Added support for adding weight hooks that aren't registered on the ModelPatcher at sampling time

comfy/hooks.py

@@ -1,5 +1,6 @@
-from typing import TYPE_CHECKING, List, Dict, Tuple
+from typing import TYPE_CHECKING, List, Dict, Tuple, Callable
 import enum
+import math
 import torch
 import numpy as np
 
@@ -15,41 +16,23 @@ class EnumHookMode(enum.Enum):
     MinVram = "minvram"
     MaxSpeed = "maxspeed"
 
-class InterpolationMethod:
-    LINEAR = "linear"
-    EASE_IN = "ease_in"
-    EASE_OUT = "ease_out"
-    EASE_IN_OUT = "ease_in_out"
+class EnumHookType(enum.Enum):
+    Weight = "weight"
+    Patch = "patch"
 
-    _LIST = [LINEAR, EASE_IN, EASE_OUT, EASE_IN_OUT]
+class EnumWeightTarget(enum.Enum):
+    Model = "model"
+    Clip = "clip"
 
-    @classmethod
-    def get_weights(cls, num_from: float, num_to: float, length: int, method: str, reverse=False):
-        diff = num_to - num_from
-        if method == cls.LINEAR:
-            weights = torch.linspace(num_from, num_to, length)
-        elif method == cls.EASE_IN:
-            index = torch.linspace(0, 1, length)
-            weights = diff * np.power(index, 2) + num_from
-        elif method == cls.EASE_OUT:
-            index = torch.linspace(0, 1, length)
-            weights = diff * (1 - np.power(1 - index, 2)) + num_from
-        elif method == cls.EASE_IN_OUT:
-            index = torch.linspace(0, 1, length)
-            weights = diff * ((1 - np.cos(index * np.pi)) / 2) + num_from
-        else:
-            raise ValueError(f"Unrecognized interpolation method '{method}'.")
-        if reverse:
-            weights = weights.flip(dims=(0,))
-        return weights
-
-class HookRef:
+class _HookRef:
     pass
 
 class Hook:
-    def __init__(self):
-        self.hook_ref = HookRef()
-        self.hook_keyframe = HookKeyframeGroup()
+    def __init__(self, hook_type: EnumHookType=None, hook_ref: _HookRef=None,
+                 hook_keyframe: 'HookKeyframeGroup'=None):
+        self.hook_type = hook_type
+        self.hook_ref = hook_ref if hook_ref else _HookRef()
+        self.hook_keyframe = hook_keyframe if hook_keyframe else HookKeyframeGroup()
 
     @property
     def strength(self):
@@ -62,18 +45,84 @@ class Hook:
     def reset(self):
         self.hook_keyframe.reset()
 
-    def clone(self):
-        c = Hook()
+    def clone(self, subtype: Callable=None):
+        if subtype is None:
+            subtype = type(self)
+        c: Hook = subtype()
+        c.hook_type = self.hook_type
         c.hook_ref = self.hook_ref
         c.hook_keyframe = self.hook_keyframe
         return c
-    
+
     def __eq__(self, other: 'Hook'):
         return self.__class__ == other.__class__ and self.hook_ref == other.hook_ref
 
     def __hash__(self):
         return hash(self.hook_ref)
 
