Marcel Hofer
163ff56c56
Add image and text Decision Fields with independent BOOLEAN conditions and bounded INT outputs. Keep probability-rich decision_json and add a readable multiline decision output. Support local in-process llama.cpp and native OpenAI/OpenRouter Decisions API requests. Preserve dynamic output connections and migrate earlier workflow layouts; add explanatory notes to both frontends and update release metadata and documentation. Feature idea contributed by JMarimon1 (Reddit feature request): https://www.reddit.com/user/JMarimon1/ Validation: 257 embedded ComfyUI Python tests and 6 frontend tests passed; catalog and mirrored-version checks passed.
79 lines
3.3 KiB
JavaScript
79 lines
3.3 KiB
JavaScript
import { app } from "/scripts/app.js";
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import { ComfyWidgets } from "/scripts/widgets.js";
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const DECISION_NODE_NAMES = new Set([
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"ThinkingLLM_DecisionRouter",
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"ThinkingLLM_DecisionRouterText",
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"ThinkingLLM_DecisionFieldsImage",
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"ThinkingLLM_DecisionFieldsText",
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]);
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const NOTES_WIDGET_NAME = "recommended_settings";
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function buildDecisionNotes(node) {
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const isFieldNode = node.comfyClass.includes("DecisionFields");
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const inputKind = node.comfyClass.endsWith("Text") ? "text" : "image";
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if (isFieldNode) {
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return [
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`Decision Fields (${inputKind})`,
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"BOOLEAN: ask a yes/no question. Fields are independent, so multiple answers can be true.",
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"Threshold (0-1): minimum probability for true; 0.50 is the default.",
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"INT: choose a whole number from Minimum through Maximum, inclusive. Useful for counts.",
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"Outside that range, the integer is clamped to its nearest bound and marked in decision_json.",
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"decision_json: detailed machine-readable values and probabilities; decision: readable multiline summary.",
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"Typed field sockets return native Boolean or Integer batches in input order.",
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"Backend: local models run in-process; API mode uses the selected provider profile.",
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"Tip: use specific questions about visible, observable facts. Accuracy depends on the selected model.",
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].join("\n");
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}
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return [
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`Decision Router (${inputKind})`,
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"Choose exactly one matching route for each input item.",
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"Route name: output label. Route description: criteria the model uses to choose it.",
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"Batch inputs are evaluated item by item; items sent to each route keep their original order.",
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"An empty route output is blocked, so its downstream branch is skipped.",
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"decision_json contains the chosen route and probabilities.",
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"Backend: local models run in-process; API mode uses the selected provider profile.",
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"Tip: use clear, distinct descriptions, including when no route should match.",
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].join("\n");
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}
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function ensureNotesWidget(node) {
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if (!Array.isArray(node.widgets)) return null;
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let widget = node.widgets.find((candidate) => candidate?.name === NOTES_WIDGET_NAME && candidate?.inputEl)
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?? node.widgets.find((candidate) => candidate?.name === NOTES_WIDGET_NAME);
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if (!widget) {
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widget = ComfyWidgets.STRING(node, NOTES_WIDGET_NAME, ["STRING", { multiline: true }], app).widget;
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}
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if (widget?.inputEl) {
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widget.inputEl.readOnly = true;
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widget.inputEl.style.opacity = 0.78;
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widget.inputEl.style.fontSize = "12px";
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widget.inputEl.style.lineHeight = "1.35";
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widget.inputEl.rows = 10;
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}
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widget.options = { ...(widget.options || {}), serialize: false };
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widget.serializeValue = async () => undefined;
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widget.value = buildDecisionNotes(node);
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return widget;
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}
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function resizeNode(node) {
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requestAnimationFrame(() => {
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const size = node.computeSize();
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if (size[0] < node.size[0]) size[0] = node.size[0];
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if (size[1] < node.size[1]) size[1] = node.size[1];
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node.setSize(size);
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app.graph?.setDirtyCanvas?.(true, false);
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});
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}
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app.registerExtension({
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name: "ThinkingLLM.DecisionNotes",
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nodeCreated(node) {
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if (!DECISION_NODE_NAMES.has(node.comfyClass) || node._thinkingllmDecisionNotesHooked) return;
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node._thinkingllmDecisionNotesHooked = true;
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if (ensureNotesWidget(node)) resizeNode(node);
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},
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});
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