Vito Sansevero
cab776af6e
- Workflow widget text now only replaces API text for metadata showing the pre-3.2.3 corruption signature (2+ PromptManager nodes with identical API text but different widgets). Otherwise the API graph wins, so expanded dynamic prompts and linked text display what actually ran. - Images with only a UI workflow are converted to the API graph shape and traced the same way: prompts, KSamplerAdvanced, UNET/checkpoint loaders, KJNodes Set/Get, Reroute, bypassed and muted nodes.
330 lines
18 KiB
JavaScript
330 lines
18 KiB
JavaScript
// Run with: node --test tests/js/
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const test = require("node:test");
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const assert = require("node:assert/strict");
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const { extractGenerationParams } = require("../../web/js/comfy-metadata.js");
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// Same shape as the workflow attached to #75: PromptManager nodes are saved as
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// CLIPTextEncode with extra inputs, and the checkpoint loader is pysssss'.
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const issue75Prompt = {
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1: {
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class_type: "KSampler",
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inputs: {
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seed: 480655185510613, steps: 25, cfg: 5.5,
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sampler_name: "euler_ancestral", scheduler: "normal", denoise: 0.8,
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model: ["20", 0], positive: ["39", 0], negative: ["40", 0], latent_image: ["36", 0],
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},
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},
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8: { class_type: "CLIPSetLastLayer", inputs: { stop_at_clip_layer: -2, clip: ["20", 1] } },
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20: {
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class_type: "CheckpointLoader|pysssss",
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inputs: { ckpt_name: "Anime\\Model-XL.safetensors", prompt: "[none]", example: "[none]" },
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},
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39: {
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class_type: "CLIPTextEncode",
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inputs: {
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text: "1girl, cat_ears, smile", category: "", tags: "", search_text: "",
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prepend_text: "masterpiece, best quality, ", append_text: "", clip: ["8", 0],
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},
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},
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40: {
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class_type: "CLIPTextEncode",
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inputs: {
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text: "3d, crowd", category: "", tags: "", search_text: "",
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prepend_text: "bad quality, worst quality, ", append_text: "", clip: ["8", 0],
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},
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},
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};
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test("#75: traces PromptManager prompts and pysssss checkpoint from the sampler", () => {
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const r = extractGenerationParams({ prompt: issue75Prompt });
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assert.equal(r.positivePrompt, "masterpiece, best quality, 1girl, cat_ears, smile");
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assert.equal(r.negativePrompt, "bad quality, worst quality, 3d, crowd");
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assert.equal(r.checkpoint, "Anime\\Model-XL.safetensors");
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assert.deepEqual(
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[r.seed, r.steps, r.cfgScale, r.sampler],
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[480655185510613, 25, 5.5, "euler_ancestral"],
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);
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});
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test("#75: workflow blob does not override values from the prompt graph", () => {
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const workflow = { nodes: [{ type: "KSampler", widgets_values: [1, "randomize", 99, 7, "ddim"] }] };
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const r = extractGenerationParams({ prompt: issue75Prompt, workflow });
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assert.equal(r.steps, 25);
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assert.equal(r.cfgScale, 5.5);
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});
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test("#75: recovers per-node text when SaveImage patch overwrote every PromptManager node", () => {
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// Pre-fix images: both nodes carry the final positive text in the prompt graph,
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// but the workflow graph still has each node's real widget values.