+
+class WeightHook(Hook):
+    def __init__(self, strength_model=1.0, strength_clip=1.0):
+        super().__init__(hook_type=EnumHookType.Weight)
+        self.weights: Dict = None
+        self.weights_clip: Dict = None
+        self.need_weight_init = True
+        self._strength_model = strength_model
+        self._strength_clip = strength_clip
+    
+    @property
+    def strength_model(self):
+        return self._strength_model * self.strength
+    
+    @property
+    def strength_clip(self):
+        return self._strength_clip * self.strength
+
+    def add_hook_patches(self, model: 'ModelPatcher', target: EnumWeightTarget):
+        weights = None
+        if target == EnumWeightTarget.Model:
+            strength = self._strength_model
+        else:
+            strength = self._strength_clip
+        
+        if self.need_weight_init:
+            key_map = {}
+            if target == EnumWeightTarget.Model:
+                key_map = comfy.lora.model_lora_keys_unet(model.model, key_map)
+            else:
+                key_map = comfy.lora.model_lora_keys_clip(model.model, key_map)
+            weights = comfy.lora.load_lora(self.weights, key_map)
+        else:
+            if target == EnumWeightTarget.Model:
+                weights = self.weights
+            else:
+                weights = self.weights_clip
+        k = model.add_hook_patches(hook=self, patches=weights, strength_patch=strength)
+        # TODO: add logs about any keys that were not applied
+
+    def clone(self, subtype: Callable=None):
+        if subtype is None:
+            subtype = type(self)
+        c: WeightHook = super().clone(subtype)
+        c.weights = self.weights
+        c.weights_clip = self.weights_clip
+        c.need_weight_init = self.need_weight_init
+        c._strength_model = self._strength_model
+        c._strength_clip = self._strength_clip
+        return c
+
+class PatchHook(Hook):
+    def __init__(self):
+        super().__init__(hook_type=EnumHookType.Patch)
+        self.patches: Dict = None
+    
+    def clone(self, subtype: Callable=None):
+        if subtype is None:
+            subtype = type(self)
+        c: PatchHook = super().clone(type(self))
+        c.patches = self.patches
+        return c
+
 class HookGroup:
     def __init__(self):
         self.hooks: List[Hook] = []
@@ -121,6 +170,7 @@ class HookGroup:
                 final_hook = final_hook.clone_and_combine(hook)
         return final_hook
 
+
 class HookKeyframe:
     def __init__(self, strength: float, start_percent=0.0, guarantee_steps=1):
         self.strength = strength
@@ -216,6 +266,35 @@ class HookKeyframeGroup:
         # return True if keyframe changed, False if no change
         return prev_index != self._current_index
 
+
+class InterpolationMethod:
+    LINEAR = "linear"
+    EASE_IN = "ease_in"
+    EASE_OUT = "ease_out"
+    EASE_IN_OUT = "ease_in_out"
+
+    _LIST = [LINEAR, EASE_IN, EASE_OUT, EASE_IN_OUT]
+
+    @classmethod
+    def get_weights(cls, num_from: float, num_to: float, length: int, method: str, reverse=False):
+        diff = num_to - num_from
+        if method == cls.LINEAR:
+            weights = torch.linspace(num_from, num_to, length)
+        elif method == cls.EASE_IN:
+            index = torch.linspace(0, 1, length)
+            weights = diff * np.power(index, 2) + num_from
+        elif method == cls.EASE_OUT:
+            index = torch.linspace(0, 1, length)
+            weights = diff * (1 - np.power(1 - index, 2)) + num_from
+        elif method == cls.EASE_IN_OUT:
+            index = torch.linspace(0, 1, length)
+            weights = diff * ((1 - np.cos(index * np.pi)) / 2) + num_from
+        else:
+            raise ValueError(f"Unrecognized interpolation method '{method}'.")
+        if reverse:
+            weights = weights.flip(dims=(0,))
+        return weights
+
 def get_sorted_list_via_attr(objects: List, attr: str) -> List:
     if not objects:
         return objects
@@ -239,15 +318,68 @@ def get_sorted_list_via_attr(objects: List, attr: str) -> List:
         sorted_list.extend(object_list)
     return sorted_list
 
