Compare commits

..
Author SHA1 Message Date
gold3bear c275a56806 # 2023-12-17 00:40:26 +08:00
gold3bear 82c3c7addd improve mix-modal ui 2023-12-16 23:59:45 +08:00
shadowcz007 561805a417 优化下3dImage 2023-12-16 15:48:35 +08:00
shadowcz007 7bb4324365 fixbug 2023-12-16 13:52:28 +08:00
shadowcz007 0608653d35 Update ui_mixlab.js 2023-12-16 00:05:41 +08:00
shadowcz007 fa3472cdc5 find_the_node 2023-12-15 23:46:46 +08:00
shadowcz007 7d553b6fcf 修复svgImage的bug 2023-12-15 19:57:22 +08:00
shadowcz007 677627630e 右击节点获取readme 2023-12-14 20:02:27 +08:00
shadow 9f23172b22 Update README.md 2023-12-13 10:35:33 +08:00
gold3bear 14ceaa472d Merge branch 'main' of https://github.com/shadowcz007/comfyui-mixlab-nodes 2023-12-12 21:17:36 +08:00
gold3bear 1188b9d3bc fix:macos fonts runtime erros 2023-12-12 21:16:51 +08:00
shadowcz007 424c9a9423 0.3.1 2023-12-12 20:26:57 +08:00
shadowcz007 8f60c81ef3 test 2023-12-12 20:22:46 +08:00
shadowcz007 dd648d1ae5 test 2023-12-12 20:14:43 +08:00
shadowcz007 02e839e272 新增示例 2023-12-12 18:11:47 +08:00
shadowcz007 ec8c56707b 0.3.0
v0.3.0 🚀🚗🚚🏃‍

- Added support for setting proxies: HTTP_PROXY, HTTPS_PROXY, http_proxy, https_proxy ✅

- Added a new Speech feature node, enabling the use of a voice assistant: SpeechRecognition & SpeechSynthesis 🎙️

- Added TextImage node, allowing conversion of text into image format 📷

- Added SvgImage node, enabling layout parsing and poster generation in conjunction with the Layer class node 🖼️

- Added an experimental 3DImage node for loading 3D models 🌟
2023-12-12 17:22:37 +08:00
shadowcz007 1e8d317ee8 Merge branch 'main' of https://github.com/shadowcz007/comfyui-mixlab-nodes 2023-12-12 17:14:55 +08:00
shadowcz007 e81df111a7 0.3 ing 2023-12-12 17:14:52 +08:00
shadow 90e55ffe14 Merge pull request #45 from shadowcz007/v0.2.8-proxy
V0.2.8 proxy
2023-12-12 14:09:55 +08:00
shadowcz007 4e73b1d3fc fixbug 2023-12-12 14:07:01 +08:00
gold3bear 21dca1e34f test ok 2023-12-12 13:56:58 +08:00
shadowcz007 c2292850bb 1 2023-12-12 12:38:37 +08:00
BearXiong 04791c92e2 proxy new_request 2023-12-12 11:55:12 +08:00
shadowcz007 d9462b6d8a Update Utils.py 2023-12-11 11:49:36 +08:00
shadowcz007 be9b83559e test 2023-12-10 17:31:59 +08:00
shadowcz007 958889afee test-3d 2023-12-10 17:31:28 +08:00
shadowcz007 dce677035f 更新-字体和颜色选择 2023-12-10 11:31:06 +08:00
shadowcz007 d98a8855ad update 2023-12-09 18:07:16 +08:00
shadowcz007 7cd0587a64 COLOR冲突,改个名字 2023-12-09 12:33:58 +08:00
shadowcz007 bb3967f11e ing 2023-12-09 00:37:38 +08:00
shadowcz007 36ee203bd7 ing 2023-12-09 00:17:29 +08:00
shadowcz007 914919ba75 test 2023-12-08 13:01:00 +08:00
shadowcz007 a4514e8565 1 2023-12-08 00:15:13 +08:00
shadowcz007 116e983c50 新增textImage 2023-12-08 00:07:34 +08:00
shadowcz007 fb667b6c42 0.2.8 layers 预发布 2023-12-07 18:28:58 +08:00
shadowcz007 b0a090cf14 1 2023-12-07 11:42:54 +08:00
shadowcz007 110b470d33 Update README.md 2023-12-06 19:51:30 +08:00
shadowcz007 c517c4d015 v0.2.7 2023-12-06 19:49:26 +08:00
shadowcz007 35ed4f9101 Update main_mixlab.js 2023-12-06 19:41:29 +08:00
shadowcz007 18c723c2e3 seed 2023-12-06 19:35:40 +08:00
shadowcz007 631223602c Update gpt_mixlab.js 2023-12-06 19:01:30 +08:00
33 changed files with 12401 additions and 302 deletions
+77 -37
View File
@@ -1,15 +1,25 @@
##
v0.3.0 🚀🚗🚚🏃‍
v0.2.6 🚀🚗🚚🏃‍
- Added support for setting proxies: HTTP_PROXY, HTTPS_PROXY, http_proxy, https_proxy ✅
- [Add getting camera video stream](./workflow/7-camera-workflow.json)
- Add a slider to the floating window, which can be used as input for denoise
- OSupport for calling multiple GPTs
- Added a new Speech feature node, enabling the use of a voice assistant: SpeechRecognition & SpeechSynthesis 🎙️
- Added TextImage node, allowing conversion of text into image format 📷
- Added SvgImage node, enabling layout parsing and poster generation in conjunction with the Layer class node 🖼️
- Added an experimental 3DImage node for loading 3D models 🌟
![screenshare](./assets/screenshare.png)
### SpeechRecognition & SpeechSynthesis
![f](./assets/audio-workflow.svg)
[Voice + Real-time Face Swap Workflow](./workflow/语音+实时换脸workflow.json)
### ScreenShareNode & FloatingVideoNode
> Now comfyui supports capturing screen pixel streams from any software and can be used for LCM-Lora integration. Let's get started with implementation and design! 💻🌐
@@ -33,7 +43,7 @@ https://github.com/shadowcz007/comfyui-mixlab-nodes/assets/12645064/e7e77f90-e43
### GPT
>Support for calling multiple GPTs.ChatGPT、ChatGLM3 , Some code provided by rui. If you are using OpenAI's service, fill in https://api.openai.com/v1 . If you are using a local LLM service, fill in http://127.0.0.1:xxxx/v1 . Azure OpenAI:https://xxxx.openai.azure.com
> Support for calling multiple GPTs.ChatGPT、ChatGLM3 , Some code provided by rui. If you are using OpenAI's service, fill in https://api.openai.com/v1 . If you are using a local LLM service, fill in http://127.0.0.1:xxxx/v1 . Azure OpenAI:https://xxxx.openai.azure.com
![gpt-workflow.svg](./assets/gpt-workflow.svg)
@@ -41,38 +51,14 @@ https://github.com/shadowcz007/comfyui-mixlab-nodes/assets/12645064/e7e77f90-e43
[workflow-5](./workflow/5-gpt-workflow.json)
## Installation
### Layers
> A new layer class node has been added, allowing you to separate the image into layers. After merging the images, you can input the controlnet for further processing.
manually install, simply clone the repo into the custom_nodes directory with this command:
![layers](./assets/layers-workflow.svg)
```
cd ComfyUI/custom_nodes
![poster](./assets/poster-workflow.svg)
git clone https://github.com/shadowcz007/comfyui-mixlab-nodes.git
```
Install the requirements:
run directly:
```
cd ComfyUI_Mixlab
install.bat
```
or install the requirements using:
```
../../../python_embeded/python.exe -s -m pip install -r requirements.txt
```
If you are using a venv, make sure you have it activated before installation and use:
```
pip3 install -r requirements.txt
```
## Nodes
## Other Nodes
![main](./assets/all-workflow.svg)
![main2](./assets/detect-face-all.png)
@@ -88,9 +74,6 @@ pip3 install -r requirements.txt
![TransparentImage](./assets/TransparentImage.png)
> Consistency Decoder
[openai Consistency Decoder]( https://github.com/openai/consistencydecoder)
@@ -120,6 +103,42 @@ An improvement has been made to directly redirect to GitHub to search for missin
<!-- ### Workflow
[Workflow](./workflow.md) -->
## Installation
manually install, simply clone the repo into the custom_nodes directory with this command:
```
cd ComfyUI/custom_nodes
git clone https://github.com/shadowcz007/comfyui-mixlab-nodes.git
```
Install the requirements:
run directly:
```
cd ComfyUI/custom_nodes/comfyui-mixlab-nodes
install.bat
```
or install the requirements using:
```
../../../python_embeded/python.exe -s -m pip install -r requirements.txt
```
If you are using a venv, make sure you have it activated before installation and use:
```
pip3 install -r requirements.txt
```
#### Chinese community
访问 [www.mixcomfy.com](https://www.mixcomfy.com),获得更多内测功能,关注微信公众号:Mixlab无界社区
#### Thanks:
[ComfyUI-CLIPSeg](https://github.com/biegert/ComfyUI-CLIPSeg/tree/main)
@@ -127,5 +146,26 @@ An improvement has been made to directly redirect to GitHub to search for missin
[discussions](https://github.com/shadowcz007/comfyui-mixlab-nodes/discussions)
### TODO:
- 音频播放节点:带可视化、支持多音轨、可配置音轨音量
- vector https://github.com/GeorgLegato/stable-diffusion-webui-vectorstudio
<picture>
<source
media="(prefers-color-scheme: dark)"
srcset="
https://api.star-history.com/svg?repos=shadowcz007/comfyui-mixlab-nodes&type=Date&theme=dark
"
/>
<source
media="(prefers-color-scheme: light)"
srcset="
https://api.star-history.com/svg?repos=shadowcz007/comfyui-mixlab-nodes&type=Date
"
/>
<img
alt="Star History Chart"
src="https://api.star-history.com/svg?repos=shadowcz007/comfyui-mixlab-nodes&type=Date"
/>
</picture>
+70 -3
View File
@@ -168,8 +168,44 @@ def save_workflow_json(data):
json.dump(data, file)
return workflow_path
def get_nodes_map():
# print("#####path::", current_path)
data_path=os.path.join(current_path, "data")
print('data_path: ',data_path)
# if not os.path.exists(data_path):
# # 使用mkdir()方法创建新目录
# os.mkdir(data_path)
json_data={}
nodes_map=os.path.join(current_path, "data/extension-node-map.json")
if os.path.exists(nodes_map):
with open(nodes_map) as json_file:
json_data = json.load(json_file)
return json_data
# 保存原始的 get 方法
_original_request = aiohttp.ClientSession._request
# 定义新的 get 方法
async def new_request(self, method, url, *args, **kwargs):
# 检查环境变量以确定是否使用代理
proxy = os.environ.get('HTTP_PROXY') or os.environ.get('HTTPS_PROXY') or os.environ.get('http_proxy') or os.environ.get('https_proxy')
# print('Proxy Config:',proxy)
if proxy and 'proxy' not in kwargs:
kwargs['proxy'] = proxy
print('Use Proxy:',proxy)
# 调用原始的 _request 方法
return await _original_request(self, method, url, *args, **kwargs)
# 应用 Monkey Patch
aiohttp.ClientSession._request = new_request
# https
async def new_start(self, address, port, verbose=True, call_on_start=None):
runner = web.AppRunner(self.app, access_log=None)
await runner.setup()
site = web.TCPSite(runner, address, port)
@@ -239,6 +275,20 @@ async def mixlab_workflow_hander(request):
return web.json_response(result)
@routes.post('/mixlab/nodes_map')
async def nodes_map_hander(request):
data = await request.json()
result={}
try:
result={
'data':get_nodes_map(),
'status':'success',
}
except Exception as e:
print(e)
return web.json_response(result)
def new_add_routes(self):
import nodes
self.app.add_routes(routes)
@@ -268,11 +318,14 @@ PromptServer.add_routes=new_add_routes
# 导入节点
from .nodes.PromptNode import RandomPrompt
from .nodes.ImageNode import TransparentImage,LoadImagesFromPath,AreaToMask,SmoothMask,FeatheredMask,SplitLongMask,ImageCropByAlpha,EnhanceImage,FaceToMask
from .nodes.ImageNode import TransparentImage,LoadImagesFromPath,TextImage,SvgImage,Image3D,EmptyLayer,ShowLayer,NewLayer,MergeLayers,AreaToMask,SmoothMask,FeatheredMask,SplitLongMask,ImageCropByAlpha,EnhanceImage,FaceToMask
from .nodes.Vae import VAELoader,VAEDecode
from .nodes.ScreenShareNode import ScreenShareNode,FloatingVideo
from .nodes.Clipseg import CLIPSeg,CombineMasks
from .nodes.ChatGPT import ChatGPTNode,ShowTextForGPT,CharacterInText
from .nodes.Audio import SpeechRecognition,SpeechSynthesis
from .nodes.Utils import ColorInput,FontInput
# 要导出的所有节点及其名称的字典
# 注意:名称应全局唯一
@@ -280,7 +333,14 @@ NODE_CLASS_MAPPINGS = {
"RandomPrompt":RandomPrompt,
"TransparentImage":TransparentImage,
"LoadImagesFromPath":LoadImagesFromPath,
"TextImage":TextImage,
"EnhanceImage":EnhanceImage,
"SvgImage":SvgImage,
"3DImage":Image3D,
"EmptyLayer":EmptyLayer,
"ShowLayer":ShowLayer,
"NewLayer":NewLayer,
"MergeLayers":MergeLayers,
"SplitLongMask":SplitLongMask,
"FeatheredMask":FeatheredMask,
"SmoothMask":SmoothMask,
@@ -295,7 +355,11 @@ NODE_CLASS_MAPPINGS = {
"CombineMasks_":CombineMasks,
"ChatGPTOpenAI":ChatGPTNode,
"ShowTextForGPT":ShowTextForGPT,
"CharacterInText":CharacterInText
"CharacterInText":CharacterInText,
"SpeechRecognition":SpeechRecognition,
"SpeechSynthesis":SpeechSynthesis,
"Color":ColorInput,
"Font":FontInput
}
# 一个包含节点友好/可读的标题的字典
@@ -307,7 +371,10 @@ NODE_DISPLAY_NAME_MAPPINGS = {
"ScreenShare":"ScreenShare ♾️Mixlab",
"FloatingVideo":"FloatingVideo ♾️Mixlab",
"ChatGPTOpenAI":"ChatGPT ♾️Mixlab",
"ShowTextForGPT":"ShowTextForGPT ♾️Mixlab"
"ShowTextForGPT":"ShowTextForGPT ♾️Mixlab",
"MergeLayers":"MergeLayers ♾️Mixlab",
"SpeechSynthesis":"SpeechSynthesis ♾️Mixlab",
"SpeechRecognition":"SpeechRecognition ♾️Mixlab"
}
# web ui的节点功能
Binary file not shown.
Binary file not shown.
File diff suppressed because one or more lines are too long

