ComfyAssets-ComfyUI_PromptManager/tests/js/comfy-metadata.test.js

// Run with: node --test tests/js/
const test = require("node:test");
const assert = require("node:assert/strict");
const { extractGenerationParams } = require("../../web/js/comfy-metadata.js");

// Same shape as the workflow attached to #75: PromptManager nodes are saved as
// CLIPTextEncode with extra inputs, and the checkpoint loader is pysssss'.
const issue75Prompt = {
    1: {
        class_type: "KSampler",
        inputs: {
            seed: 480655185510613, steps: 25, cfg: 5.5,
            sampler_name: "euler_ancestral", scheduler: "normal", denoise: 0.8,
            model: ["20", 0], positive: ["39", 0], negative: ["40", 0], latent_image: ["36", 0],
        },
    },
    8: { class_type: "CLIPSetLastLayer", inputs: { stop_at_clip_layer: -2, clip: ["20", 1] } },
    20: {
        class_type: "CheckpointLoader|pysssss",
        inputs: { ckpt_name: "Anime\\Model-XL.safetensors", prompt: "[none]", example: "[none]" },
    },
    39: {
        class_type: "CLIPTextEncode",
        inputs: {
            text: "1girl, cat_ears, smile", category: "", tags: "", search_text: "",
            prepend_text: "masterpiece, best quality, ", append_text: "", clip: ["8", 0],
        },
    },
    40: {
        class_type: "CLIPTextEncode",
        inputs: {
            text: "3d, crowd", category: "", tags: "", search_text: "",
            prepend_text: "bad quality, worst quality, ", append_text: "", clip: ["8", 0],
        },
    },
};

test("#75: traces PromptManager prompts and pysssss checkpoint from the sampler", () => {
    const r = extractGenerationParams({ prompt: issue75Prompt });
    assert.equal(r.positivePrompt, "masterpiece, best quality, 1girl, cat_ears, smile");
    assert.equal(r.negativePrompt, "bad quality, worst quality, 3d, crowd");
    assert.equal(r.checkpoint, "Anime\\Model-XL.safetensors");
    assert.deepEqual(
        [r.seed, r.steps, r.cfgScale, r.sampler],
        [480655185510613, 25, 5.5, "euler_ancestral"],
    );
});

test("#75: workflow blob does not override values from the prompt graph", () => {
    const workflow = { nodes: [{ type: "KSampler", widgets_values: [1, "randomize", 99, 7, "ddim"] }] };
    const r = extractGenerationParams({ prompt: issue75Prompt, workflow });
    assert.equal(r.steps, 25);
    assert.equal(r.cfgScale, 5.5);
});

test("#75: recovers per-node text when SaveImage patch overwrote every PromptManager node", () => {
    // Pre-fix images: both nodes carry the final positive text in the prompt graph,
    // but the workflow graph still has each node's real widget values.
    const positiveFinal = "masterpiece, best quality, 1girl, smile";
    const prompt = {
        1: { class_type: "KSampler", inputs: { seed: 1, steps: 20, cfg: 7, sampler_name: "euler", positive: ["39", 0], negative: ["40", 0], model: ["20", 0] } },
        20: { class_type: "CheckpointLoaderSimple", inputs: { ckpt_name: "m.safetensors" } },
        39: { class_type: "PromptManager", inputs: { text: positiveFinal, prepend_text: "masterpiece, best quality,", append_text: "" } },
        40: { class_type: "PromptManager", inputs: { text: positiveFinal, prepend_text: "bad quality,", append_text: "" } },
    };
    const workflow = {
        nodes: [
            { id: 39, type: "PromptManager", widgets_values: ["1girl, smile", "", "", "", "masterpiece, best quality,", ""] },
            { id: 40, type: "PromptManager", widgets_values: ["3d, crowd", "", "", "", "bad quality,", ""] },
        ],
    };
    const r = extractGenerationParams({ prompt, workflow });
    assert.equal(r.positivePrompt, "masterpiece, best quality, 1girl, smile");
    assert.equal(r.negativePrompt, "bad quality, 3d, crowd");
});