+def create_hook_lora(lora: Dict[str, torch.Tensor], strength_model: float, strength_clip: float):
+    hook_group = HookGroup()
+    hook = WeightHook(strength_model=strength_model, strength_clip=strength_clip)
+    hook_group.add(hook)
+    hook.weights = lora
+    hook.need_weight_init = True
+    return hook_group
+
+def create_hook_model_as_lora(model: 'ModelPatcher', clip: 'CLIP',
+                              model_loaded: 'ModelPatcher', clip_loaded: 'CLIP',
+                              strength_model: float, strength_clip: float):
+    hook_group = HookGroup()
+    hook = WeightHook(strength_model=strength_model, strength_clip=strength_clip)
+    hook_group.add(hook)
+    if model is not None and model_loaded is not None:
+        expected_model_keys = set(model_loaded.model.state_dict().keys())
+        patches_model: Dict[str, torch.Tensor] = model_loaded.model.state_dict()
+        # do not include ANY model_sampling components of the model that should act as a patch
+        for key in list(patches_model.keys()):
+            if key.startswith("model_sampling"):
+                expected_model_keys.discard(key)
+                patches_model.pop(key, None)
+        weights_model, k = model.get_weight_diffs(patches_model)
+    else:
+        weights_model = {}
+        k = ()
+
+    if clip is not None and clip_loaded is not None:
+        expected_clip_keys = clip_loaded.patcher.model.state_dict().copy()
+        patches_clip: Dict[str, torch.Tensor] = clip_loaded.cond_stage_model.state_dict()
+        weights_clip, k1 = clip.patcher.get_weight_diffs(patches_clip)
+    else:
+        weights_clip = {}
+        k1 = ()
+    
+    k = set(k)
+    k1 = set(k1)
+    if model is not None and model_loaded is not None:
+        for key in expected_model_keys:
+            if key not in k:
+                print(f"MODEL-AS-LORA NOT LOADED {key}")
+    if clip is not None and clip_loaded is not None:
+        for key in expected_clip_keys:
+            if key not in k1:
+                print(f"CLIP-AS-LORA NOT LOADED {key}")
+
+    hook.weights = weights_model
+    hook.weights_clip = weights_clip
+    hook.need_weight_init = False
+    return hook_group
 
 def load_hook_lora_for_models(model: 'ModelPatcher', clip: 'CLIP', lora: Dict[str, torch.Tensor],
-                              hook: Hook, strength_model: float, strength_clip: float):
+                              strength_model: float, strength_clip: float):
     key_map = {}
     if model is not None:
         key_map = comfy.lora.model_lora_keys_unet(model.model, key_map)
     if clip is not None:
         key_map = comfy.lora.model_lora_keys_clip(clip.cond_stage_model, key_map)
 
+    hook_group = HookGroup()
+    hook = WeightHook()
+    hook_group.add(hook)
     loaded: Dict[str] = comfy.lora.load_lora(lora, key_map)
     if model is not None:
         new_modelpatcher = model.clone()
@@ -267,11 +399,14 @@ def load_hook_lora_for_models(model: 'ModelPatcher', clip: 'CLIP', lora: Dict[st
     for x in loaded:
         if (x not in k) and (x not in k1):
             print(f"NOT LOADED {x}")
-    return (new_modelpatcher, new_clip)
+    return (new_modelpatcher, new_clip, hook_group)
 
 def load_hook_model_as_lora_for_models(model: 'ModelPatcher', clip: 'CLIP',
                                        model_loaded: 'ModelPatcher', clip_loaded: 'CLIP',
-                                       hook: Hook, strength_model: float, strength_clip: float):
+                                       strength_model: float, strength_clip: float):
+    hook_group = HookGroup()
+    hook = WeightHook()
+    hook_group.add(hook)
     if model is not None and model_loaded is not None:
         new_modelpatcher = model.clone()
         expected_model_keys = set(model_loaded.model.state_dict().keys())
@@ -307,7 +442,7 @@ def load_hook_model_as_lora_for_models(model: 'ModelPatcher', clip: 'CLIP',
             if key not in k1:
                 print(f"CLIP-AS-LORA NOT LOADED {key}")
     
-    return (new_modelpatcher, new_clip)
+    return (new_modelpatcher, new_clip, hook_group)
 
 def set_hooks_for_conditioning(cond, hooks: HookGroup):
     if hooks is None:

comfy/model_patcher.py

@@ -16,7 +16,7 @@
     along with this program.  If not, see <https://www.gnu.org/licenses/>.
 """
 
-from typing import Dict, List, Tuple, Optional
+from typing import Dict, List, Tuple, Optional, Callable
 import torch
 import copy
 import inspect
@@ -113,7 +113,8 @@ class ModelPatcher:
         self.weight_inplace_update = weight_inplace_update
         self.patches_uuid = uuid.uuid4()
 