After

Width:  |  Height:  |  Size: 73 KiB

File diff suppressed because one or more lines are too long

After

Width:  |  Height:  |  Size: 7.4 MiB

File diff suppressed because one or more lines are too long

After

Width:  |  Height:  |  Size: 51 KiB

File diff suppressed because one or more lines are too long

After

Width:  |  Height:  |  Size: 8.7 MiB

Binary file not shown.
Binary file not shown.
File diff suppressed because it is too large Load Diff
+46
View File
@@ -0,0 +1,46 @@
class SpeechRecognition:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"upload":("AUDIOINPUTMIX",), },
}
RETURN_TYPES = ("STRING",)
RETURN_NAMES = ("prompt",)
FUNCTION = "run"
CATEGORY = "♾️Mixlab/audio"
INPUT_IS_LIST = False
OUTPUT_IS_LIST = (False,)
def run(self,upload):
return (upload,)
class SpeechSynthesis:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"text": ("STRING", {"forceInput": True}),
}
}
INPUT_IS_LIST = True
RETURN_TYPES = ("STRING",)
FUNCTION = "run"
OUTPUT_NODE = True
OUTPUT_IS_LIST = (True,)
CATEGORY = "♾️Mixlab/audio"
def run(self, text):
# print(session_history)
return {"ui": {"text": text}, "result": (text,)}
+1 -1
View File
@@ -46,7 +46,7 @@ def chat(client, model_name,messages ):
except (urllib.error.HTTPError, openai.OpenAIError) as ex:
if try_count >= 3:
raise ex
time.sleep(5)
time.sleep(3)
continue
finish_reason = response.choices[0].finish_reason
+7 -4
View File
@@ -1,3 +1,6 @@
#### Thanks:
# [ComfyUI-CLIPSeg](https://github.com/biegert/ComfyUI-CLIPSeg/tree/main)
from transformers import CLIPSegProcessor, CLIPSegForImageSegmentation
from PIL import Image
@@ -252,7 +255,7 @@ class CombineMasks:
# A dictionary that contains all nodes you want to export with their names
# NOTE: names should be globally unique
NODE_CLASS_MAPPINGS = {
"CLIPSeg": CLIPSeg,
"CombineSegMasks": CombineMasks,
}
# NODE_CLASS_MAPPINGS = {
# "CLIPSeg": CLIPSeg,
# "CombineSegMasks": CombineMasks,
# }
+516 -3
View File
@@ -1,17 +1,18 @@
import numpy as np
import torch
from PIL import Image, ImageOps,ImageFilter,ImageEnhance,ImageDraw,ImageSequence
from PIL import Image, ImageOps,ImageFilter,ImageEnhance,ImageDraw,ImageSequence, ImageFont
from PIL.PngImagePlugin import PngInfo
import base64,os
from io import BytesIO
import folder_paths
import json
import json,io
from comfy.cli_args import args
import cv2
from .Watcher import FolderWatcher
FONT_PATH= os.path.abspath(os.path.join(os.path.dirname(__file__),'../assets/王汉宗颜楷体繁.ttf'))
MAX_RESOLUTION=8192
@@ -322,6 +323,117 @@ def areaToMask(x,y,w,h,image):
return mask
# def merge_images(bg_image, layer_image,mask, x, y, width, height):
# # 打开底图
# # bg_image = Image.open(background)
# bg_image=bg_image.convert("RGBA")
# # 打开图层
# layer_image=layer_image.convert("RGBA")
# layer_image = layer_image.resize((width, height))
# # mask = Image.new("L", layer_image.size, 255)
# mask = mask.resize((width, height))
# # 在底图上粘贴图层
# bg_image.paste(layer_image, (x, y), mask=mask)
# # 输出合成后的图片
# # bg_image.save("output.jpg")
# return bg_image
def merge_images(bg_image, layer_image, mask, x, y, width, height, scale_option):
# 打开底图
bg_image = bg_image.convert("RGBA")
# 打开图层
layer_image = layer_image.convert("RGBA")
# layer_image = layer_image.resize((width, height))
# 根据缩放选项调整图像大小
if scale_option == "height":
# 按照高度比例缩放
original_width, original_height = layer_image.size
scale = height / original_height
new_width = int(original_width * scale)
layer_image = layer_image.resize((new_width, height))
elif scale_option == "width":
# 按照宽度比例缩放
original_width, original_height = layer_image.size
scale = width / original_width
new_height = int(original_height * scale)
layer_image = layer_image.resize((width, new_height))
elif scale_option == "overall":
# 整体缩放
layer_image = layer_image.resize((width, height))
# 调整mask的大小
nw, nh = layer_image.size
mask = mask.resize((nw, nh))
# 在底图上粘贴图层
bg_image.paste(layer_image, (x, y), mask=mask)
# 输出合成后的图片
return bg_image
def generate_text_image(text_list, font_path, font_size, text_color, vertical=True, spacing=0):
# Load Chinese font
font = ImageFont.truetype(font_path, font_size)
# Calculate image size based on the number of characters and orientation
if vertical:
width = font_size + 100
height = font_size * len(text_list) + (len(text_list) - 1) * spacing + 100
else:
width = font_size * len(text_list) + (len(text_list) - 1) * spacing + 100
height = font_size + 100
# Create a blank image
image = Image.new('RGBA', (width, height), (255, 255, 255,0))
draw = ImageDraw.Draw(image)
# Draw text
if vertical:
for i, char in enumerate(text_list):
char_position = (50, 50 + i * font_size)
draw.text(char_position, char, font=font, fill=text_color)
else:
for i, char in enumerate(text_list):
char_position = (50 + i * (font_size + spacing), 50)
draw.text(char_position, char, font=font, fill=text_color)
# Save the image
# image.save(output_image_path)
# 分离alpha通道
alpha_channel = image.split()[3]
# 创建一个只有alpha通道的新图像
alpha_image = Image.new('L', image.size)
alpha_image.putdata(alpha_channel.getdata())
image=image.convert('RGB')
return (image,alpha_image)
def base64_to_image(base64_string):
# 去除前缀
prefix, base64_data = base64_string.split(",", 1)
# 从base64字符串中解码图像数据
image_data = base64.b64decode(base64_data)
# 创建一个内存流对象
image_stream = io.BytesIO(image_data)
# 使用PIL的Image模块打开图像数据
image = Image.open(image_stream)
return image
class SmoothMask:
@classmethod
@@ -494,6 +606,7 @@ class TransparentImage:
}
RETURN_TYPES = ('STRING','IMAGE','RGBA')
RETURN_NAMES = ("file_path","IMAGE","RGBA",)
OUTPUT_NODE = True
@@ -671,7 +784,7 @@ class LoadImagesFromPath:
imgs=[imgs[index_variable]] if index_variable < len(imgs) else None
masks=[masks[index_variable]] if index_variable < len(masks) else None
print('#prompt::::',prompt)
# print('#prompt::::',prompt)
return (imgs,masks,prompt,)
@@ -708,6 +821,117 @@ class ImageCropByAlpha:
return (img,)
class TextImage:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"text": ("STRING",{"multiline": False,"default": "龍馬精神迎新歲"}),
"font_path": ("STRING",{"multiline": False,"default": FONT_PATH}),
"font_size": ("INT",{
"default":100,
"min": 100, #Minimum value
"max": 1000, #Maximum value
"step": 1, #Slider's step
"display": "number" # Cosmetic only: display as "number" or "slider"
}),
"spacing": ("INT",{
"default":12,
"min": 1, #Minimum value
"max": 200, #Maximum value
"step": 1, #Slider's step
"display": "number" # Cosmetic only: display as "number" or "slider"
}),
"text_color":("STRING",{"multiline": False,"default": "#000000"}),
"vertical":("BOOLEAN", {"default": True},),
},
}
RETURN_TYPES = ("IMAGE","MASK")
# RETURN_NAMES = ("WIDTH","HEIGHT","X","Y",)
FUNCTION = "run"
CATEGORY = "♾️Mixlab/image"
INPUT_IS_LIST = False
OUTPUT_IS_LIST = (False,False,)
def run(self,text,font_path,font_size,spacing,text_color,vertical):
text_list=list(text)
img,mask=generate_text_image(text_list,font_path,font_size,text_color,vertical,spacing)
img=pil2tensor(img)
mask=pil2tensor(mask)
return (img,mask,)
class SvgImage:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"upload":("SVG",),},
}
RETURN_TYPES = ("IMAGE","LAYER")
RETURN_NAMES = ("IMAGE","layers",)
FUNCTION = "run"
CATEGORY = "♾️Mixlab/image"
INPUT_IS_LIST = False
OUTPUT_IS_LIST = (False,True,)
def run(self,upload):
layers=[]
image = base64_to_image(upload['image'])
image=image.convert('RGB')
image=pil2tensor(image)
for layer in upload['data']:
layers.append(layer)
return (image,layers,)
class Image3D:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"upload":("THREED",), },
}
RETURN_TYPES = ("IMAGE","MASK",)
# RETURN_NAMES = ("IMAGE",)
FUNCTION = "run"
CATEGORY = "♾️Mixlab/image"
INPUT_IS_LIST = False
OUTPUT_IS_LIST = (False,False,)
def run(self,upload):
# print(upload['image'])
image = base64_to_image(upload['image'])
image=image.convert('RGB')
mask=image.convert('L')
mask=pil2tensor(mask)
image=pil2tensor(image)
return (image,mask,)
class AreaToMask:
@classmethod
def INPUT_TYPES(s):
@@ -770,3 +994,292 @@ class FaceToMask:
return (mask,)
class EmptyLayer:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"width": ("INT",{
"default":512,
"min": 1, #Minimum value
"max": 8192, #Maximum value
"step": 1, #Slider's step
"display": "number" # Cosmetic only: display as "number" or "slider"
}),
"height": ("INT",{
"default": 512,
"min": 1, #Minimum value
"max": 8192, #Maximum value
"step": 1, #Slider's step
"display": "number" # Cosmetic only: display as "number" or "slider"
}),
},
}
RETURN_TYPES = ("LAYER",)
RETURN_NAMES = ("layers",)
FUNCTION = "run"
CATEGORY = "♾️Mixlab/layer"
OUTPUT_IS_LIST = (True,)
def run(self, width,height):
blank_image = Image.new("RGB", (width, height))
mask=blank_image.convert('L')
blank_image=pil2tensor(blank_image)
mask=pil2tensor(mask)
layer_n=[{
"x":0,
"y":0,
"width":width,
"height":height,
"z_index":0,
"scale_option":'width',
"image":blank_image,
"mask":mask
}]
return (layer_n,)
class NewLayer:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"x": ("INT",{
"default": 0,
"min": -100, #Minimum value
"max": 8192, #Maximum value
"step": 1, #Slider's step
"display": "number" # Cosmetic only: display as "number" or "slider"
}),
"y": ("INT",{
"default": 0,
"min": 0, #Minimum value
"max": 8192, #Maximum value
"step": 1, #Slider's step
"display": "number" # Cosmetic only: display as "number" or "slider"
}),
"width": ("INT",{
"default": 512,
"min": 1, #Minimum value
"max": 8192, #Maximum value
"step": 1, #Slider's step
"display": "number" # Cosmetic only: display as "number" or "slider"
}),
"height": ("INT",{
"default": 512,
"min": 1, #Minimum value
"max": 8192, #Maximum value
"step": 1, #Slider's step
"display": "number" # Cosmetic only: display as "number" or "slider"
}),
"z_index": ("INT",{
"default": 0,
"min":0, #Minimum value
"max": 100, #Maximum value
"step": 1, #Slider's step
"display": "number" # Cosmetic only: display as "number" or "slider"
}),
"scale_option": (["width","height",'overall'],),
"image": ("IMAGE",),
},
"optional":{
"mask": ("MASK",{"default": None}),
"layers": ("LAYER",{"default": None}),
}
}
RETURN_TYPES = ("LAYER",)
RETURN_NAMES = ("layers",)
FUNCTION = "run"
CATEGORY = "♾️Mixlab/layer"
INPUT_IS_LIST = True
OUTPUT_IS_LIST = (True,)
def run(self,x,y,width,height,z_index,scale_option,image,mask,layers):
# print(x,y,width,height,z_index,image,mask)
if mask==None:
im=tensor2pil(image)
mask=im.convert('L')
mask=pil2tensor(mask)
else:
mask=mask[0]
layer_n=[{
"x":x[0],
"y":y[0],
"width":width[0],
"height":height[0],
"z_index":z_index[0],
"scale_option":scale_option[0],
"image":image[0],
"mask":mask
}]
if layers!=None:
layer_n=layer_n+layers
return (layer_n,)
class ShowLayer:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"edit": ("EDIT",),
"x": ("INT",{
"default": 0,
"min": -100, #Minimum value
"max": 8192, #Maximum value
"step": 1, #Slider's step
"display": "number" # Cosmetic only: display as "number" or "slider"
}),
"y": ("INT",{
"default": 0,
"min": 0, #Minimum value
"max": 8192, #Maximum value
"step": 1, #Slider's step
"display": "number" # Cosmetic only: display as "number" or "slider"
}),
"width": ("INT",{
"default": 512,
"min": 1, #Minimum value
"max": 8192, #Maximum value
"step": 1, #Slider's step
"display": "number" # Cosmetic only: display as "number" or "slider"
}),
"height": ("INT",{
"default": 512,
"min": 1, #Minimum value
"max": 8192, #Maximum value
"step": 1, #Slider's step
"display": "number" # Cosmetic only: display as "number" or "slider"
}),
"z_index": ("INT",{
"default": 0,
"min":0, #Minimum value
"max": 100, #Maximum value
"step": 1, #Slider's step
"display": "number" # Cosmetic only: display as "number" or "slider"
}),
"scale_option": (["width","height",'overall'],),
# "image": ("IMAGE",),
},
"optional":{
# "mask": ("MASK",{"default": None}),
"layers": ("LAYER",{"default": None}),
}
}
RETURN_TYPES = ( )
RETURN_NAMES = ( )
FUNCTION = "run"
CATEGORY = "♾️Mixlab/layer"
INPUT_IS_LIST = True
# OUTPUT_IS_LIST = (True,)
def run(self,edit,x,y,width,height,z_index,scale_option,layers):
# print(x,y,width,height,z_index,image,mask)
# if mask==None:
# im=tensor2pil(image)
# mask=im.convert('L')
# mask=pil2tensor(mask)
# else:
# mask=mask[0]
# layers[edit[0]]={
# "x":x[0],
# "y":y[0],
# "width":width[0],
# "height":height[0],
# "z_index":z_index[0],
# "scale_option":scale_option[0],
# "image":image[0],
# "mask":mask
# }
return ( )
class MergeLayers:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"layers": ("LAYER",),
"image": ("IMAGE",),
},
}
RETURN_TYPES = ("IMAGE",)
# RETURN_NAMES = ("WIDTH","HEIGHT","X","Y",)
FUNCTION = "run"
CATEGORY = "♾️Mixlab/layer"
INPUT_IS_LIST = True
# OUTPUT_IS_LIST = (False,)
def run(self,layers,image):
# print(len(layers),len(image))
bg_image=image[0]
bg_image=tensor2pil(bg_image)
# 按z-index排序
layers_new = sorted(layers, key=lambda x: x["z_index"])
for layer in layers_new:
