mirror of
https://github.com/comfyanonymous/ComfyUI.git
synced 2025-04-13 12:23:30 +00:00
227 lines
9.7 KiB
Python
227 lines
9.7 KiB
Python
from __future__ import annotations
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import torch
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import uuid
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import comfy.model_management
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import comfy.conds
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import comfy.model_patcher
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import comfy.utils
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import comfy.hooks
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import comfy.patcher_extension
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from typing import TYPE_CHECKING
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if TYPE_CHECKING:
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from comfy.model_patcher import ModelPatcher
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from comfy.model_base import BaseModel
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from comfy.controlnet import ControlBase
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def prepare_mask(noise_mask, shape, device):
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return comfy.utils.reshape_mask(noise_mask, shape).to(device)
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def get_models_from_cond(cond, model_type):
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models = []
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for c in cond:
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if model_type in c:
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if isinstance(c[model_type], list):
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models += c[model_type]
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else:
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models += [c[model_type]]
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return models
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def get_hooks_from_cond(cond, full_hooks: comfy.hooks.HookGroup):
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# get hooks from conds, and collect cnets so they can be checked for extra_hooks
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cnets: list[ControlBase] = []
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for c in cond:
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if 'hooks' in c:
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for hook in c['hooks'].hooks:
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full_hooks.add(hook)
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if 'control' in c:
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cnets.append(c['control'])
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def get_extra_hooks_from_cnet(cnet: ControlBase, _list: list):
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if cnet.extra_hooks is not None:
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_list.append(cnet.extra_hooks)
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if cnet.previous_controlnet is None:
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return _list
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return get_extra_hooks_from_cnet(cnet.previous_controlnet, _list)
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hooks_list = []
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cnets = set(cnets)
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for base_cnet in cnets:
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get_extra_hooks_from_cnet(base_cnet, hooks_list)
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extra_hooks = comfy.hooks.HookGroup.combine_all_hooks(hooks_list)
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if extra_hooks is not None:
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for hook in extra_hooks.hooks:
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full_hooks.add(hook)
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return full_hooks
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def convert_cond(cond):
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out = []
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for c in cond:
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temp = c[1].copy()
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model_conds = temp.get("model_conds", {})
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if c[0] is not None:
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temp["cross_attn"] = c[0]
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temp["model_conds"] = model_conds
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temp["uuid"] = uuid.uuid4()
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out.append(temp)
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return out
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def get_additional_models(conds, dtype):
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"""loads additional models in conditioning"""
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cnets: list[ControlBase] = []
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gligen = []
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add_models = []
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for k in conds:
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cnets += get_models_from_cond(conds[k], "control")
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gligen += get_models_from_cond(conds[k], "gligen")
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add_models += get_models_from_cond(conds[k], "additional_models")
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control_nets = set(cnets)
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inference_memory = 0
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control_models = []
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for m in control_nets:
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control_models += m.get_models()
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inference_memory += m.inference_memory_requirements(dtype)
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gligen = [x[1] for x in gligen]
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models = control_models + gligen + add_models
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return models, inference_memory
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def get_additional_models_from_model_options(model_options: dict[str]=None):
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"""loads additional models from registered AddModels hooks"""
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models = []
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if model_options is not None and "registered_hooks" in model_options:
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registered: comfy.hooks.HookGroup = model_options["registered_hooks"]
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for hook in registered.get_type(comfy.hooks.EnumHookType.AdditionalModels):
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hook: comfy.hooks.AdditionalModelsHook
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models.extend(hook.models)
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return models
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def cleanup_additional_models(models):
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"""cleanup additional models that were loaded"""
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for m in models:
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if hasattr(m, 'cleanup'):
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m.cleanup()
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def preprocess_multigpu_conds(conds: dict[str, list[dict[str]]], model: ModelPatcher, model_options: dict[str]):
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'''If multigpu acceleration required, creates deepclones of ControlNets and GLIGEN per device.'''
