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Code Issues Actions 10 Packages Projects Releases Wiki Activity

Visualize region for conditioning area & support extra model paths

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This commit is contained in:
space-nuko 2023-02-12 00:51:36 -08:00
parent 7e5645e956
commit 75ea299e74
13 changed files with 280 additions and 85 deletions
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12
comfy/sd.py
View File

@ -244,7 +244,7 @@ def load_lora_for_models(model, clip, lora_path, strength_model, strength_clip):
class CLIP:
def __init__(self, config={}, embedding_directory=None, no_init=False):
def __init__(self, config={}, embedding_directories=None, no_init=False):
if no_init:
return
self.target_clip = config["target"]
@ -261,7 +261,7 @@ class CLIP:
tokenizer = sd1_clip.SD1Tokenizer
self.cond_stage_model = clip(**(params))
self.tokenizer = tokenizer(embedding_directory=embedding_directory)
self.tokenizer = tokenizer(embedding_directories=embedding_directories)
self.patcher = ModelPatcher(self.cond_stage_model)
def clone(self):
@ -323,18 +323,18 @@ class VAE:
samples = samples.cpu()
return samples
def load_clip(ckpt_path, embedding_directory=None):
def load_clip(ckpt_path, embedding_directories=None):
clip_data = load_torch_file(ckpt_path)
config = {}
if "text_model.encoder.layers.22.mlp.fc1.weight" in clip_data:
config['target'] = 'ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder'
else:
config['target'] = 'ldm.modules.encoders.modules.FrozenCLIPEmbedder'
clip = CLIP(config=config, embedding_directory=embedding_directory)
clip = CLIP(config=config, embedding_directories=embedding_directories)
clip.load_from_state_dict(clip_data)
return clip
def load_checkpoint(config_path, ckpt_path, output_vae=True, output_clip=True, embedding_directory=None):
def load_checkpoint(config_path, ckpt_path, output_vae=True, output_clip=True, embedding_directories=None):
config = OmegaConf.load(config_path)
model_config_params = config['model']['params']
clip_config = model_config_params['cond_stage_config']
@ -355,7 +355,7 @@ def load_checkpoint(config_path, ckpt_path, output_vae=True, output_clip=True, e
load_state_dict_to = [w]
if output_clip:
clip = CLIP(config=clip_config, embedding_directory=embedding_directory)
clip = CLIP(config=clip_config, embedding_directories=embedding_directories)
w.cond_stage_model = clip.cond_stage_model
load_state_dict_to = [w]

13
comfy/sd1_clip.py
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@ -2,6 +2,7 @@ import os
from transformers import CLIPTokenizer, CLIPTextModel, CLIPTextConfig
import torch
import shared
class ClipTokenWeightEncoder:
def encode_token_weights(self, token_weight_pairs):
@ -167,8 +168,8 @@ def unescape_important(text):
text = text.replace("\0\2", "(")
return text
def load_embed(embedding_name, embedding_directory):
embed_path = os.path.join(embedding_directory, embedding_name)
def load_embed(embedding_name, embedding_directories):
embed_path = shared.find_model_file("embeddings", embedding_name)
if not os.path.isfile(embed_path):
extensions = ['.safetensors', '.pt', '.bin']
valid_file = None
@ -195,7 +196,7 @@ def load_embed(embedding_name, embedding_directory):
return next(iter(values))
class SD1Tokenizer:
def __init__(self, tokenizer_path=None, max_length=77, pad_with_end=True, embedding_directory=None):
def __init__(self, tokenizer_path=None, max_length=77, pad_with_end=True, embedding_directories=None):
if tokenizer_path is None:
tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "sd1_tokenizer")
self.tokenizer = CLIPTokenizer.from_pretrained(tokenizer_path)
@ -208,7 +209,7 @@ class SD1Tokenizer:
self.pad_with_end = pad_with_end
vocab = self.tokenizer.get_vocab()
self.inv_vocab = {v: k for k, v in vocab.items()}
self.embedding_directory = embedding_directory
self.embedding_directories = embedding_directories
self.max_word_length = 8
def tokenize_with_weights(self, text):
@ -221,9 +222,9 @@ class SD1Tokenizer:
for word in to_tokenize:
temp_tokens = []
embedding_identifier = "embedding:"
if word.startswith(embedding_identifier) and self.embedding_directory is not None:
if word.startswith(embedding_identifier) and self.embedding_directories is not None:
embedding_name = word[len(embedding_identifier):].strip('\n')
embed = load_embed(embedding_name, self.embedding_directory)
embed = load_embed(embedding_name, self.embedding_directories)
if embed is not None:
if len(embed.shape) == 1:
temp_tokens += [(embed, t[1])]

