nodes/sn0w12-ComfyUI-Sn0w-Scripts
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sn0w12
2024-05-12 02:40:00 +02:00
parent 3956ac074c
commit 8f7a3d3afe
7 changed files with 192 additions and 73 deletions
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__init__.py
+3 -1
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@@ -15,6 +15,7 @@ from .src.load_lora_from_folder import LoadLoraFolderNode
from .src.textbox import TextboxNode
from .src.simple_sampler_custom import SimpleSamplerCustom
from .src.show_sigmas import ShowSigmasNode
from .src.combine_schedulers import CombineSchedulersNode
NODE_CLASS_MAPPINGS = {
"Find SDXL Resolution": FindResolutionNode,
@@ -33,6 +34,7 @@ NODE_CLASS_MAPPINGS = {
"Copy/Paste Textbox": TextboxNode,
"Simple Sampler Custom": SimpleSamplerCustom,
"Show Sigmas": ShowSigmasNode,
"Combine Sigmas": CombineSchedulersNode,
}
NODE_DISPLAY_NAME_MAPPINGS = {
@@ -51,7 +53,7 @@ NODE_DISPLAY_NAME_MAPPINGS = {
"Load Lora Folder": "Load Lora Folder",
"Copy/Paste Textbox": "Textbox",
"Simple Sampler Custom": "Simple Sampler Custom",
"Show Sigmas": "Show Sigmas",
"Combine Sigmas": "Combine Sigmas",
}
WEB_DIRECTORY = "./web"
src/combine_schedulers.py
+35
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@@ -0,0 +1,35 @@
from comfy_extras.nodes_custom_sampler import SplitSigmasDenoise, SplitSigmas
class CombineSchedulersNode:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"sigmas_a": ("SIGMAS",),
"sigmas_b": ("SIGMAS",),
"split_at": ("INT", {"default": 10, "min": 10, "max": 10000}),
"denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step":0.01, "round": 0.01}),
},
}
RETURN_TYPES = ("SIGMAS", )
RETURN_NAMES = ("SIGMAS", )
FUNCTION = "get_combined_sigmas"
CATEGORY = "sampling/custom_sampling"
def get_combined_sigmas(self, sigmas_a, sigmas_b, split_at, denoise):
split_sigmas_a = SplitSigmas.get_sigmas(self, sigmas_a, split_at)
split_sigmas_b = SplitSigmas.get_sigmas(self, sigmas_b, split_at)
sigmas = split_sigmas_a[0] + split_sigmas_b[1]
sigmas = self.get_denoised_sigmas(sigmas, denoise)
return (sigmas,)
def get_denoised_sigmas(self, sigmas, denoise):
if denoise == 1:
return (None, sigmas, )
return SplitSigmasDenoise.get_sigmas(self, sigmas, denoise)
src/show_sigmas.py
+18 -12
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@@ -22,9 +22,9 @@ class ShowSigmasNode:
FUNCTION = "run"
CATEGORY = "utils"
def run(self, sigmas, unique_id):
# Convert tensor to a numpy array if it's not already
def sigmas_to_list(self, sigmas):
# Convert tensor to a numpy array if it's not already
if isinstance(sigmas, np.ndarray):
temporary_sigmas = sigmas
else:
@@ -42,13 +42,10 @@ class ShowSigmasNode:
sigmas_list = [[float(value)] for value in temporary_sigmas]
else:
sigmas_list = temporary_sigmas.tolist()
PromptServer.instance.send_sync("sn0w_get_sigmas", {
"id": unique_id,
"sigmas": sigmas_list,
})
outputs = MessageHolder.waitForMessage(unique_id)
return sigmas_list
def image_to_tensor(self, outputs):
output_data = outputs['output']
if ',' in output_data:
image_data = output_data.split(',', 1)[1]
@@ -78,8 +75,17 @@ class ShowSigmasNode:
print("Tensor shape for SaveImage node:", image_tensor.shape)
# Print final tensor shape
final_tensor = image_tensor
print("Final tensor shape to pass to SaveImage:", final_tensor.shape)
