# 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