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Merge branch 'comfyanonymous:master' into refactor/execution

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Dr.Lt.Data 2023-06-15 09:06:10 +09:00 committed by GitHub
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11 changed files with 139 additions and 69 deletions
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5
comfy/clip_vision.py
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@ -1,4 +1,4 @@
from transformers import CLIPVisionModelWithProjection, CLIPVisionConfig, CLIPImageProcessor from transformers import CLIPVisionModelWithProjection, CLIPVisionConfig, CLIPImageProcessor, modeling_utils
from .utils import load_torch_file, transformers_convert from .utils import load_torch_file, transformers_convert
import os import os
import torch import torch
@ -6,7 +6,8 @@ import torch
class ClipVisionModel(): class ClipVisionModel():
def __init__(self, json_config): def __init__(self, json_config):
config = CLIPVisionConfig.from_json_file(json_config) config = CLIPVisionConfig.from_json_file(json_config)
self.model = CLIPVisionModelWithProjection(config) with modeling_utils.no_init_weights():
self.model = CLIPVisionModelWithProjection(config)
self.processor = CLIPImageProcessor(crop_size=224, self.processor = CLIPImageProcessor(crop_size=224,
do_center_crop=True, do_center_crop=True,
do_convert_rgb=True, do_convert_rgb=True,

