Merge branch 'a1111-meta-v2' of https://github.com/pythongosssss/ComfyUI into...

Merge branch 'a1111-meta-v2' of https://github.com/pythongosssss/ComfyUI into pythongosssss-a1111-meta-v2

server.py

@@ -73,6 +73,14 @@ class PromptServer():
         async def get_root(request):
             return web.FileResponse(os.path.join(self.web_root, "index.html"))
 
+        @routes.get("/embeddings")
+        def get_embeddings(self):
+            models_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "models")
+            embed_dir = os.path.join(models_dir, "embeddings")
+            embeddings = nodes.filter_files_extensions(nodes.recursive_search(embed_dir), nodes.supported_pt_extensions)
+
+            return web.json_response(list(map(lambda a: os.path.splitext(a)[0].lower(), embeddings)))
+
         @routes.get("/extensions")
         async def get_extensions(request):
             files = glob.glob(os.path.join(self.web_root, 'extensions/**/*.js'), recursive=True)

web/scripts/api.js

@@ -106,6 +106,15 @@ class ComfyApi extends EventTarget {
 		return await resp.json();
 	}
 
+	/**
+	 * Gets a list of embedding names
+	 * @returns An array of script urls to import
+	 */
+	async getEmbeddings() {
+		const resp = await fetch("/embeddings", { cache: "no-store" });
+		return await resp.json();
+	}
+
 	/**
 	 * Loads node object definitions for the graph
 	 * @returns The node definitions

web/scripts/app.js

@@ -2,7 +2,7 @@ import { ComfyWidgets } from "./widgets.js";
 import { ComfyUI } from "./ui.js";
 import { api } from "./api.js";
 import { defaultGraph } from "./defaultGraph.js";
-import { getPngMetadata } from "./pnginfo.js";
+import { getPngMetadata, importA1111 } from "./pnginfo.js";
 
 class ComfyApp {
 	constructor() {
@@ -743,8 +743,12 @@ class ComfyApp {
 	async handleFile(file) {
 		if (file.type === "image/png") {
 			const pngInfo = await getPngMetadata(file);
-			if (pngInfo && pngInfo.workflow) {
-				this.loadGraphData(JSON.parse(pngInfo.workflow));
+			if (pngInfo) {
+				if (pngInfo.workflow) {
+					this.loadGraphData(JSON.parse(pngInfo.workflow));
+				} else if (pngInfo.parameters) {
+					importA1111(this.graph, pngInfo.parameters);
+				}
 			}
 		} else if (file.type === "application/json" || file.name.endsWith(".json")) {
 			const reader = new FileReader();

web/scripts/pnginfo.js

+import { api } from "./api.js";
+
 export function getPngMetadata(file) {
 	return new Promise((r) => {
 		const reader = new FileReader();
@@ -43,3 +45,262 @@ export function getPngMetadata(file) {
 		reader.readAsArrayBuffer(file);
 	});
 }
+
+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(",")
+			.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) {
+				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);
+		}
+	}
+}