image=layer['image']
mask=layer['mask']
if 'type' in layer and layer['type']=='base64' and type(image) == str:
im=base64_to_image(image)
im=im.convert('RGB')
image=pil2tensor(im)
mask=base64_to_image(mask)
mask=mask.convert('L')
mask=pil2tensor(mask)
layer_image=tensor2pil(image)
layer_mask=tensor2pil(mask)
bg_image=merge_images(bg_image,
layer_image,
layer_mask,
layer['x'],
layer['y'],
layer['width'],
layer['height'],
layer['scale_option']
)
mask=bg_image.convert('RGBA')
mask=pil2tensor(mask)
bg_image=bg_image.convert('RGB')
bg_image=pil2tensor(bg_image)
channels = ["red", "green", "blue", "alpha"]
# print(mask,mask.shape)
mask = mask[:, :, :, channels.index("green")]
return (bg_image,mask,)
+5 -4
View File
@@ -83,11 +83,12 @@ class ScreenShareNode:
"optional":{
"prompt": ("PROMPT",),
"slide": ("SLIDE",),
"seed": ("SEED",),
# "seed": ("INT", {"default": 1, "min": 0, "max": 0xffffffffffffffff}),
} }
RETURN_TYPES = ('IMAGE','STRING','FLOAT')
RETURN_NAMES = ("IMAGE","PROMPT","FLOAT",)
RETURN_TYPES = ('IMAGE','STRING','FLOAT',"INT")
RETURN_NAMES = ("IMAGE","PROMPT","FLOAT","INT")
FUNCTION = "run"
CATEGORY = "♾️Mixlab/image"
@@ -96,10 +97,10 @@ class ScreenShareNode:
OUTPUT_IS_LIST = (False,False,False,False)
# 运行的函数
def run(self,image_base64,prompt,slide):
def run(self,image_base64,prompt,slide,seed):
im,mask=base64_save(image_base64)
# print('##########prompt',prompt)
return (im,prompt,slide)
return (im,prompt,slide,seed)
class FloatingVideo:
+83
View File
@@ -0,0 +1,83 @@
import os
# FONT_PATH= os.path.abspath(os.path.join(os.path.dirname(__file__),'../assets/王汉宗颜楷体繁.ttf'))
import matplotlib.font_manager as fm
def get_font_files(directory):
font_files = {}
# 从指定目录加载字体
for file in os.listdir(directory):
if file.endswith('.ttf') or file.endswith('.otf'):
font_name = os.path.splitext(file)[0]
font_path = os.path.join(directory, file)
font_files[font_name] = os.path.abspath(font_path)
# 尝试获取系统字体
try:
font_paths = fm.findSystemFonts()
for path in font_paths:
try:
font_prop = fm.FontProperties(fname=path)
font_name = font_prop.get_name()
font_files[font_name] = path
except Exception as e:
print(f"Error processing font {path}: {e}")
except Exception as e:
print(f"Error finding system fonts: {e}")
return font_files
r_directory = os.path.join(os.path.dirname(__file__), '../assets/')
font_files = get_font_files(r_directory)
# print(font_files)
class ColorInput:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"color":("TCOLOR",),
},
}
RETURN_TYPES = ("STRING",)
# RETURN_NAMES = ("WIDTH","HEIGHT","X","Y",)
FUNCTION = "run"
CATEGORY = "♾️Mixlab/utils"
INPUT_IS_LIST = False
OUTPUT_IS_LIST = (False,False,)
def run(self,color):
return (color,)
class FontInput:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"font": (list(font_files.keys()),),
},
}
RETURN_TYPES = ("STRING",)
# RETURN_NAMES = ("WIDTH","HEIGHT","X","Y",)
FUNCTION = "run"
CATEGORY = "♾️Mixlab/utils"
INPUT_IS_LIST = False
OUTPUT_IS_LIST = (False,False,)
def run(self,font):
return (font_files[font],)
+311
View File
@@ -0,0 +1,311 @@
import { app } from '../../../scripts/app.js'
// import { api } from '../../../scripts/api.js'
import { ComfyWidgets } from '../../../scripts/widgets.js'
import { $el } from '../../../scripts/ui.js'
function get_position_style (ctx, widget_width, y, node_height) {
const MARGIN = 4 // the margin around the html element
/* Create a transform that deals with all the scrolling and zooming */
const elRect = ctx.canvas.getBoundingClientRect()
const transform = new DOMMatrix()
.scaleSelf(
elRect.width / ctx.canvas.width,
elRect.height / ctx.canvas.height
)
.multiplySelf(ctx.getTransform())
.translateSelf(MARGIN, MARGIN + y)
return {
transformOrigin: '0 0',
transform: transform,
left: `0`,
top: `0`,
cursor: 'pointer',
position: 'absolute',
maxWidth: `${widget_width - MARGIN * 2}px`,
// maxHeight: `${node_height - MARGIN * 2}px`, // we're assuming we have the whole height of the node
width: `${widget_width - MARGIN * 2}px`,
// height: `${node_height * 0.3 - MARGIN * 2}px`,
// background: '#EEEEEE',
display: 'flex',
flexDirection: 'column',
// alignItems: 'center',
justifyContent: 'space-around'
}
}
const getLocalData = key => {
let data = {}
try {
data = JSON.parse(localStorage.getItem(key)) || {}
} catch (error) {
return {}
}
return data
}
function speakText (text) {
const speechMsg = new SpeechSynthesisUtterance()
speechMsg.text = text
// 语音合成结束时触发的事件
speechMsg.onend = function (event) {
console.log('语音播放结束')
window._mixlab_speech_synthesis_onend = true
}
// 语音合成错误时触发的事件
speechMsg.onerror = function (event) {
console.error('语音播放错误:', event.error)
}
// 使用浏览器默认语音合成器进行语音播放
speechSynthesis.speak(speechMsg)
}
// 调用方法,将文字转换为语音播放
// speakText('Hello, how are you?');
// #MixCopilot
const start = (element, id, startBtn) => {
startBtn.className='loading_mixlab'
window.recognition = new webkitSpeechRecognition()
window.recognition.continuous = true
window.recognition.interimResults = true
window.recognition.lang = navigator.language
let timeoutId, intervalId
window.recognition.onstart = () => {
console.log('开始语音输入', window._mixlab_speech_synthesis_onend)
window._mixlab_speech_synthesis_onend = false
}
window.recognition.onresult = function (event) {
const result = event.results[event.results.length - 1][0].transcript
console.log('识别结果:', result)
element.value = result
let data = getLocalData('_mixlab_speech_recognition')
data[id] = result.trim()
localStorage.setItem('_mixlab_speech_recognition', JSON.stringify(data))
if (timeoutId) clearTimeout(timeoutId)
if (!window.recognition) return
timeoutId = setTimeout(function () {
console.log('结果传递::', result)
app.queuePrompt(0, 1)
window.recognition?.stop()
window.recognition = null;
startBtn.className=''
startBtn.innerText = 'START'
timeoutId = null
intervalId = setInterval(() => {
if (
app.ui.lastQueueSize === 0 &&
!window.recognition &&
window._mixlab_speech_synthesis_onend
) {
start(element, id, startBtn)
startBtn.innerText = 'STOP'
if (intervalId) {
clearInterval(intervalId)
}
}
}, 2200)
}, 2000)
}
window.recognition.onend = function () {
console.log('语音输入结束')
}
window.recognition.onspeechend = function () {
console.log('onspeechend')
}
window.recognition.onerror = function (event) {
console.log('Error occurred in recognition: ' + event.error)
}
window.recognition.start()
}
app.registerExtension({
name: 'Mixlab.audio.SpeechRecognition',
async getCustomWidgets (app) {
return {
AUDIOINPUTMIX (node, inputName, inputData, app) {
// console.log('##node', node)
const widget = {
type: inputData[0], // the type, CHEESE
name: inputName, // the name, slice
size: [128, 32], // a default size
draw (ctx, node, width, y) {},
computeSize (...args) {
return [128, 32] // a method to compute the current size of the widget
},
async serializeValue (nodeId, widgetIndex) {
let data = getLocalData('_mixlab_speech_recognition')
return data[node.id] || 'Hello Mixlab'
}
}
// widget.something = something; // maybe adds stuff to it
node.addCustomWidget(widget) // adds it to the node
return widget // and returns it.
}
}
},
async beforeRegisterNodeDef (nodeType, nodeData, app) {
if (nodeType.comfyClass == 'SpeechRecognition') {
const orig_nodeCreated = nodeType.prototype.onNodeCreated
nodeType.prototype.onNodeCreated = function () {
orig_nodeCreated?.apply(this, arguments)
const widget = {
type: 'div',
name: 'chatgptdiv',
draw (ctx, node, widget_width, y, widget_height) {
Object.assign(
this.div.style,
get_position_style(ctx, widget_width, 44, node.size[1])
)
}
}
widget.div = $el('div', {})
document.body.appendChild(widget.div)
const inputDiv = (key, placeholder) => {
let div = document.createElement('div')
const startBtn = document.createElement('button')
const textArea = document.createElement('textarea')
textArea.className = `${'comfy-multiline-input'} ${placeholder}`
textArea.style = `margin-top: 14px;
height: 44px;`
div.style = `flex-direction: column;
display: flex;
margin: 0px 8px 6px;`
startBtn.style = `
outline: none;
border: none;
padding: 4px; `
startBtn.innerText = 'START'
div.appendChild(startBtn)
div.appendChild(textArea)
startBtn.addEventListener('click', () => {
if (window.recognition) {
window.recognition.stop()
window.recognition = null
startBtn.innerText = 'START'
startBtn.className=''
} else {
start(textArea, this.id, startBtn)
startBtn.innerText = 'STOP'
}
})
return div
}
let inputAudio = inputDiv('_mixlab_speech_recognition', 'audio')
widget.div.appendChild(inputAudio)
this.addCustomWidget(widget)
const onRemoved = this.onRemoved
this.onRemoved = () => {
inputAudio.remove()
widget.div.remove()
return onRemoved?.()
}
this.serialize_widgets = true //需要保存参数
}
}
},
async loadedGraphNode (node, app) {
if (node.type === 'SpeechRecognition') {
let data = getLocalData('_mixlab_speech_recognition')
// console.log('_mixlab_speech_recognition', node.widgets)
let div = node.widgets.filter(f => f.type === 'div')[0]
if (div && data[node.id]) {
div.div.querySelector('textarea').value = data[node.id]
}
}
}
})
app.registerExtension({
name: 'Mixlab.audio.SpeechSynthesis',
async beforeRegisterNodeDef (nodeType, nodeData, app) {
if (nodeData.name === 'SpeechSynthesis') {
function populate (text) {
// console.log('SpeechSynthesis',this.widgets)
if (this.widgets) {
const pos = this.widgets.findIndex(w => w.name === 'text')
if (pos !== -1) {
for (let i = pos; i < this.widgets.length; i++) {
this.widgets[i].onRemove?.()
}
this.widgets.length = pos
}
}
for (let list of text) {
const w = ComfyWidgets['STRING'](
this,
'text',
['STRING', { multiline: true }],
app
).widget
w.inputEl.readOnly = true
w.inputEl.style.opacity = 0.6
w.value = list
}
speakText(text.join('\n'))
// console.log('ShowTextForGPT',this.widgets.length)
requestAnimationFrame(() => {
const sz = this.computeSize()
if (sz[0] < this.size[0]) {
sz[0] = this.size[0]
}
if (sz[1] < this.size[1]) {
sz[1] = this.size[1]
}
this.onResize?.(sz)
app.graph.setDirtyCanvas(true, false)
})
}
// When the node is executed we will be sent the input text, display this in the widget
const onExecuted = nodeType.prototype.onExecuted
nodeType.prototype.onExecuted = function (message) {
onExecuted?.apply(this, arguments)
populate.call(this, message.text)
}
this.serialize_widgets = true //需要保存参数
}
}
})
+1 -1
View File
@@ -3,7 +3,7 @@ import { app } from '../../../scripts/app.js'
const repoOwner = 'shadowcz007' // 替换为仓库的所有者
const repoName = 'comfyui-mixlab-nodes' // 替换为仓库的名称
const version = 'v0.2.6'
const version = 'v0.3.1'
fetch(`https://api.github.com/repos/${repoOwner}/${repoName}/releases/latest`)
.then(response => response.json())
+23 -8
View File
@@ -65,10 +65,10 @@ app.registerExtension({
const widget = {
type: inputData[0], // the type, CHEESE
name: inputName, // the name, slice
size: [128, 24], // a default size
size: [128, 32], // a default size
draw (ctx, node, width, y) {},
computeSize (...args) {
return [128, 24] // a method to compute the current size of the widget
return [128,32] // a method to compute the current size of the widget
},
async serializeValue (nodeId, widgetIndex) {
let data = getLocalData('_mixlab_api_key')
@@ -84,12 +84,12 @@ app.registerExtension({
const widget = {
type: inputData[0], // the type, CHEESE
name: inputName, // the name, slice
size: [128, 24], // a default size
size: [128, 32], // a default size
draw (ctx, node, width, y) {
// a method to draw the widget (ctx is a CanvasRenderingContext2D)
},
computeSize (...args) {
return [128, 24] // a method to compute the current size of the widget
return [128, 32] // a method to compute the current size of the widget
},
async serializeValue (nodeId, widgetIndex) {
let data = getLocalData('_mixlab_api_url')
@@ -192,7 +192,7 @@ app.registerExtension({
let id = node.id
console.log('ChatGPTOpenAI serialize_widgets', this)
// console.log('ChatGPTOpenAI serialize_widgets', this)
widget.div.querySelector('.Key').value = apiKey[id] || 'by Mixlab'
widget.div.querySelector('.URL').value =
@@ -216,12 +216,27 @@ app.registerExtension({
this.widgets.length = pos;
}
}
// console.log('ShowTextForGPT',this.widgets.length)
for (const list of text) {
// console.log('ShowTextForGPT',text)
for (let list of text) {
const w = ComfyWidgets["STRING"](this, "text", ["STRING", { multiline: true }], app).widget;
w.inputEl.readOnly = true;
w.inputEl.style.opacity = 0.6;
w.value = list;
try {
let data=JSON.parse(list);
data=Array.from(data,d=>{
return {
...d,
content:decodeURIComponent(d.content)
}
})
list=JSON.stringify(data,null,2)
} catch (error) {
// console.log(error)
}
w.value =list;
}
// console.log('ShowTextForGPT',this.widgets.length)
requestAnimationFrame(() => {
+659
View File
@@ -0,0 +1,659 @@
import { app } from '../../../scripts/app.js'
import { api } from '../../../scripts/api.js'
import { ComfyWidgets } from '../../../scripts/widgets.js'