56
comfy/ldm/modules/attention.py
View File

@ -10,6 +10,7 @@ from .diffusionmodules.util import checkpoint
from .sub_quadratic_attention import efficient_dot_product_attention from .sub_quadratic_attention import efficient_dot_product_attention
from comfy import model_management from comfy import model_management
import comfy.ops
from . import tomesd from . import tomesd
@ -52,7 +53,7 @@ def init_(tensor):
class GEGLU(nn.Module): class GEGLU(nn.Module):
def __init__(self, dim_in, dim_out): def __init__(self, dim_in, dim_out):
super().__init__() super().__init__()
self.proj = nn.Linear(dim_in, dim_out * 2) self.proj = comfy.ops.Linear(dim_in, dim_out * 2)
def forward(self, x): def forward(self, x):
x, gate = self.proj(x).chunk(2, dim=-1) x, gate = self.proj(x).chunk(2, dim=-1)
@ -65,14 +66,14 @@ class FeedForward(nn.Module):
inner_dim = int(dim * mult) inner_dim = int(dim * mult)
dim_out = default(dim_out, dim) dim_out = default(dim_out, dim)
project_in = nn.Sequential( project_in = nn.Sequential(
nn.Linear(dim, inner_dim), comfy.ops.Linear(dim, inner_dim),
nn.GELU() nn.GELU()
) if not glu else GEGLU(dim, inner_dim) ) if not glu else GEGLU(dim, inner_dim)
self.net = nn.Sequential( self.net = nn.Sequential(
project_in, project_in,
nn.Dropout(dropout), nn.Dropout(dropout),
nn.Linear(inner_dim, dim_out) comfy.ops.Linear(inner_dim, dim_out)
) )
def forward(self, x): def forward(self, x):
@ -154,12 +155,12 @@ class CrossAttentionBirchSan(nn.Module):
self.scale = dim_head ** -0.5 self.scale = dim_head ** -0.5
self.heads = heads self.heads = heads
self.to_q = nn.Linear(query_dim, inner_dim, bias=False) self.to_q = comfy.ops.Linear(query_dim, inner_dim, bias=False)
self.to_k = nn.Linear(context_dim, inner_dim, bias=False) self.to_k = comfy.ops.Linear(context_dim, inner_dim, bias=False)
self.to_v = nn.Linear(context_dim, inner_dim, bias=False) self.to_v = comfy.ops.Linear(context_dim, inner_dim, bias=False)
self.to_out = nn.Sequential( self.to_out = nn.Sequential(
nn.Linear(inner_dim, query_dim), comfy.ops.Linear(inner_dim, query_dim),
nn.Dropout(dropout) nn.Dropout(dropout)
) )
@ -251,12 +252,12 @@ class CrossAttentionDoggettx(nn.Module):
self.scale = dim_head ** -0.5 self.scale = dim_head ** -0.5
self.heads = heads self.heads = heads
self.to_q = nn.Linear(query_dim, inner_dim, bias=False) self.to_q = comfy.ops.Linear(query_dim, inner_dim, bias=False)
self.to_k = nn.Linear(context_dim, inner_dim, bias=False) self.to_k = comfy.ops.Linear(context_dim, inner_dim, bias=False)
self.to_v = nn.Linear(context_dim, inner_dim, bias=False) self.to_v = comfy.ops.Linear(context_dim, inner_dim, bias=False)
self.to_out = nn.Sequential( self.to_out = nn.Sequential(
nn.Linear(inner_dim, query_dim), comfy.ops.Linear(inner_dim, query_dim),
nn.Dropout(dropout) nn.Dropout(dropout)
) )
@ -349,12 +350,12 @@ class CrossAttention(nn.Module):
self.scale = dim_head ** -0.5 self.scale = dim_head ** -0.5
self.heads = heads self.heads = heads
self.to_q = nn.Linear(query_dim, inner_dim, bias=False) self.to_q = comfy.ops.Linear(query_dim, inner_dim, bias=False)
self.to_k = nn.Linear(context_dim, inner_dim, bias=False) self.to_k = comfy.ops.Linear(context_dim, inner_dim, bias=False)
self.to_v = nn.Linear(context_dim, inner_dim, bias=False) self.to_v = comfy.ops.Linear(context_dim, inner_dim, bias=False)
self.to_out = nn.Sequential( self.to_out = nn.Sequential(
nn.Linear(inner_dim, query_dim), comfy.ops.Linear(inner_dim, query_dim),
nn.Dropout(dropout) nn.Dropout(dropout)
) )
@ -407,11 +408,11 @@ class MemoryEfficientCrossAttention(nn.Module):
self.heads = heads self.heads = heads
self.dim_head = dim_head self.dim_head = dim_head
self.to_q = nn.Linear(query_dim, inner_dim, bias=False) self.to_q = comfy.ops.Linear(query_dim, inner_dim, bias=False)
self.to_k = nn.Linear(context_dim, inner_dim, bias=False) self.to_k = comfy.ops.Linear(context_dim, inner_dim, bias=False)
self.to_v = nn.Linear(context_dim, inner_dim, bias=False) self.to_v = comfy.ops.Linear(context_dim, inner_dim, bias=False)
self.to_out = nn.Sequential(nn.Linear(inner_dim, query_dim), nn.Dropout(dropout)) self.to_out = nn.Sequential(comfy.ops.Linear(inner_dim, query_dim), nn.Dropout(dropout))
self.attention_op: Optional[Any] = None self.attention_op: Optional[Any] = None
def forward(self, x, context=None, value=None, mask=None): def forward(self, x, context=None, value=None, mask=None):
@ -456,11 +457,11 @@ class CrossAttentionPytorch(nn.Module):
self.heads = heads self.heads = heads
self.dim_head = dim_head self.dim_head = dim_head
self.to_q = nn.Linear(query_dim, inner_dim, bias=False) self.to_q = comfy.ops.Linear(query_dim, inner_dim, bias=False)
self.to_k = nn.Linear(context_dim, inner_dim, bias=False) self.to_k = comfy.ops.Linear(context_dim, inner_dim, bias=False)
self.to_v = nn.Linear(context_dim, inner_dim, bias=False) self.to_v = comfy.ops.Linear(context_dim, inner_dim, bias=False)
self.to_out = nn.Sequential(nn.Linear(inner_dim, query_dim), nn.Dropout(dropout)) self.to_out = nn.Sequential(comfy.ops.Linear(inner_dim, query_dim), nn.Dropout(dropout))
self.attention_op: Optional[Any] = None self.attention_op: Optional[Any] = None
def forward(self, x, context=None, value=None, mask=None): def forward(self, x, context=None, value=None, mask=None):
@ -601,7 +602,7 @@ class SpatialTransformer(nn.Module):
stride=1, stride=1,
padding=0) padding=0)
else: else:
self.proj_in = nn.Linear(in_channels, inner_dim) self.proj_in = comfy.ops.Linear(in_channels, inner_dim)
self.transformer_blocks = nn.ModuleList( self.transformer_blocks = nn.ModuleList(
[BasicTransformerBlock(inner_dim, n_heads, d_head, dropout=dropout, context_dim=context_dim[d], [BasicTransformerBlock(inner_dim, n_heads, d_head, dropout=dropout, context_dim=context_dim[d],
@ -609,13 +610,12 @@ class SpatialTransformer(nn.Module):
for d in range(depth)] for d in range(depth)]
) )
if not use_linear: if not use_linear:
self.proj_out = zero_module(nn.Conv2d(inner_dim, self.proj_out = nn.Conv2d(inner_dim,in_channels,
in_channels,
kernel_size=1, kernel_size=1,
stride=1, stride=1,
padding=0)) padding=0)
else: else:
self.proj_out = zero_module(nn.Linear(in_channels, inner_dim)) self.proj_out = comfy.ops.Linear(in_channels, inner_dim)
self.use_linear = use_linear self.use_linear = use_linear
def forward(self, x, context=None, transformer_options={}): def forward(self, x, context=None, transformer_options={}):

