Ethanfel
980f406573
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>
72 lines
2.6 KiB
Python
72 lines
2.6 KiB
Python
"""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)
|