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29 Commits
Author SHA1 Message Date
Dijkstra 38e89f9567 Merge pull request #52 from idrirap/dev/fix_advanced_nodes
fix advanced nodes
2025-07-26 19:43:22 +02:00
Dijkstra f752500e74 chore: forgot to update the version 2025-07-26 19:39:42 +02:00
Dijkstra 11af9469ee fix: removed all the advanced node stuff. Replaced it with vanilla node with extra js took from custom-node repo. And added explanation for view info in the readme 2025-07-26 19:39:20 +02:00
Dijkstra abb9b39448 Merge pull request #51 from idrirap/dev/clear_readme
chore: detail the difference between tags and trigger words
2025-07-26 17:48:51 +02:00
Dijkstra 2e889c7018 chore: detail the difference between tags and trigger words 2025-07-26 17:46:52 +02:00
Dijkstra 52157dff5a Merge pull request #39 from idrirap/dev/issue38
fix: no longer crash when internet is unavailable
2025-01-16 09:38:20 +01:00
Dijkstra 8c18075e9f chore: update node version 2025-01-16 09:37:53 +01:00
Dijkstra e0ec5dee30 fix: no longer crash when internet is unavailable 2025-01-16 09:35:17 +01:00
Dijkstra c09e56b21f Merge pull request #36 from idrirap/dev/issue33
Dev/issue33
2024-10-31 19:53:14 +01:00
Dijkstra 763152c041 chore: set clip strength to 0 when there is no CLIP. (just in case) 2024-10-31 19:51:46 +01:00
Dijkstra c85b302a61 chore: CLIP is not optional in the LoraLoaderVanilla and LoraLoaderAdvanced 2024-10-31 19:49:13 +01:00
Dijkstra 6484968fbe Merge pull request #35 from idrirap/dev/issue34
fix: sometimes the preview image extension is prefixed with '.preview'
2024-10-31 19:15:58 +01:00
Dijkstra 9a212d13e7 fix: sometimes the preview image extension is prefixed with '.preview' 2024-10-31 19:14:31 +01:00
Dijkstra b02bea5480 chore: bump version + added MIT license 2024-06-20 17:56:49 +02:00
Dijkstra 8c4b7941a6 chore: improved description 2024-06-20 17:50:07 +02:00
dijkstra 070751dbf1 Merge pull request #24 from idrirap/dev/add_registryId
feat: set publisherId
2024-06-20 17:35:06 +02:00
Dijkstra 41f6277830 feat: set publisherId 2024-06-20 17:34:27 +02:00
dijkstra 03ddfbfe66 Merge pull request #21 from haohaocreates/publish
Add Github Action for Publishing to Comfy Registry
2024-06-20 17:28:55 +02:00
dijkstra 885a1c8371 Merge pull request #22 from haohaocreates/pyproject
Add pyproject.toml for Custom Node Registry
2024-06-20 17:28:45 +02:00
haohaocreates 8656b2ebaf chore(pyproject): Add pyproject.toml for Custom Node Registry 2024-05-22 17:25:44 -04:00
haohaocreates c3759e7b30 chore(publish): Add Github Action for Publishing to Comfy Registry 2024-05-22 17:25:41 -04:00
Dijkstra 236e230515 doc: added trigger explanation 2024-04-09 22:35:48 +02:00
dijkstra 23ef04b1cb Merge pull request #20 from idrirap/update_readme
Update readme
2024-04-09 22:18:38 +02:00
Dijkstra a95cba4e3b doc: last fix 2024-04-09 22:18:14 +02:00
Dijkstra 40f09582e2 doc: added View info in readme 2024-04-09 22:17:08 +02:00
Dijkstra 6635d30566 doc: updated readme 2024-04-09 22:11:55 +02:00
dijkstra 5cf49c9565 Merge pull request #19 from idrirap/issue14
feat: added pyssss's lora view info
2024-04-09 21:24:05 +02:00
Dijkstra 1e8ed1e16f feat: added pyssss's lora view info 2024-04-09 21:09:44 +02:00
dijkstra 64113afd7f Merge pull request #18 from idrirap/issue16
feat: added lora name to output
2024-04-09 18:16:19 +02:00
15 changed files with 605 additions and 217 deletions
+21
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@@ -0,0 +1,21 @@
name: Publish to Comfy registry
on:
workflow_dispatch:
push:
branches:
- main
paths:
- "pyproject.toml"
jobs:
publish-node:
name: Publish Custom Node to registry
runs-on: ubuntu-latest
steps:
- name: Check out code
uses: actions/checkout@v4
- name: Publish Custom Node
uses: Comfy-Org/publish-node-action@main
with:
## Add your own personal access token to your Github Repository secrets and reference it here.
personal_access_token: ${{ secrets.REGISTRY_ACCESS_TOKEN }}
+88 -22
View File
@@ -1,38 +1,92 @@
# ComfyUI-Lora-Auto-Trigger-Words
This project is a fork of https://github.com/Extraltodeus/LoadLoraWithTags
The aim of these custom nodes is to get an _easy_ access to the tags used to trigger a lora.