17
comfy/ops.py Normal file
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@ -0,0 +1,17 @@
import torch
class Linear(torch.nn.Module):
def __init__(self, in_features: int, out_features: int, bias: bool = True,
device=None, dtype=None) -> None:
factory_kwargs = {'device': device, 'dtype': dtype}
super().__init__()
self.in_features = in_features
self.out_features = out_features
self.weight = torch.nn.Parameter(torch.empty((out_features, in_features), **factory_kwargs))
if bias:
self.bias = torch.nn.Parameter(torch.empty(out_features, **factory_kwargs))
else:
self.register_parameter('bias', None)
def forward(self, input):
return torch.nn.functional.linear(input, self.weight, self.bias)

3
comfy/samplers.py
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@ -273,7 +273,8 @@ def sampling_function(model_function, x, timestep, uncond, cond, cond_scale, con
max_total_area = model_management.maximum_batch_area() max_total_area = model_management.maximum_batch_area()
cond, uncond = calc_cond_uncond_batch(model_function, cond, uncond, x, timestep, max_total_area, cond_concat, model_options) cond, uncond = calc_cond_uncond_batch(model_function, cond, uncond, x, timestep, max_total_area, cond_concat, model_options)
if "sampler_cfg_function" in model_options: if "sampler_cfg_function" in model_options:
return model_options["sampler_cfg_function"](cond, uncond, cond_scale) args = {"cond": cond, "uncond": uncond, "cond_scale": cond_scale, "timestep": timestep}
return model_options["sampler_cfg_function"](args)
else: else:
return uncond + (cond - uncond) * cond_scale return uncond + (cond - uncond) * cond_scale

11
comfy/sd.py
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@ -1,6 +1,7 @@
import torch import torch
import contextlib import contextlib
import copy import copy
import inspect
from . import sd1_clip from . import sd1_clip
from . import sd2_clip from . import sd2_clip
@ -313,8 +314,10 @@ class ModelPatcher:
self.model_options["transformer_options"]["tomesd"] = {"ratio": ratio} self.model_options["transformer_options"]["tomesd"] = {"ratio": ratio}
def set_model_sampler_cfg_function(self, sampler_cfg_function): def set_model_sampler_cfg_function(self, sampler_cfg_function):
self.model_options["sampler_cfg_function"] = sampler_cfg_function if len(inspect.signature(sampler_cfg_function).parameters) == 3:
self.model_options["sampler_cfg_function"] = lambda args: sampler_cfg_function(args["cond"], args["uncond"], args["cond_scale"]) #Old way
else:
self.model_options["sampler_cfg_function"] = sampler_cfg_function
def set_model_patch(self, patch, name): def set_model_patch(self, patch, name):
to = self.model_options["transformer_options"] to = self.model_options["transformer_options"]
@ -1152,9 +1155,9 @@ def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, o
else: else:
model = model_base.BaseModel(unet_config, v_prediction=v_prediction) model = model_base.BaseModel(unet_config, v_prediction=v_prediction)
model = load_model_weights(model, sd, verbose=False, load_state_dict_to=load_state_dict_to)
if fp16: if fp16:
model = model.half() model = model.half()
model = load_model_weights(model, sd, verbose=False, load_state_dict_to=load_state_dict_to)
return (ModelPatcher(model), clip, vae, clipvision) return (ModelPatcher(model), clip, vae, clipvision)