import { $el } from '../../../scripts/ui.js'
async function uploadImage (blob, fileType = '.svg') {
// const blob = await (await fetch(src)).blob();
const body = new FormData()
body.append('image', new File([blob], new Date().getTime() + fileType))
const resp = await api.fetchApi('/upload/image', {
method: 'POST',
body
})
// console.log(resp)
let data = await resp.json()
let { name, subfolder } = data
let src = api.apiURL(
`/view?filename=${encodeURIComponent(
name
)}&type=input&subfolder=${subfolder}${app.getPreviewFormatParam()}${app.getRandParam()}`
)
return src
}
function base64ToBlobFromURL (base64URL, contentType) {
return fetch(base64URL).then(response => response.blob())
}
function getContentTypeFromBase64 (base64Data) {
const regex = /^data:(.+);base64,/
const matches = base64Data.match(regex)
if (matches && matches.length >= 2) {
return matches[1]
}
return null
}
// 示例用法
// const base64Data = 'data:image/jpeg;base64,/9j/4AAQSkZJRgABAQEAAAAAAAD/...'; // 替换为实际的base64图片数据
// const contentType = getContentTypeFromBase64(base64Data);
// console.log(contentType);
// // 示例用法
// const base64Data = '...'; // 替换为实际的base64图片数据
// const contentType = 'image/jpeg'; // 替换为实际的图片类型
// const blob = base64ToBlob(base64Data, contentType);
// console.log(blob);
function get_position_style (ctx, widget_width, y, node_height) {
const MARGIN = 4 // the margin around the html element
/* Create a transform that deals with all the scrolling and zooming */
const elRect = ctx.canvas.getBoundingClientRect()
const transform = new DOMMatrix()
.scaleSelf(
elRect.width / ctx.canvas.width,
elRect.height / ctx.canvas.height
)
.multiplySelf(ctx.getTransform())
.translateSelf(MARGIN, MARGIN + y)
return {
transformOrigin: '0 0',
transform: transform,
left: `0`,
top: `0`,
cursor: 'pointer',
position: 'absolute',
maxWidth: `${widget_width - MARGIN * 2}px`,
// maxHeight: `${node_height - MARGIN * 2}px`, // we're assuming we have the whole height of the node
width: `${widget_width - MARGIN * 2}px`,
// height: `${node_height * 0.3 - MARGIN * 2}px`,
// background: '#EEEEEE',
display: 'flex',
flexDirection: 'column',
// alignItems: 'center',
justifyContent: 'space-around'
}
}
const getLocalData = key => {
let data = {}
try {
data = JSON.parse(localStorage.getItem(key)) || {}
} catch (error) {
return {}
}
return data
}
const setLocalDataOfWin = (key, value) => {
localStorage.setItem(key, JSON.stringify(value))
// window[key] = value
}
function createImage (url) {
let im = new Image()
return new Promise((res, rej) => {
im.onload = () => res(im)
im.src = url
})
}
const parseImage = url => {
return new Promise((res, rej) => {
fetch(url)
.then(response => response.blob())
.then(blob => {
const reader = new FileReader()
reader.onloadend = () => {
const base64data = reader.result
res(base64data)
// 在这里可以将base64数据用于进一步处理或显示图片
}
reader.readAsDataURL(blob)
})
.catch(error => {
console.log('发生错误:', error)
})
})
}
const parseSvg = async svgContent => {
let scale = 2
// 创建一个临时的DOM元素来解析SVG
const tempContainer = document.createElement('div')
tempContainer.innerHTML = svgContent
// 提取SVG元素
const svgElement = tempContainer.querySelector('svg')
if (!svgElement) return
// 获取SVG中 rect元素
var rectElements = svgElement?.querySelectorAll('rect') || []
// console.log(rectElements,svgElement)
// 定义一个数组来存储处理后的数据
var data = []
Array.from(rectElements, (rectElement, i) => {
// 获取rect元素的属性值
var x = ~~(rectElement.getAttribute('x') || 0)
var y = ~~(rectElement.getAttribute('y') || 0)
var width = ~~rectElement.getAttribute('width')
var height = ~~rectElement.getAttribute('height')
// console.log('rectElements',rectElement,x,y,width,height)
if (x != undefined && y != undefined && width && height) {
// 创建一个新的canvas元素
var canvas = document.createElement('canvas')
canvas.width = width
canvas.height = height
var context = canvas.getContext('2d')
// 填充颜色到canvas
var fill = rectElement.getAttribute('fill')
context.fillStyle = fill
context.fillRect(0, 0, width, height)
// 将canvas转换为base64格式
var base64 = canvas.toDataURL()
// 将数据转化为指定的JSON格式
var rectData = {
x: parseInt(x),
y: parseInt(y),
width: parseInt(width),
height: parseInt(height),
z_index: i + 1,
scale_option: 'width',
image: base64,
mask: base64,
type: 'base64',
_t: 'rect'
}
// 将处理后的数据添加到数组中
data.push(rectData)
}
})
var svgWidth = svgElement.getAttribute('width')
var svgHeight = svgElement.getAttribute('height')
if (!(svgWidth && svgHeight)) {
// viewBox
let viewBox = svgElement.viewBox.baseVal
svgWidth = viewBox.width
svgHeight = viewBox.height
}
// 创建一个新的canvas元素
var canvas = document.createElement('canvas')
canvas.width = svgWidth
canvas.height = svgHeight
var context = canvas.getContext('2d')
// 绘制SVG到canvas
var svgString = new XMLSerializer().serializeToString(svgElement)
var DOMURL = window.URL || window.webkitURL || window
var svgBlob = new Blob([svgString], { type: 'image/svg+xml;charset=utf-8' })
var url = DOMURL.createObjectURL(svgBlob)
let img = await createImage(url)
context.drawImage(img, 0, 0)
let base64 = canvas.toDataURL()
var rectData = {
x: 0,
y: 0,
width: parseInt(svgWidth),
height: parseInt(svgHeight),
z_index: 0,
scale_option: 'width',
image: base64,
mask: base64,
type: 'base64',
_t: 'canvas'
}
data.push(rectData)
// 打印处理后的数据
console.log('layers', { data, image: base64, svgElement })
return { data, image: base64, svgElement }
}
app.registerExtension({
name: 'Mixlab.image.SvgImage',
async getCustomWidgets (app) {
return {
SVG (node, inputName, inputData, app) {
// console.log('##node', node, inputName, inputData)
const widget = {
type: inputData[0], // the type, CHEESE
name: inputName, // the name, slice
size: [128, 88], // a default size
draw (ctx, node, width, y) {},
computeSize (...args) {
return [128, 88] // a method to compute the current size of the widget
},
async serializeValue (nodeId, widgetIndex) {
let d = getLocalData('_mixlab_svg_image')
// console.log('serializeValue',d)
if (d) {
let url = d[node.id]
let dt = await fetch(url)
let svgStr = await dt.text()
const { data, image } = (await parseSvg(svgStr)) || {}
// console.log(data, image)
return JSON.parse(JSON.stringify({ data, image }))
} else {
return
}
}
}
// console.log('##node',node.serialize)
// widget.something = something; // maybe adds stuff to it
node.addCustomWidget(widget) // adds it to the node
return widget // and returns it.
}
}
},
async beforeRegisterNodeDef (nodeType, nodeData, app) {
if (nodeType.comfyClass == 'SvgImage') {
const orig_nodeCreated = nodeType.prototype.onNodeCreated
nodeType.prototype.onNodeCreated = async function () {
orig_nodeCreated?.apply(this, arguments)
const uploadWidget = this.widgets.filter(w => w.name == 'upload')[0]
// console.log('SvgImage nodeData',await uploadWidget.serializeValue())
const widget = {
type: 'div',
name: 'upload-preview',
draw (ctx, node, widget_width, y, widget_height) {
Object.assign(
this.div.style,
get_position_style(ctx, widget_width, 44, node.size[1])
)
}
}
widget.div = $el('div', {})
document.body.appendChild(widget.div)
const inputDiv = (key, placeholder, svgContainer) => {
let div = document.createElement('div')
const ip = document.createElement('input')
ip.type = 'file'
ip.className = `${'comfy-multiline-input'} ${placeholder}`
div.style = `display: flex;
align-items: center;
margin: 6px 8px;
margin-top: 0;`
ip.placeholder = placeholder
// ip.value = value
ip.style = `outline: none;
border: none;
padding: 4px;
width: 60%;cursor: pointer;
height: 32px;`
const label = document.createElement('label')
label.style = 'font-size: 10px;min-width:32px'
label.innerText = placeholder
div.appendChild(label)
div.appendChild(ip)
let that = this
ip.addEventListener('change', event => {
const file = event.target.files[0]
const reader = new FileReader()
// 读取文件内容
reader.onload = async e => {
const svgContent = e.target.result
var blob = new Blob([svgContent], { type: 'image/svg+xml' })
let url = await uploadImage(blob)
// console.log(url)
const { svgElement, data, image } = await parseSvg(svgContent)
// 将提取的SVG元素显示在页面上
let dd = getLocalData(key)
dd[that.id] = url
setLocalDataOfWin(key, dd)
// console.log(this.id, ip.value.trim())
svgElement.style = `width: 90%;padding: 5%;`
// 将提取的SVG元素显示在页面上
svgContainer.innerHTML = ''
svgContainer.appendChild(svgElement)
let h = ~~getComputedStyle(svgElement).height.replace('px', '')
if (that.size && that.size[1] < h) {
that.setSize([that.size[0], that.size[1] + h])
app.canvas.draw(true, true)
}
// console.log(that.size,~~getComputedStyle(svgElement).height.replace('px',''))
uploadWidget.value = await uploadWidget.serializeValue()
}
// 以文本形式读取文件
reader.readAsText(file)
})
return div
}
let svg = document.createElement('div')
svg.className = 'preview'
svg.style = `background:#eee;margin-top: 12px;`
let upload = inputDiv('_mixlab_svg_image', 'Svg', svg)
widget.div.appendChild(upload)
widget.div.appendChild(svg)
this.addCustomWidget(widget)
const onRemoved = this.onRemoved
this.onRemoved = () => {
upload.remove()
svg.remove()
widget.div.remove()
return onRemoved?.()
}
if (this.onResize) {
this.onResize(this.size)
}
this.serialize_widgets = true //需要保存参数
}
}
},
async loadedGraphNode (node, app) {
// Fires every time a node is constructed
// You can modify widgets/add handlers/etc here
const sleep = (t = 1000) => {
return new Promise((res, rej) => {
setTimeout(() => res(1), t)
})
}
if (node.type === 'SvgImage') {
// await sleep(0)
let widget = node.widgets.filter(w => w.name === 'upload-preview')[0]
let dd = getLocalData('_mixlab_svg_image')
let id = node.id
console.log('SvgImage load', node.widgets[0], node.widgets)
if (!dd[id]) return
let dt = await fetch(dd[id])
let svgStr = await dt.text()
const { svgElement, data, image } = await parseSvg(svgStr)
svgElement.style = `width: 90%;padding: 5%;`
// 将提取的SVG元素显示在页面上
widget.div.querySelector('.preview').innerHTML = ''
widget.div.querySelector('.preview').appendChild(svgElement)
const uploadWidget = node.widgets.filter(w => w.name == 'upload')[0]
uploadWidget.value = await uploadWidget.serializeValue()
// let h=~~getComputedStyle(widget.div).height.replace('px','');
// let w=~~getComputedStyle(widget.div).width.replace('px','');
// // console.log('svg', w,h,node.size)
// node.setSize([
// w,h
// ])
// app.graph.setDirtyCanvas(true)
// console.log(node.widgets_values)
}
}
})
app.registerExtension({
name: 'Mixlab.image.3DImage',
async getCustomWidgets (app) {
return {
THREED (node, inputName, inputData, app) {
// console.log('##node', node, inputName, inputData)
const widget = {
type: inputData[0], // the type, CHEESE
name: inputName, // the name, slice
size: [128, 88], // a default size
draw (ctx, node, width, y) {},
computeSize (...args) {
return [128, 88] // a method to compute the current size of the widget
},
async serializeValue (nodeId, widgetIndex) {
let d = getLocalData('_mixlab_3d_image')
// console.log('serializeValue',d)
if (d) {
let url = d[node.id]
let base64 = await parseImage(url)
return JSON.parse(JSON.stringify({ image: base64 }))
} else {
return {}
}
}
}
node.addCustomWidget(widget)
return widget
}
}
},
async beforeRegisterNodeDef (nodeType, nodeData, app) {
if (nodeType.comfyClass == '3DImage') {
const orig_nodeCreated = nodeType.prototype.onNodeCreated
nodeType.prototype.onNodeCreated = async function () {
orig_nodeCreated?.apply(this, arguments)
const uploadWidget = this.widgets.filter(w => w.name == 'upload')[0]
console.log('3d nodeData', this.inputs)
const widget = {
type: 'div',
name: 'upload-preview',
draw (ctx, node, widget_width, y, widget_height) {
Object.assign(
this.div.style,
get_position_style(ctx, widget_width, 44, node.size[1])
)
}
}
widget.div = $el('div', {})
document.body.appendChild(widget.div)
const inputDiv = (key, placeholder, preview) => {
let div = document.createElement('div')
const ip = document.createElement('input')
ip.type = 'file'
ip.className = `${'comfy-multiline-input'} ${placeholder}`
div.style = `display: flex;
align-items: center;
margin: 6px 8px;
margin-top: 0;`
ip.placeholder = placeholder
// ip.value = value
ip.style = `outline: none;
border: none;
padding: 4px;
width: 60%;cursor: pointer;
height: 32px;`
const label = document.createElement('label')
label.style = 'font-size: 10px;min-width:32px'
label.innerText = placeholder
div.appendChild(label)
div.appendChild(ip)
let that = this
ip.addEventListener('change', event => {
const file = event.target.files[0]
const reader = new FileReader()
// 读取文件内容
reader.onload = async e => {
const fileURL = URL.createObjectURL(file)
// console.log('文件URL: ', fileURL)
let html = `<model-viewer
alt="Neil Armstrong's Spacesuit from the Smithsonian Digitization Programs Office and National Air and Space Museum"
src="${fileURL}"
ar
shadow-intensity="1"
camera-controls
touch-action="pan-y">
<div class="controls">
<div>Variant: <select class="variant"></select></div>