This project is compatible with Stacked Loras from https://github.com/LucianoCirino/efficiency-nodes-comfyui/releases
I talk about **lora**, but works with **lycoris** too.
The aim of these custom nodes is to get an _easy_ access to the **trigger words** (or **tags**) used to trigger a lora.
*Note that trigger words and tags are used interchangeably in this readme and means the same thing.*
This project is compatible with Stacked Loras from https://github.com/jags111/efficiency-nodes-comfyui
When I talk about **lora**, I also mean **lycoris** too.
## Install
Some of this project nodes depends on https://github.com/pythongosssss/ComfyUI-Custom-Scripts :
- LoraLoaderAdvanced
- LoraLoaderStackedAdvanced
They get their vanilla equivalents.
- `View info...`
They get their vanilla equivalents with less features
Overall, Custom-Scripts is recommended to be able to know the content of the tag lists with the node `showText`
## Features
### Main nodes
### Lora Trigger Words
Lora trigger words are imported from two sources :
- Civitai api (only for civitai models)
![image](./images/CivitaiTriggerWords.png)
- Model training metadata (when available)
#### Vanilla vs Advanced
Vanilla refers to nodes that have no lora preview from the menu, nor the lora list. But the features provided are the same.
> Vanilla refers to nodes that have no lora preview from the menu, nor the lora list. But the features provided are the same.
![image](./images/main.png)
#### Nodes
- LoraLoader (Vanilla or Advanced)
- LoraLoaderStacked (Vanilla or Avanced). The stacked lora input is optional.
Allow to load a lora, either the normal way, or the efficiency-nodes way.
These loaders have two custom outputs:
- civitai_tags_list: a python list of the tags related to this lora on civitai
- meta_tags_list: a python list of the tags used for training the lora embeded in it (if any)
This outputs needs to be filtered by two othere nodes:
- TagsFormater: Helper to show the available tag and their indexes
- tagsSelector: allow to filter tags and apply a weight to it.
- TagSelector contains four parameters. First the selector (see Filtering next)
- The weight `(tag:weight)`
- The boolean `ensure_comma`. To properly append comma if a prefix or suffix is added.
- The boolean `append_loraname_if_empty` which will add the name of the lora in the list of outputs if they are empty.
#### Filtering
### Nodes
#### LoraLoader (Vanilla and Advanced)
![image](./images/LoraLoader.png)
INPUT
- `override_lora_name` (optional): Used to ignore the field `lora_name` and use the name passed. Should use [LoraListNames](#loralistnames) or the `lora_name` output.
FIELDS
- `force_fetch`: Force the civitai fetching of data even if there is already something saved
- `enable_preview`: Toggle on/off the saved lora preview if any (only in advanced)
- `append_lora_if_empty`: Add the name of the lora to the list of tags if the list is empty
OUTPUT
- `civitai_tags_list`: a python list of the tags related to this lora on civitai
- `meta_tags_list`: a python list of the tags used for training the lora embeded in it (if any)
- `lora_name`: the name of the current selected lora
#### LoraLoaderStacked (Vanilla and Avanced).
![image](./images/LoraLoaderStacked.png)
INPUT
- `lora_stack` (optional): another stack of lora.
- `override_lora_name` (optional): Used to ignore the field `lora_name` and use the name passed. Should use [LoraListNames](#loralistnames) or the `lora_name` output.
FIELDS
- `force_fetch`: Force the civitai fetching of data even if there is already something saved
- `enable_preview`: Toggle on/off the saved lora preview if any (only in advanced)
- `append_lora_if_empty`: Add the name of the lora to the list of tags if the list is empty
OUTPUT
- `civitai_tags_list`: a python list of the tags related to this lora on civitai
- `meta_tags_list`: a python list of the tags used for training the lora embeded in it (if any)
- `lora_name`: the name of the current selected lora
#### LoraTagsOnly
![image](./images/LoraTagsOnly.png)
To get the tags without using the lora.
- `override_lora_name` (optional): Used to ignore the field `lora_name` and use the name passed. Should use [LoraListNames](#loralistnames) or the `lora_name` output.
