File size: 3,647 Bytes
226c7c9 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 |
# 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_transfer1.diffusion.config.training.ema import PowerEMAConfig
from cosmos_transfer1.diffusion.training.modules.edm_sde import EDMSDE
from cosmos_transfer1.utils.lazy_config import LazyCall as L
from cosmos_transfer1.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:
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
# training related
ema: LazyDict = PowerEMAConfig
sde: LazyDict = L(EDMSDE)(
p_mean=0.0,
p_std=1.0,
sigma_max=80,
sigma_min=0.0002,
)
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
input_image_key: str = "images_1024" # key to fetch input image from data_batch
loss_reduce: str = "sum"
loss_scale: float = 1.0
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
context_parallel_size: int = 1 # Number of context parallel groups
# `num_latents_to_drop` is mechanism to satisfy the CP%8==0 and (1I,N*P,1I) latents setup.
# Since our tokenizer is causal and has the `T+1` input frames setup, it makes it
# a little challenging to sample exact number of frames from file, and encode those.
# Instead, we sample as many frame from file, run the tokenizer twice, and discard the second
# chunk's P-latents, ensuring the above two requirements. By default, this flag does not have any effect.
num_latents_to_drop: int = 0 # number of latents to drop
@attrs.define(slots=False)
class MultiviewModelConfig(DefaultModelConfig):
n_views: int = 6
@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
|