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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 List
import attrs
from cosmos_predict1.diffusion.training.config.base.ema import PowerEMAConfig
from cosmos_predict1.diffusion.training.modules.edm_sde import EDMSDE
from cosmos_predict1.utils.lazy_config import LazyCall as L
from cosmos_predict1.utils.lazy_config import LazyDict
@attrs.define(slots=False)
class FSDPConfig:
policy: str = "block"
checkpoint: bool = False
min_num_params: int = 1024
sharding_group_size: int = 8
sharding_strategy: str = "full"
@attrs.define(slots=False)
class DefaultModelConfig:
vae: LazyDict = None
conditioner: LazyDict = None
net: LazyDict = None
ema: LazyDict = PowerEMAConfig
sde: LazyDict = L(EDMSDE)(
p_mean=0.0,
p_std=1.0,
sigma_max=80,
sigma_min=0.0002,
)
sigma_data: float = 0.5
camera_sample_weight: LazyDict = LazyDict(
dict(
enabled=False,
weight=5.0,
)
)
aesthetic_finetuning: LazyDict = LazyDict(
dict(
enabled=False,
)
)
loss_mask_enabled: bool = False
loss_masking: LazyDict = None
loss_add_logvar: bool = True
precision: str = "bfloat16"
input_data_key: str = "video" # key to fetch input data from data_batch
input_image_key: str = "images_1024" # key to fetch input image from data_batch
loss_reduce: str = "sum"
loss_scale: float = 1.0
latent_shape: List[int] = [16, 24, 44, 80] # 24 corresponig to 136 frames
fsdp_enabled: bool = False
use_torch_compile: bool = False
fsdp: FSDPConfig = attrs.field(factory=FSDPConfig)
use_dummy_temporal_dim: bool = False # Whether to use dummy temporal dimension in data
adjust_video_noise: bool = False # whether or not adjust video noise accroding to the video length
peft_control: LazyDict | None = None
@attrs.define(slots=False)
class MultiviewModelConfig(DefaultModelConfig):
n_views: int = 6
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