feat(nodes): add Promp (LoraManager) node and autocomplete support

py/metadata_collector/node_extractors.py

@@ -666,6 +666,7 @@ NODE_EXTRACTORS = {
     "LoraManagerLoader": LoraLoaderManagerExtractor,
     # Conditioning
     "CLIPTextEncode": CLIPTextEncodeExtractor,
+    "PromptLoraManager": CLIPTextEncodeExtractor,
     "CLIPTextEncodeFlux": CLIPTextEncodeFluxExtractor,  # Add CLIPTextEncodeFlux
     "WAS_Text_to_Conditioning": CLIPTextEncodeExtractor,
     "AdvancedCLIPTextEncode": CLIPTextEncodeExtractor,  # From https://github.com/BlenderNeko/ComfyUI_ADV_CLIP_emb

py/nodes/prompt.py

@@ -0,0 +1,59 @@
+from typing import Any, Optional
+from nodes import CLIPTextEncode  # type: ignore
+
+class PromptLoraManager:
+    """Encodes text (and optional trigger words) into CLIP conditioning."""
+
+    NAME = "Prompt (LoraManager)"
+    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."
+    )
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "text": (
+                    'STRING',
+                    {
+                        "multiline": True,
+                        "pysssss.autocomplete": False, 
+                        "dynamicPrompts": True,
+                        "tooltip": "The text to be encoded.",
+                    },
+                ),
+                "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."
+                        )
+                    },
+                )
+            },
+        }
+
+    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
+        if trigger_words:
+            prompt = ", ".join([trigger_words, text])
+
+        conditioning = CLIPTextEncode().encode(clip, prompt)[0]
+        return (conditioning, prompt,)