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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.diffusion.networks.general_dit_video_conditioned import VideoExtendGeneralDIT
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
Cosmos_Predict1_WorldInterpolator_7B: LazyDict = LazyDict(
dict(
defaults=[
{"override /net": "faditv2_7b"},
{"override /conditioner": "video_cond"},
{"override /tokenizer": "cosmos_diffusion_tokenizer_res720_comp8x8x8_t121_ver092624"},
"_self_",
],
model=dict(
sde=L(EDMSDE)(
p_mean=0.0,
p_std=1.0,
sigma_max=80,
sigma_min=0.0002,
),
input_image_key="images_1024",
latent_shape=[
16,
4,
88,
160,
],
tokenizer=dict(
video_vae=dict(
pixel_chunk_duration=9,
)
),
vae=dict( # Added VAE field
pixel_chunk_duration=9,
latent_ch=16,
),
adjust_video_noise=True,
num_latents_to_drop=1,
context_parallel_size=1,
conditioner=dict(
video_cond_bool=dict(
condition_location="first_and_last_1",
cfg_unconditional_type="zero_condition_region_condition_mask",
apply_corruption_to_condition_region="noise_with_sigma",
condition_on_augment_sigma=False,
dropout_rate=0.0,
first_random_n_num_condition_t_max=2,
normalize_condition_latent=False,
augment_sigma_sample_p_mean=-3.0,
augment_sigma_sample_p_std=2.0,
augment_sigma_sample_multiplier=1.0,
apply_corruption_to_condition_region_sigma_value=[0.001],
),
text=dict(
dropout_rate=0.5,
),
),
net=L(VideoExtendGeneralDIT)(
extra_per_block_abs_pos_emb=True,
rope_h_extrapolation_ratio=1.0,
rope_w_extrapolation_ratio=1.0,
rope_t_extrapolation_ratio=2.0,
extra_per_block_abs_pos_emb_type="learnable",
),
),
job=dict(group="WorldInterpolator", name="Cosmos_Predict1_WorldInterpolator_7B"),
)
)
Cosmos_Predict1_WorldInterpolator_7B_Post_trained: LazyDict = LazyDict(
dict(
defaults=[
"/experiment/Cosmos_Predict1_WorldInterpolator_7B",
],
job=dict(
name="Cosmos_Predict1_WorldInterpolator_7B_Post_trained",
),
)
)
cs = ConfigStore.instance()
for _item in [
Cosmos_Predict1_WorldInterpolator_7B,
Cosmos_Predict1_WorldInterpolator_7B_Post_trained,
]:
cs.store(group="experiment", package="_global_", name=_item["job"]["name"], node=_item)
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