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const positiveFinal = "masterpiece, best quality, 1girl, smile";
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const prompt = {
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1: { class_type: "KSampler", inputs: { seed: 1, steps: 20, cfg: 7, sampler_name: "euler", positive: ["39", 0], negative: ["40", 0], model: ["20", 0] } },
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20: { class_type: "CheckpointLoaderSimple", inputs: { ckpt_name: "m.safetensors" } },
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39: { class_type: "PromptManager", inputs: { text: positiveFinal, prepend_text: "masterpiece, best quality,", append_text: "" } },
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40: { class_type: "PromptManager", inputs: { text: positiveFinal, prepend_text: "bad quality,", append_text: "" } },
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};
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const workflow = {
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nodes: [
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{ id: 39, type: "PromptManager", widgets_values: ["1girl, smile", "", "", "", "masterpiece, best quality,", ""] },
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{ id: 40, type: "PromptManager", widgets_values: ["3d, crowd", "", "", "", "bad quality,", ""] },
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],
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};
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const r = extractGenerationParams({ prompt, workflow });
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assert.equal(r.positivePrompt, "masterpiece, best quality, 1girl, smile");
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assert.equal(r.negativePrompt, "bad quality, 3d, crowd");
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});
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test("current metadata: API text wins over a stale or unexpanded workflow widget", () => {
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// Dynamic prompt expanded at queue time: API holds what ran, workflow holds the template
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const prompt = {
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1: { class_type: "KSampler", inputs: { positive: ["39", 0], negative: ["40", 0], model: ["20", 0] } },
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20: { class_type: "CheckpointLoaderSimple", inputs: { ckpt_name: "m.safetensors" } },
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39: { class_type: "PromptManager", inputs: { text: "a red cat", prepend_text: "", append_text: "" } },
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40: { class_type: "PromptManager", inputs: { text: "blurry", prepend_text: "", append_text: "" } },
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};
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const workflow = {
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nodes: [
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{ id: 39, type: "PromptManager", widgets_values: ["a {red|blue} cat", "", "", "", "", ""] },
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{ id: 40, type: "PromptManager", widgets_values: ["blurry", "", "", "", "", ""] },
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],
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};
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const r = extractGenerationParams({ prompt, workflow });
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assert.equal(r.positivePrompt, "a red cat");
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assert.equal(r.negativePrompt, "blurry");
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});
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test("current metadata: linked text is followed, not replaced by the widget's stale value", () => {
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const prompt = {
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1: { class_type: "KSampler", inputs: { positive: ["39", 0], negative: ["40", 0], model: ["20", 0] } },
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20: { class_type: "CheckpointLoaderSimple", inputs: { ckpt_name: "m.safetensors" } },
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39: { class_type: "PromptManager", inputs: { text: ["50", 0], prepend_text: "", append_text: "" } },
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40: { class_type: "PromptManager", inputs: { text: ["50", 0], prepend_text: "bad,", append_text: "" } },
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50: { class_type: "PrimitiveString", inputs: { value: "shared text" } },
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};
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const workflow = {
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nodes: [
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{ id: 39, type: "PromptManager", widgets_values: ["old value A", "", "", "", "", ""] },
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{ id: 40, type: "PromptManager", widgets_values: ["old value B", "", "", "", "bad,", ""] },
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],
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};
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const r = extractGenerationParams({ prompt, workflow });
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assert.equal(r.positivePrompt, "shared text");
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});
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test("prepend/append are not doubled when text is already the combined prompt", () => {
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const prompt = {
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1: { class_type: "KSampler", inputs: { positive: ["2", 0], negative: ["3", 0], model: ["4", 0] } },
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2: { class_type: "PromptManager", inputs: { text: "best quality, a cat, 8k", prepend_text: "best quality,", append_text: "8k" } },
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3: { class_type: "CLIPTextEncode", inputs: { text: "blurry" } },
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};
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assert.equal(extractGenerationParams({ prompt }).positivePrompt, "best quality, a cat, 8k");
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});
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test("linked prepend/append inputs are resolved (no longer baked into text since 3.2.3)", () => {
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const prompt = {
