added_new_node
This commit is contained in:
Satadal Dhara
@@ -6,6 +6,7 @@ from .nodes.resolution_machine import Resolution_Machine
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from .nodes.save_image_metadata import ImageSaveWithMetadata
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from .nodes.upscale_machine import Upscale_Machine
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from .nodes.prompt_autocomplete import PromptAutocomplete
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from .nodes.latent_machine import Latent_Machine
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# A dictionary that maps class names to class objects
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NODE_CLASS_MAPPINGS = {
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@@ -14,6 +15,7 @@ NODE_CLASS_MAPPINGS = {
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"ImageSaveWithMetadata": ImageSaveWithMetadata,
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"Upscale_Machine": Upscale_Machine,
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"PromptAutocomplete": PromptAutocomplete,
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"Latent_Machine": Latent_Machine,
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}
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# A dictionary that maps display names to class names
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@@ -23,6 +25,7 @@ NODE_DISPLAY_NAME_MAPPINGS = {
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"Save Image w/Metadata": "ImageSaveWithMetadata",
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"Upscale Machine": "Upscale_Machine",
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"Prompt Autocomplete": "PromptAutocomplete",
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"Latent Machine": "Latent_Machine",
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}
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# A dictionary that contains web directory information
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+38
-3
@@ -20,7 +20,7 @@ app.registerExtension({
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node.widgets.splice(index, 1);
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// Add proper dropdown
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const nameWidget = node.addWidget("combo", "name", "None", () => {}, {
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const nameWidget = node.addWidget("combo", "name", "None", () => { }, {
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values: ["None"]
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});
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@@ -50,17 +50,52 @@ app.registerExtension({
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nameWidget.value = available[0]; // safe fallback
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}
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// Force update previews for the new name
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updatePreviews(nameWidget.value);
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app.graph.change();
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}
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// Hook CSV change
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const oldCsvCallback = csvWidget.callback;
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csvWidget.callback = function(value) {
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csvWidget.callback = function (value) {
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if (oldCsvCallback) oldCsvCallback(value);
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loadNames(value); // refresh dynamically
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};
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// --- Preview Logic ---
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const posPreview = node.widgets.find(w => w.name === "positive_preview");
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const negPreview = node.widgets.find(w => w.name === "negative_preview");
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const notePreview = node.widgets.find(w => w.name === "note_preview");
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async function updatePreviews(name) {
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const csv = csvWidget.value;
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if (!csv || !name || name === "None") return;
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try {
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const resp = await fetch(`/sata/prompt_machine/get?csv=${encodeURIComponent(csv)}&name=${encodeURIComponent(name)}`);
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const data = await resp.json();
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if (posPreview) posPreview.value = data.positive || "";
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if (negPreview) negPreview.value = data.negative || "";
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if (notePreview) notePreview.value = data.note || "";
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app.graph.change();
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} catch (err) {
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console.error("[PromptMachineFrontend] Failed to load preview:", err);
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}
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}
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// Hook Name change
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const oldNameCallback = nameWidget.callback;
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nameWidget.callback = function (value) {
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if (oldNameCallback) oldNameCallback(value);
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updatePreviews(value);
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};
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// Initial sync
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loadNames(csvWidget.value);
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loadNames(csvWidget.value).then(() => {
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updatePreviews(nameWidget.value);
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});
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}
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});
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@@ -77,21 +77,70 @@ app.registerExtension({
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app.graph.change();
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}
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// --- Preview Logic ---
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const previewWidget = node.widgets.find(w => w.name === "dimension_preview");
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function updatePreview() {
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if (!previewWidget) return;
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const w = widthWidget.value;
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const h = heightWidget.value;
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previewWidget.value = `${w} x ${h}`;
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}
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// --- Hook model change ---
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const oldModelCallback = modelWidget.callback;
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modelWidget.callback = function(value) {
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modelWidget.callback = function (value) {
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if (oldModelCallback) oldModelCallback(value);
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refreshResolutions(value);
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};
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// --- Hook resolution change ---
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const oldResolutionCallback = resolutionWidget.callback;
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resolutionWidget.callback = function(value) {
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resolutionWidget.callback = function (value) {
