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_nodes.py
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_nodes.py
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import uuid
from typing import Optional, Tuple
import folder_paths
import torch
from nodes import CheckpointLoaderSimple, ControlNetLoader
from onediff.utils.import_utils import ( # type: ignore
is_nexfort_available,
is_oneflow_available,
)
from ._config import is_disable_oneflow_backend
from .modules import BoosterExecutor, BoosterScheduler, BoosterSettings
if is_oneflow_available() and not is_disable_oneflow_backend():
from .modules.oneflow import BasicOneFlowBoosterExecutor
BasicBoosterExecutor = BasicOneFlowBoosterExecutor
print("\033[1;31mUsing OneFlow backend\033[0m (Default)")
elif is_nexfort_available():
from .modules.nexfort.booster_basic import BasicNexFortBoosterExecutor
BasicBoosterExecutor = BasicNexFortBoosterExecutor
print("\033[1;32mUsing Nexfort backend\033[0m (Default)")
else:
raise RuntimeError(
"Neither OneFlow nor Nexfort is available. Please ensure at least one of them is installed."
)
__all__ = [
"ModelSpeedup",
"VaeSpeedup",
"ControlnetSpeedup",
"OneDiffApplyModelBooster",
"OneDiffControlNetLoader",
"OneDiffCheckpointLoaderSimple",
]
class SpeedupMixin:
"""A mix-in class to provide speedup functionality."""
FUNCTION = "speedup"
CATEGORY = "OneDiff"
@torch.inference_mode()
def speedup(
self,
model,
inplace: bool = False,
custom_booster: Optional[BoosterScheduler] = None,
booster_settings: Optional[BoosterSettings] = None,
*args,
**kwargs
) -> Tuple:
"""
Speed up the model inference.
Args:
model: The input model to be sped up.
inplace (bool, optional): Whether to perform the operation inplace. Defaults to False.
custom_booster (BoosterScheduler, optional): Custom booster scheduler to use. Defaults to None.
*args: Additional positional arguments to be passed to the underlying functions.
**kwargs: Additional keyword arguments to be passed to the underlying functions.
Returns:
Tuple: Tuple containing the optimized model.
"""
if booster_settings is None and not hasattr(self, "booster_settings"):
self.booster_settings = BoosterSettings(tmp_cache_key=str(uuid.uuid4()))
if custom_booster:
booster = custom_booster
booster.inplace = inplace
else:
booster = BoosterScheduler(BasicBoosterExecutor(), inplace=inplace)
booster.settings = (
self.booster_settings if booster_settings is None else booster_settings
)
return (booster(model, *args, **kwargs),)
class ModelSpeedup(SpeedupMixin):
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"model": ("MODEL",),
},
"optional": {
"custom_booster": ("CUSTOM_BOOSTER",),
"inplace": (
"BOOLEAN",
{"default": True, "label_on": "yes", "label_off": "no"},
),
},
}
RETURN_TYPES = ("MODEL",)
class VaeSpeedup(SpeedupMixin):
@classmethod
def INPUT_TYPES(s):
return {
"required": {"vae": ("VAE",)},
"optional": {
"custom_booster": ("CUSTOM_BOOSTER",),
"inplace": (
"BOOLEAN",
{"default": True, "label_on": "yes", "label_off": "no"},
),
},
}
RETURN_TYPES = ("VAE",)
def speedup(self, vae, inplace=False, custom_booster: BoosterScheduler = None):
return super().speedup(vae, inplace, custom_booster)
class ControlnetSpeedup(SpeedupMixin):
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"control_net": ("CONTROL_NET",),
},
"optional": {
"inplace": (
"BOOLEAN",
{"default": True, "label_on": "yes", "label_off": "no"},
),
"custom_booster": ("CUSTOM_BOOSTER",),
},
}
RETURN_TYPES = ("CONTROL_NET",)
FUNCTION = "speedup"
CATEGORY = "OneDiff"
def speedup(
self,
control_net=None,
inplace=True,
custom_booster: BoosterScheduler = None,
**kwargs
):
return super().speedup(control_net, inplace, custom_booster)
class OneDiffApplyModelBooster:
"""Main class responsible for optimizing models."""
@classmethod
def INPUT_TYPES(s):
return {
"required": {},
"optional": {
"quantization_booster": ("QuantizationBooster",),
"deepcache_booster": ("DeepCacheBooster",),
"torchcompile_booster": ("TorchCompileBooster",),
},
}
CATEGORY = "OneDiff/Booster"
RETURN_TYPES = ("CUSTOM_BOOSTER",)
FUNCTION = "speedup_module"
@torch.no_grad()
def speedup_module(
self,
quantization_booster: BoosterExecutor = None,
deepcache_booster=None,
torchcompile_booster=None,
):
"""Apply the optimization technique to the model."""
booster_executors = []
if deepcache_booster:
booster_executors.append(deepcache_booster)
if quantization_booster:
booster_executors.append(quantization_booster)
if torchcompile_booster:
booster_executors.append(torchcompile_booster)
assert len(booster_executors) > 0
return (BoosterScheduler(booster_executors),)
class OneDiffControlNetLoader(ControlNetLoader):
@classmethod
def INPUT_TYPES(s):
ret = super().INPUT_TYPES()
ret.update(
{
"optional": {
"custom_booster": ("CUSTOM_BOOSTER",),
}
}
)
return ret
CATEGORY = "OneDiff/Loaders"
FUNCTION = "onediff_load_controlnet"
@torch.inference_mode()
def onediff_load_controlnet(self, control_net_name, custom_booster=None):
controlnet = super().load_controlnet(control_net_name)[0]
if custom_booster is None:
custom_booster = BoosterScheduler(BasicBoosterExecutor())
controlnet = custom_booster(controlnet, ckpt_name=control_net_name)
return (controlnet,)
class OneDiffCheckpointLoaderSimple(CheckpointLoaderSimple, SpeedupMixin):
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"ckpt_name": (folder_paths.get_filename_list("checkpoints"),),
"vae_speedup": (["disable", "enable"],),
},
"optional": {
"custom_booster": ("CUSTOM_BOOSTER",),
},
}
CATEGORY = "OneDiff/Loaders"
FUNCTION = "onediff_load_checkpoint"
def __init__(self) -> None:
super().__init__()
self.unet_booster_settings = BoosterSettings(tmp_cache_key=str(uuid.uuid4()))
self.vae_booster_settings = BoosterSettings(tmp_cache_key=str(uuid.uuid4()))
@torch.inference_mode()
def onediff_load_checkpoint(
self,
ckpt_name,
vae_speedup="disable",
custom_booster: BoosterScheduler = None,
):
modelpatcher, clip, vae = self.load_checkpoint(ckpt_name)
modelpatcher = self.speedup(
modelpatcher,
inplace=True,
custom_booster=custom_booster,
booster_settings=self.unet_booster_settings,
)[0]
if vae_speedup == "enable":
vae = self.speedup(
vae,
inplace=True,
custom_booster=custom_booster,
booster_settings=self.vae_booster_settings,
)[0]
# Set weight inplace update
modelpatcher.weight_inplace_update = True
return (
modelpatcher,
clip,
vae,
)