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# Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
# Copyright 2024 Black Forest Labs and The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from peft.tuners.tuners_utils import BaseTunerLayer
from typing import List, Any, Optional, Type
class enable_lora:
def __init__(self, lora_modules: List[BaseTunerLayer], dit_activated: bool, cond_activated: bool=False, latent_sblora_weight: float=None, condition_sblora_weight: float=None) -> None:
self.dit_activated = dit_activated
self.cond_activated = cond_activated
self.latent_sblora_weight = latent_sblora_weight
self.condition_sblora_weight = condition_sblora_weight
# assert not (dit_activated and cond_activated)
self.lora_modules: List[BaseTunerLayer] = [
each for each in lora_modules if isinstance(each, BaseTunerLayer)
]
self.scales = [
{
active_adapter: lora_module.scaling[active_adapter] if active_adapter in lora_module.scaling else 1
for active_adapter in lora_module.active_adapters
} for lora_module in self.lora_modules
]
def __enter__(self) -> None:
for i, lora_module in enumerate(self.lora_modules):
if not isinstance(lora_module, BaseTunerLayer):
continue
for active_adapter in lora_module.active_adapters:
if active_adapter == "default":
if self.dit_activated:
lora_module.scaling[active_adapter] = self.scales[0]["default"] if self.latent_sblora_weight is None else self.latent_sblora_weight
else:
lora_module.scaling[active_adapter] = 0
else:
assert active_adapter == "condition"
if self.cond_activated:
lora_module.scaling[active_adapter] = self.scales[0]["condition"] if self.condition_sblora_weight is None else self.condition_sblora_weight
else:
lora_module.scaling[active_adapter] = 0
def __exit__(
self,
exc_type: Optional[Type[BaseException]],
exc_val: Optional[BaseException],
exc_tb: Optional[Any],
) -> None:
for i, lora_module in enumerate(self.lora_modules):
if not isinstance(lora_module, BaseTunerLayer):
continue
for active_adapter in lora_module.active_adapters:
lora_module.scaling[active_adapter] = self.scales[i][active_adapter]
class set_lora_scale:
def __init__(self, lora_modules: List[BaseTunerLayer], scale: float) -> None:
self.lora_modules: List[BaseTunerLayer] = [
each for each in lora_modules if isinstance(each, BaseTunerLayer)
]
self.scales = [
{
active_adapter: lora_module.scaling[active_adapter]
for active_adapter in lora_module.active_adapters
}
for lora_module in self.lora_modules
]
self.scale = scale
def __enter__(self) -> None:
for lora_module in self.lora_modules:
if not isinstance(lora_module, BaseTunerLayer):
continue
lora_module.scale_layer(self.scale)
def __exit__(
self,
exc_type: Optional[Type[BaseException]],
exc_val: Optional[BaseException],
exc_tb: Optional[Any],
) -> None:
for i, lora_module in enumerate(self.lora_modules):
if not isinstance(lora_module, BaseTunerLayer):
continue
for active_adapter in lora_module.active_adapters:
lora_module.scaling[active_adapter] = self.scales[i][active_adapter]
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