widgets updated and working for the most part
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Hamilton
+13
-8
@@ -9,20 +9,25 @@ NODE_CLASS_MAPPINGS = {
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"FS: Fit Image And Resize": FitResizeLatent,
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"FS: Load Image And Resize To Fit": LoadToFitResizeLatent,
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"FS: Pick Image From Batch": RandomImageFromBatch,
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"FS: Pick Image From Batches": RandomImageFromBatches,
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"FS: Pick Image From List": RandomImageFromList,
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"FS: Crop Image Into Even Pieces": CropImageIntoEvenPieces,
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"FS: Image Region To Mask": ImageRegionMask,
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"FS: Fit Size From Int": "Fit Size From Int",
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"FS: Fit Size From Image": "Fit Size From Image",
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"FS: Fit Image And Resize": "Fit Image And Resize",
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"FS: Load Image And Resize To Fit": "Load Image And Resize To Fit",
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"FS: Pick Image From Batch": "Pick Image From Batch",
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"FS: Crop Image Into Even Pieces": "Crop Image Into Even Pieces",
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"FS: Fit Size From Int": "Fit Size From Int (FS)",
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"FS: Fit Size From Image": "Fit Size From Image (FS)",
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"FS: Fit Image And Resize": "Fit Image And Resize (FS)",
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"FS: Load Image And Resize To Fit": "Load Image And Resize To Fit (FS)",
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"FS: Pick Image From Batch": "Pick Image From Batch (FS)",
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"FS: Pick Image From Batches": "Pick Image From Batches (FS)",
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"FS: Pick Image From List": "Pick Image From List (FS)",
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"FS: Crop Image Into Even Pieces": "Crop Image Into Even Pieces (FS)",
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"FS: Image Region To Mask": "Image Region To Mask (FS)",
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}
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__all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS']
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EXTENSION_NAME = "Fitsize"
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symlink_web_dir("js", EXTENSION_NAME)
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WEB_DIRECTORY = "./js"
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@@ -0,0 +1,87 @@
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import { app } from '../../scripts/app.js'
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// from: https://github.com/melMass/comfy_mtb
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export const setupDynamicConnections = (nodeType, prefix, inputType) => {
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const onNodeCreated = nodeType.prototype.onNodeCreated
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nodeType.prototype.onNodeCreated = function () {
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const r = onNodeCreated ? onNodeCreated.apply(this, arguments) : undefined
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console.log('onNodeCreated', `${prefix}_1`)
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this.addInput(`${prefix}_1`, inputType)
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return r
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}
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const onConnectionsChange = nodeType.prototype.onConnectionsChange
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nodeType.prototype.onConnectionsChange = function (
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type,
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index,
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connected,
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link_info
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) {
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const r = onConnectionsChange
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? onConnectionsChange.apply(this, arguments)
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: undefined
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dynamic_connection(this, index, connected, `${prefix}_`, inputType)
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}
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}
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export const dynamic_connection = (
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node,
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index,
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connected,
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connectionPrefix = 'input_',
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connectionType = 'PSDLAYER',
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nameArray = []
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) => {
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if (!node.inputs[index].name.startsWith(connectionPrefix)) {
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return
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}
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// remove all non connected inputs
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if (!connected && node.inputs.length > 1) {
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if (node.widgets) {
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const w = node.widgets.find((w) => w.name === node.inputs[index].name)
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if (w) {
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w.onRemoved?.()
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node.widgets.length = node.widgets.length - 1
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}
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}
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node.removeInput(index)
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// make inputs sequential again
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for (let i = 0; i < node.inputs.length; i++) {
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const name =
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i < nameArray.length ? nameArray[i] : `${connectionPrefix}${i + 1}`
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node.inputs[i].label = name
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node.inputs[i].name = name
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}
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}
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// add an extra input
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if (node.inputs[node.inputs.length - 1].link != undefined) {
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const nextIndex = node.inputs.length
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const name =
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nextIndex < nameArray.length
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? nameArray[nextIndex]
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: `${connectionPrefix}${nextIndex + 1}`
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node.addInput(name, connectionType)
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}
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}
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app.registerExtension({
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name: "Comfy.Fitsize.PickImageFromBatches",
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async beforeRegisterNodeDef(nodeType, nodeData, app) {
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if (!nodeData.name.startsWith('FS:')) {
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return
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}
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if(nodeData.name === "FS: Pick Image From Batches") {
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setupDynamicConnections(nodeType, 'batches', 'IMAGE')
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}
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}
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})
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@@ -1,3 +1,4 @@
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import random
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from PIL import Image, ImageOps
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import torch