test("current metadata: API text wins over a stale or unexpanded workflow widget", () => {
    // Dynamic prompt expanded at queue time: API holds what ran, workflow holds the template
    const prompt = {
        1: { class_type: "KSampler", inputs: { positive: ["39", 0], negative: ["40", 0], model: ["20", 0] } },
        20: { class_type: "CheckpointLoaderSimple", inputs: { ckpt_name: "m.safetensors" } },
        39: { class_type: "PromptManager", inputs: { text: "a red cat", prepend_text: "", append_text: "" } },
        40: { class_type: "PromptManager", inputs: { text: "blurry", prepend_text: "", append_text: "" } },
    };
    const workflow = {
        nodes: [
            { id: 39, type: "PromptManager", widgets_values: ["a {red|blue} cat", "", "", "", "", ""] },
            { id: 40, type: "PromptManager", widgets_values: ["blurry", "", "", "", "", ""] },
        ],
    };
    const r = extractGenerationParams({ prompt, workflow });
    assert.equal(r.positivePrompt, "a red cat");
    assert.equal(r.negativePrompt, "blurry");
});

test("current metadata: linked text is followed, not replaced by the widget's stale value", () => {
    const prompt = {
        1: { class_type: "KSampler", inputs: { positive: ["39", 0], negative: ["40", 0], model: ["20", 0] } },
        20: { class_type: "CheckpointLoaderSimple", inputs: { ckpt_name: "m.safetensors" } },
        39: { class_type: "PromptManager", inputs: { text: ["50", 0], prepend_text: "", append_text: "" } },
        40: { class_type: "PromptManager", inputs: { text: ["50", 0], prepend_text: "bad,", append_text: "" } },
        50: { class_type: "PrimitiveString", inputs: { value: "shared text" } },
    };
    const workflow = {
        nodes: [
            { id: 39, type: "PromptManager", widgets_values: ["old value A", "", "", "", "", ""] },
            { id: 40, type: "PromptManager", widgets_values: ["old value B", "", "", "", "bad,", ""] },
        ],
    };
    const r = extractGenerationParams({ prompt, workflow });
    assert.equal(r.positivePrompt, "shared text");
});

test("prepend/append are not doubled when text is already the combined prompt", () => {
    const prompt = {
        1: { class_type: "KSampler", inputs: { positive: ["2", 0], negative: ["3", 0], model: ["4", 0] } },
        2: { class_type: "PromptManager", inputs: { text: "best quality, a cat, 8k", prepend_text: "best quality,", append_text: "8k" } },
        3: { class_type: "CLIPTextEncode", inputs: { text: "blurry" } },
    };
    assert.equal(extractGenerationParams({ prompt }).positivePrompt, "best quality, a cat, 8k");
});

test("linked prepend/append inputs are resolved (no longer baked into text since 3.2.3)", () => {
    const prompt = {
        1: { class_type: "KSampler", inputs: { positive: ["2", 0], negative: ["3", 0], model: ["4", 0] } },
        2: { class_type: "PromptManager", inputs: { text: "a fox", prepend_text: ["5", 0], append_text: ["6", 0] } },
        3: { class_type: "CLIPTextEncode", inputs: { text: "blurry" } },
        5: { class_type: "PrimitiveString", inputs: { value: "masterpiece," } },
        6: { class_type: "PrimitiveString", inputs: { value: "8k" } },
    };
    assert.equal(extractGenerationParams({ prompt }).positivePrompt, "masterpiece, a fox 8k");
});

test("negative prompt is identified by link, not by keywords", () => {
    const prompt = {
        3: { class_type: "KSampler", inputs: { seed: 1, steps: 20, cfg: 7, sampler_name: "euler", positive: ["6", 0], negative: ["7", 0], model: ["4", 0] } },
        4: { class_type: "CheckpointLoaderSimple", inputs: { ckpt_name: "sd15.safetensors" } },
        6: { class_type: "CLIPTextEncode", inputs: { text: "embedding:goodstyle, a lighthouse" } },
        7: { class_type: "CLIPTextEncode", inputs: { text: "blurry" } },
    };
    const r = extractGenerationParams({ prompt });
    assert.equal(r.positivePrompt, "embedding:goodstyle, a lighthouse");
    assert.equal(r.negativePrompt, "blurry");
});

