mirror of
https://github.com/willmiao/ComfyUI-Lora-Manager.git
synced 2026-03-23 06:02:11 -03:00
d5a2bd1e24
Adds custom words autocomplete functionality similar to comfyui-custom-scripts, with the following features: Backend (Python): - Create CustomWordsService for CSV parsing and priority-based search - Add API endpoints: GET/POST /api/lm/custom-words and GET /api/lm/custom-words/search - Share storage with pysssss plugin (checks for their user/autocomplete.txt first) - Fallback to Lora Manager's user directory for storage Frontend (JavaScript/Vue): - Add 'custom_words' and 'prompt' model types to autocomplete system - Prompt node now supports dual-mode autocomplete: * Type 'emb:' prefix → search embeddings * Type normally → search custom words (no prefix required) - Add AUTOCOMPLETE_TEXT_PROMPT widget type - Update Vue component and composable types Key Features: - CSV format: word[,priority] compatible with danbooru-tags.txt - Priority-based sorting: 20% top priority + prefix + include matches - Preview tooltip for embeddings (not for custom words) - Dynamic endpoint switching based on prefix detection Breaking Changes: - Prompt (LoraManager) node widget type changed from AUTOCOMPLETE_TEXT_EMBEDDINGS to AUTOCOMPLETE_TEXT_PROMPT - Removed standalone web/comfyui/prompt.js (integrated into main widgets) Fixes comfy_dir path calculation by prioritizing folder_paths.base_path from ComfyUI when available, with fallback to computed path.
57 lines
1.9 KiB
Python
57 lines
1.9 KiB
Python
from typing import Any, Optional
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class PromptLM:
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"""Encodes text (and optional trigger words) into CLIP conditioning."""
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NAME = "Prompt (LoraManager)"
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CATEGORY = "Lora Manager/conditioning"
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DESCRIPTION = (
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"Encodes a text prompt using a CLIP model into an embedding that can be used "
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"to guide the diffusion model towards generating specific images."
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)
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"text": (
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"AUTOCOMPLETE_TEXT_PROMPT",
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{
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"placeholder": "Enter prompt...",
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"tooltip": "The text to be encoded.",
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},
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),
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"clip": (
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'CLIP',
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{"tooltip": "The CLIP model used for encoding the text."},
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),
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},
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"optional": {
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"trigger_words": (
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'STRING',
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{
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"forceInput": True,
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"tooltip": (
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"Optional trigger words to prepend to the text before "
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"encoding."
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)
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},
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)
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},
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}
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RETURN_TYPES = ('CONDITIONING', 'STRING',)
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RETURN_NAMES = ('CONDITIONING', 'PROMPT',)
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OUTPUT_TOOLTIPS = (
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"A conditioning containing the embedded text used to guide the diffusion model.",
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)
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FUNCTION = "encode"
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def encode(self, text: str, clip: Any, trigger_words: Optional[str] = None):
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prompt = text
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if trigger_words:
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prompt = ", ".join([trigger_words, text])
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from nodes import CLIPTextEncode # type: ignore
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conditioning = CLIPTextEncode().encode(clip, prompt)[0]
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return (conditioning, prompt,) |