OUTPUT
- `civitai_tags_list`: a python list of the tags related to this lora on civitai
- `meta_tags_list`: a python list of the tags used for training the lora embeded in it (if any)
- `lora_name`: the name of the current selected lora
#### TagsFormater
![image](./images/TagsFormater.png)
Helper to show the available tag and their indexes. Tags are sorted by training frequence. The more a tag was used, the higher in the list it is. Works for both `civitai_tags_list` and `meta_tags_list`
#### TagsSelector
![image](./images/TagsSelector.png)
Allow to filter tags and apply a weight to it.
TagSelector contains four parameters.
- `selector` (see the [Filtering](#filtering) section next)
- `weight`: to format the tag like `(tag:weight)`. Default set to 1 without the weight like `tag`.
- `ensure_comma`. To properly append comma if a prefix or suffix is added.
#### LoraListNames
![image](./images/LoraListNames.png)
List all the existing lora names. It is used as an input for `override_lora_name`
### Filtering
The format is simple. It's the same as python list index, but can select multiple index or ranges of indexes separated by comas.
`Ex: 0, 3, 5:8, -8:`
- Select a specific list of indexes: `0, 2, 3, 15`...
@@ -42,6 +96,18 @@ The format is simple. It's the same as python list index, but can select multipl
- You can use negative indexes. Like `-1` to select the last tag
- By default `:` selects everything
### View Info
![image](./images/ViewInfo.png)
Pythongossss's [View Info...](https://github.com/pythongosssss/ComfyUI-Custom-Scripts?tab=readme-ov-file#checkpointloraembedding-info) feature from ComfyUI-Custom-Scripts
To enable this feature go into Settings > Pysssss > ModelInfo > 🐍 Model Info - Lora Nodes/Widgets
And add the following at the end of the line:
```
LoraLoaderVanilla.lora_name,LoraLoaderStackedVanilla.lora_name,LoraLoaderAdvanced.lora_name,LoraLoaderStackedAdvanced.lora_name,LoraTagsOnly.lora_name
```
### Examples
#### Example of normal workflow
![image](./images/loaderAdvanced.png)
@@ -55,5 +121,5 @@ Lora Stack can also be chained together to load multiple loras into an efficient
### Side nodes I made and kept here
- FusionText: takes two text input and join them together
- Randomizer: takes two couples text+lorastack and return randomly one them
- Randomizer: takes two couples text+lorastack and return randomly one of them
- TextInputBasic: just a text input with two additional input for text chaining
+2 -1
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@@ -5,4 +5,5 @@ from .nodes_utils import NODE_CLASS_MAPPINGS as nu_NCM
NODE_CLASS_MAPPINGS = dict(na_NCM, **nu_NCM)
#NODE_DISPLAY_NAME_MAPPINGS = dict(na_NDNM, **nu_NDNM)
__all__ = ["NODE_CLASS_MAPPINGS"]#, "NODE_DISPLAY_NAME_MAPPINGS"]
WEB_DIRECTORY = "./web"
__all__ = ["NODE_CLASS_MAPPINGS", "WEB_DIRECTORY"]#, "NODE_DISPLAY_NAME_MAPPINGS"]
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+6 -130
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@@ -14,7 +14,6 @@ class LoraLoaderVanilla:
return {
"required": {
"model": ("MODEL",),
"clip": ("CLIP", ),
"lora_name": (LORA_LIST, ),
"strength_model": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
"strength_clip": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
@@ -22,6 +21,7 @@ class LoraLoaderVanilla:
"append_loraname_if_empty": ("BOOLEAN", {"default": False}),
},
"optional": {
"clip": ("CLIP", ),
"override_lora_name":("STRING", {"forceInput": True}),
}
}
@@ -31,7 +31,9 @@ class LoraLoaderVanilla:
FUNCTION = "load_lora"
CATEGORY = "autotrigger"
def load_lora(self, model, clip, lora_name, strength_model, strength_clip, force_fetch, append_loraname_if_empty, override_lora_name=""):
def load_lora(self, model, lora_name, strength_model, strength_clip, force_fetch, append_loraname_if_empty, clip=None, override_lora_name=""):
if clip is None:
strength_clip=0
if override_lora_name != "":
lora_name = override_lora_name
meta_tags_list = sort_tags_by_frequency(get_metadata(lora_name, "loras"))
@@ -55,7 +57,6 @@ class LoraLoaderVanilla:
self.loaded_lora = (lora_path, lora)
