init

.gitignore

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+node_modules

README.md

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+# sd-playtime
+
+Just for fun...
+
+Model link: https://civitai.com/models/4823/deliberate

index.js

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+import rl from 'node:readline';
+import { StableDiffusionApi } from 'stable-diffusion-api';
+import sharp from 'sharp';
+
+const PROMPT = "extremely detailed cinematic close up photo of an (Nikolay Valuev:1.3) as ethereal neural network organism, anatomical face, biomechanical details";
+
+const api = new StableDiffusionApi({
+    host: "127.0.0.1",
+    port: 7860,
+    protocol: "http",
+    defaultSampler: "DPM++ 2M Karras",
+    defaultStepCount: 22,
+});
+
+await api.setModel("deliberate_v3");
+
+function printProgress() {
+    const progressInterval = setInterval(async () => {
+        const response = await api.getProgress();
+
+        if (response.progress === 0.0 && response.state.job_count === 0) {
+            clearInterval(progressInterval);
+        }
+
+        rl.cursorTo(process.stdout, 0);
+        rl.clearLine(process.stdout, 0);
+        process.stdout.write(`[WAIT]: progress = ${response.progress.toFixed(2)}, jobs: ${response.state.job_count}`);
+    }, 200);
+}
+
+async function predict(prompt) {
+    printProgress();
+
+    return api.txt2img({
+        prompt: prompt,
+        negative_prompt: '[deformed | disfigured], poorly drawn, [bad : wrong] anatomy, [extra | missing | floating | disconnected] limb, (mutated hands and fingers), blurry',
+        batch_size: 4,
+        cfg_scale: 7,
+        width: 640,
+        height: 640,
+        enable_hr: false,
+        hr_resize_x: 1280,
+        hr_resize_y: 1280,
+        hr_upscaler: "4x_NMKD-Siax_200k",
+        hr_second_pass_steps: 8,
+        denoising_strength: 0.36,
+        seed: -1
+    });
+}
+
+const results = [];
+const prediction = await predict(PROMPT);
+
+for (let result of prediction.images) {
+    const image = await result.png().toBuffer();
+    results.push(image);
+}
+
+const canvas = await sharp({
+    create: {
+        width: 1280,
+        height: 1280,
+        channels: 3,
+        background: { r: 0, g: 0, b: 0 }
+    }
+}).png().toBuffer();
+
+const result = sharp(canvas).composite([
+    { input: results[0], gravity: 'northwest' },
+    { input: results[1], gravity: 'northeast' },
+    { input: results[2], gravity: 'southwest' },
+    { input: results[3], gravity: 'southeast' },
+]);
+
+await result.jpeg().toFile('result.jpeg');

package.json

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+{
+	"scripts": {
+		"postinstall": "patch-package"
+	},
+	"type": "module",
+	"dependencies": {
+		"patch-package": "^8.0.0",
+		"sharp": "^0.32.5",
+		"stable-diffusion-api": "^0.0.7"
+	}
+}

patches/stable-diffusion-api+0.0.7.patch

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+diff --git a/node_modules/stable-diffusion-api/dist/lib/StableDiffusionApi.js b/node_modules/stable-diffusion-api/dist/lib/StableDiffusionApi.js
+index efb62a9..6b03349 100644
+--- a/node_modules/stable-diffusion-api/dist/lib/StableDiffusionApi.js
++++ b/node_modules/stable-diffusion-api/dist/lib/StableDiffusionApi.js
+@@ -21,7 +21,7 @@ const base64_1 = require("../utils/base64");
+ const createScriptsWithCnUnits = (initScripts, controlNetUnit) => __awaiter(void 0, void 0, void 0, function* () {
+     const promises = controlNetUnit.map((unit) => __awaiter(void 0, void 0, void 0, function* () { return yield unit.toJson(); }));
+     const args = yield Promise.all(promises);
+-    const ControlNet = { args };
++    const ControlNet = args.length ? { args } : undefined;
+     const scripts = Object.assign(Object.assign({}, initScripts), { ControlNet });
+     return scripts;
+ });
+@@ -457,7 +457,7 @@ class StableDiffusionApi {
+     setModel(name, findClosest = true) {
+         return __awaiter(this, void 0, void 0, function* () {
+             const models = yield this.getSdModels();
+-            const modelNames = models.map((model) => model.name);
++            const modelNames = models.map((model) => model.model_name);
+             let foundModel = null;
+             if (modelNames.includes(name)) {
+                 foundModel = name;