return image_tensor
def run(self, sigmas, unique_id):
sigmas_list = self.sigmas_to_list(sigmas)
PromptServer.instance.send_sync("sn0w_get_sigmas", {
"id": unique_id,
"sigmas": sigmas_list,
})
outputs = MessageHolder.waitForMessage(unique_id)
final_tensor = self.image_to_tensor(outputs)
return (sigmas, final_tensor,)
src/simple_sampler_custom.py
+15 -5
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@@ -4,6 +4,7 @@ from comfy_extras.nodes_align_your_steps import AlignYourStepsScheduler
from server import PromptServer
from ..sn0w import Logger, MessageHolder
from .custom_schedulers.custom_schedulers import CustomSchedulers
from .show_sigmas import ShowSigmasNode
import comfy.samplers
class SimpleSamplerCustom:
@@ -17,7 +18,7 @@ class SimpleSamplerCustom:
@classmethod
def build_scheduler_list(cls):
custom_scheduler_names = list(cls.custom_schedulers.get_scheduler_settings().keys())
print(custom_scheduler_names)
cls.logger.log(custom_scheduler_names, "DEBUG")
cls.scheduler_list = comfy.samplers.KSampler.SCHEDULERS + ["align_your_steps", "polyexponential", "vp"] + custom_scheduler_names
cls.scheduler_settings = {name: settings for name, settings in cls.custom_schedulers.get_scheduler_settings().items()}
@@ -55,8 +56,8 @@ class SimpleSamplerCustom:
},
}
RETURN_TYPES = ("IMAGE", "LATENT", "CONDITIONING", "CONDITIONING")
RETURN_NAMES = ("IMAGE", "LATENT", "POSITIVE", "NEGATIVE")
RETURN_TYPES = ("IMAGE", "LATENT", "CONDITIONING", "CONDITIONING", "IMAGE")
RETURN_NAMES = ("IMAGE", "LATENT", "POSITIVE", "NEGATIVE", "SIGMAS GRAPH")
FUNCTION = "sample"
@@ -94,7 +95,17 @@ class SimpleSamplerCustom:
else:
image = (None, )
return (image[0], samples[1], positive_prompt, negative_prompt)
sigmas_list = ShowSigmasNode.sigmas_to_list(self, sigmas)
PromptServer.instance.send_sync("sampler_get_sigmas", {
"id": kwargs["unique_id"],
"sigmas": sigmas_list,
})
outputs = MessageHolder.waitForMessage(kwargs["unique_id"])
final_tensor = ShowSigmasNode.image_to_tensor(self, outputs)
return (image[0], samples[1], positive_prompt, negative_prompt, final_tensor,)
def get_sampler(self, sampler_name):
sampler = comfy.samplers.sampler_object(sampler_name)
@@ -174,5 +185,4 @@ class SimpleSamplerCustom:
"id": unique_id,
})
outputs = MessageHolder.waitForMessage(unique_id)
print(outputs)
return outputs
web/show_sigmas.js
+1 -43
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@@ -6,55 +6,13 @@ app.registerExtension({
name: "sn0w.ShowSigmas",
async beforeRegisterNodeDef(nodeType, nodeData, app) {
if (nodeData.name === "Show Sigmas") {
// The populate function now returns the Base64 encoded image
function populate(sigmas) {
// Define the size of the canvas
const width = 800;
const height = 400;
// Create a canvas element
const canvas = document.createElement('canvas');
canvas.width = width;
canvas.height = height;
const ctx = canvas.getContext('2d');
// Background
ctx.fillStyle = 'white';
ctx.fillRect(0, 0, width, height);
// Normalize sigma values to fit within the canvas height
const maxSigma = Math.max(...sigmas.map(s => s[0]));
const minSigma = Math.min(...sigmas.map(s => s[0]));
const range = maxSigma - minSigma;
// Function to scale a sigma value to the canvas coordinates
const scaleY = sigma => (height - 20) - (((sigma - minSigma) / range) * (height - 40));