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if (oldResolutionCallback) oldResolutionCallback(value);
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applyResolution();
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};
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// Hook width/height changes for preview
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const oldWidthCallback = widthWidget.callback;
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widthWidget.callback = function (value) {
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if (oldWidthCallback) oldWidthCallback(value);
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updatePreview();
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};
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const oldHeightCallback = heightWidget.callback;
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heightWidget.callback = function (value) {
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if (oldHeightCallback) oldHeightCallback(value);
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updatePreview();
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};
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// --- Add Swap Button ---
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node.addWidget("button", "Swap Dimensions", null, () => {
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const w = widthWidget.value;
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const h = heightWidget.value;
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// Swap values
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widthWidget.value = h;
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heightWidget.value = w;
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// Force "Custom (manual)" to prevent preset overwrite
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if (resolutionWidget.value !== "Custom (manual)") {
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// Check if "Custom (manual)" exists in options
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if (!resolutionWidget.options.values.includes("Custom (manual)")) {
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resolutionWidget.options.values.push("Custom (manual)");
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}
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resolutionWidget.value = "Custom (manual)";
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// Unlock widgets since we are now in custom mode
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widthWidget.disabled = false;
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heightWidget.disabled = false;
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}
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updatePreview();
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app.graph.change();
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});
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// --- Initial sync ---
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refreshResolutions(modelWidget.value);
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updatePreview();
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}
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});
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@@ -0,0 +1,148 @@
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import torch
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import torch.fft
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import random
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class Latent_Machine:
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"""
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Creates an empty latent initialized with Power-Law (1/f) noise, Perlin-like noise, or Plasma noise.
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Supports 4-channel (SD1.5/SDXL) and 16-channel (Flux/SD3) latents.
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"""
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def __init__(self):
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self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"width": ("INT", {"default": 512, "min": 64, "max": 8192, "step": 8}),
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"height": ("INT", {"default": 512, "min": 64, "max": 8192, "step": 8}),
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"batch_size": ("INT", {"default": 1, "min": 1, "max": 64}),
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"model_type": ([
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"SD1.5/SDXL/PicsArt/Kolors/Auraflow (4ch)",
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"Flux/Qwen/SD3/Lumina (16ch)"
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],),
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"noise_type": ([
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"Gaussian (White): Sharp Architecture, Text, intricate mechanics",
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"Pink (1/f): Photorealism, Portraits, Nature",
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"Brown (1/f²): Anime, Digital Art, Bokeh/Backgrounds",
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"Perlin : Fantasy landscapes, Fluids, Hair/Fabric",
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"Plasma : Sci-Fi, Abstract, Alien terrain"
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],),
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"intensity": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.1}),
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"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
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}
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}
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RETURN_TYPES = ("LATENT",)
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FUNCTION = "generate_noise"
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CATEGORY = "SATA_UtilityNode"
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def generate_noise(self, width, height, batch_size, model_type, noise_type, intensity, seed):
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# Set seed for reproducibility
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torch.manual_seed(seed)
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random.seed(seed)
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# Determine channels based on model type
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if "16ch" in model_type:
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c = 16
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else:
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c = 4
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# Latent dimensions (compressed by 8)
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h = height // 8
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w = width // 8
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# Generate base noise
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if "Gaussian" in noise_type:
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noise = torch.randn((batch_size, c, h, w), device=self.device)
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elif "Perlin" in noise_type:
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# Multi-Octave Value Noise Approximation
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noise = self.generate_perlin_approx(batch_size, c, h, w)
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else:
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# FFT-based Power Law Noise (Pink, Brown, Plasma)
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if "Pink" in noise_type:
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alpha = 1.0
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elif "Brown" in noise_type:
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alpha = 2.0
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elif "Plasma" in noise_type:
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alpha = 3.0 # Very smooth
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else:
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alpha = 0.0 # Fallback to white
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noise = self.generate_power_law_noise(batch_size, c, h, w, alpha)