5
comfy/sd1_clip.py
View File

@ -1,6 +1,6 @@
import os import os
from transformers import CLIPTokenizer, CLIPTextModel, CLIPTextConfig from transformers import CLIPTokenizer, CLIPTextModel, CLIPTextConfig, modeling_utils
import torch import torch
import traceback import traceback
import zipfile import zipfile
@ -38,7 +38,8 @@ class SD1ClipModel(torch.nn.Module, ClipTokenWeightEncoder):
if textmodel_json_config is None: if textmodel_json_config is None:
textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "sd1_clip_config.json") textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "sd1_clip_config.json")
config = CLIPTextConfig.from_json_file(textmodel_json_config) config = CLIPTextConfig.from_json_file(textmodel_json_config)
self.transformer = CLIPTextModel(config) with modeling_utils.no_init_weights():
self.transformer = CLIPTextModel(config)
self.device = device self.device = device
self.max_length = max_length self.max_length = max_length

6
execution.py
View File

@ -310,7 +310,6 @@ class PromptExecutor:
else: else:
self.server.client_id = None self.server.client_id = None
execution_start_time = time.perf_counter()
if self.server.client_id is not None: if self.server.client_id is not None:
self.server.send_sync("execution_start", { "prompt_id": prompt_id}, self.server.client_id) self.server.send_sync("execution_start", { "prompt_id": prompt_id}, self.server.client_id)
@ -358,12 +357,7 @@ class PromptExecutor:
for x in executed: for x in executed:
self.old_prompt[x] = copy.deepcopy(prompt[x]) self.old_prompt[x] = copy.deepcopy(prompt[x])
self.server.last_node_id = None self.server.last_node_id = None
if self.server.client_id is not None:
self.server.send_sync("executing", { "node": None, "prompt_id": prompt_id }, self.server.client_id)
print("Prompt executed in {:.2f} seconds".format(time.perf_counter() - execution_start_time))
gc.collect()
comfy.model_management.soft_empty_cache()
def validate_inputs(prompt, item, validated): def validate_inputs(prompt, item, validated):

13
main.py
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@ -3,6 +3,8 @@ import itertools
import os import os
import shutil import shutil
import threading import threading
import gc
import time
from comfy.cli_args import args from comfy.cli_args import args
import comfy.utils import comfy.utils
@ -29,15 +31,22 @@ import folder_paths
import server import server
from server import BinaryEventTypes from server import BinaryEventTypes
from nodes import init_custom_nodes from nodes import init_custom_nodes
import comfy.model_management
def prompt_worker(q, server): def prompt_worker(q, server):
e = worklist_execution.PromptExecutor(server) e = worklist_execution.PromptExecutor(server)
while True: while True:
item, item_id = q.get() item, item_id = q.get()
e.execute(item[2], item[1], item[3], item[4]) execution_start_time = time.perf_counter()
prompt_id = item[1]
e.execute(item[2], prompt_id, item[3], item[4])
q.task_done(item_id, e.outputs_ui) q.task_done(item_id, e.outputs_ui)
if server.client_id is not None:
server.send_sync("executing", { "node": None, "prompt_id": prompt_id }, server.client_id)
print("Prompt executed in {:.2f} seconds".format(time.perf_counter() - execution_start_time))
gc.collect()
comfy.model_management.soft_empty_cache()
async def run(server, address='', port=8188, verbose=True, call_on_start=None): async def run(server, address='', port=8188, verbose=True, call_on_start=None):
await asyncio.gather(server.start(address, port, verbose, call_on_start), server.publish_loop()) await asyncio.gather(server.start(address, port, verbose, call_on_start), server.publish_loop())

