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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, List, Union
import attrs
from .ar_configs_base_model import ModelConfig, TokenizerConfig
@attrs.define(slots=False)
class DataShapeConfig:
latent_shape: list = []
num_video_frames: Union[None, int] = None
height: Union[None, int] = None
width: Union[None, int] = None
@attrs.define(slots=False)
class SamplingConfig:
"""
Sampling config
Args:
temperature (float): Temperature value for controlling randomness in sampling. Defaults to 0.6.
top_p (float): Top-p probability threshold for nucleus sampling. Defaults to 0.9.
logprobs (bool): Flag indicating whether to compute token log probabilities. Defaults to False.
echo (bool): Flag indicating whether to include prompt tokens in the generated output. Defaults to False.
"""
temperature: float = 0.6
top_k: int = None
top_p: float = 0.9
compile_prefill: bool = False
compile_sampling: bool = True
logprobs: bool = False
echo: bool = False
@attrs.define(slots=False)
class DiffusionDecoderSamplingConfig:
"""
Diffusion decoder sampling config
Args:
guidance (float): Guidance scale for the diffusion process. Controls how much the model follows the conditioning. Defaults to 0.8.
sigma_min (float): Minimum noise level for the diffusion process. Defaults to 0.02.
sigma (float): Initial noise level for the diffusion process. Defaults to 8.
num_steps (int): Number of denoising steps to perform. Defaults to 35.
overlap (int): Number of overlapping frames between video chunks during processing. Defaults to 2.
continuous_tokenizer_channel (int): Number of channels in the continuous tokenizer of diffusion decoder. Defaults to 16.
continuous_tokenizer_spatial_compression_ratio (int): Spatial compression ratio for the continuous tokenizer of diffusion decoder. Defaults to 8.
dd_train_num_video_frames (int): Number of video frames used during training for diffusion decoder. Defaults to 57.
"""
guidance: float = 1.8
sigma_min: float = 0.02
sigma: float = 8
num_steps: int = 15
overlap: int = 2
continuous_tokenizer_channel = 16
continuous_tokenizer_spatial_compression_ratio = 8
dd_train_num_video_frames: int = 57
max_iter: int = 99
fps: int = 24
@attrs.define(slots=False)
class InferenceConfig:
"""
Inference config
Args:
model_config (ModelConfig): Model config
tokenizer_config (TokenizerConfig): Tokenizer config
ckpt_path (str): Path to the checkpoint
latent_shape (list): Shape of the latent
"""
model_config: ModelConfig = None
tokenizer_config: TokenizerConfig = None
ckpt_path: str = ""
data_shape_config: DataShapeConfig = None
defaults: List[Any] = attrs.field(
factory=lambda: [
"_self_",
{"data_val": None},
{"data_shape_config": "video_shape_as_model_config"},
{"eval_job": None},
]
)
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