model_lora, clip_lora = load_lora_for_models(model, clip, lora, strength_model, strength_clip)
return (model_lora, clip_lora, civitai_tags_list, meta_tags_list, lora_name)
class LoraLoaderStackedVanilla:
@@ -98,130 +99,6 @@ class LoraLoaderStackedVanilla:
return (civitai_tags_list, meta_tags_list, loras, lora_name)
class LoraLoaderAdvanced:
def __init__(self):
self.loaded_lora = None
@classmethod
def INPUT_TYPES(s):
LORA_LIST = sorted(folder_paths.get_filename_list("loras"), key=str.lower)
populate_items(LORA_LIST, "loras")
return {
"required": {
"model": ("MODEL",),
"clip": ("CLIP", ),
"lora_name": (LORA_LIST, ),
"strength_model": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
"strength_clip": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
"force_fetch": ("BOOLEAN", {"default": False}),
"enable_preview": ("BOOLEAN", {"default": False}),
"append_loraname_if_empty": ("BOOLEAN", {"default": False}),
},
"optional": {
"override_lora_name":("STRING", {"forceInput": True}),
}
}
RETURN_TYPES = ("MODEL", "CLIP", "LIST", "LIST", "STRING")
RETURN_NAMES = ("MODEL", "CLIP", "civitai_tags_list", "meta_tags_list", "lora_name")
FUNCTION = "load_lora"
CATEGORY = "autotrigger"
def load_lora(self, model, clip, lora_name, strength_model, strength_clip, force_fetch, enable_preview, append_loraname_if_empty, override_lora_name=""):
if override_lora_name != "":
has_preview, prev = get_preview_path(override_lora_name, "loras")
prev = f"loras/{prev}" if has_preview else None
lora_name = {"content": override_lora_name, "image": prev, "type": "loras"}
meta_tags_list = sort_tags_by_frequency(get_metadata(lora_name["content"], "loras"))
civitai_tags_list = load_and_save_tags(lora_name["content"], force_fetch)
civitai_tags_list = append_lora_name_if_empty(civitai_tags_list, lora_name["content"], append_loraname_if_empty)
meta_tags_list = append_lora_name_if_empty(meta_tags_list, lora_name["content"], append_loraname_if_empty)
lora_path = folder_paths.get_full_path("loras", lora_name["content"])
lora = None
if self.loaded_lora is not None:
if self.loaded_lora[0] == lora_path:
lora = self.loaded_lora[1]
else:
temp = self.loaded_lora
self.loaded_lora = None
del temp
if lora is None:
lora = load_torch_file(lora_path, safe_load=True)
self.loaded_lora = (lora_path, lora)
model_lora, clip_lora = load_lora_for_models(model, clip, lora, strength_model, strength_clip)
if enable_preview:
_, preview = copy_preview_to_temp(lora_name["image"])
if preview is not None:
preview_output = {
"filename": preview,
"subfolder": "lora_preview",
"type": "temp"
}
return {"ui": {"images": [preview_output]}, "result": (model_lora, clip_lora, civitai_tags_list, meta_tags_list, lora_name["content"])}
return (model_lora, clip_lora, civitai_tags_list, meta_tags_list, lora_name["content"])
class LoraLoaderStackedAdvanced:
@classmethod
def INPUT_TYPES(s):
LORA_LIST = folder_paths.get_filename_list("loras")
populate_items(LORA_LIST, "loras")
return {
"required": {
"lora_name": (LORA_LIST,),
"lora_weight": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
"force_fetch": ("BOOLEAN", {"default": False}),
"enable_preview": ("BOOLEAN", {"default": False}),
"append_loraname_if_empty": ("BOOLEAN", {"default": False}),
},
"optional": {
"lora_stack": ("LORA_STACK", ),
"override_lora_name":("STRING", {"forceInput": True}),
}
}
RETURN_TYPES = ("LIST", "LIST", "LORA_STACK", "STRING")
RETURN_NAMES = ("civitai_tags_list", "meta_tags_list", "LORA_STACK", "lora_name")
FUNCTION = "set_stack"
#OUTPUT_NODE = False
CATEGORY = "autotrigger"
def set_stack(self, lora_name, lora_weight, force_fetch, enable_preview, append_loraname_if_empty, lora_stack=None, override_lora_name=""):
if override_lora_name != "":
has_preview, prev = get_preview_path(override_lora_name, "loras")
prev = f"loras/{prev}" if has_preview else None
lora_name = {"content": override_lora_name, "image": prev, "type": "loras"}
civitai_tags_list = load_and_save_tags(lora_name["content"], force_fetch)
meta_tags = get_metadata(lora_name["content"], "loras")
meta_tags_list = sort_tags_by_frequency(meta_tags)