4
comfy/sd2_clip.py
View File

@ -30,5 +30,5 @@ class SD2ClipModel(sd1_clip.SD1ClipModel):
self.layer_idx = layer_idx
class SD2Tokenizer(sd1_clip.SD1Tokenizer):
def __init__(self, tokenizer_path=None, embedding_directory=None):
super().__init__(tokenizer_path, pad_with_end=False, embedding_directory=embedding_directory)
def __init__(self, tokenizer_path=None, embedding_directories=None):
super().__init__(tokenizer_path, pad_with_end=False, embedding_directories=embedding_directories)

10
config.yml Normal file
View File

@ -0,0 +1,10 @@
config:
alignToGrid: True
gridSize: 20
paths:
configs: []
checkpoints: []
vae: []
clip: []
embeddings: []
loras: []

7
main.py
View File

@ -27,6 +27,7 @@ if '--dont-upcast-attention' in sys.argv:
import torch
import nodes
import shared
def get_input_data(inputs, class_def, outputs={}, prompt={}, extra_data={}):
valid_inputs = class_def.INPUT_TYPES()
@ -230,6 +231,9 @@ def validate_inputs(prompt, item):
if type_input == "STRING":
val = str(val)
inputs[x] = val
if type_input == "REGION":
val = {"x": val["x"], "y": val["y"], "width": val["width"], "height": val["height"]}
inputs[x] = val
if len(info) > 1:
if "min" in info[1] and val < info[1]["min"]:
@ -378,6 +382,9 @@ class PromptServer(BaseHTTPRequestHandler):
info['category'] = obj_class.CATEGORY
out[x] = info
self.wfile.write(json.dumps(out).encode('utf-8'))
elif self.path == "/config":
self._set_headers(ct='application/json')
self.wfile.write(json.dumps(shared.config).encode('utf-8'))
elif self.path[1:] in os.listdir(self.server.server_dir):
if self.path[1:].endswith('.css'):
self._set_headers(ct='text/css')

