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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 hydra.core.config_store import ConfigStore
from cosmos_predict1.tokenizer.training.configs.experiments.utils import create_debug_job_with_mock_data
from cosmos_predict1.utils import log
from cosmos_predict1.utils.lazy_config import LazyDict
# Post-training config for Cosmos-Tokenize1-CV8x8x8-720p-HDVILA
Cosmos_Tokenize1_CV8x8x8_720p_HDVILA: LazyDict = LazyDict(
dict(
defaults=[
"/experiment/video_basic",
{"override /network": "continuous_factorized_video"},
{"override /data_train": "hdvila_video720"},
{"override /data_val": "hdvila_video720"},
"_self_",
],
dataloader_train=dict(
dataset=dict(
crop_height=256,
num_video_frames=121,
),
batch_size=1,
),
dataloader_val=dict(
dataset=dict(
crop_height=256,
num_video_frames=121,
),
batch_size=1,
),
model=dict(
config=dict(
network=dict(
channels_mult=[2, 4, 4],
patch_size=4,
legacy_mode=False,
temporal_compression=8,
spatial_compression=8,
)
)
),
job=dict(
project="posttraining",
group="tokenizer",
name="Cosmos-Tokenize1-CV8x8x8-720p-HDVILA",
),
checkpoint=dict(
load_path="checkpoints/Cosmos-Tokenize1-CV8x8x8-720p/model.pt",
strict_resume=True,
load_training_state=True,
jit=dict(input_shape=[1, 3, 17, 512, 512]),
),
)
)
# Post-training config for Cosmos-Tokenize1-DV8x16x16-720p-HDVILA
Cosmos_Tokenize1_DV8x16x16_720p_HDVILA: LazyDict = LazyDict(
dict(
defaults=[
"/experiment/video_basic",
{"override /network": "discrete_factorized_video"},
{"override /data_train": "hdvila_video720"},
{"override /data_val": "hdvila_video720"},
"_self_",
],
dataloader_train=dict(
dataset=dict(
crop_height=256,
num_video_frames=49,
),
batch_size=1,
),
dataloader_val=dict(
dataset=dict(
crop_height=256,
num_video_frames=49,
),
batch_size=1,
),
model=dict(
config=dict(
network=dict(
persistent_quantizer=False,
z_channels=16,
channels_mult=[2, 4, 4],
patch_size=4,
legacy_mode=False,
temporal_compression=8,
spatial_compression=16,
)
)
),
job=dict(
project="posttraining",
group="tokenizer",
name="Cosmos-Tokenize1-DV8x16x16-720p-HDVILA",
),
checkpoint=dict(
load_path="checkpoints/Cosmos-Tokenize1-DV8x16x16-720p/model.pt",
strict_resume=True,
load_training_state=True,
jit=dict(input_shape=[1, 3, 17, 512, 512]),
),
)
)
# Post-training config for Cosmos-Tokenize1-CV4x8x8-360p-HDVILA
Cosmos_Tokenize1_CV4x8x8_360p_HDVILA: LazyDict = LazyDict(
dict(
defaults=[
"/experiment/video_basic",
{"override /network": "continuous_factorized_video"},
{"override /data_train": "hdvila_video360"},
{"override /data_val": "hdvila_video360"},
"_self_",
],
dataloader_train=dict(
dataset=dict(
crop_height=256,
num_video_frames=49,
),
batch_size=1,
),
dataloader_val=dict(
dataset=dict(
crop_height=256,
num_video_frames=49,
),
batch_size=1,
),
model=dict(
config=dict(
network=dict(
channels_mult=[2, 4, 4],
patch_size=2,
legacy_mode=False,
temporal_compression=4,
spatial_compression=8,
)
)
),
job=dict(
project="posttraining",
group="tokenizer",
name="Cosmos-Tokenize1-CV4x8x8-360p-HDVILA",
),
checkpoint=dict(
load_path="checkpoints/Cosmos-Tokenize1-CV4x8x8-360p/model.pt",
strict_resume=True,
load_training_state=True,
jit=dict(input_shape=[1, 3, 17, 512, 512]),
),
)
)
# Post-training config for Cosmos-Tokenize1-DV4x8x8-360p-HDVILA
Cosmos_Tokenize1_DV4x8x8_360p_HDVILA: LazyDict = LazyDict(
dict(
defaults=[
"/experiment/video_basic",
{"override /network": "discrete_factorized_video"},
{"override /data_train": "hdvila_video360"},
{"override /data_val": "hdvila_video360"},
"_self_",
],
dataloader_train=dict(
dataset=dict(
crop_height=256,
num_video_frames=49,
),
batch_size=1,
),
dataloader_val=dict(
dataset=dict(
crop_height=256,
num_video_frames=49,
),
batch_size=1,
),
model=dict(
config=dict(
network=dict(
persistent_quantizer=False,
z_channels=256,
channels_mult=[2, 4, 4],
patch_size=2,
legacy_mode=False,
temporal_compression=4,
spatial_compression=8,
)
)
),
job=dict(
project="posttraining",
group="tokenizer",
name="Cosmos-Tokenize1-DV4x8x8-360p-HDVILA",
),
checkpoint=dict(
load_path="checkpoints/Cosmos-Tokenize1-DV4x8x8-360p/model.pt",
strict_resume=True,
load_training_state=True,
jit=dict(input_shape=[1, 3, 17, 512, 512]),
),
)
)
cs = ConfigStore.instance()
for _item in [
Cosmos_Tokenize1_CV8x8x8_720p_HDVILA,
Cosmos_Tokenize1_DV8x16x16_720p_HDVILA,
Cosmos_Tokenize1_CV4x8x8_360p_HDVILA,
Cosmos_Tokenize1_DV4x8x8_360p_HDVILA,
]:
experiment_name = [name for name, value in globals().items() if value is _item][0]
log.info(f"Registering experiment: {experiment_name}")
cs.store(
group="experiment",
package="_global_",
name=experiment_name,
node=_item,
)
mock_experiment = f"mock_{experiment_name}"
log.info(f"Registering mock experiment: {mock_experiment}")
_debug_item = create_debug_job_with_mock_data(_item["job"]["name"])
cs.store(
group="experiment",
package="_global_",
name=mock_experiment,
node=_debug_item,
)
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