civitai_tags_list = append_lora_name_if_empty(civitai_tags_list, lora_name["content"], append_loraname_if_empty)
meta_tags_list = append_lora_name_if_empty(meta_tags_list, lora_name["content"], append_loraname_if_empty)
loras = [(lora_name["content"],lora_weight,lora_weight,)]
if lora_stack is not None:
loras.extend(lora_stack)
if enable_preview:
_, preview = copy_preview_to_temp(lora_name["image"])
if preview is not None:
preview_output = {
"filename": preview,
"subfolder": "lora_preview",
"type": "temp"
}
return {"ui": {"images": [preview_output]}, "result": (civitai_tags_list, meta_tags_list, loras, lora_name["content"])}
return {"result": (civitai_tags_list, meta_tags_list, loras, lora_name["content"])}
class LoraTagsOnly:
@classmethod
def INPUT_TYPES(s):
@@ -253,14 +130,13 @@ class LoraTagsOnly:
return (civitai_tags_list, meta_tags_list)
# A dictionary that contains all nodes you want to export with their names
# NOTE: names should be globally unique
NODE_CLASS_MAPPINGS = {
"LoraLoaderVanilla": LoraLoaderVanilla,
"LoraLoaderStackedVanilla": LoraLoaderStackedVanilla,
"LoraLoaderAdvanced": LoraLoaderAdvanced,
"LoraLoaderStackedAdvanced": LoraLoaderStackedAdvanced,
"LoraLoaderAdvanced": LoraLoaderVanilla,
"LoraLoaderStackedAdvanced": LoraLoaderStackedVanilla,
"LoraTagsOnly": LoraTagsOnly,
}
+14
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@@ -0,0 +1,14 @@
[project]
name = "comfyui-lora-auto-trigger-words"
description = "The aim of these custom nodes is to get an easy access to the tags used to trigger a lora / lycoris. Extract the tags from civitai or from the safetensors metadatas when available."
version = "1.1.0"
license = "MIT"
[project.urls]
Repository = "https://github.com/idrirap/ComfyUI-Lora-Auto-Trigger-Words"
# Used by Comfy Registry https://comfyregistry.org
[tool.comfy]
PublisherId = "dijkstra"
DisplayName = "ComfyUI-Lora-Auto-Trigger-Words"
Icon = ""
+5 -64
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@@ -3,60 +3,6 @@ import hashlib
import json
import os
import requests
import shutil
def get_preview_path(name, type):
file_name = os.path.splitext(name)[0]
file_path = folder_paths.get_full_path(type, name)
if file_path is None:
print(f"Unable to get path for {type} {name}")
return None
file_path_no_ext = os.path.splitext(file_path)[0]
item_image=None
for ext in ["png", "jpg", "jpeg", "preview.png"]:
has_image = os.path.isfile(file_path_no_ext + "." + ext)
if has_image:
item_image = f"{file_name}.{ext}"
break
return has_image, item_image
def copy_preview_to_temp(file_name):
if file_name is None:
return None, None
base_name = os.path.basename(file_name)
lora_less = "/".join(file_name.split("/")[1:])
file_path = folder_paths.get_full_path("loras", lora_less)
if file_path is None:
return None, None
temp_path = folder_paths.get_temp_directory()
preview_path = os.path.join(temp_path, "lora_preview")
if not os.path.isdir(preview_path) :
os.makedirs(preview_path)
preview_path = os.path.join(preview_path, base_name)
shutil.copyfile(file_path, preview_path)
return preview_path, base_name
# add previews in selectors
def populate_items(names, type):
for idx, item_name in enumerate(names):
has_image, item_image = get_preview_path(item_name, type)
names[idx] = {
"content": item_name,
"image": f"{type}/{item_image}" if has_image else None,
"type": "loras",
}
names.sort(key=lambda i: i["content"].lower())
def load_json_from_file(file_path):
try:
@@ -80,8 +26,11 @@ def save_dict_to_json(data_dict, file_path):
def get_model_version_info(hash_value):
api_url = f"https://civitai.com/api/v1/model-versions/by-hash/{hash_value}"
response = requests.get(api_url)
try:
response = requests.get(api_url)
except Exception as e:
print(f"[Lora-Auto-Trigger] {e}")
return None
if response.status_code == 200:
return response.json()
else:
@@ -127,14 +76,6 @@ def load_and_save_tags(lora_name, force_fetch):
return output_tags_list
def show_list(list_input):
i = 0
output = ""
for debug in list_input:
output += f"{i} : {debug}\n"
i+=1
return output
def get_metadata(filepath, type):
filepath = folder_paths.get_full_path(type, filepath)