test("follows LoRA chains, ControlNet pass-through, concatenated text and primitive inputs", () => {
    const prompt = {
        1: { class_type: "KSamplerAdvanced", inputs: { noise_seed: 42, steps: ["9", 0], cfg: 6, sampler_name: "dpmpp_2m", positive: ["5", 0], negative: ["5", 1], model: ["3", 0] } },
        2: { class_type: "CheckpointLoaderSimple", inputs: { ckpt_name: "base.safetensors" } },
        3: { class_type: "LoraLoader", inputs: { lora_name: "style.safetensors", model: ["2", 0], clip: ["2", 1] } },
        5: { class_type: "ControlNetApplyAdvanced", inputs: { positive: ["6", 0], negative: ["7", 0], strength: 1 } },
        6: { class_type: "CLIPTextEncode", inputs: { text: ["8", 0] } },
        7: { class_type: "CLIPTextEncode", inputs: { text: "lowres" } },
        8: { class_type: "Text Concatenate", inputs: { delimiter: ", ", text_a: "a castle", text_b: "at dusk" } },
        9: { class_type: "PrimitiveInt", inputs: { value: 30 } },
    };
    const r = extractGenerationParams({ prompt });
    assert.equal(r.positivePrompt, "a castle, at dusk");
    assert.equal(r.negativePrompt, "lowres");
    assert.equal(r.checkpoint, "base.safetensors");
    assert.equal(r.seed, 42);
    assert.equal(r.steps, 30);
});

test("custom sampler graphs (Flux: SamplerCustomAdvanced + CFGGuider) resolve every field", () => {
    const prompt = {
        85: { class_type: "UNETLoader", inputs: { unet_name: "flux2-klein.safetensors" } },
        135: { class_type: "Lora Loader (LoraManager)", inputs: { model: ["85", 0], clip: ["90", 0] } },
        149: { class_type: "PromptManager", inputs: { text: "a red fox", prepend_text: ["133", 0], append_text: "" } },
        152: { class_type: "SeedHistory", inputs: { seed: 1725922648 } },
        // ControlNet-style node with positive/negative but no model: must not be taken as the sampler
        200: { class_type: "ControlNetApplyAdvanced", inputs: { positive: ["149", 0], negative: ["274", 0], strength: 1 } },
        265: { class_type: "SamplerCustomAdvanced", inputs: { noise: ["267", 0], guider: ["268", 0], sampler: ["269", 0], sigmas: ["266", 0] } },
        266: { class_type: "Flux2Scheduler", inputs: { steps: 50, width: ["153", 1] } },
        267: { class_type: "RandomNoise", inputs: { noise_seed: ["152", 0] } },
        268: { class_type: "CFGGuider", inputs: { cfg: 5.0, model: ["135", 0], positive: ["149", 0], negative: ["274", 0] } },
        269: { class_type: "KSamplerSelect", inputs: { sampler_name: "uni_pc" } },
        274: { class_type: "CLIPTextEncode", inputs: { text: "watermark" } },
    };
    const r = extractGenerationParams({ prompt });
    assert.deepEqual(
        [r.positivePrompt, r.negativePrompt, r.checkpoint, r.cfgScale, r.seed, r.steps, r.sampler],
        ["a red fox", "watermark", "flux2-klein.safetensors", 5.0, 1725922648, 50, "uni_pc"],
    );
});

test("multi-output settings nodes (SamplerCombo) map each field to its own value", () => {
    const prompt = {
        5: {
            class_type: "KSampler",
            inputs: {
                seed: ["162", 0], steps: ["165", 2], cfg: ["165", 3], sampler_name: ["165", 0],
                positive: ["6", 0], negative: ["7", 0], model: ["4", 0],
            },
        },
        4: { class_type: "CheckpointLoaderSimple", inputs: { ckpt_name: "m.safetensors" } },
        6: { class_type: "CLIPTextEncode", inputs: { text: "a cat" } },
        7: { class_type: "CLIPTextEncode", inputs: { text: "blurry" } },
        162: { class_type: "SeedHistory", inputs: { seed: 1495646836, seed_history_ui: "" } },
        165: { class_type: "SamplerCombo", inputs: { sampler_name: "euler_ancestral", scheduler: "normal", steps: 50, cfg: 7 } },
    };
    const r = extractGenerationParams({ prompt });
    assert.deepEqual([r.seed, r.steps, r.cfgScale, r.sampler], [1495646836, 50, 7, "euler_ancestral"]);
});