98
nodes.py
View File

@ -16,26 +16,7 @@ sys.path.insert(0, os.path.join(sys.path[0], "comfy"))
import comfy.samplers
import comfy.sd
import model_management
supported_ckpt_extensions = ['.ckpt']
supported_pt_extensions = ['.ckpt', '.pt', '.bin']
try:
import safetensors.torch
supported_ckpt_extensions += ['.safetensors']
supported_pt_extensions += ['.safetensors']
except:
print("Could not import safetensors, safetensors support disabled.")
def recursive_search(directory):
result = []
for root, subdir, file in os.walk(directory, followlinks=True):
for filepath in file:
#we os.path,join directory with a blank string to generate a path separator at the end.
result.append(os.path.join(root, filepath).replace(os.path.join(directory,''),''))
return result
def filter_files_extensions(files, extensions):
return sorted(list(filter(lambda a: os.path.splitext(a)[-1].lower() in extensions, files)))
import shared
class CLIPTextEncode:
@classmethod
@ -65,10 +46,8 @@ class ConditioningSetArea:
@classmethod
def INPUT_TYPES(s):
return {"required": {"conditioning": ("CONDITIONING", ),
"width": ("INT", {"default": 64, "min": 64, "max": 4096, "step": 64}),
"height": ("INT", {"default": 64, "min": 64, "max": 4096, "step": 64}),
"x": ("INT", {"default": 0, "min": 0, "max": 4096, "step": 64}),
"y": ("INT", {"default": 0, "min": 0, "max": 4096, "step": 64}),
"latent": ("LATENT", ),
"region": ("REGION", ),
"strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
}}
RETURN_TYPES = ("CONDITIONING",)
@ -76,7 +55,11 @@ class ConditioningSetArea:
CATEGORY = "conditioning"
def append(self, conditioning, width, height, x, y, strength, min_sigma=0.0, max_sigma=99.0):
def append(self, conditioning, latent, region, strength, min_sigma=0.0, max_sigma=99.0):
width = region["width"]
height = region["height"]
x = region["x"]
y = region["y"]
c = copy.deepcopy(conditioning)
for t in c:
t[1]['area'] = (height // 8, width // 8, y // 8, x // 8)
@ -120,33 +103,28 @@ class VAEEncode:
return (vae.encode(pixels), )
class CheckpointLoader:
models_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "models")
config_dir = os.path.join(models_dir, "configs")
ckpt_dir = os.path.join(models_dir, "checkpoints")
embedding_directory = os.path.join(models_dir, "embeddings")
embedding_directories = shared.get_model_paths("embeddings")
@classmethod
def INPUT_TYPES(s):
return {"required": { "config_name": ("COMBO", { "choices": filter_files_extensions(recursive_search(s.config_dir), '.yaml') }),
"ckpt_name": ("COMBO", { "choices": filter_files_extensions(recursive_search(s.ckpt_dir), supported_ckpt_extensions) })}}
return {"required": { "config_name": ("COMBO", { "choices": shared.get_model_files("configs") }),
"ckpt_name": ("COMBO", { "choices": shared.get_model_files("checkpoints") })}}
RETURN_TYPES = ("MODEL", "CLIP", "VAE")
FUNCTION = "load_checkpoint"
CATEGORY = "loaders"
def load_checkpoint(self, config_name, ckpt_name, output_vae=True, output_clip=True):
config_path = os.path.join(self.config_dir, config_name)
ckpt_path = os.path.join(self.ckpt_dir, ckpt_name)
return comfy.sd.load_checkpoint(config_path, ckpt_path, output_vae=True, output_clip=True, embedding_directory=self.embedding_directory)
config_path = shared.find_model_file("configs", config_name)
ckpt_path = shared.find_model_file("checkpoints", ckpt_name)
return comfy.sd.load_checkpoint(config_path, ckpt_path, output_vae=True, output_clip=True, embedding_directories=self.embedding_directories)
class LoraLoader:
models_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "models")
lora_dir = os.path.join(models_dir, "loras")
@classmethod
def INPUT_TYPES(s):
return {"required": { "model": ("MODEL",),
"clip": ("CLIP", ),
"lora_name": ("COMBO", { "choices": filter_files_extensions(recursive_search(s.lora_dir), supported_pt_extensions) }),
"lora_name": ("COMBO", { "choices": shared.get_model_files("loras") }),
"strength_model": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
"strength_clip": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
}}
@ -156,16 +134,14 @@ class LoraLoader:
CATEGORY = "loaders"
def load_lora(self, model, clip, lora_name, strength_model, strength_clip):
lora_path = os.path.join(self.lora_dir, lora_name)
lora_path = shared.find_model_file("loras", lora_name)
model_lora, clip_lora = comfy.sd.load_lora_for_models(model, clip, lora_path, strength_model, strength_clip)
return (model_lora, clip_lora)
class VAELoader:
models_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "models")
vae_dir = os.path.join(models_dir, "vae")
@classmethod
def INPUT_TYPES(s):
return {"required": { "vae_name": ("COMBO", { "choices": filter_files_extensions(recursive_search(s.vae_dir), supported_pt_extensions) })}}
return {"required": { "vae_name": ("COMBO", { "choices": shared.get_model_files("vae") })}}
RETURN_TYPES = ("VAE",)
FUNCTION = "load_vae"
@ -173,16 +149,14 @@ class VAELoader:
#TODO: scale factor?
def load_vae(self, vae_name):
vae_path = os.path.join(self.vae_dir, vae_name)
vae_path = shared.find_model_file("vae", vae_name)
vae = comfy.sd.VAE(ckpt_path=vae_path)
return (vae,)
class CLIPLoader:
models_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "models")
clip_dir = os.path.join(models_dir, "clip")
@classmethod
def INPUT_TYPES(s):
return {"required": { "clip_name": ("COMBO", { "choices": filter_files_extensions(recursive_search(s.clip_dir), supported_pt_extensions) }),
return {"required": { "clip_name": ("COMBO", { "choices": shared.get_model_files("clip") }),
"stop_at_clip_layer": ("INT", {"default": -1, "min": -24, "max": -1, "step": 1}),
}}
RETURN_TYPES = ("CLIP",)
@ -191,8 +165,8 @@ class CLIPLoader:
CATEGORY = "loaders"
def load_clip(self, clip_name, stop_at_clip_layer):
clip_path = os.path.join(self.clip_dir, clip_name)
clip = comfy.sd.load_clip(ckpt_path=clip_path, embedding_directory=CheckpointLoader.embedding_directory)
clip_path = shared.find_model_file("clip", clip_name)
clip = comfy.sd.load_clip(ckpt_path=clip_path, embedding_directories=CheckpointLoader.embedding_directories)
clip.clip_layer(stop_at_clip_layer)
return (clip,)
@ -215,21 +189,21 @@ class EmptyLatentImage:
return (latent, )
def common_upscale(samples, width, height, upscale_method, crop):
if crop == "center":
old_width = samples.shape[3]
old_height = samples.shape[2]
old_aspect = old_width / old_height
new_aspect = width / height
x = 0
y = 0
if old_aspect > new_aspect:
x = round((old_width - old_width * (new_aspect / old_aspect)) / 2)
elif old_aspect < new_aspect:
y = round((old_height - old_height * (old_aspect / new_aspect)) / 2)
s = samples[:,:,y:old_height-y,x:old_width-x]
else:
s = samples
return torch.nn.functional.interpolate(s, size=(height, width), mode=upscale_method)
if crop == "center":
old_width = samples.shape[3]
old_height = samples.shape[2]
old_aspect = old_width / old_height
new_aspect = width / height
x = 0
y = 0
if old_aspect > new_aspect:
x = round((old_width - old_width * (new_aspect / old_aspect)) / 2)
elif old_aspect < new_aspect:
y = round((old_height - old_height * (old_aspect / new_aspect)) / 2)
s = samples[:,:,y:old_height-y,x:old_width-x]
else:
s = samples
return torch.nn.functional.interpolate(s, size=(height, width), mode=upscale_method)
class LatentUpscale:
upscale_methods = ["nearest-exact", "bilinear", "area"]

