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BlenderNeko 2023-04-14 13:13:14 +02:00 committed by GitHub
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3 changed files with 303 additions and 25 deletions
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263
comfy_extras/nodes_mask.py Normal file
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@ -0,0 +1,263 @@
import torch
from nodes import MAX_RESOLUTION
class LatentCompositeMasked:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"destination": ("LATENT",),
"source": ("LATENT",),
"x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
"y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
},
"optional": {
"mask": ("MASK",),
}
}
RETURN_TYPES = ("LATENT",)
FUNCTION = "composite"
CATEGORY = "latent"
def composite(self, destination, source, x, y, mask = None):
output = destination.copy()
destination = destination["samples"].clone()
source = source["samples"]
x = max(-source.shape[3] * 8, min(x, destination.shape[3] * 8))
y = max(-source.shape[2] * 8, min(y, destination.shape[2] * 8))
left, top = (x // 8, y // 8)
right, bottom = (left + source.shape[3], top + source.shape[2],)
if mask is None:
mask = torch.ones_like(source)
else:
mask = mask.clone()
mask = torch.nn.functional.interpolate(mask[None, None], size=(source.shape[2], source.shape[3]), mode="bilinear")
mask = mask.repeat((source.shape[0], source.shape[1], 1, 1))
# calculate the bounds of the source that will be overlapping the destination
# this prevents the source trying to overwrite latent pixels that are out of bounds
# of the destination
visible_width, visible_height = (destination.shape[3] - left + min(0, x), destination.shape[2] - top + min(0, y),)
mask = mask[:, :, :visible_height, :visible_width]
inverse_mask = torch.ones_like(mask) - mask
source_portion = mask * source[:, :, :visible_height, :visible_width]
destination_portion = inverse_mask * destination[:, :, top:bottom, left:right]
destination[:, :, top:bottom, left:right] = source_portion + destination_portion
output["samples"] = destination
return (output,)
class MaskToImage:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"mask": ("MASK",),
}
}
CATEGORY = "mask"
RETURN_TYPES = ("IMAGE",)
FUNCTION = "mask_to_image"
def mask_to_image(self, mask):
result = mask[None, :, :, None].expand(-1, -1, -1, 3)
return (result,)
class ImageToMask:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"image": ("IMAGE",),
"channel": (["red", "green", "blue"],),
}
}
CATEGORY = "mask"
RETURN_TYPES = ("MASK",)
FUNCTION = "image_to_mask"
def image_to_mask(self, image, channel):
channels = ["red", "green", "blue"]
mask = image[0, :, :, channels.index(channel)]
return (mask,)
class SolidMask:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"value": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
"width": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}),
"height": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}),
}
}
CATEGORY = "mask"
RETURN_TYPES = ("MASK",)
FUNCTION = "solid"
def solid(self, value, width, height):
out = torch.full((height, width), value, dtype=torch.float32, device="cpu")
return (out,)
class InvertMask:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"mask": ("MASK",),
}
}
CATEGORY = "mask"
RETURN_TYPES = ("MASK",)
FUNCTION = "invert"
def invert(self, mask):
out = 1.0 - mask
return (out,)
class CropMask:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"mask": ("MASK",),
"x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
"y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
"width": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}),
"height": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}),
}
}
CATEGORY = "mask"
RETURN_TYPES = ("MASK",)
FUNCTION = "crop"
def crop(self, mask, x, y, width, height):
out = mask[y:y + height, x:x + width]
return (out,)
class MaskComposite:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"destination": ("MASK",),
"source": ("MASK",),
"x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
"y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
"operation": (["multiply", "add", "subtract"],),
}
}
CATEGORY = "mask"
RETURN_TYPES = ("MASK",)
FUNCTION = "combine"
def combine(self, destination, source, x, y, operation):
output = destination.clone()
left, top = (x, y,)
right, bottom = (min(left + source.shape[1], destination.shape[1]), min(top + source.shape[0], destination.shape[0]))
visible_width, visible_height = (right - left, bottom - top,)
source_portion = source[:visible_height, :visible_width]
destination_portion = destination[top:bottom, left:right]
match operation:
case "multiply":
output[top:bottom, left:right] = destination_portion * source_portion
case "add":
output[top:bottom, left:right] = destination_portion + source_portion
case "subtract":
output[top:bottom, left:right] = destination_portion - source_portion