test("workflow-only images account for the hidden control_after_generate widget", () => {
    const workflow = {
        nodes: [
            { id: 1, type: "CheckpointLoader|pysssss", widgets_values: ["model.safetensors", "[none]"] },
            { id: 2, type: "KSampler", widgets_values: [123, "randomize", 25, 5.5, "euler", "normal", 1] },
        ],
    };
    const r = extractGenerationParams({ workflow });
    assert.deepEqual(
        [r.checkpoint, r.seed, r.steps, r.cfgScale, r.sampler],
        ["model.safetensors", 123, 25, 5.5, "euler"],
    );
});

test("cyclic, fan-out graphs from crafted PNGs resolve quickly", () => {
    // Every node links to itself and its neighbour through several inputs and has
    // no scalars: without a visited set this explores ~inputs^MAX_DEPTH paths.
    const loop = (id, next) => ({
        class_type: "Evil",
        inputs: {
            text_a: [id, 0], text_b: [next, 0], text_c: [id, 0], text_d: [next, 0],
            value_a: [id, 0], value_b: [next, 0], model: [next, 0],
        },
    });
    const prompt = {
        1: { class_type: "KSampler", inputs: { seed: ["2", 0], steps: ["3", 0], positive: ["2", 0], negative: ["3", 0], model: ["2", 0] } },
        2: loop("2", "3"),
        3: loop("3", "2"),
    };
    const started = Date.now();
    const r = extractGenerationParams({ prompt });
    assert.ok(Date.now() - started < 500, `took ${Date.now() - started}ms`);
    assert.equal(r.positivePrompt, "No prompt found");
    assert.equal(r.seed, "Unknown");
});

test("chained encoders whose prepend and append both link onward stay linear", () => {
    const prompt = { 1: { class_type: "KSampler", inputs: { positive: ["n0", 0], negative: ["n0", 0], model: ["n0", 0] } } };
    for (let i = 0; i < 60; i++) {
        prompt[`n${i}`] = {
            class_type: "PromptManager",
            inputs: { text: `t${i}`, prepend_text: [`n${i + 1}`, 0], append_text: [`n${i + 1}`, 0] },
        };
    }
    const started = Date.now();
    extractGenerationParams({ prompt });
    assert.ok(Date.now() - started < 500, `took ${Date.now() - started}ms`);
});

// UI-format link: [id, from_node, from_slot, to_node, to_slot, type]
test("workflow-only: traces prompts, model and KSamplerAdvanced settings through Set/Get and Reroute", () => {
    const workflow = {
        nodes: [
            { id: 1, type: "UNETLoader", widgets_values: ["flux.safetensors", "default"], outputs: [{ links: [1] }] },
            { id: 2, type: "SetNode", widgets_values: ["model"], inputs: [{ name: "MODEL", link: 1 }] },
            { id: 3, type: "GetNode", widgets_values: ["model"], outputs: [{ links: [2] }] },
            { id: 4, type: "PromptManager", widgets_values: ["a castle", "", "", "", "masterpiece,", ""], inputs: [], outputs: [{ links: [3] }] },
            { id: 5, type: "Reroute", inputs: [{ name: "", link: 3 }], outputs: [{ links: [4] }] },
            { id: 6, type: "CLIPTextEncode", widgets_values: ["lowres"], outputs: [{ links: [5] }] },
            {
                id: 7, type: "KSamplerAdvanced",
                inputs: [{ name: "model", link: 2 }, { name: "positive", link: 4 }, { name: "negative", link: 5 }],
                widgets_values: ["enable", 77, "randomize", 30, 6.5, "dpmpp_2m", "karras", 0, 10000, "disable"],
            },
        ],
        links: [
            [1, 1, 0, 2, 0, "MODEL"], [2, 3, 0, 7, 0, "MODEL"], [3, 4, 0, 5, 0, "CONDITIONING"],
            [4, 5, 0, 7, 1, "CONDITIONING"], [5, 6, 0, 7, 2, "CONDITIONING"],
        ],
    };
    const r = extractGenerationParams({ workflow });
    assert.deepEqual(
        [r.positivePrompt, r.negativePrompt, r.checkpoint, r.seed, r.steps, r.cfgScale, r.sampler],
        ["masterpiece, a castle", "lowres", "flux.safetensors", 77, 30, 6.5, "dpmpp_2m"],
    );
});