70
web/extensions/core/colorPalette.js
View File

@ -1,6 +1,5 @@
import { app } from "/scripts/app.js"; import {app} from "/scripts/app.js";
import { $el } from "/scripts/ui.js"; import {$el} from "/scripts/ui.js";
import { api } from "/scripts/api.js";
// Manage color palettes // Manage color palettes
@ -24,6 +23,8 @@ const colorPalettes = {
"TAESD": "#DCC274", // cheesecake "TAESD": "#DCC274", // cheesecake
}, },
"litegraph_base": { "litegraph_base": {
"BACKGROUND_IMAGE": "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAGQAAABkCAIAAAD/gAIDAAAAGXRFWHRTb2Z0d2FyZQBBZG9iZSBJbWFnZVJlYWR5ccllPAAAAQBJREFUeNrs1rEKwjAUhlETUkj3vP9rdmr1Ysammk2w5wdxuLgcMHyptfawuZX4pJSWZTnfnu/lnIe/jNNxHHGNn//HNbbv+4dr6V+11uF527arU7+u63qfa/bnmh8sWLBgwYJlqRf8MEptXPBXJXa37BSl3ixYsGDBMliwFLyCV/DeLIMFCxYsWLBMwSt4Be/NggXLYMGCBUvBK3iNruC9WbBgwYJlsGApeAWv4L1ZBgsWLFiwYJmCV/AK3psFC5bBggULloJX8BpdwXuzYMGCBctgwVLwCl7Be7MMFixYsGDBsu8FH1FaSmExVfAxBa/gvVmwYMGCZbBg/W4vAQYA5tRF9QYlv/QAAAAASUVORK5CYII=",
"CLEAR_BACKGROUND_COLOR": "#222",
"NODE_TITLE_COLOR": "#999", "NODE_TITLE_COLOR": "#999",
"NODE_SELECTED_TITLE_COLOR": "#FFF", "NODE_SELECTED_TITLE_COLOR": "#FFF",
"NODE_TEXT_SIZE": 14, "NODE_TEXT_SIZE": 14,
@ -77,6 +78,8 @@ const colorPalettes = {
"VAE": "#FF7043", // deep orange "VAE": "#FF7043", // deep orange
}, },
"litegraph_base": { "litegraph_base": {
"BACKGROUND_IMAGE": "data:image/gif;base64,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",
"CLEAR_BACKGROUND_COLOR": "lightgray",
"NODE_TITLE_COLOR": "#222", "NODE_TITLE_COLOR": "#222",
"NODE_SELECTED_TITLE_COLOR": "#000", "NODE_SELECTED_TITLE_COLOR": "#000",
"NODE_TEXT_SIZE": 14, "NODE_TEXT_SIZE": 14,
@ -191,7 +194,7 @@ app.registerExtension({
const nodeData = defs[nodeId]; const nodeData = defs[nodeId];
var inputs = nodeData["input"]["required"]; var inputs = nodeData["input"]["required"];
if (nodeData["input"]["optional"] != undefined){ if (nodeData["input"]["optional"] != undefined) {
inputs = Object.assign({}, nodeData["input"]["required"], nodeData["input"]["optional"]) inputs = Object.assign({}, nodeData["input"]["required"], nodeData["input"]["optional"])
} }
@ -232,12 +235,9 @@ app.registerExtension({
"id": "my_color_palette_unique_id", "id": "my_color_palette_unique_id",
"name": "My Color Palette", "name": "My Color Palette",
"colors": { "colors": {
"node_slot": { "node_slot": {},
}, "litegraph_base": {},
"litegraph_base": { "comfy_base": {}
},
"comfy_base": {
}
} }
}; };
@ -266,7 +266,7 @@ app.registerExtension({
}; };
const addCustomColorPalette = async (colorPalette) => { const addCustomColorPalette = async (colorPalette) => {
if (typeof(colorPalette) !== "object") { if (typeof (colorPalette) !== "object") {
app.ui.dialog.show("Invalid color palette"); app.ui.dialog.show("Invalid color palette");
return; return;
} }
@ -286,7 +286,7 @@ app.registerExtension({
return; return;
} }
if (colorPalette.colors.node_slot && typeof(colorPalette.colors.node_slot) !== "object") { if (colorPalette.colors.node_slot && typeof (colorPalette.colors.node_slot) !== "object") {
app.ui.dialog.show("Invalid color palette colors.node_slot"); app.ui.dialog.show("Invalid color palette colors.node_slot");
return; return;
} }
@ -301,7 +301,11 @@ app.registerExtension({
} }
} }
els.select.append($el("option", { textContent: colorPalette.name + " (custom)", value: "custom_" + colorPalette.id, selected: true })); els.select.append($el("option", {
textContent: colorPalette.name + " (custom)",
value: "custom_" + colorPalette.id,
selected: true
}));
setColorPalette("custom_" + colorPalette.id); setColorPalette("custom_" + colorPalette.id);
await loadColorPalette(colorPalette); await loadColorPalette(colorPalette);
@ -350,7 +354,7 @@ app.registerExtension({
if (colorPalette.colors.comfy_base) { if (colorPalette.colors.comfy_base) {
const rootStyle = document.documentElement.style; const rootStyle = document.documentElement.style;
for (const key in colorPalette.colors.comfy_base) { for (const key in colorPalette.colors.comfy_base) {
rootStyle.setProperty('--' + key, colorPalette.colors.comfy_base[key]); rootStyle.setProperty('--' + key, colorPalette.colors.comfy_base[key]);
} }
} }
app.canvas.draw(true, true); app.canvas.draw(true, true);
@ -380,7 +384,7 @@ app.registerExtension({
const fileInput = $el("input", { const fileInput = $el("input", {
type: "file", type: "file",
accept: ".json", accept: ".json",
style: { display: "none" }, style: {display: "none"},
