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# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# 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 typing import Dict, Type, TypeVar
from cosmos_predict1.diffusion.training.models.extend_model import ExtendDiffusionModel
from cosmos_predict1.diffusion.training.models.model import DiffusionModel as VideoDiffusionModel
from cosmos_predict1.diffusion.training.utils.layer_control.peft_control_config_parser import LayerControlConfigParser
from cosmos_predict1.diffusion.training.utils.peft.peft import add_lora_layers, setup_lora_requires_grad
from cosmos_predict1.diffusion.utils.customization.customization_manager import CustomizationType
from cosmos_predict1.utils import misc
from cosmos_predict1.utils.lazy_config import instantiate as lazy_instantiate
T = TypeVar("T")
def video_peft_decorator(base_class: Type[T]) -> Type[T]:
class PEFTVideoDiffusionModel(base_class):
def __init__(self, config: dict, fsdp_checkpointer=None):
super().__init__(config)
@misc.timer("PEFTVideoDiffusionModel: set_up_model")
def set_up_model(self):
config = self.config
peft_control_config_parser = LayerControlConfigParser(config=config.peft_control)
peft_control_config = peft_control_config_parser.parse()
self.model = self.build_model()
if peft_control_config and peft_control_config["customization_type"] == CustomizationType.LORA:
add_lora_layers(self.model, peft_control_config)
num_lora_params = setup_lora_requires_grad(self.model)
if num_lora_params == 0:
raise ValueError("No LoRA parameters found. Please check the model configuration.")
if config.ema.enabled:
with misc.timer("PEFTDiffusionModel: instantiate ema"):
config.ema.model = self.model
self.model_ema = lazy_instantiate(config.ema)
config.ema.model = None
else:
self.model_ema = None
def state_dict_model(self) -> Dict:
return {
"model": self.model.state_dict(),
"ema": self.model_ema.state_dict() if self.model_ema else None,
}
return PEFTVideoDiffusionModel
@video_peft_decorator
class PEFTVideoDiffusionModel(VideoDiffusionModel):
pass
@video_peft_decorator
class PEFTExtendDiffusionModel(ExtendDiffusionModel):
pass
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