2
requirements.txt
View File

@ -7,6 +7,6 @@ open-clip-torch
transformers
safetensors
pytorch_lightning
pyyaml
accelerate

62
shared.py Normal file
View File

@ -0,0 +1,62 @@
import os.path
import yaml
supported_ckpt_extensions = ['.ckpt']
supported_pt_extensions = ['.ckpt', '.pt', '.bin']
try:
import safetensors.torch
supported_ckpt_extensions += ['.safetensors']
supported_pt_extensions += ['.safetensors']
except:
print("Could not import safetensors, safetensors support disabled.")
model_kinds = {
"configs": [".yml"],
"checkpoints": supported_ckpt_extensions,
"vae": supported_pt_extensions,
"clip": supported_pt_extensions,
"embeddings": supported_pt_extensions,
"loras": supported_pt_extensions,
}
def recursive_search(directory):
result = []
for root, subdir, file in os.walk(directory, followlinks=True):
for filepath in file:
#we os.path,join directory with a blank string to generate a path separator at the end.
result.append(os.path.join(root, filepath).replace(os.path.join(directory,''),''))
return result
def filter_files_extensions(files, extensions):
return sorted(list(filter(lambda a: os.path.splitext(a)[-1].lower() in extensions, files)))
def get_files(directories, extensions):
files = []
for dir in directories:
files.extend(recursive_search(dir))
return filter_files_extensions(files, extensions)
def get_model_paths(kind):
models_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "models")
model_dir = os.path.join(models_dir, kind)
return [model_dir] + config["paths"][kind]
def get_model_files(kind):
exts = model_kinds[kind]
paths = get_model_paths(kind)
return get_files(paths, exts)
def find_model_file(kind, basename):
# TODO: find by model hash instead of filename
for path in get_model_paths(kind):
file = os.path.join(path, basename)
if os.path.isfile(file):
return file
raise FileNotFoundError("Model not found: " + basename)
config = {}
with open("config.yml", "r") as f:
config = yaml.safe_load(f)["config"]