<div><button class="capture">Capture</button></div>
</div></model-viewer>`
preview.innerHTML = html;
that.setSize([that.size[0],that.size[1]+300])
app.canvas.draw(true, true)
const modelViewerVariants = preview.querySelector('model-viewer')
const select = preview.querySelector('.variant')
const capture = preview.querySelector('.capture')
modelViewerVariants.addEventListener('load', () => {
const names = modelViewerVariants.availableVariants
for (const name of names) {
const option = document.createElement('option')
option.value = name
option.textContent = name
select.appendChild(option)
}
// Adds a default option.
const option = document.createElement('option')
option.value = 'default'
option.textContent = 'Default'
select.appendChild(option)
})
select.addEventListener('input', event => {
modelViewerVariants.variantName =
event.target.value === 'default' ? null : event.target.value
})
capture.addEventListener('click', async () => {
let base64Data = modelViewerVariants.toDataURL()
const contentType = getContentTypeFromBase64(base64Data)
const blob = await base64ToBlobFromURL(base64Data, contentType)
// const fileBlob = new Blob([e.target.result], { type: file.type });
let url = await uploadImage(blob, '.png')
console.log(url)
let dd = getLocalData(key)
dd[that.id] = url
setLocalDataOfWin(key, dd)
})
uploadWidget.value = await uploadWidget.serializeValue()
}
// 以文本形式读取文件
reader.readAsDataURL(file)
})
return div
}
let preview = document.createElement('div')
preview.className = 'preview'
preview.style = `margin-top: 12px;display: flex;
justify-content: center;
align-items: center;`
let upload = inputDiv('_mixlab_3d_image', '3D Model', preview)
widget.div.appendChild(upload)
widget.div.appendChild(preview)
this.addCustomWidget(widget);
const onResize = this.onResize;
this.onResize = function () {
let m=preview.querySelector('model-viewer')
m.style.width=`${this.size[0]-24}px`
m.style.height=`${this.size[1]-48}px`
// console.log(this.size,preview)
return onResize?.apply(this, arguments);
};
const onRemoved = this.onRemoved
this.onRemoved = () => {
upload.remove()
preview.remove()
widget.div.remove()
return onRemoved?.()
}
if (this.onResize) {
this.onResize(this.size)
}
this.serialize_widgets = true //需要保存参数
}
}
},
async loadedGraphNode (node, app) {
// Fires every time a node is constructed
// You can modify widgets/add handlers/etc here
const sleep = (t = 1000) => {
return new Promise((res, rej) => {
setTimeout(() => res(1), t)
})
}
if (node.type === '3DImage') {
// await sleep(0)
let widget = node.widgets.filter(w => w.name === 'upload-preview')[0]
let dd = getLocalData('_mixlab_3d_image')
let id = node.id
console.log('3dImage load', node.widgets[0], node.widgets)
if (!dd[id]) return
let url = dd[id]
// let base64 = await parseImage(url)
widget.div.querySelector('.preview').innerHTML = `<img src="${url}"/>`
const uploadWidget = node.widgets.filter(w => w.name == 'upload')[0]
uploadWidget.value = await uploadWidget.serializeValue()
// let h=~~getComputedStyle(widget.div).height.replace('px','');
// let w=~~getComputedStyle(widget.div).width.replace('px','');
// // console.log('svg', w,h,node.size)
// node.setSize([
// w,h
// ])
// app.graph.setDirtyCanvas(true)
// console.log(node.widgets_values)
}
}
})
+352
View File
@@ -0,0 +1,352 @@
import { app } from '../../../scripts/app.js'
// import { api } from '../../../scripts/api.js'
import { ComfyWidgets } from '../../../scripts/widgets.js'
import { $el } from '../../../scripts/ui.js'
function get_position_style (ctx, widget_width, y, node_height) {
const MARGIN = 4 // the margin around the html element
/* Create a transform that deals with all the scrolling and zooming */
const elRect = ctx.canvas.getBoundingClientRect()
const transform = new DOMMatrix()
.scaleSelf(
elRect.width / ctx.canvas.width,
elRect.height / ctx.canvas.height
)
.multiplySelf(ctx.getTransform())
.translateSelf(MARGIN, MARGIN + y)
return {
transformOrigin: '0 0',
transform: transform,
left: `0`,
top: `0`,
cursor: 'pointer',
position: 'absolute',
maxWidth: `${widget_width - MARGIN * 2}px`,
// maxHeight: `${node_height - MARGIN * 2}px`, // we're assuming we have the whole height of the node
width: `${widget_width - MARGIN * 2}px`,
// height: `${node_height * 0.3 - MARGIN * 2}px`,
// background: '#EEEEEE',
display: 'flex',
// flexDirection: 'column',
// alignItems: 'center',
justifyContent: 'space-around'
}
}
const getLocalData = key => {
let data = {}
try {
data = JSON.parse(localStorage.getItem(key)) || {}
} catch (error) {
return {}
}
return data
}
function createImage (url) {
let im = new Image()
return new Promise((res, rej) => {
im.onload = () => res(im)
im.src = url
})
}
const parseSvg = async svgContent => {
// 创建一个临时的DOM元素来解析SVG
const tempContainer = document.createElement('div')
tempContainer.innerHTML = svgContent
// 提取SVG元素
const svgElement = tempContainer.querySelector('svg')
if (!svgElement) return
// 获取SVG中 rect元素
var rectElements = svgElement?.querySelectorAll('rect') || []
// console.log(rectElements,svgElement)
// 定义一个数组来存储处理后的数据
var data = []
Array.from(rectElements, (rectElement, i) => {
// 获取rect元素的属性值
var x = ~~(rectElement.getAttribute('x')||0);
var y = ~~(rectElement.getAttribute('y')||0);
var width = ~~rectElement.getAttribute('width')
var height = ~~rectElement.getAttribute('height')
// console.log('rectElements',rectElement,x,y,width,height)
if (x != undefined && y != undefined&&width&&height) {
// 创建一个新的canvas元素
var canvas = document.createElement('canvas')
canvas.width = width
canvas.height = height
var context = canvas.getContext('2d')
// 填充颜色到canvas
var fill = rectElement.getAttribute('fill')
context.fillStyle = fill
context.fillRect(0, 0, width, height)
// 将canvas转换为base64格式
var base64 = canvas.toDataURL()
// 将数据转化为指定的JSON格式
var rectData = {
x: parseInt(x),
y: parseInt(y),
width: parseInt(width),
height: parseInt(height),
z_index: i + 1,
scale_option: 'width',
image: base64,
mask: base64,
type: 'base64',
_t:'rect'
}
// 将处理后的数据添加到数组中
data.push(rectData)
}
})
var svgWidth = svgElement.getAttribute('width')
var svgHeight = svgElement.getAttribute('height')
if (!(svgWidth && svgHeight)) {
// viewBox
let viewBox = svgElement.viewBox.baseVal
svgWidth =viewBox.width
svgHeight =viewBox.height
}
// 创建一个新的canvas元素
var canvas = document.createElement('canvas')
canvas.width = svgWidth
canvas.height = svgHeight
var context = canvas.getContext('2d')
// 绘制SVG到canvas
var svgString = new XMLSerializer().serializeToString(svgElement)
var DOMURL = window.URL || window.webkitURL || window
var svgBlob = new Blob([svgString], { type: 'image/svg+xml;charset=utf-8' })
var url = DOMURL.createObjectURL(svgBlob)
let img = await createImage(url)
context.drawImage(img, 0, 0)
let base64 = canvas.toDataURL()
var rectData = {
x: 0,
y: 0,
width: parseInt(svgWidth),
height: parseInt(svgHeight),
z_index: 0,
scale_option: 'width',
image: base64,
mask: base64,
type: 'base64',
_t:'canvas'
}
data.push(rectData)
// 打印处理后的数据
console.log('layers',{ data, image: base64, svgElement })
return { data, image: base64, svgElement }
}
app.registerExtension({
name: 'Mixlab.layer.ShowLayer',
async getCustomWidgets (app) {
return {
EDIT (node, inputName, inputData, app) {
// console.log('EditLayer##node', node,inputName, inputData)
const widget = {
type: inputData[0], // the type, CHEESE
name: inputName, // the name, slice
size: [128, 44], // a default size
draw (ctx, node, widget_width, y, widget_height) {
// console.log('EditLayer', this)
if (this.input)
Object.assign(
this.input.style,
get_position_style(ctx, widget_width, 32, node.size[1])
)
},
computeSize (...args) {
return [128, 44] // a method to compute the current size of the widget
},
async serializeValue (nodeId, widgetIndex) {
let d = getLocalData('_mixlab_edit_layer')
// console.log('EditLayer',d[node.id])
return d[node.id]
}
}
// widget.something = something; // maybe adds stuff to it
node.addCustomWidget(widget) // adds it to the node
return widget // and returns it.
}
}
},
async beforeRegisterNodeDef (nodeType, nodeData, app) {
if (nodeType.comfyClass == 'ShowLayer') {
const orig_nodeCreated = nodeType.prototype.onNodeCreated
nodeType.prototype.onNodeCreated = async function () {
orig_nodeCreated?.apply(this, arguments)
const findNode = nodeId => {
let node = app.graph._nodes_by_id[nodeId]
if (node?.type == 'Reroute') {
let linkId =node.inputs.filter(i=>i.type=='*')[0].link
nodeId = app.graph.links.filter(link => link.id == linkId)[0]
?.origin_id
return findNode(nodeId)
} else {
return nodeId
}
}
// 获取layers数据
const getLayers = async () => {
console.log('getLayers1',this.inputs.filter(ip => ip.name === 'layers'))
let linkId = this.inputs.filter(ip => ip.name === 'layers')[0].link
let nodeId = app.graph.links?.filter(link => link.id == linkId)[0]
?.origin_id;
if(nodeId){
nodeId = findNode(nodeId)
}
// let node = app.graph._nodes_by_id[nodeId]
// if (node?.type == 'Reroute') {
// linkId = node.inputs[0].link
// nodeId = app.graph.links.filter(link => link.id == linkId)[0]
// ?.origin_id
// }
let d = getLocalData('_mixlab_svg_image')
console.log('test',d[nodeId])
if (d[nodeId]) {
let url = d[nodeId]
let dt = await fetch(url)
let svgStr = await dt.text()
const { data } = (await parseSvg(svgStr)) || {}
console.log('fetch',data)
return data
} else {
return []
}
}
// 修改layers数据
const setLayer = async (editIndex, layers = null) => {
// let editIndex = 0
let lys = layers || (await getLayers())
let layer = lys[editIndex]
// console.log(layer)
const updateValue = name => {
const x = this.widgets.filter(w => w.name == name)[0]
x.value = layer[name]
}
if (layer) {
Array.from(['x', 'y', 'width', 'height', 'z_index'], n =>
updateValue(n)
)
}
}
let that = this
const save_edit_layer_index = i => {
let data = getLocalData('_mixlab_edit_layer')
data[that.id] = i
localStorage.setItem('_mixlab_edit_layer', JSON.stringify(data))
}
await setLayer(0)
save_edit_layer_index(0)
const edit = this.widgets.filter(w => w.name == 'edit')[0]
edit.input = $el('div', {})
edit.input.style = `
display: flex;
flex-direction:row;
align-items: center;
margin-top: 0;`
const ip = $el('input', {})
ip.className = 'comfy-multiline-input'
ip.type = 'number'
ip.min = 0
ip.step = 1
ip.max = Math.max(0, (await getLayers()).length - 1)
// ip.className = `${'comfy-multiline-input'} `
ip.value = 0
ip.style = `
background-color: var(--comfy-input-bg);
color: var(--input-text);
outline: none;
border: none;
padding: 4px;
width: 60%;
cursor: pointer;
height: 24px;`
const label = document.createElement('label')
label.style = 'font-size: 10px;min-width:32px'
label.innerText = 'Layer Index'
edit.input.appendChild(label)
edit.input.appendChild(ip)
document.body.appendChild(edit.input)
ip.addEventListener('click', async event => {
console.log(await getLayers())
ip.max = Math.max(0, (await getLayers()).length - 1)
})
ip.addEventListener('change', async event => {
let index = ~~ip.value
let lys = await getLayers()
await setLayer(index, lys)
app.graph.setDirtyCanvas(true, true)
save_edit_layer_index(index)
})
// console.log('EditLayer nodeData', edit)
const onRemoved = this.onRemoved
this.onRemoved = () => {
edit.input.remove()
return onRemoved?.()
}
if (this.onResize) {
this.onResize(this.size)
}
this.serialize_widgets = false //需要保存参数
}
}
},
async loadedGraphNode (node, app) {
// Fires every time a node is constructed
// You can modify widgets/add handlers/etc here
if (node.type === 'SvgImage') {
let widget = node.widgets.filter(w => w.div)[0]
let data = getLocalData('_mixlab_svg_image')
let id = node.id
// widget.div.querySelector('.Svg').value = data[id] || '#000000'
}
}
})
+85 -19
View File
@@ -560,6 +560,26 @@ app.registerExtension({
// console.log('###widget', widget)
node.addCustomWidget(widget) // adds it to the node
return widget // and returns it.
},
SEED (node, inputName, inputData, app) {
// We return an object containing a field CHEESE which has a function (taking node, name, data, app)
const widget = {
type: inputData[0], // the type, CHEESE
name: inputName, // the name, slice
size: [128, 12], // a default size
draw (ctx, node, width, y) {
// a method to draw the widget (ctx is a CanvasRenderingContext2D)
},
computeSize (...args) {
return [128, 12] // a method to compute the current size of the widget
},
async serializeValue (nodeId, widgetIndex) {
return window._mixlab_screen_seed_input || 0
}
}
// console.log('###widget', widget)
node.addCustomWidget(widget) // adds it to the node
return widget // and returns it.
}
}
},
@@ -576,9 +596,15 @@ app.registerExtension({
type: 'HTML', // whatever
name: 'sreen_share', // whatever
draw (ctx, node, widget_width, y, widget_height) {
console.log('ScreenSHare', y, widget_height)
Object.assign(
this.card.style,
get_position_style(ctx, widget_width, y, node.size[1])
get_position_style(
ctx,
widget_width,