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1: { class_type: "KSampler", inputs: { positive: ["2", 0], negative: ["3", 0], model: ["4", 0] } },
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2: { class_type: "PromptManager", inputs: { text: "a fox", prepend_text: ["5", 0], append_text: ["6", 0] } },
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3: { class_type: "CLIPTextEncode", inputs: { text: "blurry" } },
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5: { class_type: "PrimitiveString", inputs: { value: "masterpiece," } },
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6: { class_type: "PrimitiveString", inputs: { value: "8k" } },
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};
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assert.equal(extractGenerationParams({ prompt }).positivePrompt, "masterpiece, a fox 8k");
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});
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test("negative prompt is identified by link, not by keywords", () => {
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const prompt = {
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3: { class_type: "KSampler", inputs: { seed: 1, steps: 20, cfg: 7, sampler_name: "euler", positive: ["6", 0], negative: ["7", 0], model: ["4", 0] } },
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4: { class_type: "CheckpointLoaderSimple", inputs: { ckpt_name: "sd15.safetensors" } },
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6: { class_type: "CLIPTextEncode", inputs: { text: "embedding:goodstyle, a lighthouse" } },
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7: { class_type: "CLIPTextEncode", inputs: { text: "blurry" } },
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};
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const r = extractGenerationParams({ prompt });
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assert.equal(r.positivePrompt, "embedding:goodstyle, a lighthouse");
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assert.equal(r.negativePrompt, "blurry");
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});
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test("follows LoRA chains, ControlNet pass-through, concatenated text and primitive inputs", () => {
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const prompt = {
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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] } },
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2: { class_type: "CheckpointLoaderSimple", inputs: { ckpt_name: "base.safetensors" } },
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3: { class_type: "LoraLoader", inputs: { lora_name: "style.safetensors", model: ["2", 0], clip: ["2", 1] } },
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5: { class_type: "ControlNetApplyAdvanced", inputs: { positive: ["6", 0], negative: ["7", 0], strength: 1 } },
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6: { class_type: "CLIPTextEncode", inputs: { text: ["8", 0] } },
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7: { class_type: "CLIPTextEncode", inputs: { text: "lowres" } },
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8: { class_type: "Text Concatenate", inputs: { delimiter: ", ", text_a: "a castle", text_b: "at dusk" } },
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9: { class_type: "PrimitiveInt", inputs: { value: 30 } },
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};
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const r = extractGenerationParams({ prompt });
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assert.equal(r.positivePrompt, "a castle, at dusk");
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assert.equal(r.negativePrompt, "lowres");
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assert.equal(r.checkpoint, "base.safetensors");
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assert.equal(r.seed, 42);
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assert.equal(r.steps, 30);
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});
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test("custom sampler graphs (Flux: SamplerCustomAdvanced + CFGGuider) resolve every field", () => {
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const prompt = {
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85: { class_type: "UNETLoader", inputs: { unet_name: "flux2-klein.safetensors" } },
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135: { class_type: "Lora Loader (LoraManager)", inputs: { model: ["85", 0], clip: ["90", 0] } },
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149: { class_type: "PromptManager", inputs: { text: "a red fox", prepend_text: ["133", 0], append_text: "" } },
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152: { class_type: "SeedHistory", inputs: { seed: 1725922648 } },
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// ControlNet-style node with positive/negative but no model: must not be taken as the sampler
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200: { class_type: "ControlNetApplyAdvanced", inputs: { positive: ["149", 0], negative: ["274", 0], strength: 1 } },
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265: { class_type: "SamplerCustomAdvanced", inputs: { noise: ["267", 0], guider: ["268", 0], sampler: ["269", 0], sigmas: ["266", 0] } },
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266: { class_type: "Flux2Scheduler", inputs: { steps: 50, width: ["153", 1] } },
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267: { class_type: "RandomNoise", inputs: { noise_seed: ["152", 0] } },
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268: { class_type: "CFGGuider", inputs: { cfg: 5.0, model: ["135", 0], positive: ["149", 0], negative: ["274", 0] } },
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269: { class_type: "KSamplerSelect", inputs: { sampler_name: "uni_pc" } },
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274: { class_type: "CLIPTextEncode", inputs: { text: "watermark" } },
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};
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const r = extractGenerationParams({ prompt });
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assert.deepEqual(
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[r.positivePrompt, r.negativePrompt, r.checkpoint, r.cfgScale, r.seed, r.steps, r.sampler],
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["a red fox", "watermark", "flux2-klein.safetensors", 5.0, 1725922648, 50, "uni_pc"],
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);
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});
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test("multi-output settings nodes (SamplerCombo) map each field to its own value", () => {