with open(filepath, "rb") as file:
+469
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@@ -0,0 +1,469 @@
// COMPLETELY STOLEN FROM https://github.com/pythongosssss/ComfyUI-Custom-Scripts/blob/main/web/js/betterCombos.js
import { app } from "../../../scripts/app.js";
import { $el } from "../../../scripts/ui.js";
import { api } from "../../../scripts/api.js";
const LORA_LOADER = "LoraLoaderAdvanced"
const LORA_LOADER_STACKED = "LoraLoaderStackedAdvanced"
const IMAGE_WIDTH = 384;
const IMAGE_HEIGHT = 384;
function getType(node) {
return "loras";
}
function getWidgetName(type) {
return type === "checkpoints" ? "ckpt_name" : "lora_name";
}
function encodeRFC3986URIComponent(str) {
return encodeURIComponent(str).replace(/[!'()*]/g, (c) => `%${c.charCodeAt(0).toString(16).toUpperCase()}`);
}
const calculateImagePosition = (el, bodyRect) => {
let { top, left, right } = el.getBoundingClientRect();
const { width: bodyWidth, height: bodyHeight } = bodyRect;
const isSpaceRight = right + IMAGE_WIDTH <= bodyWidth;
if (isSpaceRight) {
left = right;
} else {
left -= IMAGE_WIDTH;
}
top = top - IMAGE_HEIGHT / 2;
if (top + IMAGE_HEIGHT > bodyHeight) {
top = bodyHeight - IMAGE_HEIGHT;
}
if (top < 0) {
top = 0;
}
return { left: Math.round(left), top: Math.round(top), isLeft: !isSpaceRight };
};
function showImage(relativeToEl, imageEl) {
const bodyRect = document.body.getBoundingClientRect();
if (!bodyRect) return;
const { left, top, isLeft } = calculateImagePosition(relativeToEl, bodyRect);
imageEl.style.left = `${left}px`;
imageEl.style.top = `${top}px`;
if (isLeft) {
imageEl.classList.add("left");
} else {
imageEl.classList.remove("left");
}
document.body.appendChild(imageEl);
}
let imagesByType = {};
const loadImageList = async (type) => {
imagesByType[type] = await (await api.fetchApi(`/pysssss/images/${type}`)).json();
};
app.registerExtension({
name: "autotrigger.Combo++",
init() {
const displayOptions = { "List (normal)": 0, "Tree (subfolders)": 1, "Thumbnails (grid)": 2 };
const displaySetting = app.ui.settings.addSetting({
id: "autotrigger.Combo++.Submenu",
name: "🐍 Lora & Checkpoint loader display mode",
defaultValue: 1,
type: "combo",
options: (value) => {
value = +value;
return Object.entries(displayOptions).map(([k, v]) => ({
value: v,
text: k,
selected: k === value,
}));
},
});
$el("style", {
textContent: `
.pysssss-combo-image {
position: absolute;
left: 0;
top: 0;
width: ${IMAGE_WIDTH}px;
height: ${IMAGE_HEIGHT}px;
object-fit: contain;
object-position: top left;
z-index: 9999;
}
.pysssss-combo-image.left {
object-position: top right;
}
.pysssss-combo-folder { opacity: 0.7 }
.pysssss-combo-folder-arrow { display: inline-block; width: 15px; }
.pysssss-combo-folder:hover { background-color: rgba(255, 255, 255, 0.1); }
.pysssss-combo-prefix { display: none }
/* Special handling for when the filter input is populated to revert to normal */
.litecontextmenu:has(input:not(:placeholder-shown)) .pysssss-combo-folder-contents {
display: block !important;
}
.litecontextmenu:has(input:not(:placeholder-shown)) .pysssss-combo-folder {
display: none;
}
.litecontextmenu:has(input:not(:placeholder-shown)) .pysssss-combo-prefix {
display: inline;
}
.litecontextmenu:has(input:not(:placeholder-shown)) .litemenu-entry {
padding-left: 2px !important;
}
/* Grid mode */
.pysssss-combo-grid {
display: grid;
grid-template-columns: repeat(auto-fit, minmax(250px, 1fr));
gap: 10px;
overflow-x: hidden;
max-width: 60vw;
}
.pysssss-combo-grid .comfy-context-menu-filter {
grid-column: 1 / -1;
position: sticky;
top: 0;
}
.pysssss-combo-grid .litemenu-entry {
word-break: break-word;
display: flex;
flex-direction: column;
justify-content: space-between;
align-items: center;
}
.pysssss-combo-grid .litemenu-entry:before {
content: "";
display: block;
width: 100%;
height: 250px;
background-size: contain;
background-position: center;
background-repeat: no-repeat;
/* No-image image attribution: Picture icons created by Pixel perfect - Flaticon */
background-image: var(--background-image, url(extensions/ComfyUI-Custom-Scripts/js/assets/no-image.png));