parent: document.body, parent: document.body,
onchange: () => { onchange: () => {
let file = fileInput.files[0]; let file = fileInput.files[0];
@ -403,17 +407,25 @@ app.registerExtension({
for (const c in colorPalettes) { for (const c in colorPalettes) {
const colorPalette = colorPalettes[c]; const colorPalette = colorPalettes[c];
options.push($el("option", { textContent: colorPalette.name, value: colorPalette.id, selected: colorPalette.id === value })); options.push($el("option", {
textContent: colorPalette.name,
value: colorPalette.id,
selected: colorPalette.id === value
}));
} }
let customColorPalettes = getCustomColorPalettes(); let customColorPalettes = getCustomColorPalettes();
for (const c in customColorPalettes) { for (const c in customColorPalettes) {
const colorPalette = customColorPalettes[c]; const colorPalette = customColorPalettes[c];
options.push($el("option", { textContent: colorPalette.name + " (custom)", value: "custom_" + colorPalette.id, selected: "custom_" + colorPalette.id === value })); options.push($el("option", {
textContent: colorPalette.name + " (custom)",
value: "custom_" + colorPalette.id,
selected: "custom_" + colorPalette.id === value
}));
} }
return $el("div", [ return $el("div", [
$el("label", { textContent: name || id }, [ $el("label", {textContent: name || id}, [
els.select = $el("select", { els.select = $el("select", {
onchange: (e) => { onchange: (e) => {
setter(e.target.value); setter(e.target.value);
@ -427,12 +439,12 @@ app.registerExtension({
const colorPaletteId = app.ui.settings.getSettingValue(id, defaultColorPaletteId); const colorPaletteId = app.ui.settings.getSettingValue(id, defaultColorPaletteId);
const colorPalette = await completeColorPalette(getColorPalette(colorPaletteId)); const colorPalette = await completeColorPalette(getColorPalette(colorPaletteId));
const json = JSON.stringify(colorPalette, null, 2); // convert the data to a JSON string const json = JSON.stringify(colorPalette, null, 2); // convert the data to a JSON string
const blob = new Blob([json], { type: "application/json" }); const blob = new Blob([json], {type: "application/json"});
const url = URL.createObjectURL(blob); const url = URL.createObjectURL(blob);
const a = $el("a", { const a = $el("a", {
href: url, href: url,
download: colorPaletteId + ".json", download: colorPaletteId + ".json",
style: { display: "none" }, style: {display: "none"},
parent: document.body, parent: document.body,
}); });
a.click(); a.click();
@ -455,12 +467,12 @@ app.registerExtension({
onclick: async () => { onclick: async () => {
const colorPalette = await getColorPaletteTemplate(); const colorPalette = await getColorPaletteTemplate();
const json = JSON.stringify(colorPalette, null, 2); // convert the data to a JSON string const json = JSON.stringify(colorPalette, null, 2); // convert the data to a JSON string
const blob = new Blob([json], { type: "application/json" }); const blob = new Blob([json], {type: "application/json"});
const url = URL.createObjectURL(blob); const url = URL.createObjectURL(blob);
const a = $el("a", { const a = $el("a", {
href: url, href: url,
download: "color_palette.json", download: "color_palette.json",
style: { display: "none" }, style: {display: "none"},
parent: document.body, parent: document.body,
}); });
a.click(); a.click();
@ -496,15 +508,25 @@ app.registerExtension({
return; return;
} }
if (colorPalettes[value]) { let palette = colorPalettes[value];
await loadColorPalette(colorPalettes[value]); if (palette) {
await loadColorPalette(palette);
} else if (value.startsWith("custom_")) { } else if (value.startsWith("custom_")) {
value = value.substr(7); value = value.substr(7);
let customColorPalettes = getCustomColorPalettes(); let customColorPalettes = getCustomColorPalettes();
if (customColorPalettes[value]) { if (customColorPalettes[value]) {
palette = customColorPalettes[value];
await loadColorPalette(customColorPalettes[value]); await loadColorPalette(customColorPalettes[value]);
} }
} }
let {BACKGROUND_IMAGE, CLEAR_BACKGROUND_COLOR} = palette.colors.litegraph_base;
if (BACKGROUND_IMAGE === undefined || CLEAR_BACKGROUND_COLOR === undefined) {
const base = colorPalettes["dark"].colors.litegraph_base;
BACKGROUND_IMAGE = base.BACKGROUND_IMAGE;
CLEAR_BACKGROUND_COLOR = base.CLEAR_BACKGROUND_COLOR;
}
app.canvas.updateBackground(BACKGROUND_IMAGE, CLEAR_BACKGROUND_COLOR);
}, },
}); });
}, },