const scaleX = index => (index / (sigmas.length - 1)) * width;
// Draw the graph line
ctx.strokeStyle = 'black';
ctx.lineWidth = 2;
ctx.beginPath();
ctx.moveTo(scaleX(0), scaleY(sigmas[0][0]));
sigmas.forEach((sigma, index) => {
ctx.lineTo(scaleX(index), scaleY(sigma[0]));
});
ctx.stroke();
// Return the canvas content as a Base64 encoded image
return canvas.toDataURL('image/png');
}
nodeType.prototype.onNodeCreated = function () {
console.log(this)
api.addEventListener('sn0w_get_sigmas', (event) => {
const data = event.detail;
console.log(`Recieved ${data}`);
if (this.id == data.id) {
console.log(`Recieved ${data.sigmas}`);
const imageBase64 = populate(data.sigmas);
const imageBase64 = SettingUtils.drawSigmas(data.sigmas);
// Send the generated image data back to the server
api.fetchApi(`${SettingUtils.API_PREFIX}/get_sigmas`, {
web/simpleSamplerCustom.js
+47 -12
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@@ -15,7 +15,6 @@ app.registerExtension({
nodeType.prototype.onConnectInput = function (targetSlot, type, output, originNode, originSlot) {
const slot = type[0]
const inputType = type[1]
console.log(JSON.parse(JSON.stringify(targetSlot.inputs[slot])))
const initialCount = countWidgetsOfType(targetSlot.widgets, "converted-widget");
const originalSize = [targetSlot.size[0], targetSlot.size[1]];
@@ -88,7 +87,6 @@ app.registerExtension({
api.addEventListener('get_scheduler_values', (event) => {
const data = event.detail
const output = getWidgetOutputs(this, data.widgets_needed);
console.log(output);
if (this.id == data.id) {
api.fetchApi(`${SettingUtils.API_PREFIX}/scheduler_values`, {
method: "POST",
@@ -108,7 +106,6 @@ app.registerExtension({
api.addEventListener('should_decode_image', (event) => {
const data = event.detail
const output = checkImageOutput(this);
console.log(output);
if (this.id == data.id) {
api.fetchApi(`${SettingUtils.API_PREFIX}/should_decode_image`, {
method: "POST",
@@ -124,6 +121,29 @@ app.registerExtension({
})
}
})
api.addEventListener('sampler_get_sigmas', (event) => {
const data = event.detail;
if (this.id == data.id) {
const imageBase64 = SettingUtils.drawSigmas(data.sigmas);
// Send the generated image data back to the server
api.fetchApi(`${SettingUtils.API_PREFIX}/get_sigmas`, {
method: "POST",
headers: {
"Content-Type": "application/json",
},
body: JSON.stringify(
{
node_id: data.id,
outputs: {
output: imageBase64
}
}
),
})
}
});
}
nodeType.prototype.onConfigure = function () {
@@ -196,25 +216,42 @@ app.registerExtension({
}
function rearrangeWidgets(node, moveWidgetsBehind, totalWidgetsToMove) {
const widgets = node.widgets;
// Calculate the starting index for removing the last three widgets
let widgets = node.widgets;
// Calculate the starting index for removing the last N widgets
let startIndex = widgets.length - totalWidgetsToMove;
// Check if there are at least three widgets to move
// Check if there are enough widgets to move
if (startIndex < 0) {
console.warning("Not enough widgets to move.");
return widgets;
}
// Extract the last N widgets to be moved
let widgetsToMove = widgets.splice(startIndex, totalWidgetsToMove);
// Find the scheduler widget and ensure it is not duplicated
const schedulerWidget = findWidget(node, "scheduler_name");