}
`,
parent: document.body,
});
const p1 = loadImageList("checkpoints");
const p2 = loadImageList("loras");
const refreshComboInNodes = app.refreshComboInNodes;
app.refreshComboInNodes = async function () {
const r = await Promise.all([
refreshComboInNodes.apply(this, arguments),
loadImageList("checkpoints").catch(() => {}),
loadImageList("loras").catch(() => {}),
]);
return r[0];
};
const imageHost = $el("img.pysssss-combo-image");
const positionMenu = (menu, fillWidth) => {
// compute best position
let left = app.canvas.last_mouse[0] - 10;
let top = app.canvas.last_mouse[1] - 10;
const body_rect = document.body.getBoundingClientRect();
const root_rect = menu.getBoundingClientRect();
if (body_rect.width && left > body_rect.width - root_rect.width - 10) left = body_rect.width - root_rect.width - 10;
if (body_rect.height && top > body_rect.height - root_rect.height - 10) top = body_rect.height - root_rect.height - 10;
menu.style.left = `${left}px`;
menu.style.top = `${top}px`;
if (fillWidth) {
menu.style.right = "10px";
}
};
const updateMenu = async (menu, type) => {
try {
await p1;
await p2;
} catch (error) {
console.error(error);
console.error("Error loading pysssss.betterCombos data");
}
// Clamp max height so it doesn't overflow the screen
const position = menu.getBoundingClientRect();
const maxHeight = window.innerHeight - position.top - 20;
menu.style.maxHeight = `${maxHeight}px`;
const images = imagesByType[type];
const items = menu.querySelectorAll(".litemenu-entry");
// Add image handler to items
const addImageHandler = (item) => {
const text = item.getAttribute("data-value").trim();
if (images[text]) {
const textNode = document.createTextNode("*");
item.appendChild(textNode);
item.addEventListener(
"mouseover",
() => {
imageHost.src = `/pysssss/view/${encodeRFC3986URIComponent(images[text])}?${+new Date()}`;
document.body.appendChild(imageHost);
showImage(item, imageHost);
},
{ passive: true }
);
item.addEventListener(
"mouseout",
() => {
imageHost.remove();
},
{ passive: true }
);
item.addEventListener(
"click",
() => {
imageHost.remove();
},
{ passive: true }
);
}
};
const createTree = () => {
// Create a map to store folder structures
const folderMap = new Map();
const rootItems = [];
const splitBy = (navigator.platform || navigator.userAgent).includes("Win") ? /\/|\\/ : /\//;
const itemsSymbol = Symbol("items");
// First pass - organize items into folder structure
for (const item of items) {
const path = item.getAttribute("data-value").split(splitBy);
// Remove path from visible text
item.textContent = path[path.length - 1];
if (path.length > 1) {
// Add the prefix path back in so it can be filtered on
const prefix = $el("span.pysssss-combo-prefix", {
textContent: path.slice(0, -1).join("/") + "/",
});
item.prepend(prefix);
}
addImageHandler(item);
if (path.length === 1) {
rootItems.push(item);
continue;
}
// Temporarily remove the item from current position
item.remove();
// Create folder hierarchy
let currentLevel = folderMap;
for (let i = 0; i < path.length - 1; i++) {
const folder = path[i];
if (!currentLevel.has(folder)) {
currentLevel.set(folder, new Map());
}
currentLevel = currentLevel.get(folder);
}
// Store the actual item in the deepest folder
if (!currentLevel.has(itemsSymbol)) {
currentLevel.set(itemsSymbol, []);
}
currentLevel.get(itemsSymbol).push(item);
}
const createFolderElement = (name) => {
const folder = $el("div.litemenu-entry.pysssss-combo-folder", {
innerHTML: `<span class="pysssss-combo-folder-arrow">▶</span> ${name}`,
style: { paddingLeft: "5px" },
});
return folder;
};
const insertFolderStructure = (parentElement, map, level = 0) => {
for (const [folderName, content] of map.entries()) {
if (folderName === itemsSymbol) continue;
const folderElement = createFolderElement(folderName);
folderElement.style.paddingLeft = `${level * 10 + 5}px`;
parentElement.appendChild(folderElement);
const childContainer = $el("div.pysssss-combo-folder-contents", {
style: { display: "none" },
});
// Add items in this folder
const items = content.get(itemsSymbol) || [];
