mirror of
https://github.com/willmiao/ComfyUI-Lora-Manager.git
synced 2026-03-22 05:32:12 -03:00
16c52877ad
- Backend: Add _AllContainer for dynamic input validation bypass - Backend: Modify INPUT_TYPES to support trigger_words1, trigger_words2, etc. - Backend: Update encode() to collect all trigger_words* from kwargs - Frontend: Create prompt_dynamic_inputs.js extension - Frontend: Implement onConnectionsChange to auto-add/remove input slots - Frontend: Renumber inputs sequentially on connect/disconnect Based on Impact Pack's Switch (Any) node dynamic input pattern.
84 lines
2.6 KiB
Python
84 lines
2.6 KiB
Python
from typing import Any
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import inspect
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class _AllContainer:
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"""Container that accepts any key for dynamic input validation."""
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def __contains__(self, item):
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return True
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def __getitem__(self, key):
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return ("STRING", {"forceInput": True})
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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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"Supports dynamic trigger words inputs."
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)
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@classmethod
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def INPUT_TYPES(cls):
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dyn_inputs = {
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"trigger_words1": (
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"STRING",
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{
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"forceInput": True,
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"tooltip": "Trigger words to prepend. Connect to add more inputs.",
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},
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),
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}
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# Bypass validation for dynamic inputs during graph execution
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stack = inspect.stack()
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if len(stack) > 2 and stack[2].function == "get_input_info":
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dyn_inputs = _AllContainer()
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return {
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"required": {
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"text": (
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"AUTOCOMPLETE_TEXT_PROMPT,STRING",
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{
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"widgetType": "AUTOCOMPLETE_TEXT_PROMPT",
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"placeholder": "Enter prompt... /char, /artist for quick tag search",
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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": dyn_inputs,
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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, **kwargs):
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# Collect all trigger words from dynamic inputs
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trigger_words = []
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for key, value in kwargs.items():
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if key.startswith("trigger_words") and value:
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trigger_words.append(value)
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# Build final prompt
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if trigger_words:
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prompt = ", ".join(trigger_words + [text])
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else:
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prompt = 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) |