feat: initial release of ComfyUI-LM-Remote

Remote-aware LoRA Manager nodes that fetch metadata via HTTP from a
remote Docker instance while loading LoRA files from local NFS/SMB
mounts. Includes reverse-proxy middleware for transparent web UI access.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

nodes/lora_stacker.py

@@ -0,0 +1,71 @@
+"""Remote LoRA Stacker — fetch metadata from the remote LoRA Manager."""
+from __future__ import annotations
+
+import logging
+import os
+
+from .remote_utils import get_lora_info_remote
+from .utils import FlexibleOptionalInputType, any_type, extract_lora_name, get_loras_list
+
+logger = logging.getLogger(__name__)
+
+
+class LoraStackerRemoteLM:
+    NAME = "Lora Stacker (Remote, LoraManager)"
+    CATEGORY = "Lora Manager/stackers"
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "text": ("AUTOCOMPLETE_TEXT_LORAS", {
+                    "placeholder": "Search LoRAs to add...",
+                    "tooltip": "Format: <lora:lora_name:strength> separated by spaces or punctuation",
+                }),
+            },
+            "optional": FlexibleOptionalInputType(any_type),
+        }
+
+    RETURN_TYPES = ("LORA_STACK", "STRING", "STRING")
+    RETURN_NAMES = ("LORA_STACK", "trigger_words", "active_loras")
+    FUNCTION = "stack_loras"
+
+    def stack_loras(self, text, **kwargs):
+        stack = []
+        active_loras = []
+        all_trigger_words = []
+
+        lora_stack = kwargs.get("lora_stack", None)
+        if lora_stack:
+            stack.extend(lora_stack)
+            for lora_path, _, _ in lora_stack:
+                lora_name = extract_lora_name(lora_path)
+                _, trigger_words = get_lora_info_remote(lora_name)
+                all_trigger_words.extend(trigger_words)
+
+        loras_list = get_loras_list(kwargs)
+        for lora in loras_list:
+            if not lora.get("active", False):
+                continue
+
+            lora_name = lora["name"]
+            model_strength = float(lora["strength"])
+            clip_strength = float(lora.get("clipStrength", model_strength))
+
+            lora_path, trigger_words = get_lora_info_remote(lora_name)
+
+            stack.append((lora_path.replace("/", os.sep), model_strength, clip_strength))
+            active_loras.append((lora_name, model_strength, clip_strength))
+            all_trigger_words.extend(trigger_words)
+
+        trigger_words_text = ",, ".join(all_trigger_words) if all_trigger_words else ""
+
+        formatted_loras = []
+        for name, model_strength, clip_strength in active_loras:
+            if abs(model_strength - clip_strength) > 0.001:
+                formatted_loras.append(f"<lora:{name}:{str(model_strength).strip()}:{str(clip_strength).strip()}>")
+            else:
+                formatted_loras.append(f"<lora:{name}:{str(model_strength).strip()}>")
+
+        active_loras_text = " ".join(formatted_loras)
+        return (stack, trigger_words_text, active_loras_text)