DemonGatanjieuandClaude Opus 5.5
c59927e701
AMB read models only from ordinary drop-downs, so rgthree's Power Lora Loader (rows are
{on, lora, strength} objects) was invisible: Model Check never reported its missing LoRAs.
Now its switched-on rows are Model Check boxes (lora_nodes.js powerLoraRowBoxes: ComfyUI's LoRA
list as choices, a fix changes only the row's LoRA; downloads put files in the same way), saved
fingerprints and a picture's model search read them, the workflow summary lists them, and saved
model references include them and Easy-Use LoRA stack rows (nodeModelValues /
_model_reference_values, mirrored). Only these widely used nodes; ComfyUI's loaders stay the
reference.
recipe_parser.js was over 600 lines: which LoRAs a node loads moves to recipe_loras.js and the
box readers to recipe_widgets.js.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
68 lines
4.3 KiB
JavaScript
68 lines
4.3 KiB
JavaScript
// LoRAs in popular third-party nodes, wherever AMB reads a workflow's models: a workflow's summary
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// (recipe_parser.js → recipe_loras.js), saved model references (recipe_identity.js) and Model
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// Check's boxes (lora_nodes.js powerLoraRowBoxes).
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import assert from 'node:assert/strict';
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import { extractRecipeMetadata } from '../web/modules/recipe_parser.js';
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import { deriveRecipeModelReferences, nodeModelValues } from '../web/modules/recipe_identity.js';
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import { POWER_LORA, powerLoraRowBoxes } from '../web/modules/lora_nodes.js';
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globalThis.LiteGraph = { registered_node_types: {
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LoraLoader: { nodeData: { input: { required: { lora_name: [['Flux\\a.safetensors', 'b.safetensors']] } } } },
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} };
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const row = (name, value) => ({ name, type: 'custom', value });
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const powerLora = {
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id: 7,
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type: POWER_LORA,
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widgets: [
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{ name: '', type: 'custom', value: {} }, // divider
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row('PowerLoraLoaderHeaderWidget', { type: 'PowerLoraLoaderHeaderWidget' }),
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row('lora_1', { on: true, lora: 'Flux\\a.safetensors', strength: 0.8, strengthTwo: null }),
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row('lora_2', { on: false, lora: 'off.safetensors', strength: 1, strengthTwo: null }),
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row('lora_3', { on: true, lora: 'b.safetensors', strength: 0.5, strengthTwo: 0.25 }),
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row('lora_4', { on: true, lora: null, strength: 1 }),
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{ name: '➕ Add Lora', type: 'custom', value: '' },
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],
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};
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const easyStack = {
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id: 8,
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type: 'easy loraStack',
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widgets: [
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{ name: 'toggle', value: true }, { name: 'mode', value: 'simple' }, { name: 'num_loras', value: 2 },
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{ name: 'lora_1_name', value: 'c.safetensors' }, { name: 'lora_1_strength', value: 0.6 },
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{ name: 'lora_2_name', value: 'None' }, { name: 'lora_2_strength', value: 1 },
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],
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};
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// The summary: switched-on rows with a LoRA, the clip strength as the model's unless set apart.
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const metadata = extractRecipeMetadata({ _nodes: [powerLora, easyStack], links: [] });
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assert.deepEqual(metadata.loras, [
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{ name: 'Flux\\a.safetensors', strength_model: 0.8, strength_clip: 0.8 },
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{ name: 'b.safetensors', strength_model: 0.5, strength_clip: 0.25 },
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{ name: 'c.safetensors', strength_model: 0.6, strength_clip: null },
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]);
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// Saved model references: rows by their place in widgets_values (rows numbered as rgthree numbers them).
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const savedPower = { id: 7, type: POWER_LORA, widgets_values: [{}, { type: 'PowerLoraLoaderHeaderWidget' },
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{ on: true, lora: 'a.safetensors', strength: 1 }, { on: false, lora: 'off.safetensors', strength: 1 },
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{ on: true, lora: 'b.safetensors', strength: 1 }, {}, ''] };
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assert.deepEqual(nodeModelValues(savedPower), [[2, 'lora', 'lora_1', 'a.safetensors'], [4, 'lora', 'lora_3', 'b.safetensors']]);
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const savedStack = { id: 8, type: 'easy loraStack', widgets_values: [true, 'simple', 2, 'c.safetensors', 1, 1, 1, 'None', 1, 1, 1, 'unused.safetensors'] };
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assert.deepEqual(nodeModelValues(savedStack), [[3, 'lora', 'lora_1_name', 'c.safetensors'], [7, 'lora', 'lora_2_name', 'None']]);
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const refs = deriveRecipeModelReferences({ workflow: { nodes: [savedPower, savedStack] }, params: {} });
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assert.deepEqual(refs.map(ref => [ref.node_id, ref.widget_index, ref.widget_name, ref.saved_value]), [
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[7, 2, 'lora_1', 'a.safetensors'], [7, 4, 'lora_3', 'b.safetensors'], [8, 3, 'lora_1_name', 'c.safetensors'],
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]); // "None" is no model
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assert.deepEqual(nodeModelValues({ type: 'CheckpointLoaderSimple', widgets_values: ['x.safetensors'] }), [[0, 'checkpoint', 'checkpoint', 'x.safetensors']]);
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// Model Check's boxes: switched-on rows naming a LoRA, read and written like a drop-down.
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const boxes = powerLoraRowBoxes(powerLora);
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assert.deepEqual(boxes.map(box => [box.name, box.type, box.value]), [['lora_1', 'combo', 'Flux\\a.safetensors'], ['lora_3', 'combo', 'b.safetensors']]);
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assert.deepEqual(boxes[0].options.values, ['Flux\\a.safetensors', 'b.safetensors']);
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boxes[1].value = 'Flux\\a.safetensors';
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assert.deepEqual(powerLora.widgets[4].value, { on: true, lora: 'Flux\\a.safetensors', strength: 0.5, strengthTwo: 0.25 }); // strength kept
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boxes[1].anomalous_replaced = { from: 'b.safetensors', to: 'Flux\\a.safetensors' };
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assert.deepEqual(powerLoraRowBoxes(powerLora)[1].anomalous_replaced, { from: 'b.safetensors', to: 'Flux\\a.safetensors' }); // lasts
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assert.deepEqual(powerLoraRowBoxes(easyStack), []);
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console.log('third_party_loras: ok');
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