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const prompt = {
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5: {
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class_type: "KSampler",
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inputs: {
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seed: ["162", 0], steps: ["165", 2], cfg: ["165", 3], sampler_name: ["165", 0],
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positive: ["6", 0], negative: ["7", 0], model: ["4", 0],
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},
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},
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4: { class_type: "CheckpointLoaderSimple", inputs: { ckpt_name: "m.safetensors" } },
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6: { class_type: "CLIPTextEncode", inputs: { text: "a cat" } },
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7: { class_type: "CLIPTextEncode", inputs: { text: "blurry" } },
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162: { class_type: "SeedHistory", inputs: { seed: 1495646836, seed_history_ui: "" } },
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165: { class_type: "SamplerCombo", inputs: { sampler_name: "euler_ancestral", scheduler: "normal", steps: 50, cfg: 7 } },
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};
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const r = extractGenerationParams({ prompt });
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assert.deepEqual([r.seed, r.steps, r.cfgScale, r.sampler], [1495646836, 50, 7, "euler_ancestral"]);
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});
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test("workflow-only images account for the hidden control_after_generate widget", () => {
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const workflow = {
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nodes: [
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{ id: 1, type: "CheckpointLoader|pysssss", widgets_values: ["model.safetensors", "[none]"] },
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{ id: 2, type: "KSampler", widgets_values: [123, "randomize", 25, 5.5, "euler", "normal", 1] },
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],
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};
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const r = extractGenerationParams({ workflow });
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assert.deepEqual(
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[r.checkpoint, r.seed, r.steps, r.cfgScale, r.sampler],
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["model.safetensors", 123, 25, 5.5, "euler"],
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);
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});
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test("cyclic, fan-out graphs from crafted PNGs resolve quickly", () => {
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// Every node links to itself and its neighbour through several inputs and has
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// no scalars: without a visited set this explores ~inputs^MAX_DEPTH paths.
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const loop = (id, next) => ({
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class_type: "Evil",
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inputs: {
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text_a: [id, 0], text_b: [next, 0], text_c: [id, 0], text_d: [next, 0],
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value_a: [id, 0], value_b: [next, 0], model: [next, 0],
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},
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});
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const prompt = {
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1: { class_type: "KSampler", inputs: { seed: ["2", 0], steps: ["3", 0], positive: ["2", 0], negative: ["3", 0], model: ["2", 0] } },
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2: loop("2", "3"),
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3: loop("3", "2"),
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};
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const started = Date.now();
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const r = extractGenerationParams({ prompt });
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assert.ok(Date.now() - started < 500, `took ${Date.now() - started}ms`);
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assert.equal(r.positivePrompt, "No prompt found");
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assert.equal(r.seed, "Unknown");
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});
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test("chained encoders whose prepend and append both link onward stay linear", () => {
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const prompt = { 1: { class_type: "KSampler", inputs: { positive: ["n0", 0], negative: ["n0", 0], model: ["n0", 0] } } };
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for (let i = 0; i < 60; i++) {
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prompt[`n${i}`] = {
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class_type: "PromptManager",
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inputs: { text: `t${i}`, prepend_text: [`n${i + 1}`, 0], append_text: [`n${i + 1}`, 0] },
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};
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}
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const started = Date.now();
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extractGenerationParams({ prompt });
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assert.ok(Date.now() - started < 500, `took ${Date.now() - started}ms`);
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});
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// UI-format link: [id, from_node, from_slot, to_node, to_slot, type]
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test("workflow-only: traces prompts, model and KSamplerAdvanced settings through Set/Get and Reroute", () => {
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const workflow = {
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nodes: [
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{ id: 1, type: "UNETLoader", widgets_values: ["flux.safetensors", "default"], outputs: [{ links: [1] }] },
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{ id: 2, type: "SetNode", widgets_values: ["model"], inputs: [{ name: "MODEL", link: 1 }] },
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{ id: 3, type: "GetNode", widgets_values: ["model"], outputs: [{ links: [2] }] },