widget_height * 5,
node.size[1]
)
)
}
}
@@ -864,6 +890,9 @@ app.registerExtension({
})
// console.log('widget.inputEl',widget.inputEl)
this.setSize([this.size[0],this.size[1]+450])
app.canvas.draw(true, true)
/*
Add the widget, make sure we clean up nicely, and we do not want to be serialized!
*/
@@ -1305,13 +1334,38 @@ app.registerExtension({
// inputDiv.style = ``
let infoDiv = document.createElement('div')
infoDiv.style = ` width: 100%;
infoDiv.style = `width: 100%;
display: flex;
justify-content: space-between;
height: 16px;
color: white;
margin-bottom: 4px;
font-size: 12px;
text-shadow: 1px 1px gray;`
infoDiv.id = 'info'
text-shadow: gray 1px 1px;
align-items: center;`
let infoText = document.createElement('div')
infoText.id = 'info'
let hideBtn = document.createElement('button')
hideBtn.innerText = '🤖'
hideBtn.style = `
border: none;
background: none;
cursor: pointer; height: 24px; margin: 4px; color: red;`
hideBtn.addEventListener('click', () => {
if (fnDiv.style.display == 'none') {
fnDiv.style.display = 'flex'
} else {
fnDiv.style.display = 'none'
}
try {
pipWindow.document.querySelector('#info').innerText = ''
} catch (error) {
console.log(error)
}
})
// Move the player to the Picture-in-Picture window.
let input = document.createElement('textarea')
@@ -1356,21 +1410,25 @@ app.registerExtension({
font-size: 16px;
margin-right: 6px;user-select: none;`
let btn = document.createElement('butotn')
btn.innerText = '❤'
btn.style = `cursor: pointer;height: 24px;margin:4px;
let seedBtn = document.createElement('butotn')
seedBtn.innerText = '🎲'
seedBtn.style = `cursor: pointer;height: 24px;margin:4px;
color: red;`
btn.addEventListener('click', () => {
if (inputDiv.style.display == 'none') {
inputDiv.style.display = 'block'
} else {
inputDiv.style.display = 'none'
}
seedBtn.addEventListener('click', () => {
window._mixlab_screen_seed_input = Math.round(
Math.floor(Math.random() * 0xffffffffffffffff)
)
try {
pipWindow.document.querySelector('#info').innerText = ''
pipWindow.document.querySelector('#info').innerText =window._mixlab_screen_seed_input
} catch (error) {
console.log(error)
}
// console.log(window._mixlab_screen_seed_input)
if (window._mixlab_screen_imagePath)
document.querySelector('#queue-button').click()
})
// TODO 需要判断是否有screenshare节点,没有的话,不需要添加
@@ -1428,7 +1486,9 @@ app.registerExtension({
console.log('##更新Prompt')
window._mixlab_screen_prompt =
window._mixlab_screen_prompt_input || window._mixlab_screen_prompt
document.querySelector('#queue-button').click()
if (window._mixlab_screen_imagePath)
document.querySelector('#queue-button').click()
try {
pipWindow.document.querySelector('#info').innerText =
@@ -1490,7 +1550,8 @@ app.registerExtension({
window._mixlab_screen_slide_input = ~~slideInp.value / 100
try {
pipWindow.document.querySelector('#info').innerText =
window._mixlab_screen_slide_input;
window._mixlab_screen_slide_input
if (window._mixlab_screen_imagePath)
document.querySelector('#queue-button').click()
} catch (error) {
console.log(error)
@@ -1505,9 +1566,12 @@ app.registerExtension({
div.appendChild(infoDiv)
div.appendChild(fnDiv)
fnDiv.appendChild(btnDiv)
infoDiv.appendChild(infoText)
infoDiv.appendChild(hideBtn)
btnDiv.appendChild(btn)
fnDiv.appendChild(btnDiv)
// 按钮区域
btnDiv.appendChild(seedBtn)
btnDiv.appendChild(pauseBtn)
btnDiv.appendChild(promptFinishBtn)
@@ -1538,7 +1602,9 @@ app.registerExtension({
window._mixlab_screen_prompt =
window._mixlab_screen_prompt_input ||
window._mixlab_screen_prompt
document.querySelector('#queue-button').click()
if (window._mixlab_screen_imagePath)
document.querySelector('#queue-button').click()
try {
pipWindow.document.querySelector('#info').innerText =
+1
View File
@@ -0,0 +1 @@
export const closeIcon = '<svg xmlns="http://www.w3.org/2000/svg" height="24" viewBox="0 -960 960 960" width="24"><path d="m256-200-56-56 224-224-224-224 56-56 224 224 224-224 56 56-224 224 224 224-56 56-224-224-224 224Z"/></svg>'
+671 -22
View File
@@ -1,4 +1,99 @@
import { app } from '../../../scripts/app.js'
import { api } from '../../../scripts/api.js'
import { ComfyWidgets } from '../../../scripts/widgets.js'
import { $el } from '../../../scripts/ui.js'
import {closeIcon} from './svg_icons.js'
function deepEqual (obj1, obj2) {
if (typeof obj1 !== typeof obj2) {
return false
}
if (typeof obj1 !== 'object' || obj1 === null || obj2 === null) {
return obj1 === obj2
}
const keys1 = Object.keys(obj1)
const keys2 = Object.keys(obj2)
if (keys1.length !== keys2.length) {
return false
}
for (let key of keys1) {
if (!deepEqual(obj1[key], obj2[key])) {
return false
}
}
return true
}
async function get_nodes_map () {
let api_host = `${window.location.hostname}:${window.location.port}`
let api_base = ''
let url = `${window.location.protocol}//${api_host}${api_base}`
const res = await fetch(`${url}/mixlab/nodes_map`, {
method: 'POST',
body: JSON.stringify({
data: 'json'
})
})
return await res.json()
}
function loadCSS(url) {
var link = document.createElement('link');
link.rel = 'stylesheet';
link.type = 'text/css';
link.href = url;
document.getElementsByTagName('head')[0].appendChild(link);
}
var cssURL = 'https://cdnjs.cloudflare.com/ajax/libs/github-markdown-css/5.5.0/github-markdown-light.min.css';
loadCSS(cssURL);
function injectCSS (css) {
// 检查页面中是否已经存在具有相同内容的style标签
const existingStyle = document.querySelector('style')
if (existingStyle && existingStyle.textContent === css) {
return // 如果已经存在相同的样式,则不进行注入
}
// 创建一个新的style标签,并将CSS内容注入其中
const style = document.createElement('style')
style.textContent = css
// 将style标签插入到页面的head元素中
const head = document.querySelector('head')
head.appendChild(style)
}
injectCSS(`::-webkit-scrollbar {
width: 2px;
}
@keyframes loading_mixlab {
0% {
background-color: green;
}
50% {
background-color: lightgreen;
}
100% {
background-color: green;
}
}
.loading_mixlab {
background-color: green;
animation-name: loading_mixlab;
animation-duration: 2s;
animation-iteration-count: infinite;
}`)
async function getCustomnodeMappings (mode = 'url') {
// mode = "local";
@@ -6,18 +101,39 @@ async function getCustomnodeMappings (mode = 'url') {
let api_base = ''
let url = `${window.location.protocol}//${api_host}${api_base}`
const response = await fetch(`${url}/customnode/getmappings?mode=${mode}`)
const data = await response.json()
let nodes = {}
try {
for (let url in data) {
let n = data[url]
for (let node of n[0]) {
nodes[node] = { url, title: n[1].title_aux }
}
const data = (await get_nodes_map()).data
for (let url in data) {
let n = data[url]
for (let node of n[0]) {
// if(node=='CLIPSeg')console.log('#CLIPSeg',n)
nodes[node] = { url, title: n[1].title_aux }
}
} catch (error) {}
}
// try {
// const response = await fetch(`${url}/customnode/getmappings?mode=${mode}`)
// const data = await response.json()
// for (let url in data) {
// let n = data[url]
// for (let node of n[0]) {
// // if(node=='CLIPSeg')console.log('#CLIPSeg',n)
// nodes[node] = { url, title: n[1].title_aux }
// }
// }
// } catch (error) {
// const data = (await get_nodes_map()).data
// for (let url in data) {
// let n = data[url]
// for (let node of n[0]) {
// // if(node=='CLIPSeg')console.log('#CLIPSeg',n)
// nodes[node] = { url, title: n[1].title_aux }
// }
// }
// }
return nodes
}
@@ -55,13 +171,51 @@ const missingNodeGithub = (missingNodeTypes, nodesMap) => {
})
}
let nodesMap
function get_position_style (ctx, widget_width, y, node_height) {
const MARGIN = 4 // the margin around the html element
/* Create a transform that deals with all the scrolling and zooming */
const elRect = ctx.canvas.getBoundingClientRect()
const transform = new DOMMatrix()
.scaleSelf(
elRect.width / ctx.canvas.width,
elRect.height / ctx.canvas.height
)
.multiplySelf(ctx.getTransform())
.translateSelf(MARGIN, MARGIN + y)
return {
transformOrigin: '0 0',
transform: transform,
left: `0`,
top: `0`,
cursor: 'pointer',
position: 'absolute',
maxWidth: `${widget_width - MARGIN * 2}px`,
// maxHeight: `${node_height - MARGIN * 2}px`, // we're assuming we have the whole height of the node
width: `${widget_width - MARGIN * 2}px`,
// height: `${node_height * 0.3 - MARGIN * 2}px`,
// background: '#EEEEEE',
display: 'flex',
flexDirection: 'column',
// alignItems: 'center',
justifyContent: 'space-around'
}
}
app.showMissingNodesError = async function (
missingNodeTypes,
hasAddedNodes = true
) {
const nodesMap = await getCustomnodeMappings()
console.log('#nodesMap', nodesMap)
// console.log('###MIXLAB', missingNodeTypes, hasAddedNodes)
nodesMap =
nodesMap && Object.keys(nodesMap).length > 0
? nodesMap
: await getCustomnodeMappings('url')
// console.log('#nodesMap', nodesMap)
// console.log('###MIXLAB', missingNodeTypes, hasAddedNodes)
this.ui.dialog.show(
`When loading the graph, the following node types were not found: <ul>${missingNodeGithub(
missingNodeTypes,
@@ -77,12 +231,507 @@ app.showMissingNodesError = async function (
})
}
// app.ui.dialog.show = function (html) {
// console.log('###MIXLAB', html)
// if (typeof html === 'string') {
// this.textElement.innerHTML = html
// } else {
// this.textElement.replaceChildren(html)
// }
// this.element.style.display = 'flex'
// }
// app.registerExtension({
// name: 'Comfy.MDNote',
// registerCustomNodes () {
// class NoteNode {
// // color = LGraphCanvas.node_colors.yellow.color
// // bgcolor = LGraphCanvas.node_colors.yellow.bgcolor
// // groupcolor = LGraphCanvas.node_colors.yellow.groupcolor
// constructor () {
// if (!this.properties) {
// this.properties = {}
// this.properties.text = ''
// }
// console.log('NoteNode1', this)
// const widget = {
// type: 'div',
// name: 'input_color',
// draw (ctx, node, widget_width, y, widget_height) {
// Object.assign(
// this.div.style,
// get_position_style(
// ctx,
// widget_width,
// 44,
// node.size[1]
// )
// )
// }
// }
// widget.div = $el('div', {});
// widget.div.innerText='1111'
// document.body.appendChild(widget.div)
// this.addCustomWidget(widget)
// this.serialize_widgets = true
// this.isVirtualNode = true
// }
// }
// // Load default visibility
// LiteGraph.registerNodeType(
// 'MDNote',
// Object.assign(NoteNode, {
// title_mode: LiteGraph.NORMAL_TITLE,
// title: 'MDNote',
// collapsable: true
// })
// )
// NoteNode.category = '♾️Mixlab/utils'
// },
// })
async function fetchReadmeContent (url) {
try {
// var repo = 'owner/repo'; // 仓库的拥有者和名称
var match = url.match(/github.com\/([^/]+\/[^/]+)/)
var repo = match[1]
var url = `https://api.github.com/repos/${repo}/readme`
var response = await fetch(url)
var data = await response.json()
var readmeUrl = data.download_url
var readmeResponse = await fetch(readmeUrl)
var content = await readmeResponse.text()
// console.log(content) // 在控制台输出readme.md文件的内容
return content
} catch (error) {
console.log('获取readme.md文件信息失败:', error)
}
}
function createModal (url, markdown,title) {
// Create modal element
var div =
document.querySelector('#mix-modal') || document.createElement('div')
div.id = 'mix-modal'
div.innerHTML = ''
div.style.cssText = `width: 100%;
z-index: 9990;
height: 100vh;
display: flex;
color: var(--descrip-text);
position: fixed;
top: 0;
left: 0;
`
var modal = document.createElement('div')
div.appendChild(modal)
modal.classList.add("modal-body")
// Set modal styles
modal.style.cssText = `
background: white;
height: 80vh;
position: fixed;
overflow:hidden;
top: 50%;
left: 50%;
transform: translate(-50%, -50%);
z-index: 9999;
border-radius: 4px;
box-shadow: 4px 4px 14px rgba(255,255,255,0.5);
`
// Create modal content area
var modalContent = document.createElement('div')
modalContent.classList.add('modal-content')
// Create modal header
const headerElement = document.createElement('div')
headerElement.classList.add('modal-header')
headerElement.style.cssText = `
display: flex;
padding: 20px 24px 8px 24px;
justify-content: space-between;
`
const headTitleElement = document.createElement('a')
headTitleElement.classList.add('header-title')
headTitleElement.style.cssText=`
color: var(--descrip-text);
font-size: 18px;
display: flex;
align-items: center;
flex: 1;
overflow: hidden;
text-decoration: none;
font-weight: bold;
`
headTitleElement.onmouseenter = function(){
headTitleElement.style.color = 'var(--comfy-menu-bg)'
}
headTitleElement.onmouseleave = function(){
headTitleElement.style.color = 'var(--descrip-text)'
}
headTitleElement.textContent= title ||'';
headTitleElement.href = url
headTitleElement.target='_blank'
const linkIcon = document.createElement('small')
linkIcon.textContent = '🔗'
headTitleElement.appendChild(linkIcon);
headerElement.appendChild(headTitleElement)
// Create close button
const closeButton = document.createElement('span')
closeButton.classList.add('close')
closeButton.innerHTML = closeIcon
// Set close button styles
closeButton.style.cssText = `
padding: 4px;
cursor: pointer;
width: 32px;
