feat: add dynamic trigger_words inputs to PromptLM node

- 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.

py/nodes/prompt.py

@@ -1,4 +1,16 @@
-from typing import Any, Optional
+from typing import Any
+import inspect
+
+
+class _AllContainer:
+    """Container that accepts any key for dynamic input validation."""
+
+    def __contains__(self, item):
+        return True
+
+    def __getitem__(self, key):
+        return ("STRING", {"forceInput": True})
+
 
 class PromptLM:
     """Encodes text (and optional trigger words) into CLIP conditioning."""
@@ -7,11 +19,27 @@ class PromptLM:
     CATEGORY = "Lora Manager/conditioning"
     DESCRIPTION = (
         "Encodes a text prompt using a CLIP model into an embedding that can be used "
-        "to guide the diffusion model towards generating specific images."
+        "to guide the diffusion model towards generating specific images. "
+        "Supports dynamic trigger words inputs."
     )
 
     @classmethod
     def INPUT_TYPES(cls):
+        dyn_inputs = {
+            "trigger_words1": (
+                "STRING",
+                {
+                    "forceInput": True,
+                    "tooltip": "Trigger words to prepend. Connect to add more inputs.",
+                },
+            ),
+        }
+
+        # Bypass validation for dynamic inputs during graph execution
+        stack = inspect.stack()
+        if len(stack) > 2 and stack[2].function == "get_input_info":
+            dyn_inputs = _AllContainer()
+
         return {
             "required": {
                 "text": (
@@ -23,36 +51,34 @@ class PromptLM:
                     },
                 ),
                 "clip": (
-                    'CLIP',
+                    "CLIP",
                     {"tooltip": "The CLIP model used for encoding the text."},
                 ),
             },
-            "optional": {
-                "trigger_words": (
-                    'STRING',
-                    {
-                        "forceInput": True,
-                        "tooltip": (
-                            "Optional trigger words to prepend to the text before "
-                            "encoding."
-                        )
-                    },
-                )
-            },
+            "optional": dyn_inputs,
         }
 
-    RETURN_TYPES = ('CONDITIONING', 'STRING',)
-    RETURN_NAMES = ('CONDITIONING', 'PROMPT',)
+    RETURN_TYPES = ("CONDITIONING", "STRING")
+    RETURN_NAMES = ("CONDITIONING", "PROMPT")
     OUTPUT_TOOLTIPS = (
         "A conditioning containing the embedded text used to guide the diffusion model.",
     )
     FUNCTION = "encode"
 
-    def encode(self, text: str, clip: Any, trigger_words: Optional[str] = None):
-        prompt = text
+    def encode(self, text: str, clip: Any, **kwargs):
+        # Collect all trigger words from dynamic inputs
+        trigger_words = []
+        for key, value in kwargs.items():
+            if key.startswith("trigger_words") and value:
+                trigger_words.append(value)
+
+        # Build final prompt
         if trigger_words:
-            prompt = ", ".join([trigger_words, text])
+            prompt = ", ".join(trigger_words + [text])
+        else:
+            prompt = text
 
         from nodes import CLIPTextEncode  # type: ignore
+
         conditioning = CLIPTextEncode().encode(clip, prompt)[0]
-        return (conditioning, prompt,)
+        return (conditioning, prompt)