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{ id: 4, type: "PromptManager", widgets_values: ["a castle", "", "", "", "masterpiece,", ""], inputs: [], outputs: [{ links: [3] }] },
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{ id: 5, type: "Reroute", inputs: [{ name: "", link: 3 }], outputs: [{ links: [4] }] },
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{ id: 6, type: "CLIPTextEncode", widgets_values: ["lowres"], outputs: [{ links: [5] }] },
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{
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id: 7, type: "KSamplerAdvanced",
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inputs: [{ name: "model", link: 2 }, { name: "positive", link: 4 }, { name: "negative", link: 5 }],
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widgets_values: ["enable", 77, "randomize", 30, 6.5, "dpmpp_2m", "karras", 0, 10000, "disable"],
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},
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],
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links: [
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[1, 1, 0, 2, 0, "MODEL"], [2, 3, 0, 7, 0, "MODEL"], [3, 4, 0, 5, 0, "CONDITIONING"],
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[4, 5, 0, 7, 1, "CONDITIONING"], [5, 6, 0, 7, 2, "CONDITIONING"],
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],
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};
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const r = extractGenerationParams({ workflow });
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assert.deepEqual(
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[r.positivePrompt, r.negativePrompt, r.checkpoint, r.seed, r.steps, r.cfgScale, r.sampler],
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["masterpiece, a castle", "lowres", "flux.safetensors", 77, 30, 6.5, "dpmpp_2m"],
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);
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});
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test("workflow-only: widgets_values_named and widget-backed inputs take precedence over positions", () => {
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const workflow = {
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nodes: [
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{ id: 1, type: "CheckpointLoaderSimple", widgets_values: ["m.safetensors"], outputs: [{ links: [1] }] },
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{ id: 2, type: "CLIPTextEncode", widgets_values_named: { text: "named text" }, widgets_values: ["ignored"], outputs: [{ links: [2] }] },
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{ id: 3, type: "CLIPTextEncode", widgets_values: ["neg"], outputs: [{ links: [3] }] },
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{
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id: 4, type: "KSampler",
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inputs: [
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{ name: "model", link: 1 }, { name: "positive", link: 2 }, { name: "negative", link: 3 },
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{ name: "seed", widget: { name: "seed" }, link: null },
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{ name: "steps", widget: { name: "steps" }, link: null },
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{ name: "cfg", widget: { name: "cfg" }, link: null },
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{ name: "sampler_name", widget: { name: "sampler_name" }, link: null },
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],
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widgets_values: [5, "fixed", 12, 4, "euler", "normal", 1],
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},
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],
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links: [[1, 1, 0, 4, 0, "MODEL"], [2, 2, 0, 4, 1, "CONDITIONING"], [3, 3, 0, 4, 2, "CONDITIONING"]],
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};
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const r = extractGenerationParams({ workflow });
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assert.deepEqual([r.positivePrompt, r.seed, r.steps, r.cfgScale, r.sampler], ["named text", 5, 12, 4, "euler"]);
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});
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test("workflow-only: bypassed nodes pass through, muted nodes are ignored", () => {
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const workflow = {
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nodes: [
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{ id: 1, type: "CheckpointLoaderSimple", widgets_values: ["m.safetensors"], outputs: [{ links: [1] }] },
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{ id: 2, type: "LoraLoader", mode: 4, inputs: [{ name: "model", type: "MODEL", link: 1 }], outputs: [{ type: "MODEL", links: [2] }] },
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{ id: 3, type: "CLIPTextEncode", widgets_values: ["pos"], outputs: [{ links: [3] }] },
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{ id: 4, type: "CLIPTextEncode", widgets_values: ["neg"], outputs: [{ links: [4] }] },
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{ 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"] },
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{ id: 6, type: "KSampler", inputs: [{ name: "model", link: 2 }, { name: "positive", link: 3 }, { name: "negative", link: 4 }], widgets_values: [1, "fixed", 20, 7, "euler"] },
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],
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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"]],
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};
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const r = extractGenerationParams({ workflow });
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assert.deepEqual([r.checkpoint, r.positivePrompt, r.steps], ["m.safetensors", "pos", 20]);
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});
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test("missing or empty metadata yields placeholders", () => {
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const r = extractGenerationParams({});
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assert.equal(r.positivePrompt, "No prompt found");
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assert.equal(r.negativePrompt, "No negative prompt found");
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assert.equal(r.checkpoint, "Unknown");
|
|
assert.equal(extractGenerationParams(null).seed, "Unknown");
|
|
});
|