height: 32px;
display: flex;
justify-content: center;
align-items: center;
user-select: none;
fill: var(--descrip-text);
`
closeButton.onmouseenter = function(){
closeButton.style.fill = 'var(--comfy-menu-bg)';
}
closeButton.onmouseleave = function(){
closeButton.style.fill = 'var(--descrip-text)';
}
headerElement.appendChild(closeButton)
// Click event to close the modal
function closeMixModal(){
div.style.display = 'none'
window.removeEventListener('keydown',MixModalEscKeyEvent)
}
closeButton.onclick = function () {
closeMixModal()
}
// Set modal content area styles
modalContent.style.cssText = `
position: relative;
padding: 0px;
overflow: hidden scroll;;
height: 100%;
min-width:300px
`
// Append close button to modal content area
modal.appendChild(headerElement)
// Create element for displaying Markdown content
var markdownContent = document.createElement('div')
markdownContent.classList.add('markdown-content','markdown-body')
markdownContent.style.cssText = `max-width: 50vw;padding: 0px 24px 100px 24px;`
showdown.setFlavor('github');
var converter = new showdown.Converter()
var html = converter.makeHtml(markdown)
// Hide images in the markdown when they fail to load
var regex = /<img[^>]+src="?([^"\s]+)"?[^>]*>/g
html = html.replace(regex, function (match, src) {
return `<img src="${src}" onerror="this.style.display='none'">`
})
// Open links in a new tab or window
html = html.replace(/<a/g, '<a target="_blank"')
// Fix href attribute to absolute path
html = html.replace(
/<a([^>]+href=["'])(?!https?:\/\/)([^"'>]+)/g,
function (match, prefix, path) {
var absolutePath = url + '/' + path
return '<a' + prefix + absolutePath
}
)
markdownContent.innerHTML = html
// Append Markdown content element to modal content area
modalContent.appendChild(markdownContent)
// Append modal content area to modal element
modal.appendChild(modalContent)
const footerElement = document.createElement('div')
footerElement.style.cssText=`
position: absolute;
left: 0;
right: 0;
text-align: right;
padding:10px;
font-size:12px
`
const footerText = document.createElement('a')
footerText.href ="https://github.com/shadowcz007/comfyui-mixlab-nodes"
footerText.innerText = "Support by Mixlab"
footerText.style.cssText=`color:inherit`
footerText.target = "_blank"
footerText.onmouseenter=function(){
footerText.style.color = 'var(--input-text)'
}
footerText.onmouseleave=function(){
footerText.style.color = 'inherit'
}
footerText.onclick = function(e){
e.stopPropagation()
}
footerElement.appendChild(footerText)
div.appendChild(footerElement)
// Append modal element to the page
if (!document.querySelector('#mix-modal')) {
document.body.appendChild(div)
}
function MixModalEscKeyEvent(event){
if(event.key == "Escape"){
closeMixModal()
}
}
window.removeEventListener('keydown',MixModalEscKeyEvent)
window.addEventListener('keydown',MixModalEscKeyEvent)
const bgElement = document.createElement('div')
bgElement.classList.add('mix-modal-bg')
bgElement.style.cssText = `
width:100%;
height:100%;
background-color: rgba(0,0,0,0.8);
`
bgElement.onclick = function( ){
closeMixModal()
}
div.appendChild(bgElement)
}
app.registerExtension({
name: 'Comfy.Mixlab.ui',
init () {
LGraphCanvas.prototype.helpAboutNode = async function (node) {
nodesMap =
nodesMap && Object.keys(nodesMap).length > 0
? nodesMap
: await getCustomnodeMappings('url')
console.log('node & node map', node, nodesMap, nodesMap[node.type])
let repo = nodesMap[node.type]
if (repo) {
let markdown = await fetchReadmeContent(repo.url)
createModal(repo.url,markdown,repo.title)
}
}
const getNodeMenuOptions = LGraphCanvas.prototype.getNodeMenuOptions // store the existing method
LGraphCanvas.prototype.getNodeMenuOptions = function (node) {
// replace it
const options = getNodeMenuOptions.apply(this, arguments) // start by calling the stored one
node.setDirtyCanvas(true, true) // force a redraw of (foreground, background)
// options.splice(
// options.length - 1,
// 0, // splice a new option in at the end
// {
// content: '♾️Mixlab', // with a name
// callback: () => {
// LGraphCanvas.prototype.helpAboutNode(node)
// } // and the callback
// },
// null // a divider
// )
return [
{
content: 'Help ♾️Mixlab', // with a name
callback: () => {
LGraphCanvas.prototype.helpAboutNode(node)
} // and the callback
},
null,
...options
] // and return the options
}
},
async setup () {
// Add canvas menu options
const orig = LGraphCanvas.prototype.getCanvasMenuOptions;
LGraphCanvas.prototype.getCanvasMenuOptions = function () {
const options = orig.apply(this, arguments);
options.push(null, {
content: `Find ♾️Mixlab`,
disabled: false, // or a function determining whether to disable
callback: async () => {
nodesMap =
nodesMap && Object.keys(nodesMap).length > 0
? nodesMap
: await getCustomnodeMappings('url')
const nodesDiv = document.createDocumentFragment()
const nodes = (await app.graphToPrompt()).output
// console.log('[Mixlab]', 'loaded graph node: ', app)
let div =
document.querySelector('#mixlab_find_the_node') ||
document.createElement('div')
div.id = 'mixlab_find_the_node'
div.style = `
flex-direction: column;
align-items: end;
display:flex;position: absolute;
top: 50px; left: 50px; width: 200px;
color: var(--descrip-text);
background-color: var(--comfy-menu-bg);
padding: 10px;
border: 1px solid black;z-index: 999999999;padding-top: 0;`
div.innerHTML = ''
let btn = document.createElement('div')
btn.style=`display: flex;
width: calc(100% - 24px);
justify-content: space-between;
align-items: center;
padding: 0 12px;
height: 32px;`
let btnB = document.createElement('button')
let textB = document.createElement('p')
btn.appendChild(textB)
btn.appendChild(btnB)
textB.innerText = `Find The Node`
btnB.style = `float: right; border: none; color: var(--input-text);
background-color: var(--comfy-input-bg); border-color: var(--border-color);cursor: pointer;`
btnB.addEventListener('click', () => {
div.style.display = 'none'
})
btnB.innerText = 'X'
// 悬浮框拖动事件
div.addEventListener('mousedown', function (e) {
var startX = e.clientX
var startY = e.clientY
var offsetX = div.offsetLeft
var offsetY = div.offsetTop
function moveBox (e) {
var newX = e.clientX
var newY = e.clientY
var deltaX = newX - startX
var deltaY = newY - startY
div.style.left = offsetX + deltaX + 'px'
div.style.top = offsetY + deltaY + 'px'
}
function stopMoving () {
document.removeEventListener('mousemove', moveBox)
document.removeEventListener('mouseup', stopMoving)
}
document.addEventListener('mousemove', moveBox)
document.addEventListener('mouseup', stopMoving)
})
div.appendChild(btn)
const updateNodes = (ns, nd) => {
for (let nodeId in ns) {
let n = ns[nodeId].class_type
const { url, title } = nodesMap[n]
let d = document.createElement('button')
d.style = `text-align: left;margin:6px;color: var(--input-text);
background-color: var(--comfy-input-bg); border-color: var(--border-color);cursor: pointer;`
d.addEventListener('click', () => {
const node = app.graph.getNodeById(nodeId)
if (!node) return
app.canvas.centerOnNode(node)
app.canvas.setZoom(1)
})
d.addEventListener('mouseover', async () => {
// console.log('mouseover')
let n = (await app.graphToPrompt()).output
if (!deepEqual(n, ns)) {
nd.innerHTML = ''
updateNodes(n, nd)
}
})
d.innerHTML = `
<span>${'#' + nodeId} ${n}</span>
<a href="${url}" target="_blank" style="text-decoration: none;">🔗</a>
`
d.title = title
nd.appendChild(d)
}
}
let nodesDivv = document.createElement('div')
for (let nodeId in nodes) {
let n = nodes[nodeId].class_type
const { url, title } = nodesMap[n]
let d = document.createElement('button')
d.style = `text-align: left;margin:6px;color: var(--input-text);
background-color: var(--comfy-input-bg); border-color: var(--border-color);cursor: pointer;`
d.addEventListener('click', () => {
const node = app.graph.getNodeById(nodeId)
if (!node) return
app.canvas.centerOnNode(node)
app.canvas.setZoom(1)
})
d.addEventListener('mouseover', async () => {
console.log('mouseover')
let n = (await app.graphToPrompt()).output
if (!deepEqual(n, nodes)) {
nodesDivv.innerHTML = ''
updateNodes(n, nodesDivv)
}
})
d.innerHTML = `
<span>${'#' + nodeId} ${n}</span>
<a href="${url}" target="_blank" style="text-decoration: none;">🔗</a>
`
d.title = title
nodesDiv.appendChild(d)
}
nodesDivv.appendChild(nodesDiv)
nodesDivv.style=`overflow: scroll;
height: 70vh;width: 100%;`
div.appendChild(nodesDivv)
if (!document.querySelector('#mixlab_find_the_node'))
document.body.appendChild(div)
}
});
return options;
};
}
})
+168
View File
@@ -0,0 +1,168 @@
import { app } from '../../../scripts/app.js'
import { api } from '../../../scripts/api.js'
import { ComfyWidgets } from '../../../scripts/widgets.js'
import { $el } from '../../../scripts/ui.js'
const getLocalData = key => {
let data = {}
try {
data = JSON.parse(localStorage.getItem(key)) || {}
} catch (error) {
return {}
}
return data
}
function get_position_style (ctx, widget_width, y, node_height) {
const MARGIN = 4 // the margin around the html element
/* Create a transform that deals with all the scrolling and zooming */
const elRect = ctx.canvas.getBoundingClientRect()
const transform = new DOMMatrix()
.scaleSelf(
elRect.width / ctx.canvas.width,
elRect.height / ctx.canvas.height
)
.multiplySelf(ctx.getTransform())
.translateSelf(MARGIN, MARGIN + y)
return {
transformOrigin: '0 0',
transform: transform,
left: `0`,
top: `0`,
cursor: 'pointer',
position: 'absolute',
maxWidth: `${widget_width - MARGIN * 2}px`,
// maxHeight: `${node_height - MARGIN * 2}px`, // we're assuming we have the whole height of the node
width: `${widget_width - MARGIN * 2}px`,
// height: `${node_height * 0.3 - MARGIN * 2}px`,
// background: '#EEEEEE',
display: 'flex',
flexDirection: 'column',
// alignItems: 'center',
justifyContent: 'space-around'
}
}
app.registerExtension({
name: 'Mixlab.utils.Color',
async getCustomWidgets (app) {
return {
TCOLOR (node, inputName, inputData, app) {
// console.log('##node', node)
const widget = {
type: inputData[0], // the type, CHEESE
name: inputName, // the name, slice
size: [128, 32], // a default size
draw (ctx, node, width, y) {},
computeSize (...args) {
return [128, 32] // a method to compute the current size of the widget
},
async serializeValue (nodeId, widgetIndex) {
let data = getLocalData('_mixlab_utils_color')
return data[node.id] || '#000000'
}
}
// widget.something = something; // maybe adds stuff to it
node.addCustomWidget(widget) // adds it to the node
return widget // and returns it.
}
}
},
async beforeRegisterNodeDef (nodeType, nodeData, app) {
if (nodeType.comfyClass == 'Color') {
const orig_nodeCreated = nodeType.prototype.onNodeCreated
nodeType.prototype.onNodeCreated = function () {
orig_nodeCreated?.apply(this, arguments)
console.log('Color nodeData', this.widgets)
const widget = {
type: 'div',
name: 'input_color',
draw (ctx, node, widget_width, y, widget_height) {
Object.assign(
this.div.style,
get_position_style(
ctx,
widget_width,
44,
node.size[1]
)
)
}
}
widget.div = $el('div', {})
document.body.appendChild(widget.div)
const inputDiv = (key, placeholder, value) => {
let div = document.createElement('div')
const ip = document.createElement('input')
ip.type = 'color'
ip.className = `${'comfy-multiline-input'} ${placeholder}`
div.style = `display: flex;
align-items: center;
margin: 6px 8px;
margin-top: 0;`
ip.placeholder = placeholder
ip.value = value
ip.style = `outline: none;
border: none;
padding: 4px;
width: 100%;cursor: pointer;
height: 32px;`
const label = document.createElement('label')
label.style = 'font-size: 10px;min-width:32px'
label.innerText = placeholder
div.appendChild(label)
div.appendChild(ip)
ip.addEventListener('change', () => {
let data = getLocalData(key)
data[this.id] = ip.value.trim()
localStorage.setItem(key, JSON.stringify(data))
// console.log(this.id, ip.value.trim())
})
return div
}
let inputColor = inputDiv('_mixlab_utils_color', 'Color', '#000000')
widget.div.appendChild(inputColor)
this.addCustomWidget(widget)
const onRemoved = this.onRemoved
this.onRemoved = () => {
inputColor.remove()
widget.div.remove()
return onRemoved?.()
}
this.serialize_widgets = true //需要保存参数
}
}
},
async loadedGraphNode (node, app) {
// Fires every time a node is constructed
// You can modify widgets/add handlers/etc here
if (node.type === 'Color') {
let widget = node.widgets.filter(w => w.div)[0]
let data = getLocalData('_mixlab_utils_color')
let id = node.id
widget.div.querySelector('.Color').value = data[id] || '#000000'
}
}
})
+1077
View File
File diff suppressed because one or more lines are too long
+3
View File
File diff suppressed because one or more lines are too long
+23 -2
View File
@@ -1,3 +1,24 @@
::-webkit-scrollbar {
width: 2px;
}
width: 2px;
}
@keyframes loading_mixlab {
0% {
background-color: green;
}
50% {
background-color: lightgreen;
}
100% {
