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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.
import os
import nemo_run as run
from huggingface_hub import snapshot_download
from nemo.collections import llm
from nemo.collections.diffusion.models.model import DiT7BConfig, DiT14BConfig
from nemo.collections.diffusion.train import pretrain, videofolder_datamodule
from nemo.lightning.pytorch.strategies.utils import RestoreConfig
@run.cli.factory(target=llm.train)
def cosmos_diffusion_7b_text2world_finetune() -> run.Partial:
# Model setup
recipe = pretrain()
recipe.model.config = run.Config(DiT7BConfig)
# Trainer setup
recipe.trainer.max_steps = 1000
recipe.optim.config.lr = 1e-6
# Tensor / Sequence parallelism
recipe.trainer.strategy.tensor_model_parallel_size = 8
recipe.trainer.strategy.sequence_parallel = True
recipe.trainer.strategy.ckpt_async_save = False
# FSDP
recipe.trainer.strategy.ddp.with_megatron_fsdp_code_path = True
recipe.trainer.strategy.ddp.data_parallel_sharding_strategy = "MODEL_AND_OPTIMIZER_STATES"
recipe.trainer.strategy.ddp.overlap_param_gather = True
recipe.trainer.strategy.ddp.overlap_grad_reduce = True
recipe.model.config.use_cpu_initialization = True
# Data setup
recipe.data = videofolder_datamodule()
recipe.data.path = "" # path to folder with processed dataset
# Checkpoint load
recipe.resume.restore_config = run.Config(RestoreConfig, load_artifacts=False)
recipe.resume.restore_config.path = os.path.join(
snapshot_download("nvidia/Cosmos-1.0-Diffusion-7B-Text2World", allow_patterns=["nemo/*"]), "nemo"
) # path to diffusion model checkpoint
recipe.resume.resume_if_exists = False
# Directory to save checkpoints / logs
recipe.log_log_dir = "nemo_experiments/cosmos_diffusion_7b_text2world_finetune"
return recipe
@run.cli.factory(target=llm.train)
def cosmos_diffusion_14b_text2world_finetune() -> run.Partial:
# Model setup
recipe = pretrain()
recipe.model.config = run.Config(DiT14BConfig)
# Trainer setup
recipe.trainer.max_steps = 1000
recipe.optim.config.lr = 1e-6
# Tensor / Sequence parallelism
recipe.trainer.strategy.tensor_model_parallel_size = 8
recipe.trainer.strategy.sequence_parallel = True
recipe.trainer.strategy.ckpt_async_save = False
# FSDP
recipe.trainer.strategy.ddp.with_megatron_fsdp_code_path = True
recipe.trainer.strategy.ddp.data_parallel_sharding_strategy = "MODEL_AND_OPTIMIZER_STATES"
recipe.trainer.strategy.ddp.overlap_param_gather = True
recipe.trainer.strategy.ddp.overlap_grad_reduce = True
recipe.model.config.use_cpu_initialization = True
# Activation Checkpointing
recipe.model.config.recompute_granularity = "full"
recipe.model.config.recompute_method = "uniform"
recipe.model.config.recompute_num_layers = 1
# Data setup
recipe.data = videofolder_datamodule()
recipe.data.path = "" # path to folder with processed dataset
# Checkpoint load
recipe.resume.restore_config = run.Config(RestoreConfig, load_artifacts=False)
recipe.resume.restore_config.path = os.path.join(
snapshot_download("nvidia/Cosmos-1.0-Diffusion-14B-Text2World", allow_patterns=["nemo/*"]), "nemo"
) # path to diffusion model checkpoint
recipe.resume.resume_if_exists = False
# Directory to save checkpoints / logs
recipe.log_log_dir = "nemo_experiments/cosmos_diffusion_14b_text2world_finetune"
return recipe
if __name__ == "__main__":
run.cli.main(llm.train, default_factory=cosmos_diffusion_7b_text2world_finetune)
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