1
web/index.html
View File

@ -7,6 +7,7 @@
<link rel="stylesheet" type="text/css" href="lib/litegraph.css" /> <link rel="stylesheet" type="text/css" href="lib/litegraph.css" />
<link rel="stylesheet" type="text/css" href="style.css" /> <link rel="stylesheet" type="text/css" href="style.css" />
<script type="text/javascript" src="lib/litegraph.core.js"></script> <script type="text/javascript" src="lib/litegraph.core.js"></script>
<script type="text/javascript" src="lib/litegraph.extensions.js" defer></script>
<script type="module"> <script type="module">
import { app } from "/scripts/app.js"; import { app } from "/scripts/app.js";
await app.setup(); await app.setup();

21
web/lib/litegraph.extensions.js Normal file
View File

@ -0,0 +1,21 @@
/**
* Changes the background color of the canvas.
*
* @method updateBackground
* @param {image} String
* @param {clearBackgroundColor} String
* @
*/
LGraphCanvas.prototype.updateBackground = function (image, clearBackgroundColor) {
this._bg_img = new Image();
this._bg_img.name = image;
this._bg_img.src = image;
this._bg_img.onload = () => {
this.draw(true, true);
};
this.background_image = image;
this.clear_background = true;
this.clear_background_color = clearBackgroundColor;
this._pattern = null
}