background-color: green;
}
}
.loading_mixlab {
background-color: green;
animation-name: loading_mixlab;
animation-duration: 2s;
animation-iteration-count: infinite;
}
+85 -63
View File
@@ -1,6 +1,6 @@
{
"last_node_id": 22,
"last_link_id": 48,
"last_node_id": 23,
"last_link_id": 51,
"nodes": [
{
"id": 7,
@@ -105,7 +105,7 @@
{
"name": "text",
"type": "STRING",
"link": 48,
"link": 51,
"widget": {
"name": "text"
}
@@ -316,13 +316,13 @@
"1": 246
},
"flags": {},
"order": 6,
"order": 7,
"mode": 0,
"inputs": [
{
"name": "images",
"type": "IMAGE",
"link": 46
"link": 50
}
],
"properties": {
@@ -445,13 +445,13 @@
"1": 58
},
"flags": {},
"order": 7,
"order": 6,
"mode": 0,
"inputs": [
{
"name": "image",
"type": "IMAGE",
"link": 47
"link": 49
}
],
"outputs": [
@@ -472,45 +472,6 @@
512
]
},
{
"id": 22,
"type": "ScreenShare",
"pos": [
-63,
483
],
"size": {
"0": 315,
"1": 98
},
"flags": {},
"order": 3,
"mode": 0,
"outputs": [
{
"name": "IMAGE",
"type": "IMAGE",
"links": [
46,
47
],
"shape": 3,
"slot_index": 0
},
{
"name": "STRING",
"type": "STRING",
"links": [
48
],
"shape": 3,
"slot_index": 1
}
],
"properties": {
"Node name for S&R": "ScreenShare"
}
},
{
"id": 20,
"type": "FloatingVideo",
@@ -518,10 +479,10 @@
1928,
295
],
"size": [
315,
58
],
"size": {
"0": 315,
"1": 58
},
"flags": {},
"order": 12,
"mode": 0,
@@ -534,7 +495,68 @@
],
"properties": {
"Node name for S&R": "FloatingVideo"
}
},
"widgets_values": [
null
]
},
{
"id": 23,
"type": "ScreenShare",
"pos": [
-65,
446
],
"size": {
"0": 315,
"1": 170
},
"flags": {},
"order": 3,
"mode": 0,
"outputs": [
{
"name": "IMAGE",
"type": "IMAGE",
"links": [
49,
50
],
"shape": 3,
"slot_index": 0
},
{
"name": "PROMPT",
"type": "STRING",
"links": [
51
],
"shape": 3,
"slot_index": 1
},
{
"name": "FLOAT",
"type": "FLOAT",
"links": null,
"shape": 3
},
{
"name": "INT",
"type": "INT",
"links": null,
"shape": 3
}
],
"properties": {
"Node name for S&R": "ScreenShare"
},
"widgets_values": [
null,
null,
null,
null,
null
]
}
],
"links": [
@@ -787,24 +809,24 @@
"CLIP"
],
[
46,
22,
0,
2,
0,
"IMAGE"
],
[
47,
22,
49,
23,
0,
18,
0,
"IMAGE"
],
[
48,
22,
50,
23,
0,
2,
0,
"IMAGE"
],
[
51,
23,
1,
8,
1,
+142 -135
View File
@@ -1,13 +1,13 @@
{
"last_node_id": 23,
"last_link_id": 54,
"last_node_id": 24,
"last_link_id": 59,
"nodes": [
{
"id": 7,
"type": "CLIPTextEncode",
"pos": [
495,
307
515,
130
],
"size": {
"0": 400,
@@ -45,8 +45,8 @@
"id": 8,
"type": "CLIPTextEncode",
"pos": [
504,
33
515,
1268
],
"size": {
"0": 400,
@@ -64,7 +64,7 @@
{
"name": "text",
"type": "STRING",
"link": 48,
"link": 59,
"widget": {
"name": "text"
}
@@ -92,8 +92,8 @@
"id": 6,
"type": "LoraLoader",
"pos": [
112,
5
515,
460
],
"size": {
"0": 315,
@@ -140,58 +140,17 @@
1
]
},
{
"id": 23,
"type": "VAEEncode",
"pos": [
563,
663
],
"size": {
"0": 210,
"1": 46
},
"flags": {},
"order": 5,
"mode": 0,
"inputs": [
{
"name": "pixels",
"type": "IMAGE",
"link": 52
},
{
"name": "vae",
"type": "VAE",
"link": 53
}
],
"outputs": [
{
"name": "LATENT",
"type": "LATENT",
"links": [
54
],
"shape": 3,
"slot_index": 0
}
],
"properties": {
"Node name for S&R": "VAEEncode"
}
},
{
"id": 5,
"type": "KSampler",
"pos": [
1085,
49
],
"size": [
315,
262
1015,
130
],
"size": {
"0": 315,
"1": 262
},
"flags": {},
"order": 7,
"mode": 0,
@@ -219,7 +178,7 @@
{
"name": "denoise",
"type": "FLOAT",
"link": 49,
"link": 56,
"widget": {
"name": "denoise"
}
@@ -253,8 +212,8 @@
"id": 11,
"type": "VAEDecode",
"pos": [
1451,
59
1430,
130
],
"size": {
"0": 210,
@@ -294,13 +253,13 @@
"id": 20,
"type": "FloatingVideo",
"pos": [
1464,
243
],
"size": [
315,
58
1740,
130
],
"size": {
"0": 315,
"1": 58
},
"flags": {},
"order": 9,
"mode": 0,
@@ -318,12 +277,78 @@
null
]
},
{
"id": 2,
"type": "PreviewImage",
"pos": [
515,
716
],
"size": {
"0": 210,
"1": 246
},
"flags": {},
"order": 4,
"mode": 0,
"inputs": [
{
"name": "images",
"type": "IMAGE",
"link": 55
}
],
"properties": {
"Node name for S&R": "PreviewImage"
}
},
{
"id": 23,
"type": "VAEEncode",
"pos": [
515,
1092
],
"size": {
"0": 210,
"1": 46
},
"flags": {},
"order": 5,
"mode": 0,
"inputs": [
{
"name": "pixels",
"type": "IMAGE",
"link": 57
},
{
"name": "vae",
"type": "VAE",
"link": 58
}
],
"outputs": [
{
"name": "LATENT",
"type": "LATENT",
"links": [
54
],
"shape": 3,
"slot_index": 0
}
],
"properties": {
"Node name for S&R": "VAEEncode"
}
},
{
"id": 10,
"type": "CheckpointLoaderSimple",
"pos": [
80,
238
100,
130
],
"size": {
"0": 315,
@@ -358,7 +383,7 @@
"type": "VAE",
"links": [
34,
53
58
],
"shape": 3,
"slot_index": 2
@@ -372,15 +397,15 @@
]
},
{
"id": 22,
"id": 24,
"type": "ScreenShare",
"pos": [
103,
412
100,
358
],
"size": {
"0": 315,
"1": 146
"1": 170
},
"flags": {},
"order": 1,
@@ -390,17 +415,17 @@
"name": "IMAGE",
"type": "IMAGE",
"links": [
46,
52
55,
57
],
"shape": 3,
"slot_index": 0
},
{
"name": "STRING",
"name": "PROMPT",
"type": "STRING",
"links": [
48
59
],
"shape": 3,
"slot_index": 1
@@ -409,10 +434,16 @@
"name": "FLOAT",
"type": "FLOAT",
"links": [
49
56
],
"shape": 3,
"slot_index": 2
},
{
"name": "INT",
"type": "INT",
"links": null,
"shape": 3
}
],
"properties": {
@@ -422,33 +453,9 @@
null,
null,
null,
null,
null
]
},
{
"id": 2,
"type": "PreviewImage",
"pos": [
-501,
918
],
"size": {
"0": 210,
"1": 246
},
"flags": {},
"order": 4,
"mode": 0,
"inputs": [
{
"name": "images",
"type": "IMAGE",
"link": 46
}
],
"properties": {
"Node name for S&R": "PreviewImage"
}
}
],
"links": [
@@ -660,30 +667,6 @@
1,
"CLIP"
],
[
46,
22,
0,
2,
0,
"IMAGE"
],
[
48,
22,
1,
8,
1,
"STRING"
],
[
49,
22,
2,
5,
4,
"FLOAT"
],
[
51,
8,
@@ -693,15 +676,39 @@
"CONDITIONING"
],
[
52,
22,
54,
23,
0,
5,
3,
"LATENT"
],
[
55,
24,
0,
2,
0,
"IMAGE"
],
[
56,
24,
2,
5,
4,
"FLOAT"
],
[
57,
24,
0,
23,
0,
"IMAGE"
],
[
53,
58,
10,
2,
23,
@@ -709,12 +716,12 @@
"VAE"
],
[
54,
23,
0,
5,
3,
"LATENT"
59,
24,
1,
8,
1,
"STRING"
]
],
"groups": [],
File diff suppressed because it is too large Load Diff
+862
View File
@@ -0,0 +1,862 @@
{
"last_node_id": 28,
"last_link_id": 64,
"nodes": [
{
"id": 7,
"type": "CLIPTextEncode",
"pos": [
515,
130
],
"size": {
"0": 400,
"1": 200
},
"flags": {},
"order": 3,
"mode": 0,
"inputs": [
{
"name": "clip",
"type": "CLIP",
"link": 33
}
],
"outputs": [
{
"name": "CONDITIONING",
"type": "CONDITIONING",
"links": [
29
],
"shape": 3,
"slot_index": 0
}
],
"properties": {
"Node name for S&R": "CLIPTextEncode"
},
"widgets_values": [
""
]
},
{
"id": 6,
"type": "LoraLoader",
"pos": [
515,
460
],
"size": {
"0": 315,
"1": 126
},
"flags": {},
"order": 4,
"mode": 0,
"inputs": [
{
"name": "model",
"type": "MODEL",
"link": 31
},
{
"name": "clip",
"type": "CLIP",
"link": 45
}
],
"outputs": [
{
"name": "MODEL",
"type": "MODEL",
"links": [
44
],
"shape": 3,
"slot_index": 0
},
{
"name": "CLIP",
"type": "CLIP",
"links": null,
"shape": 3
}
],
"properties": {
"Node name for S&R": "LoraLoader"
},
"widgets_values": [
"lcm-lora-sdv1-5.safetensors",
1,
1
]
},
{
"id": 11,
"type": "VAEDecode",
"pos": [
1430,
130
],
"size": {
"0": 210,
"1": 46
},
"flags": {},
"order": 11,
"mode": 0,
"inputs": [
{
"name": "samples",
"type": "LATENT",
"link": 35
},
{
"name": "vae",
"type": "VAE",
"link": 34
}
],
"outputs": [
{
"name": "IMAGE",
"type": "IMAGE",
"links": [
41
],
"shape": 3,
"slot_index": 0
}
],
"properties": {
"Node name for S&R": "VAEDecode"
}
},
{
"id": 23,
"type": "VAEEncode",
"pos": [
515,
1092
],
"size": {
"0": 210,
"1": 46
},
"flags": {},
"order": 6,
"mode": 0,
"inputs": [
{
"name": "pixels",
"type": "IMAGE",
"link": 57
},
{
"name": "vae",
"type": "VAE",
"link": 58
}
],
"outputs": [
{
"name": "LATENT",
"type": "LATENT",
"links": [
54
],
"shape": 3,
"slot_index": 0
}
],
"properties": {
"Node name for S&R": "VAEEncode"
}
},
{
"id": 10,
"type": "CheckpointLoaderSimple",
"pos": [
100,
130
],
"size": {
"0": 315,
"1": 98
},
"flags": {},
"order": 0,
"mode": 0,
"outputs": [
{
"name": "MODEL",
"type": "MODEL",
"links": [
31
],
"shape": 3,
"slot_index": 0
},
{
"name": "CLIP",
"type": "CLIP",
"links": [
32,
33,
45
],
"shape": 3,
"slot_index": 1
},
{
"name": "VAE",
"type": "VAE",
"links": [
34,
58
],
"shape": 3,
"slot_index": 2
}
],
"properties": {
"Node name for S&R": "CheckpointLoaderSimple"
},
"widgets_values": [
"deliberate_v2.safetensors"
]
},
{
"id": 8,
"type": "CLIPTextEncode",
"pos": [
515,
1268
],
"size": {
"0": 400,
"1": 200
},
"flags": {},
"order": 8,
"mode": 0,
"inputs": [
{
"name": "clip",
"type": "CLIP",
"link": 32
},
{
"name": "text",
"type": "STRING",
"link": 62,
"widget": {
"name": "text"
}
}
],
"outputs": [
{
"name": "CONDITIONING",
"type": "CONDITIONING",
"links": [
51
],
"shape": 3,
"slot_index": 0
}
],
"properties": {
"Node name for S&R": "CLIPTextEncode"
},
"widgets_values": [
""
]
},
{
"id": 20,
"type": "FloatingVideo",
"pos": [
1274,
536
],
"size": [
315,
58
],
"flags": {},
"order": 12,
"mode": 0,
"inputs": [
{
"name": "images",
"type": "IMAGE",
"link": 41
}
],
"properties": {
"Node name for S&R": "FloatingVideo"
},
"widgets_values": [
null
]
},
{
"id": 5,
"type": "KSampler",
"pos": [
1004,
130
],
"size": [
315,
262
],
"flags": {},
"order": 10,
"mode": 0,
"inputs": [
{
"name": "model",
"type": "MODEL",
"link": 44
},
{
"name": "positive",
"type": "CONDITIONING",
"link": 51
},
{
"name": "negative",
"type": "CONDITIONING",
"link": 29
},
{
"name": "latent_image",
"type": "LATENT",
"link": 54
}
],
"outputs": [
{
"name": "LATENT",
"type": "LATENT",
"links": [
35
],
"shape": 3,
"slot_index": 0
}
],
"properties": {
"Node name for S&R": "KSampler"
},
"widgets_values": [
758428944049342,
"fixed",
4,
1.6,
"lcm",
"karras",
0.61
]
},
{
"id": 24,
"type": "ScreenShare",
"pos": [
62,
543
],
"size": {
"0": 315,
"1": 194
},
"flags": {},
"order": 1,
"mode": 0,
"outputs": [
{
"name": "IMAGE",
"type": "IMAGE",
"links": [
55,
57
],
"shape": 3,
"slot_index": 0
},
{
"name": "PROMPT",
"type": "STRING",
"links": [],
"shape": 3,
"slot_index": 1
},
{
"name": "FLOAT",
"type": "FLOAT",
"links": [],
"shape": 3,
"slot_index": 2
},
{
"name": "INT",
"type": "INT",
"links": null,
"shape": 3
}
],
"properties": {
"Node name for S&R": "ScreenShare"
},
"widgets_values": [
null,
null,
null,
null,
null
]
},
{
"id": 2,
"type": "PreviewImage",
"pos": [
-416,
516
],
"size": {
"0": 210,
"1": 246
},
"flags": {},
"order": 5,
"mode": 0,
"inputs": [
{
"name": "images",
"type": "IMAGE",
"link": 55
}
],
"properties": {
"Node name for S&R": "PreviewImage"
}
},
{
"id": 26,
"type": "ChatGPTOpenAI",
"pos": [
5,
1325
],
"size": [
400,
357.99993896484375
],
"flags": {},
"order": 7,
"mode": 0,
"inputs": [
{
"name": "prompt",
"type": "STRING",
"link": 61,
"widget": {
"name": "prompt"
}
}
],
"outputs": [
{
"name": "text",
"type": "STRING",
"links": [
62,
63
],
"shape": 3,
"slot_index": 0
},
{
"name": "messages",
"type": "STRING",
"links": null,
"shape": 3
},
{
"name": "session_history",
"type": "STRING",
"links": null,
"shape": 3
}
],
"properties": {
"Node name for S&R": "ChatGPTOpenAI"
},
"widgets_values": [
null,
null,
"",
"你需要根据用户的输入,转化为英文单词的短句子组合,输出英文",
"gpt-35-turbo",
1249,
"randomize",
1,
null
]
},
{
"id": 28,
"type": "SpeechSynthesis",
"pos": [
542,
1570
],
"size": [
315,
76
],
"flags": {},
"order": 9,
"mode": 0,
"inputs": [
{
"name": "text",
"type": "STRING",
"link": 63,
"widget": {
"name": "text"
}
}
],
"outputs": [
{
"name": "STRING",
"type": "STRING",
"links": null,
"shape": 6
}
],
"properties": {
"Node name for S&R": "SpeechSynthesis"
},
"widgets_values": [
"A very handsome elderly gentleman."
]
},
{
"id": 25,
"type": "SpeechRecognition",
"pos": [
-5,
1101
],
"size": [
392.5606180664065,
166.64923671875022
],
"flags": {},
"order": 2,
"mode": 0,
"outputs": [
{
"name": "prompt",
"type": "STRING",
"links": [
61
],
"shape": 3,
"slot_index": 0
}
],
"properties": {
"Node name for S&R": "SpeechRecognition"
},
"widgets_values": [
null,
null
]
}
],
"links": [
[
1,
1,
0,
2,
0,
"IMAGE"
],
[
3,
6,
0,
5,
0,
"MODEL"
],
[
5,
8,
0,
5,
2,
"CONDITIONING"
],
[
6,
9,
0,
5,
3,
"LATENT"
],
[
7,
10,
0,
6,
0,
"MODEL"
],
[
8,
10,
1,
6,
1,
"CLIP"
],
[
9,
5,
0,
11,
0,
"LATENT"
],
[
10,
10,
2,
11,
1,
"VAE"
],
[
11,
6,
1,
7,
0,
"CLIP"
],
[
12,
6,
1,
8,
0,
"CLIP"
],
[
14,
11,
0,
3,
0,
"IMAGE"
],
[
17,
1,
2,
7,
1,
"STRING"
],
[
18,
7,
0,
15,
0,
"CONDITIONING"
],
[
19,
15,
0,
5,
1,
"CONDITIONING"
],
[
20,
16,
0,
15,
1,
"CONTROL_NET"
],
[
24,
1,
0,
18,
0,
"IMAGE"
],
[
25,
18,
0,
15,
2,
"IMAGE"
],
[
29,
7,
0,
5,
2,
"CONDITIONING"
],
[
31,
10,
0,
6,
0,
"MODEL"
],
[
32,
10,
1,
8,
0,
"CLIP"
],
[
33,
10,
1,
7,
0,
"CLIP"
],
[
34,
10,
2,
11,
1,
"VAE"
],
[
35,
5,
0,
11,
0,
"LATENT"
],
[
41,
11,
0,
20,
0,
"IMAGE"
],
[
44,
6,
0,
5,
0,
"MODEL"
],
[
45,
10,
1,
6,
1,
"CLIP"
],
[
51,
8,
0,
5,
1,
"CONDITIONING"
],
[
54,
23,
0,
5,
3,
"LATENT"
],
[
55,
24,
0,
2,
0,
"IMAGE"
],
[
57,
24,
0,
23,
0,
"IMAGE"
],
[
58,
10,
2,
23,
1,
"VAE"
],
[
61,
25,
0,
26,
0,
"STRING"
],
[
62,
26,
0,
8,
1,
"STRING"
],
[
63,
26,
0,
28,
0,
"STRING"
]
],
"groups": [],
"config": {},
"extra": {},
"version": 0.4
}