test("workflow-only: widgets_values_named and widget-backed inputs take precedence over positions", () => {
    const workflow = {
        nodes: [
            { id: 1, type: "CheckpointLoaderSimple", widgets_values: ["m.safetensors"], outputs: [{ links: [1] }] },
            { id: 2, type: "CLIPTextEncode", widgets_values_named: { text: "named text" }, widgets_values: ["ignored"], outputs: [{ links: [2] }] },
            { id: 3, type: "CLIPTextEncode", widgets_values: ["neg"], outputs: [{ links: [3] }] },
            {
                id: 4, type: "KSampler",
                inputs: [
                    { name: "model", link: 1 }, { name: "positive", link: 2 }, { name: "negative", link: 3 },
                    { name: "seed", widget: { name: "seed" }, link: null },
                    { name: "steps", widget: { name: "steps" }, link: null },
                    { name: "cfg", widget: { name: "cfg" }, link: null },
                    { name: "sampler_name", widget: { name: "sampler_name" }, link: null },
                ],
                widgets_values: [5, "fixed", 12, 4, "euler", "normal", 1],
            },
        ],
        links: [[1, 1, 0, 4, 0, "MODEL"], [2, 2, 0, 4, 1, "CONDITIONING"], [3, 3, 0, 4, 2, "CONDITIONING"]],
    };
    const r = extractGenerationParams({ workflow });
    assert.deepEqual([r.positivePrompt, r.seed, r.steps, r.cfgScale, r.sampler], ["named text", 5, 12, 4, "euler"]);
});

test("workflow-only: bypassed nodes pass through, muted nodes are ignored", () => {
    const workflow = {
        nodes: [
            { id: 1, type: "CheckpointLoaderSimple", widgets_values: ["m.safetensors"], outputs: [{ links: [1] }] },
            { id: 2, type: "LoraLoader", mode: 4, inputs: [{ name: "model", type: "MODEL", link: 1 }], outputs: [{ type: "MODEL", links: [2] }] },
            { id: 3, type: "CLIPTextEncode", widgets_values: ["pos"], outputs: [{ links: [3] }] },
            { id: 4, type: "CLIPTextEncode", widgets_values: ["neg"], outputs: [{ links: [4] }] },
            { id: 5, type: "KSampler", mode: 2, inputs: [{ name: "model", link: 2 }, { name: "positive", link: 3 }, { name: "negative", link: 4 }], widgets_values: [9, "fixed", 99, 9, "ddim"] },
            { id: 6, type: "KSampler", inputs: [{ name: "model", link: 2 }, { name: "positive", link: 3 }, { name: "negative", link: 4 }], widgets_values: [1, "fixed", 20, 7, "euler"] },
        ],
        links: [[1, 1, 0, 2, 0, "MODEL"], [2, 2, 0, 6, 0, "MODEL"], [3, 3, 0, 6, 1, "CONDITIONING"], [4, 4, 0, 6, 2, "CONDITIONING"]],
    };
    const r = extractGenerationParams({ workflow });
    assert.deepEqual([r.checkpoint, r.positivePrompt, r.steps], ["m.safetensors", "pos", 20]);
});

test("missing or empty metadata yields placeholders", () => {
    const r = extractGenerationParams({});
    assert.equal(r.positivePrompt, "No prompt found");
    assert.equal(r.negativePrompt, "No negative prompt found");
    assert.equal(r.checkpoint, "Unknown");
    assert.equal(extractGenerationParams(null).seed, "Unknown");
});