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# Normalize standard deviation to match expected latent variance (approx 1.0 for standard Gaussian)
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# This ensures intensity works consistently across different noise types
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current_std = noise.std()
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if current_std > 1e-6:
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noise = noise / current_std
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# Apply intensity
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noise = noise * intensity
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return ({"samples": noise},)
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def generate_power_law_noise(self, batch_size, c, h, w, alpha):
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# Generate White Noise (Standard Gaussian)
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white_noise = torch.randn((batch_size, c, h, w), device=self.device)
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# Convert to Frequency Domain (FFT)
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fft_noise = torch.fft.fft2(white_noise)
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# Create Frequency Grid
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y = torch.fft.fftfreq(h, device=self.device)
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x = torch.fft.fftfreq(w, device=self.device)
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dy, dx = torch.meshgrid(y, x, indexing='ij')
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# Calculate distance from center (frequency magnitude)
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# Add epsilon to avoid division by zero at DC component
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frequency_magnitude = torch.sqrt(dy**2 + dx**2) + 1e-8
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# Apply Power Law Scaling: Amplitude ~ 1 / f^(alpha/2)
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scale = 1.0 / (frequency_magnitude ** (alpha / 2.0))
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# Zero out the DC component (mean brightness) to keep it neutral
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scale[0, 0] = 0
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fft_structured = fft_noise * scale
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# Convert back to Spatial Domain (Inverse FFT)
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structured_noise = torch.fft.ifft2(fft_structured).real
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return structured_noise
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def generate_perlin_approx(self, batch_size, c, h, w):
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"""
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Generates a Perlin-like noise by blending upsampled noise from multiple octaves.
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This is faster than calculating actual Perlin noise per pixel on the CPU/GPU.
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"""
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noise = torch.zeros((batch_size, c, h, w), device=self.device)
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# Octaves: scale down, generate noise, scale up
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octaves = [2, 4, 8, 16]
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weights = [0.5, 0.25, 0.125, 0.0625] # 1/f amplitude falloff
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total_weight = 0
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for scale, weight in zip(octaves, weights):
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# Calculate small dimensions
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sh, sw = max(1, h // scale), max(1, w // scale)
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# Generate random noise at this scale
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small_noise = torch.randn((batch_size, c, sh, sw), device=self.device)
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# Upsample to full size using bicubic interpolation for smoothness
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upsampled = torch.nn.functional.interpolate(
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small_noise, size=(h, w), mode='bicubic', align_corners=False
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)
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noise += upsampled * weight
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total_weight += weight
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# Add a bit of fine-grained white noise for texture
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noise += torch.randn((batch_size, c, h, w), device=self.device) * 0.05
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return noise
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@@ -107,6 +107,11 @@ class Prompt_Machine:
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}),
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# make 'name' a free text field (STRING) — avoids list validation problems
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"name": ("STRING", {"default": default_name}),
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},
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"optional": {
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"positive_preview": ("STRING", {"multiline": True, "forceInput": False, "default": ""}),
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"negative_preview": ("STRING", {"multiline": True, "forceInput": False, "default": ""}),
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"note_preview": ("STRING", {"multiline": True, "forceInput": False, "default": ""}),
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}
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}
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@@ -115,7 +120,7 @@ class Prompt_Machine:
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FUNCTION = "get_prompts"
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CATEGORY = "SATA_UtilityNode"
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def get_prompts(self, csv_file, name):
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def get_prompts(self, csv_file, name, positive_preview=None, negative_preview=None, note_preview=None):
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print(f"[PromptMachine] get_prompts - csv_file={csv_file}, name={name}")
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pos, neg, note = read_prompt_row(csv_file, name)
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print(f"[PromptMachine] get_prompts -> pos_len={len(pos)}, neg_len={len(neg)}, note_len={len(note)}")
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@@ -40,6 +40,9 @@ class Resolution_Machine:
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"resolution": (resolutions, {"default": default_resolution}),
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"custom_width": ("INT", {"default": 512, "min": 1, "max": 8192}),
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"custom_height": ("INT", {"default": 512, "min": 1, "max": 8192}),
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},
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"optional": {
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"dimension_preview": ("STRING", {"multiline": False, "forceInput": False, "default": ""}),
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}
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}
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@@ -48,7 +51,7 @@ class Resolution_Machine:
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FUNCTION = "get_resolution"
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CATEGORY = "SATA_UtilityNode"
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def get_resolution(self, model, resolution, custom_width, custom_height):
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def get_resolution(self, model, resolution, custom_width, custom_height, dimension_preview=None):
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config = load_config()
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if model not in config:
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