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multigpu_models: list[ModelPatcher] = model.get_additional_models_with_key("multigpu")
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if len(multigpu_models) == 0:
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return
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extra_devices = [x.load_device for x in multigpu_models]
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# handle controlnets
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controlnets: set[ControlBase] = set()
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for k in conds:
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for kk in conds[k]:
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if 'control' in kk:
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controlnets.add(kk['control'])
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if len(controlnets) > 0:
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# first, unload all controlnet clones
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for cnet in list(controlnets):
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cnet_models = cnet.get_models()
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for cm in cnet_models:
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comfy.model_management.unload_model_and_clones(cm, unload_additional_models=True)
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# next, make sure each controlnet has a deepclone for all relevant devices
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for cnet in controlnets:
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curr_cnet = cnet
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while curr_cnet is not None:
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for device in extra_devices:
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if device not in curr_cnet.multigpu_clones:
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curr_cnet.deepclone_multigpu(device, autoregister=True)
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curr_cnet = curr_cnet.previous_controlnet
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# since all device clones are now present, recreate the linked list for cloned cnets per device
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for cnet in controlnets:
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curr_cnet = cnet
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while curr_cnet is not None:
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prev_cnet = curr_cnet.previous_controlnet
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for device in extra_devices:
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device_cnet = curr_cnet.get_instance_for_device(device)
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prev_device_cnet = None
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if prev_cnet is not None:
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prev_device_cnet = prev_cnet.get_instance_for_device(device)
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device_cnet.set_previous_controlnet(prev_device_cnet)
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curr_cnet = prev_cnet
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# potentially handle gligen - since not widely used, ignored for now
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def prepare_sampling(model: ModelPatcher, noise_shape, conds, model_options=None):
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executor = comfy.patcher_extension.WrapperExecutor.new_executor(
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_prepare_sampling,
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comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.PREPARE_SAMPLING, model_options, is_model_options=True)
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)
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return executor.execute(model, noise_shape, conds, model_options=model_options)
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def _prepare_sampling(model: ModelPatcher, noise_shape, conds, model_options=None):
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model.match_multigpu_clones()
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preprocess_multigpu_conds(conds, model, model_options)
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models, inference_memory = get_additional_models(conds, model.model_dtype())
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models += get_additional_models_from_model_options(model_options)
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models += model.get_nested_additional_models() # TODO: does this require inference_memory update?
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memory_required = model.memory_required([noise_shape[0] * 2] + list(noise_shape[1:])) + inference_memory
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minimum_memory_required = model.memory_required([noise_shape[0]] + list(noise_shape[1:])) + inference_memory
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comfy.model_management.load_models_gpu([model] + models, memory_required=memory_required, minimum_memory_required=minimum_memory_required)
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real_model: BaseModel = model.model
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return real_model, conds, models
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def cleanup_models(conds, models):
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cleanup_additional_models(models)
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control_cleanup = []
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for k in conds:
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control_cleanup += get_models_from_cond(conds[k], "control")
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cleanup_additional_models(set(control_cleanup))
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def prepare_model_patcher(model: ModelPatcher, conds, model_options: dict):
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'''
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Registers hooks from conds.
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'''
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# check for hooks in conds - if not registered, see if can be applied
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hooks = comfy.hooks.HookGroup()
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for k in conds:
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get_hooks_from_cond(conds[k], hooks)
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# add wrappers and callbacks from ModelPatcher to transformer_options
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model_options["transformer_options"]["wrappers"] = comfy.patcher_extension.copy_nested_dicts(model.wrappers)
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model_options["transformer_options"]["callbacks"] = comfy.patcher_extension.copy_nested_dicts(model.callbacks)
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# begin registering hooks
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registered = comfy.hooks.HookGroup()
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target_dict = comfy.hooks.create_target_dict(comfy.hooks.EnumWeightTarget.Model)
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# handle all TransformerOptionsHooks
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for hook in hooks.get_type(comfy.hooks.EnumHookType.TransformerOptions):
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hook: comfy.hooks.TransformerOptionsHook
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hook.add_hook_patches(model, model_options, target_dict, registered)
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# handle all AddModelsHooks
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for hook in hooks.get_type(comfy.hooks.EnumHookType.AdditionalModels):
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hook: comfy.hooks.AdditionalModelsHook
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hook.add_hook_patches(model, model_options, target_dict, registered)
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# handle all WeightHooks by registering on ModelPatcher
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model.register_all_hook_patches(hooks, target_dict, model_options, registered)
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# add registered_hooks onto model_options for further reference
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if len(registered) > 0:
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model_options["registered_hooks"] = registered
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# merge original wrappers and callbacks with hooked wrappers and callbacks
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to_load_options: dict[str] = model_options.setdefault("to_load_options", {})
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for wc_name in ["wrappers", "callbacks"]:
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comfy.patcher_extension.merge_nested_dicts(to_load_options.setdefault(wc_name, {}), model_options["transformer_options"][wc_name],
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copy_dict1=False)
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return to_load_options
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def prepare_model_patcher_multigpu_clones(model_patcher: ModelPatcher, loaded_models: list[ModelPatcher], model_options: dict):
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'''
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In case multigpu acceleration is enabled, prep ModelPatchers for each device.
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'''
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multigpu_patchers: list[ModelPatcher] = [x for x in loaded_models if x.is_multigpu_base_clone]
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if len(multigpu_patchers) > 0:
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multigpu_dict: dict[torch.device, ModelPatcher] = {}
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multigpu_dict[model_patcher.load_device] = model_patcher
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for x in multigpu_patchers:
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x.hook_patches = comfy.model_patcher.create_hook_patches_clone(model_patcher.hook_patches, copy_tuples=True)
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x.hook_mode = model_patcher.hook_mode # match main model's hook_mode
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multigpu_dict[x.load_device] = x
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model_options["multigpu_clones"] = multigpu_dict
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return multigpu_patchers
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