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import os
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@@ -345,10 +346,55 @@ class CropImageIntoEvenPieces:
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crop = image[: , y : y + crop_height , x : x + crop_width , :]
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pieces.append(torch.from_numpy(crop))
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# image[:, y : y + height, x : x + width, :]
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return (torch.cat(pieces, dim=0), )
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class ImageRegionMask:
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def __init__(self):
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pass
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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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"image": ("IMAGE",),
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"rows": ("INT", {"default": 3, "min": 1, "max": 32, "step": 1,}),
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"columns": ("INT", {"default": 1, "min": 1, "max": 32, "step": 1,}),
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"chosen_row": ("INT", {"default": 0, "min": 0, "max": 32, "step": 1,}),
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"chosen_column": ("INT", {"default": 0, "min": 0, "max": 32, "step": 1,}),
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},
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}
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RETURN_TYPES = ("MASK",)
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FUNCTION = "run"
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CATEGORY = "Fitsize/Mask"
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def run(self, image, rows, columns, chosen_row, chosen_column):
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if rows < 1:
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rows = 1
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if columns < 1:
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columns = 1
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w = image.shape[2] # width
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h = image.shape[1] # height
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crop_width = int(w / columns)
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crop_height = int(h / rows)
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mask = torch.zeros((h, w))
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min_y = crop_height * chosen_row
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max_y = min_y + crop_height
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min_x = crop_width * chosen_column
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max_x = min_x + crop_width
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mask[int(min_y):int(max_y), int(min_x):int(max_x)] = 1
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return (mask.unsqueeze(0), )
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class RandomImageFromBatch:
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@@ -387,15 +433,107 @@ class RandomImageFromBatch:
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if select_amount < 1:
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select_amount = 1
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print(
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f"RandomImageFromBatch: start_index {start_index},",
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f"select_amount {select_amount}",
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f"total_images {images.shape[0]}",)
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selected = images[start_index:start_index + select_amount]
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print(f"RandomImageFromBatch: selected {selected.shape[0]} images")
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return (selected, )
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class RandomImageFromList:
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def __init__(self):
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pass
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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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"list": ("IMAGE", ),
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"seed": ("INT", {"default": 0}),
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"start_index": ("INT", {"default": -1, "min": -1, "max": 32, "step": 1,}),
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"select_amount": ("INT", {"default": 1, "min": 1, "max": 32, "step": 1,}),
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},
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "run"
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CATEGORY = "Fitsize/Image"
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def run(self, list, seed, start_index, select_amount):
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print(f'type of list: {type(list)}, length: {len(list)}')
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list_length = len(list)
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if start_index == -1:
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start_index = np.random.randint(0, list_length)
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# return random.choice(list, select_amount)
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if start_index >= list_length:
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start_index = list_length-1
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if select_amount > list_length:
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select_amount = list_length
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if select_amount < 1:
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select_amount = 1
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selected = list[start_index:start_index + select_amount]
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print(f'selected: {start_index} to {start_index + select_amount} found {len(selected)}')
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return (selected, )
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class RandomImageFromBatches:
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def __init__(self):
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pass
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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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"seed": ("INT",{"default": 0}),
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"start_index": ("INT", {"default": -1, "min": -1, "max": 32, "step": 1,}),
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"select_amount": ("INT", {"default": 1, "min": 1, "max": 32, "step": 1,}),
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},
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "run"
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CATEGORY = "Fitsize/Image"
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def run(self, seed=0, start_index=0, select_amount=1, **kwargs):
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batches = kwargs.values()
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selected = []
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print(f'len(batches): {len(batches)}')
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for img in batches:
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# if type(images) == torch.Tensor:
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# images = images.numpy()
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if start_index == -1:
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start_index = np.random.randint(0, img.shape[0])
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if start_index >= img.shape[0]:
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start_index = img.shape[0]-1
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if select_amount > img.shape[0]:
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select_amount = img.shape[0]
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if select_amount < 1:
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select_amount = 1
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# add images to selected
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selected.append(img[start_index:start_index + select_amount])
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# try to return a tensor of images if all widths and heights match
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try:
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selected = torch.cat(selected, dim=0)
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except:
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pass
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return (selected, )
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