output = torch.clamp(output, 0.0, 1.0)
return (output,)
class FeatherMask:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"mask": ("MASK",),
"left": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
"top": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
"right": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
"bottom": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
}
}
CATEGORY = "mask"
RETURN_TYPES = ("MASK",)
FUNCTION = "feather"
def feather(self, mask, left, top, right, bottom):
output = mask.clone()
left = min(left, output.shape[1])
right = min(right, output.shape[1])
top = min(top, output.shape[0])
bottom = min(bottom, output.shape[0])
for x in range(left):
feather_rate = (x + 1.0) / left
output[:, x] *= feather_rate
for x in range(right):
feather_rate = (x + 1) / right
output[:, -x] *= feather_rate
for y in range(top):
feather_rate = (y + 1) / top
output[y, :] *= feather_rate
for y in range(bottom):
feather_rate = (y + 1) / bottom
output[-y, :] *= feather_rate
return (output,)
NODE_CLASS_MAPPINGS = {
"LatentCompositeMasked": LatentCompositeMasked,
"MaskToImage": MaskToImage,
"ImageToMask": ImageToMask,
"SolidMask": SolidMask,
"InvertMask": InvertMask,
"CropMask": CropMask,
"MaskComposite": MaskComposite,
"FeatherMask": FeatherMask,
}
NODE_DISPLAY_NAME_MAPPINGS = {
"ImageToMask": "Convert Image to Mask",
"MaskToImage": "Convert Mask to Image",
}

5
nodes.py
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@ -872,7 +872,7 @@ class SaveImage:
"filename": file,
"subfolder": subfolder,
"type": self.type
});
})
counter += 1
return { "ui": { "images": results } }
@ -933,7 +933,7 @@ class LoadImageMask:
"channel": (["alpha", "red", "green", "blue"], ),}
}
CATEGORY = "image"
CATEGORY = "mask"
RETURN_TYPES = ("MASK",)
FUNCTION = "load_image"
@ -1193,3 +1193,4 @@ def init_custom_nodes():
load_custom_nodes()
load_custom_node(os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras"), "nodes_upscale_model.py"))
load_custom_node(os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras"), "nodes_post_processing.py"))
load_custom_node(os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras"), "nodes_mask.py"))

60
web/extensions/core/widgetInputs.js
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@ -159,27 +159,31 @@ app.registerExtension({
const r = origOnInputDblClick ? origOnInputDblClick.apply(this, arguments) : undefined;
const input = this.inputs[slot];
if (input.widget && !input[ignoreDblClick]) {
const node = LiteGraph.createNode("PrimitiveNode");
app.graph.add(node);
// Calculate a position that wont directly overlap another node
const pos = [this.pos[0] - node.size[0] - 30, this.pos[1]];
while (isNodeAtPos(pos)) {
pos[1] += LiteGraph.NODE_TITLE_HEIGHT;
}
node.pos = pos;
node.connect(0, this, slot);
node.title = input.name;
// Prevent adding duplicates due to triple clicking
input[ignoreDblClick] = true;
setTimeout(() => {
delete input[ignoreDblClick];
}, 300);
if (!input.widget || !input[ignoreDblClick])// Not a widget input or already handled input
{
if (!(input.type in ComfyWidgets)) return r;//also Not a ComfyWidgets input (do nothing)
}
// Create a primitive node
const node = LiteGraph.createNode("PrimitiveNode");
app.graph.add(node);
// Calculate a position that wont directly overlap another node
const pos = [this.pos[0] - node.size[0] - 30, this.pos[1]];
while (isNodeAtPos(pos)) {
pos[1] += LiteGraph.NODE_TITLE_HEIGHT;
}
node.pos = pos;
node.connect(0, this, slot);
node.title = input.name;
// Prevent adding duplicates due to triple clicking
input[ignoreDblClick] = true;
setTimeout(() => {
delete input[ignoreDblClick];
}, 300);
return r;
};
},
@ -233,7 +237,9 @@ app.registerExtension({
// Fires before the link is made allowing us to reject it if it isn't valid
// No widget, we cant connect
if (!input.widget) return false;
if (!input.widget) {
if (!(input.type in ComfyWidgets)) return false;
}
if (this.outputs[slot].links?.length) {
return this.#isValidConnection(input);
@ -252,9 +258,17 @@ app.registerExtension({
const input = theirNode.inputs[link.target_slot];
if (!input) return;
const widget = input.widget;
const { type, linkType } = getWidgetType(widget.config);
var _widget;
if (!input.widget) {
if (!(input.type in ComfyWidgets)) return;
_widget = { "name": input.name, "config": [input.type, {}] }//fake widget
} else {
_widget = input.widget;
}
const widget = _widget;
const { type, linkType } = getWidgetType(widget.config);
// Update our output to restrict to the widget type
this.outputs[0].type = linkType;
this.outputs[0].name = type;
@ -274,7 +288,7 @@ app.registerExtension({
if (type in ComfyWidgets) {
widget = (ComfyWidgets[type](this, "value", inputData, app) || {}).widget;
} else {
widget = this.addWidget(type, "value", null, () => {}, {});
widget = this.addWidget(type, "value", null, () => { }, {});
}
if (node?.widgets && widget) {