add node：Load Lora With Shared

node.py

@@ -25,6 +25,8 @@ import time
 import random
 import logging
 import uuid
+import folder_paths
+import nodes
 # from ..ComfyUI_IPAdapter_plus import IPAdapterPlus
 from PIL import Image, ImageOps, ImageSequence, ImageFile,UnidentifiedImageError
 from PIL.PngImagePlugin import PngInfo
@@ -51,6 +53,36 @@ import importlib
 import folder_paths
 import latent_preview
 import node_helpers
+from .libs.utils import TaggedCache, any_typ
+
+root_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+settings_file = os.path.join(root_dir, 'cache_settings.json')
+try:
+    with open(settings_file) as f:
+        cache_settings = json.load(f)
+except Exception as e:
+    print(e)
+    cache_settings = {}
+cache = TaggedCache(cache_settings)
+cache_count = {}
+
+
+def update_cache(k, tag, v):
+    cache[k] = (tag, v)
+    cnt = cache_count.get(k)
+    if cnt is None:
+        cnt = 0
+        cache_count[k] = cnt
+    else:
+        cache_count[k] += 1
+
+
+def cache_weak_hash(k):
+    cnt = cache_count.get(k)
+    if cnt is None:
+        cnt = 0
+
+    return k, cnt
 
 # 获取当前文件的目录
 current_dir = os.path.dirname(os.path.abspath(__file__))
@@ -443,4 +475,103 @@ class IPAdapter_FaceID_Bool():
             return (work_model, face_image, True)
         except Exception as e:
             print(f"Error: {e}")
-            return (model, image, False)
+            return (model, image, False)
+        
+class LoraLoaderShared(nodes.LoraLoader):
+    def __init__(self):
+        self.loaded_lora = None
+
+    @classmethod
+    def INPUT_TYPES(s):
+        file_list = folder_paths.get_filename_list("loras")
+        file_list.insert(0, "None")
+        return {
+            "required": { 
+                "model": ("MODEL", {"tooltip": "The diffusion model the LoRA will be applied to."}),
+                "clip": ("CLIP", {"tooltip": "The CLIP model the LoRA will be applied to."}),
+                "lora_name": (file_list, {"tooltip": "The name of the LoRA."}),
+                "strength_model": ("FLOAT", {"default": 1.0, "min": -100.0, "max": 100.0, "step": 0.01, "tooltip": "How strongly to modify the diffusion model. This value can be negative."}),
+                "strength_clip": ("FLOAT", {"default": 1.0, "min": -100.0, "max": 100.0, "step": 0.01, "tooltip": "How strongly to modify the CLIP model. This value can be negative."}),
+                "key_opt": ("STRING", {"multiline": False, "placeholder": "If empty, use 'lora_name' as the key."}),
+            },
+            "optional": {
+                "mode": (['Auto', 'Override Cache', 'Read Only'],),
+            }
+        }
+    
+    RETURN_TYPES = ("MODEL", "CLIP", "STRING")
+    RETURN_NAMES = ("model", "clip", "cache key")
+    FUNCTION = "doit"
+
+    CATEGORY = "InspirePack/Backend"
+
+    def doit(self, model, clip, lora_name, strength_model, strength_clip, key_opt, mode='Auto'):
+        if mode == 'Read Only':
+            if key_opt.strip() == '':
+                raise Exception("[LoraLoaderShared] key_opt cannot be omit if mode is 'Read Only'")
+            key = key_opt.strip()
+        elif key_opt.strip() == '':
+            key = lora_name
+        else:
+            key = key_opt.strip()
+
+        if strength_model == 0 and strength_clip == 0:
+            return (model, clip, key)
+
+        if lora_name == "None":
+            return (model, clip, key)
+        
+        if key not in cache or mode == 'Override Cache':
+            res = self.load_lora(model, clip, lora_name, strength_model, strength_clip)
+            update_cache(key, "lora", (False, res))
+            cache_kind = 'lora'
+            print(f"[Inspire Pack] LoraLoaderShared: Lora '{lora_name}' is cached to '{key}'.")
+        else:
+            cache_kind, (_, res) = cache[key]
+            print(f"[Inspire Pack] LoraLoaderShared: Cached lora '{key}' is loaded. (Loading skip)")
+
+        if cache_kind == 'lora':
+            model, clip = res
+        else:
+            raise Exception(f"[LoraLoaderShared] Unexpected cache_kind '{cache_kind}'")
+
+        return model, clip, key
+
+    @staticmethod
+    def IS_CHANGED(model, clip, lora_name, strength_model, strength_clip, key_opt, mode='Auto'):
+        if mode == 'Read Only':
+            if key_opt.strip() == '':
+                raise Exception("[LoraLoaderShared] key_opt cannot be omit if mode is 'Read Only'")
+            key = key_opt.strip()
+        elif key_opt.strip() == '':
+            key = lora_name
+        else:
+            key = key_opt.strip()
+
+        if mode == 'Read Only':
+            return (None, cache_weak_hash(key))
+        elif mode == 'Override Cache':
+            return (lora_name, key)
+
+        return (None, cache_weak_hash(key))
+
+    def load_lora(self, model, clip, lora_name, strength_model, strength_clip):
+        if strength_model == 0 and strength_clip == 0:
+            return (model, clip)
+
+        lora_path = folder_paths.get_full_path("loras", lora_name)
+        lora = None
+        if self.loaded_lora is not None:
+            if self.loaded_lora[0] == lora_path:
+                lora = self.loaded_lora[1]
+            else:
+                temp = self.loaded_lora
+                self.loaded_lora = None
+                del temp
+
+        if lora is None:
+            lora = comfy.utils.load_torch_file(lora_path, safe_load=True)
+            self.loaded_lora = (lora_path, lora)
+
+        model_lora, clip_lora = comfy.sd.load_lora_for_models(model, clip, lora, strength_model, strength_clip)
+        return (model_lora, clip_lora)