-        self.hook_patches: Dict[comfy.hooks.HookRef] = {}
+        self.hook_patches: Dict[comfy.hooks._HookRef] = {}
+        self.hook_patches_backup: Dict[comfy.hooks._HookRef] = {}
         self.hook_backup: Dict[str, Tuple[torch.Tensor, torch.device]] = {}
         self.cached_hook_patches: Dict[comfy.hooks.HookGroup, Dict[str, torch.Tensor]] = {}
         self.current_hooks: Optional[comfy.hooks.HookGroup] = None
@@ -155,10 +156,8 @@ class ModelPatcher:
         n.object_patches_backup = self.object_patches_backup
 
         # hooks
-        for hook_ref in self.hook_patches:
-            n.hook_patches[hook_ref] = {}
-            for k in self.hook_patches[hook_ref]:
-                n.hook_patches[hook_ref][k] = self.hook_patches[hook_ref][k][:]
+        n.hook_patches = self.create_hook_patches_clone(self.hook_patches)
+        n.hook_patches_backup = self.create_hook_patches_clone(self.hook_patches_backup)
         # TODO: do we really need to clone cached_hook_patches/current_hooks?
         for group in self.cached_hook_patches:
             n.cached_hook_patches[group] = {}
@@ -170,6 +169,15 @@ class ModelPatcher:
         n.hook_mode = self.hook_mode
         return n
 
+    @staticmethod
+    def create_hook_patches_clone(orig_hook_patches):
+        new_hook_patches = {}
+        for hook_ref in orig_hook_patches:
+            new_hook_patches[hook_ref] = {}
+            for k in orig_hook_patches[hook_ref]:
+                new_hook_patches[hook_ref][k] = orig_hook_patches[hook_ref][k][:]
+        return new_hook_patches
+
     def is_clone(self, other):
         if hasattr(other, 'model') and self.model is other.model:
             return True
@@ -570,6 +578,12 @@ class ModelPatcher:
 
     def clean(self):
         self.clean_hooks()
+        self.restore_hook_patches()
+
+    def restore_hook_patches(self):
+        if len(self.hook_patches_backup) > 0:
+            self.hook_patches = self.hook_patches_backup
+            self.hook_patches_backup = {}
 
     def set_hook_mode(self, hook_mode: comfy.hooks.EnumHookMode):
         self.hook_mode = hook_mode
@@ -591,8 +605,22 @@ class ModelPatcher:
                     if cached_group.contains(hook):
                         self.cached_hook_patches.pop(cached_group)
 
-    def add_hook_patches(self, hook: comfy.hooks.Hook, patches, strength_patch=1.0, strength_model=1.0, is_diff=False):
+    def register_all_hook_patches(self, hooks_dict: Dict[comfy.hooks.Hook, None], target: comfy.hooks.EnumWeightTarget):
+        self.restore_hook_patches()
+        weight_hooks_to_register: List[comfy.hooks.WeightHook] = []
+        for hook in hooks_dict:
+            if hook.hook_type == comfy.hooks.EnumHookType.Weight:
+                if hook.hook_ref not in self.hook_patches:
+                    weight_hooks_to_register.append(hook)
+        if len(weight_hooks_to_register) > 0:
+            self.hook_patches_backup = self.create_hook_patches_clone(self.hook_patches)
+            for hook in weight_hooks_to_register:
+                hook.add_hook_patches(self, target)
+
+    def add_hook_patches(self, hook: comfy.hooks.WeightHook, patches, strength_patch=1.0, strength_model=1.0, is_diff=False):
         # NOTE: this mirrors behavior of add_patches func
+        if is_diff:
+            comfy.model_management.unload_model_clones(self)
         current_hook_patches: Dict[str,List] = self.hook_patches.get(hook.hook_ref, {})
         p = set()
         model_sd = self.model.state_dict()
@@ -613,7 +641,6 @@ class ModelPatcher:
                 if is_diff:
                     # take difference between desired weight and existing weight to get diff
                     # TODO: try to implement diff via strength_path/strength_model diff
-                    comfy.model_management.unload_model_clones(self)
                     model_dtype = comfy.utils.get_attr(self.model, key).dtype
                     if model_dtype in [torch.float8_e5m2, torch.float8_e4m3fn]:
                         diff_weight = (patches[k].to(torch.float32)-comfy.utils.get_attr(self.model, key).to(torch.float32)).to(model_dtype)
@@ -628,6 +655,22 @@ class ModelPatcher:
         self.patches_uuid = uuid.uuid4()
         return list(p)
 