for (const item of items) {
item.style.paddingLeft = `${(level + 1) * 10 + 14}px`;
childContainer.appendChild(item);
}
// Recursively add subfolders
insertFolderStructure(childContainer, content, level + 1);
parentElement.appendChild(childContainer);
// Add click handler for folder
folderElement.addEventListener("click", (e) => {
e.stopPropagation();
const arrow = folderElement.querySelector(".pysssss-combo-folder-arrow");
const contents = folderElement.nextElementSibling;
if (contents.style.display === "none") {
contents.style.display = "block";
arrow.textContent = "▼";
} else {
contents.style.display = "none";
arrow.textContent = "▶";
}
});
}
};
insertFolderStructure(items[0]?.parentElement || menu, folderMap);
positionMenu(menu);
};
const addImageData = (item) => {
const text = item.getAttribute("data-value").trim();
if (images[text]) {
item.style.setProperty("--background-image", `url(/pysssss/view/${encodeRFC3986URIComponent(images[text])})`);
}
};
if (displaySetting.value === 1 || displaySetting.value === true) {
createTree();
} else if (displaySetting.value === 2) {
menu.classList.add("pysssss-combo-grid");
for (const item of items) {
addImageData(item);
}
positionMenu(menu, true);
} else {
for (const item of items) {
addImageHandler(item);
}
}
};
const mutationObserver = new MutationObserver((mutations) => {
const node = app.canvas.current_node;
if (!node || (node.comfyClass !== LORA_LOADER && node.comfyClass !== LORA_LOADER_STACKED)) {
return;
}
for (const mutation of mutations) {
for (const removed of mutation.removedNodes) {
if (removed.classList?.contains("litecontextmenu")) {
imageHost.remove();
}
}
for (const added of mutation.addedNodes) {
if (added.classList?.contains("litecontextmenu")) {
const overWidget = app.canvas.getWidgetAtCursor();
const type = getType(node);
if (overWidget?.name === getWidgetName(type)) {
requestAnimationFrame(() => {
// Bad hack to prevent showing on right click menu by checking for the filter input
if (!added.querySelector(".comfy-context-menu-filter")) return;
updateMenu(added, type);
});
}
return;
}
}
}
});
mutationObserver.observe(document.body, { childList: true, subtree: false });
},
async beforeRegisterNodeDef(nodeType, nodeData, app) {
const isLora = (nodeData.name === LORA_LOADER || nodeData.name === LORA_LOADER_STACKED);
if (isLora) {
const onAdded = nodeType.prototype.onAdded;
nodeType.prototype.onAdded = function () {
onAdded?.apply(this, arguments);
const modelWidget = this.widgets[0];
const modelCb = modelWidget.callback;
let prev = undefined;
modelWidget.callback = function () {
let ret = modelCb?.apply(this, arguments) ?? modelWidget.value;
if (typeof ret === "object" && "content" in ret) {
ret = ret.content;
modelWidget.value = ret;
}
let v = ret;
if (prev !== v) {
prev = v;
}
return ret;
};
setTimeout(() => {
modelWidget.callback();
}, 30);
};
}
const getExtraMenuOptions = nodeType.prototype.getExtraMenuOptions;
nodeType.prototype.getExtraMenuOptions = function (_, options) {
if (this.imgs) {
// If this node has images then we add an open in new tab item
let img;
if (this.imageIndex != null) {
// An image is selected so select that
img = this.imgs[this.imageIndex];
} else if (this.overIndex != null) {
// No image is selected but one is hovered
img = this.imgs[this.overIndex];
}
if (img) {
const nodes = app.graph._nodes.filter((n) => n.comfyClass === LORA_LOADER || n.comfyClass === LORA_LOADER_STACKED);
if (nodes.length) {
options.unshift({
content: "Save as Preview",
submenu: {
options: nodes.map((n) => ({
content: n.widgets[0].value,
callback: async () => {
const url = new URL(img.src);
await api.fetchApi("/pysssss/save/" + encodeRFC3986URIComponent(`${getType(n)}/${n.widgets[0].value}`), {
method: "POST",
body: JSON.stringify({
filename: url.searchParams.get("filename"),
subfolder: url.searchParams.get("subfolder"),
type: url.searchParams.get("type"),
}),
headers: {
"content-type": "application/json",
},
});
loadImageList(getType(n));
},
})),
},
});
}
}
}
return getExtraMenuOptions?.apply(this, arguments);
};
},
});