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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 Any, Dict, List, Optional
import attrs
from cosmos_predict1.utils.lazy_config import LazyDict
@attrs.define(slots=False)
class DefaultModelConfig:
tokenizer: LazyDict = None
conditioner: LazyDict = None
net: LazyDict = None
sigma_data: float = 0.5
precision: str = "bfloat16"
input_data_key: str = "video" # key to fetch input data from data_batch
latent_shape: List[int] = [16, 24, 44, 80] # 24 corresponig to 136 frames
input_image_key: str = "images_1024"
adjust_video_noise: bool = False # Added field with default value
context_parallel_size: int = 1 # Added field with default value
# `num_latents_to_drop` is a flag that helps satisfy (1I,N*P,1I) latents setup.
# Since the tokenizer is causal and has the `T+1` input frames setup, it's
# challenging to encode arbitrary number of frames. To circumvent this,
# we sample as many frames, run the tokenizer twice, and discard the last
# chunk's P-latents, ensuring the requirement: I-latents for the input frames
# and P-latent for the-to-be-predicted in-between frames.
# By default, this flag does not have any effect.
num_latents_to_drop: int = 0 # number of P-latents to discard after encoding
sde: Optional[Dict] = None
vae: Optional[Dict] = None # Add this line to include the vae field
peft_control: LazyDict | None = None
frame_buffer_max: Optional[int] = 1
@attrs.define(slots=False)
class LatentDiffusionDecoderModelConfig(DefaultModelConfig):
tokenizer_corruptor: LazyDict = None
latent_corruptor: LazyDict = None
pixel_corruptor: LazyDict = None
diffusion_decoder_cond_sigma_low: float = None
diffusion_decoder_cond_sigma_high: float = None
diffusion_decoder_corrupt_prob: float = None
condition_on_tokenizer_corruptor_token: bool = False
@attrs.define(slots=False)
class MultiviewModelConfig(DefaultModelConfig):
n_views: int = 4
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