// If the schedulerWidget is found, remove existing occurrences from the current widgets array
if (schedulerWidget) {
widgets = widgets.filter(widget => widget !== schedulerWidget);
}
// Ensure the insertion index is within the bounds of the current array
moveWidgetsBehind = Math.max(0, Math.min(moveWidgetsBehind, widgets.length));
// Prepare the new widgets array by inserting schedulerWidget followed by widgetsToMove
let newWidgets = [
...widgets.slice(0, moveWidgetsBehind),
schedulerWidget,
...widgetsToMove,
...widgets.slice(moveWidgetsBehind)
];
// Update the node's widgets with the newly ordered widgets
node.widgets = newWidgets;
return newWidgets;
}
@@ -295,8 +332,6 @@ app.registerExtension({
if (node.inputs.some(obj => obj.name === "height")) {
node.inputs = node.inputs.filter(obj => obj.name !== "height");
}
console.log(node)
}
function showAllLatentWidgets(node) {
@@ -328,7 +363,7 @@ app.registerExtension({
const originalSize = [node.size[0], node.size[1]];
rearrangeWidgets(node, 8, 10);
rearrangeWidgets(node, 7, allSettings.size);
const nodesHidden = hideAllSchedulerWidgets(node);
node.size[0] = originalSize[0];
web/sn0w.js
+73
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@@ -1,3 +1,5 @@
import { api } from '../../scripts/api.js';
export class SettingUtils {
static API_PREFIX = '/sn0w';
@@ -176,4 +178,75 @@ export class SettingUtils {
}
}
}
static drawSigmas(sigmas) {
// Define the size of the canvas
const width = 800;
const height = 400;
// Create a canvas element
const canvas = document.createElement('canvas');
canvas.width = width;
canvas.height = height;
const ctx = canvas.getContext('2d');
// Background
ctx.fillStyle = '#333'; // Dark background
ctx.fillRect(0, 0, width, height);
// Normalize sigma values to fit within the canvas height
const maxSigma = Math.max(...sigmas.map(s => s[0]));
const minSigma = Math.min(...sigmas.map(s => s[0]));
const range = maxSigma - minSigma;
// Function to scale a sigma value to the canvas coordinates
const scaleY = sigma => (height - 20) - (((sigma - minSigma) / range) * (height - 40));
const scaleX = index => (index / (sigmas.length - 1)) * width;
// Draw horizontal grid lines and labels
const gridColor = '#555';
const textColor = '#ccc';
const numGridLines = 10;
const gridSpacing = (height - 40) / numGridLines;
ctx.strokeStyle = gridColor;
ctx.lineWidth = 1;
ctx.font = '10px Arial';
ctx.fillStyle = textColor;
for (let i = 0; i <= numGridLines; i++) {
let y = 20 + i * gridSpacing;
ctx.beginPath();
ctx.moveTo(0, y);
ctx.lineTo(width, y);
ctx.stroke();
// Add text labels for grid lines
let gridValue = maxSigma - ((i / numGridLines) * range);
ctx.fillText(gridValue.toFixed(2), 5, y - 2);
}
// Draw the graph line
ctx.strokeStyle = 'white'; // Line color
ctx.lineWidth = 2;
ctx.beginPath();
ctx.moveTo(scaleX(0), scaleY(sigmas[0][0]));
sigmas.forEach((sigma, index) => {
ctx.lineTo(scaleX(index), scaleY(sigma[0]));
});
ctx.stroke();
// Draw a dot at each sigma point
ctx.fillStyle = 'red'; // Dot color
const dotRadius = 4;
sigmas.forEach((sigma, index) => {
ctx.beginPath();
ctx.arc(scaleX(index), scaleY(sigma[0]), dotRadius, 0, 2 * Math.PI);
ctx.fill();
});
// Return the canvas content as a Base64 encoded image
return canvas.toDataURL('image/png');
}
}