29
webshit/index.html
View File

@ -54,6 +54,7 @@
<canvas id='mycanvas' width='1000' height='1000' style='width: 100%; height: 100%;'></canvas>
<script type="text/javascript" src="widgets.js"></script>
<script type="text/javascript" src="nodes.js"></script>
<script>
var graph = new LGraph();
@ -109,6 +110,22 @@ function afterLoadGraph()
}
function onConfig(json) {
if (json['alignToGrid']) {
graph.config.align_to_grid = json['alignToGrid'];
canvas.align_to_grid = json['alignToGrid'];
}
if (json['gridSize']) {
LiteGraph.CANVAS_GRID_SIZE = json['gridSize'];
}
}
fetch("config", {cache: "no-store"})
.then(response => response.json())
.then(json => onConfig(json));
function onObjectInfo(json) {
for (let key in json) {
function MyNode()
@ -128,10 +145,7 @@ function onObjectInfo(json) {
let type = inp[x][0];
let widgetClass = COMFY_WIDGETS[type];
if (Array.isArray(type)) {
w = this.addWidget("combo", x, type[0], function(v){}, { values: type } );
this._widgets += [w]
} else if (widgetClass) {
if (widgetClass) {
new widgetClass(opts).addWidget(this, x);
} else {
this.addInput(x, type);
@ -153,6 +167,13 @@ function onObjectInfo(json) {
MyNode.class_type_comfy = json[key]['name'];
MyNode.__json_data = json[key]
let props = COMFY_NODES[json[key]['name']];
if (props) {
for (const prop in props) {
MyNode.prototype[prop] = props[prop];
}
}
LiteGraph.registerNodeType(key, MyNode);
MyNode.category = json[key]['category'];
};

19
webshit/litegraph.core.js
View File

@ -6744,6 +6744,7 @@ LGraphNode.prototype.executeAction = function(action)
this.node_dragged.pos[0] = Math.round(this.node_dragged.pos[0]);
this.node_dragged.pos[1] = Math.round(this.node_dragged.pos[1]);
if (this.graph.config.align_to_grid || this.align_to_grid ) {
console.warn("AAA")
this.node_dragged.alignToGrid();
}
if( this.onNodeMoved )
@ -9916,6 +9917,9 @@ LGraphNode.prototype.executeAction = function(action)
var range = w.options.max - w.options.min;
var nvalue = Math.clamp((x - 15) / (widget_width - 30), 0, 1);
w.value = w.options.min + (w.options.max - w.options.min) * nvalue;
if (w.options.step) {
w.value = Math.round( w.value / w.options.step ) * w.options.step;
}
if (w.callback) {
setTimeout(function() {
inner_value_change(w, w.value);
@ -9927,7 +9931,12 @@ LGraphNode.prototype.executeAction = function(action)
case "combo":
var old_value = w.value;
if (event.type == LiteGraph.pointerevents_method+"move" && w.type == "number") {
w.value += event.deltaX * 0.1 * (w.options.step || 1);
if (w.options.step) {
w.value += Math.ceil( Math.abs( event.deltaX * 0.01 / w.options.step ) ) * w.options.step * Math.sign(event.deltaX);
}
else {
w.value += event.deltaX * 0.1;
}
if ( w.options.min != null && w.value < w.options.min ) {
w.value = w.options.min;
}
@ -9946,7 +9955,13 @@ LGraphNode.prototype.executeAction = function(action)
var delta = x < 40 ? -1 : x > widget_width - 40 ? 1 : 0;
if (w.type == "number") {
w.value += delta * 0.1 * (w.options.step || 1);
console.log(w);
if (w.options.step) {
w.value += w.options.step * Math.sign(delta);
}
else {
w.value += delta * 0.1;
}
if ( w.options.min != null && w.value < w.options.min ) {
w.value = w.options.min;
}

10
webshit/nodes.js Normal file
View File

@ -0,0 +1,10 @@
var COMFY_NODES = [];
COMFY_NODES["EmptyLatentImage"] = {
onExecute: function() {
for (var idx of this.outputs[0].links) {
let link = this.graph.links[idx];
link.data = this.widgets.map(function(w) { return w.value; });
}
}
};