+    def get_weight_diffs(self, patches):
+        comfy.model_management.unload_model_clones(self)
+        weights: Dict[str, Tuple] = {}
+        p = set()
+        model_sd = self.model.state_dict()
+        for k in patches:
+            if k in model_sd:
+                p.add(k)
+                model_dtype = comfy.utils.get_attr(self.model, k).dtype
+                if model_dtype in [torch.float8_e5m2, torch.float8_e4m3fn]:
+                    diff_weight = (patches[k].to(torch.float32)-comfy.utils.get_attr(self.model, k).to(torch.float32)).to(model_dtype)
+                else:
+                    diff_weight = patches[k]-comfy.utils.get_attr(self.model, k)
+                weights[k] = (diff_weight,)
+        return weights, p
+
     def get_combined_hook_patches(self, hooks: comfy.hooks.HookGroup):
         # combined_patches will contain  weights of all relevant hooks, per key
         combined_patches = {}

comfy/sampler_helpers.py

@@ -1,6 +1,7 @@
 import torch
 import comfy.model_management
 import comfy.conds
+import comfy.hooks
 
 def prepare_mask(noise_mask, shape, device):
     """ensures noise mask is of proper dimensions"""
@@ -77,3 +78,13 @@ def cleanup_models(conds, models):
         control_cleanup += get_models_from_cond(conds[k], "control")
 
     cleanup_additional_models(set(control_cleanup))
+
+def prepare_model_patcher(model, conds):
+    # check for hooks in conds - if not registered, see if can be applied
+    hooks = {}
+    for k in conds:
+        for cond in conds[k]:
+            if 'hooks' in cond:
+                for hook in cond['hooks'].hooks:
+                    hooks[hook] = None
+    model.register_all_hook_patches(hooks, comfy.hooks.EnumWeightTarget.Model)

comfy/samplers.py

@@ -779,9 +779,12 @@ class CFGGuider:
         latent_image = latent_image.to(device)
         sigmas = sigmas.to(device)
 
-        output = self.inner_sample(noise, latent_image, device, sampler, sigmas, denoise_mask, callback, disable_pbar, seed)
+        try:
+            comfy.sampler_helpers.prepare_model_patcher(self.model_patcher, self.conds)
+            output = self.inner_sample(noise, latent_image, device, sampler, sigmas, denoise_mask, callback, disable_pbar, seed)
+        finally:
+            self.model_patcher.clean()
 
-        self.model_patcher.clean()
         comfy.sampler_helpers.cleanup_models(self.conds, self.loaded_models)
         del self.inner_model
         del self.conds

comfy_extras/nodes_hooks.py

@@ -8,6 +8,7 @@ if TYPE_CHECKING:
 
 import comfy.hooks
 import comfy.sd
+import comfy.utils
 import folder_paths
 
 ###########################################
@@ -39,7 +40,7 @@ class PairConditioningSetProperties:
 
     def set_properties(self, positive_NEW, negative_NEW,
                        strength: float, set_cond_area: str,
-                       opt_mask: torch.Tensor=None, opt_hooks: comfy.hooks.Hook=None, opt_timesteps: Tuple=None):
+                       opt_mask: torch.Tensor=None, opt_hooks: comfy.hooks.HookGroup=None, opt_timesteps: Tuple=None):
         final_positive, final_negative = comfy.hooks.set_mask_conds(conds=[positive_NEW, negative_NEW],
                                                                     strength=strength, set_cond_area=set_cond_area,
                                                                     opt_mask=opt_mask, opt_hooks=opt_hooks, opt_timestep_range=opt_timesteps)
@@ -70,10 +71,10 @@ class ConditioningSetProperties:
 
