| setWidgetValue(imageNode, "width", w); |
| setWidgetValue(imageNode, "height", h); |
|
|
| if (hrUp || hrSz) { |
| let uw, uh; |
| if (hrUp) { |
| uw = w * hrUp; |
| uh = h * hrUp; |
| } else { |
| const s = hrSz.split("x"); |
| uw = +s[0]; |
| uh = +s[1]; |
| } |
|
|
| let upscaleNode; |
| let latentNode; |
|
|
| if (hrMethod.startsWith("Latent")) { |
| latentNode = upscaleNode = LiteGraph.createNode("LatentUpscale"); |
| graph.add(upscaleNode); |
| samplerNode.connect(0, upscaleNode, 0); |
|
|
| switch (hrMethod) { |
| case "Latent (nearest-exact)": |
| hrMethod = "nearest-exact"; |
| break; |
| } |
| setWidgetValue(upscaleNode, "upscale_method", hrMethod, true); |
| } else { |
| const decode = LiteGraph.createNode("VAEDecodeTiled"); |
| graph.add(decode); |
| samplerNode.connect(0, decode, 0); |
| vaeLoaderNode.connect(0, decode, 1); |
|
|
| const upscaleLoaderNode = LiteGraph.createNode("UpscaleModelLoader"); |
| graph.add(upscaleLoaderNode); |
| setWidgetValue(upscaleLoaderNode, "model_name", hrMethod, true); |
|
|
| const modelUpscaleNode = LiteGraph.createNode("ImageUpscaleWithModel"); |
| graph.add(modelUpscaleNode); |
| decode.connect(0, modelUpscaleNode, 1); |
| upscaleLoaderNode.connect(0, modelUpscaleNode, 0); |
|
|
| upscaleNode = LiteGraph.createNode("ImageScale"); |
| graph.add(upscaleNode); |
| modelUpscaleNode.connect(0, upscaleNode, 0); |
|
|
| const vaeEncodeNode = (latentNode = LiteGraph.createNode("VAEEncodeTiled")); |
| graph.add(vaeEncodeNode); |
| upscaleNode.connect(0, vaeEncodeNode, 0); |
| vaeLoaderNode.connect(0, vaeEncodeNode, 1); |
| } |
|
|
| setWidgetValue(upscaleNode, "width", ceil64(uw)); |
| setWidgetValue(upscaleNode, "height", ceil64(uh)); |
|
|
| hrSamplerNode = LiteGraph.createNode("KSampler"); |
| graph.add(hrSamplerNode); |
| ckptNode.connect(0, hrSamplerNode, 0); |
| positiveNode.connect(0, hrSamplerNode, 1); |
| negativeNode.connect(0, hrSamplerNode, 2); |
| latentNode.connect(0, hrSamplerNode, 3); |
| hrSamplerNode.connect(0, vaeNode, 0); |
| } |
| }, |
| steps(v) { |
| setWidgetValue(samplerNode, "steps", +v); |
| }, |
| seed(v) { |
| setWidgetValue(samplerNode, "seed", +v); |
| }, |
| }; |
|
|
| for (const opt in opts) { |
| if (opt in handlers) { |
| handlers[opt](popOpt(opt)); |
| } |
| } |
|
|
| if (hrSamplerNode) { |
| setWidgetValue(hrSamplerNode, "steps", getWidget(samplerNode, "steps").value); |
| setWidgetValue(hrSamplerNode, "cfg", getWidget(samplerNode, "cfg").value); |
| setWidgetValue(hrSamplerNode, "scheduler", getWidget(samplerNode, "scheduler").value); |
| setWidgetValue(hrSamplerNode, "sampler_name", getWidget(samplerNode, "sampler_name").value); |
| setWidgetValue(hrSamplerNode, "denoise", +(popOpt("denoising strength") || "1")); |
| } |
|
|
| let n = createLoraNodes(positiveNode, positive, { node: clipSkipNode, index: 0 }, { node: ckptNode, index: 0 }); |
| positive = n.text; |
| n = createLoraNodes(negativeNode, negative, n.prevClip, n.prevModel); |
| negative = n.text; |
|
|
| setWidgetValue(positiveNode, "text", replaceEmbeddings(positive)); |
| setWidgetValue(negativeNode, "text", replaceEmbeddings(negative)); |
|
|
| graph.arrange(); |
|
|
| for (const opt of ["model hash", "ensd"]) { |
| delete opts[opt]; |
| } |
|
|
| console.warn("Unhandled parameters:", opts); |
| } |
| } |
| } |
|
|