98
webshit/widgets.js
View File

@ -21,7 +21,7 @@ class IntWidget extends BaseWidget {
let s = this.options.step / 10;
this.value = Math.round( v / s ) * s;
};
let w = node.addWidget("number", x, this.default_val, onSet, { min: this.min_val, max: this.max_val, step: 10.0 * this.step_val});
let w = node.addWidget("number", x, this.default_val, onSet, { min: this.min_val, max: this.max_val, step: this.step_val});
node._widgets += [w]
if (x == "seed" || x == "noise_seed") {
let w1 = node.addWidget("toggle", "Random seed after every gen", true, function(v){}, { on: "enabled", off: "disabled" } );
@ -46,7 +46,7 @@ class FloatWidget extends BaseWidget {
// if (min_val == 0.0 && max_val == 1.0) {
// w = this.slider = this.addWidget("slider", x, default_val, function(v){}, { min: min_val, max: max_val} );
// } else {
let w = node.addWidget("number", x, this.default_val, function(v){}, { min: this.min_val, max: this.max_val, step: 10.0 * this.step_val} );
let w = node.addWidget("number", x, this.default_val, function(v){}, { min: this.min_val, max: this.max_val, step: this.step_val} );
// }
node._widgets += [w];
}
@ -160,3 +160,97 @@ class ComboWidget extends BaseWidget {
}
}
COMFY_WIDGETS["COMBO"] = ComboWidget;
class RegionWidget extends BaseWidget {
constructor(opts) {
super();
}
addWidget(node, x) {
var w = {
type: "region",
name: x,
region: { x: 0, y: 0, width: 2048, height: 2048 },
get value() {
return this.region;
},
set value(x) {
this.region = x;
},
callback: function(v){console.log("CB!", v);},
options: {},
draw: function(ctx, node, widget_width, y, H){
ctx.save();
var size = this.size[1] * 0.5;
var margin = 0.25;
var h = this.size[1] * 0.8;
// ctx.font = this.properties.font || (size * 0.8).toFixed(0) + "px Arial";
var w = ctx.measureText(this.title).width;
var x = margin * this.size[0] * 0.25;
var latentParams = node.getInputData(1, true);
if (latentParams) {
var latentWidth = latentParams[0];
var latentHeight = latentParams[1];
var widgetWidth = node.widgets[1]
var widgetHeight = node.widgets[2]
var widgetX = node.widgets[3]
var widgetY = node.widgets[4]
widgetWidth.options.max = latentWidth;
widgetWidth.value = Math.min(widgetWidth.value, latentWidth);
this.region.width = widgetWidth.value;
widgetHeight.options.max = latentHeight;
widgetHeight.value = Math.min(widgetHeight.value, latentHeight);
this.region.height = widgetHeight.value;
widgetX.options.max = latentWidth;
widgetX.value = Math.min(widgetX.value, latentWidth - widgetWidth.value);
this.region.x = widgetX.value;
widgetY.options.max = latentHeight;
widgetY.value = Math.min(widgetY.value, latentHeight - widgetHeight.value);
this.region.y = widgetY.value;
ctx.fillStyle = "#FFF";
ctx.strokeRect(x, y, this.size[0], this.size[1]);
ctx.fillStyle = "#AAF";
ctx.fillRect(
x + (this.region.x / latentWidth) * this.size[0],
y + (this.region.y / latentHeight) * this.size[1],
(this.region.width / latentWidth) * this.size[0],
(this.region.height / latentHeight) * this.size[1]
);
}
// ctx.textAlign = "left";
// ctx.fillStyle = "#AAA";
// ctx.fillText(this.title, size * 1.2 + x, h * 0.85);
// ctx.textAlign = "left";
ctx.restore();
},
mouse: function(event, pos, node) {
return true;
},
computeSize() {
return this.size;
}
};
w.size = [150, 150];
w.parent = node;
w = node.addCustomWidget(w);
node.addWidget("number", "width", 512, (v) => w.region.width = v, { property: "region_width", min: 64, max: 2048, step: 64 });
node.addWidget("number", "height", 512, (v) => w.region.height = v, { property: "region_height", min: 64, max: 2048, step: 64 });
node.addWidget("number", "x", 0, (v) => w.region.x = v, { property: "region_x", min: 0, max: 2048, step: 64 });
node.addWidget("number", "y", 0, (v) => w.region.y = v, { property: "region_y", min: 0, max: 2048, step: 64 });
// console.log(node.getInputData("Width"))
node._widgets += [w]
console.log(node);
}
}
COMFY_WIDGETS["REGION"] = RegionWidget;

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workflows/area_composition.json Normal file
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