     def set_properties(self, cond_NEW,
                        strength: float, set_cond_area: str,
-                       opt_mask: torch.Tensor=None, opt_hooks: comfy.hooks.Hook=None, opt_timesteps: Tuple=None):
+                       opt_mask: torch.Tensor=None, opt_hooks: comfy.hooks.HookGroup=None, opt_timesteps: Tuple=None):
         (final_cond,) = comfy.hooks.set_mask_conds(conds=[cond_NEW],
-                                                                    strength=strength, set_cond_area=set_cond_area,
-                                                                    opt_mask=opt_mask, opt_hooks=opt_hooks, opt_timestep_range=opt_timesteps)
+                                                   strength=strength, set_cond_area=set_cond_area,
+                                                   opt_mask=opt_mask, opt_hooks=opt_hooks, opt_timestep_range=opt_timesteps)
         return (final_cond,)
 
 class PairConditioningCombine:
@@ -198,6 +199,124 @@ class ConditioningTimestepsRange:
 ###########################################
 
 
+###########################################
+# Create Hooks
+#------------------------------------------
+class CreateHookLora:
+    NodeId = 'CreateHookLora'
+    NodeName = 'Create Hook LoRA'
+    def __init__(self):
+        self.loaded_lora = None
+    
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "lora_name": (folder_paths.get_filename_list("loras"), ),
+                "strength_model": ("FLOAT", {"default": 1.0, "min": -20.0, "max": 20.0, "step": 0.01}),
+                "strength_clip": ("FLOAT", {"default": 1.0, "min": -20.0, "max": 20.0, "step": 0.01}),
+            }
+        }
+    
+    RETURN_TYPES = ("HOOKS",)
+    CATEGORY = "advanced/hooks/create"
+    FUNCTION = "create_hook"
+
+    def create_hook(self, lora_name: str, strength_model: float, strength_clip: float):
+        if strength_model == 0 and strength_clip == 0:
+            return (None,)
+        
+        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)
+
+        hooks = comfy.hooks.create_hook_lora(lora=lora, strength_model=strength_model, strength_clip=strength_clip)
+        return (hooks,)
+
+class CreateHookLoraModelOnly(CreateHookLora):
+    NodeId = 'CreateHookLoraModelOnly'
+    NodeName = 'Create Hook LoRA (MO)'
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "lora_name": (folder_paths.get_filename_list("loras"), ),
+                "strength_model": ("FLOAT", {"default": 1.0, "min": -20.0, "max": 20.0, "step": 0.01}),
+            }
+        }
+    
+    RETURN_TYPES = ("HOOKS",)
+    CATEGORY = "advanced/hooks/create"
+    FUNCTION = "create_hook_model_only"
+
+    def create_hook_model_only(self, lora_name: str, strength_model: float):
+        return self.create_hook(lora_name=lora_name, strength_model=strength_model, strength_clip=0)
+
+class CreateHookModelAsLora:
+    NodeId = 'CreateHookModelAsLora'
+    NodeName = 'Create Hook Model as LoRA'
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "model": ("MODEL",),
+                "clip": ("CLIP",),
+                "ckpt_name": (folder_paths.get_filename_list("checkpoints"), ),
+                "strength_model": ("FLOAT", {"default": 1.0, "min": -20.0, "max": 20.0, "step": 0.01}),
+                "strength_clip": ("FLOAT", {"default": 1.0, "min": -20.0, "max": 20.0, "step": 0.01}),
+            }
+        }
+    
+    RETURN_TYPES = ("HOOKS",)
+    CATEGORY = "advanced/hooks/create"
+    FUNCTION = "create_hook"
+
+    def create_hook(self, model: 'ModelPatcher', clip: 'CLIP', ckpt_name: str,
+                                 strength_model: float, strength_clip: float):
+        ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name)
+        out = comfy.sd.load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, embedding_directory=folder_paths.get_folder_paths("embeddings"))
+        model_loaded = out[0]
+        clip_loaded = out[1]
+
+        hooks = comfy.hooks.create_hook_model_as_lora(model=model, clip=clip,
+                                                     model_loaded=model_loaded, clip_loaded=clip_loaded,
+                                                     strength_model=strength_model, strength_clip=strength_clip)
+        return (hooks,)
+
+class CreateHookModelAsLoraModelOnly:
+    NodeId = 'CreateHookModelAsLoraModelOnly'
+    NodeName = 'Create Hook Model as LoRA (MO)'
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "model": ("MODEL",),
+                "ckpt_name": (folder_paths.get_filename_list("checkpoints"), ),
+                "strength_model": ("FLOAT", {"default": 1.0, "min": -20.0, "max": 20.0, "step": 0.01}),
+            }
+        }
+    
+    RETURN_TYPES = ("HOOKS",)
+    CATEGORY = "advanced/hooks/create"
+    FUNCTION = "create_hook_model_only"
+
+    def create_hook_model_only(self, model: 'ModelPatcher', ckpt_name: str, strength_model: float):
+        return CreateHookModelAsLora.create_hook(self, model=model, clip=None, ckpt_name=ckpt_name,
+                                                                              strength_model=strength_model, strength_clip=0)
+#------------------------------------------
+###########################################
+
+
 ###########################################
 # Register Hooks
 #------------------------------------------
@@ -242,12 +361,9 @@ class RegisterHookLora:
             lora = comfy.utils.load_torch_file(lora_path, safe_load=True)
             self.loaded_lora = (lora_path, lora)
         
