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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 cosmos1.models.autoregressive.diffusion_decoder.config.base.conditioner import (
VideoLatentDiffusionDecoderConditionerConfig,
)
from discrete_video import DiscreteVideoFSQJITTokenizer
from pretrained_vae import JITVAE, JointImageVideoSharedJITTokenizer, VideoJITTokenizer
from lazy_config_init import LazyCall as L
def get_cosmos_video_discrete_tokenizer_comp8x16x16(
resolution: str,
chunk_duration: int,
checkpoint_path: str,
):
assert resolution in ["720"]
pixel_chunk_duration = chunk_duration
temporal_compression_factor = 8
spatial_compression_factor = 16
return L(DiscreteVideoFSQJITTokenizer)(
enc_fp=checkpoint_path.replace(".jit", "encoder.jit"),
dec_fp=checkpoint_path.replace(".jit", "decoder.jit"),
name="discrete_video_fsq",
latent_ch=6,
is_bf16=True,
pixel_chunk_duration=pixel_chunk_duration,
latent_chunk_duration=1 + (pixel_chunk_duration - 1) // temporal_compression_factor,
max_enc_batch_size=8,
max_dec_batch_size=4,
levels=[8, 8, 8, 5, 5, 5],
compression_ratio=[temporal_compression_factor, spatial_compression_factor, spatial_compression_factor],
)
def get_cosmos_video_tokenizer_comp8x8x8(resolution: str, chunk_duration: int, checkpoint_path=None):
pixel_chunk_duration = chunk_duration
temporal_compression_factor = 8
spatial_compression_factor = 8
return L(JointImageVideoSharedJITTokenizer)(
video_vae=L(VideoJITTokenizer)(
name="cosmos_1_0_diffusion_tokenizer",
latent_ch=16,
is_bf16=True,
pixel_chunk_duration=pixel_chunk_duration,
temporal_compression_factor=temporal_compression_factor,
spatial_compression_factor=spatial_compression_factor,
spatial_resolution=resolution,
),
image_vae=L(JITVAE)(
name="cosmos_1_0_diffusion_tokenizer",
latent_ch=16,
is_image=False,
is_bf16=True,
),
name="cosmos_diffusion_tokenizer_res720_comp8x8x8_t121_ver092624",
latent_ch=16,
)
def register_tokenizer(cs):
cs.store(
group="tokenizer",
package="model.tokenizer",
name="cosmos_video_tokenizer_res720_comp8x8x8_t121_ver092624",
node=get_cosmos_video_tokenizer_comp8x8x8(
resolution="720",
chunk_duration=121,
checkpoint_path="checkpoints/Cosmos-1.0-Tokenizer-CV8x8x8/.jit",
),
)
def register_corruptor(cs):
cs.store(
group="tokenizer_corruptor",
package="model.tokenizer_corruptor",
name="cosmos_video_discrete_tokenizer_res720_comp8x16x16_t49_ver110224",
node=get_cosmos_video_discrete_tokenizer_comp8x16x16(
resolution="720",
chunk_duration=49,
checkpoint_path="checkpoints/Cosmos-1.0-Tokenizer-DV8x16x16/.jit",
),
)
def register_conditioner(cs):
cs.store(
group="conditioner",
package="model.conditioner",
name="video_latent_diffusion_decoder_cond",
node=VideoLatentDiffusionDecoderConditionerConfig,
)
def register_configs():
cs = ConfigStore.instance()
register_conditioner(cs)
register_corruptor(cs)
register_tokenizer(cs)
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