-        hook = comfy.hooks.Hook()
-        hook_group = comfy.hooks.HookGroup()
-        hook_group.add(hook)
-        model_lora, clip_lora = comfy.hooks.load_hook_lora_for_models(model=model, clip=clip, lora=lora, hook=hook,
+        model_lora, clip_lora, hooks = comfy.hooks.load_hook_lora_for_models(model=model, clip=clip, lora=lora,
                                                                       strength_model=strength_model, strength_clip=strength_clip)
-        return (model_lora, clip_lora, hook_group)
+        return (model_lora, clip_lora, hooks)
 
 class RegisterHookLoraModelOnly(RegisterHookLora):
     NodeId = 'RegisterHookLoraModelOnly'
@@ -257,10 +373,8 @@ class RegisterHookLoraModelOnly(RegisterHookLora):
         return {
             "required": {
                 "model": ("MODEL",),
-                "clip": ("CLIP",),
                 "lora_name": (folder_paths.get_filename_list("loras"), ),
                 "strength_model": ("FLOAT", {"default": 1.0, "min": -20.0, "max": 20.0, "step": 0.01}),
-                "strength_clip": ("FLOAT", {"default": 1.0, "min": -20.0, "max": 20.0, "step": 0.01}),
             }
         }
     
@@ -299,14 +413,10 @@ class RegisterHookModelAsLora:
         model_loaded = out[0]
         clip_loaded = out[1]
 
-        hook = comfy.hooks.Hook()
-        hook_group = comfy.hooks.HookGroup()
-        hook_group.add(hook)
-        model_lora, clip_lora = comfy.hooks.load_hook_model_as_lora_for_models(model=model, clip=clip,
-                                                                               model_loaded=model_loaded, clip_loaded=clip_loaded,
-                                                                               hook=hook,
-                                                                               strength_model=strength_model, strength_clip=strength_clip)
-        return (model_lora, clip_lora, hook_group)
+        model_lora, clip_lora, hooks = comfy.hooks.load_hook_model_as_lora_for_models(model=model, clip=clip,
+                                                                                     model_loaded=model_loaded, clip_loaded=clip_loaded,
+                                                                                     strength_model=strength_model, strength_clip=strength_clip)
+        return (model_lora, clip_lora, hooks)
 
 class RegisterHookModelAsLoraModelOnly:
     NodeId = 'RegisterHookModelAsLoraModelOnly'
@@ -551,6 +661,11 @@ class CombineHooksEight:
 ###########################################
 
 node_list = [
+    # Create
+    CreateHookLora,
+    CreateHookLoraModelOnly,
+    CreateHookModelAsLora,
+    CreateHookModelAsLoraModelOnly,
     # Register
     RegisterHookLora,
     RegisterHookLoraModelOnly,