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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.
import omegaconf
from cosmos_predict1.diffusion.training.module.pretrained_vae import VideoJITTokenizer
from cosmos_predict1.utils.lazy_config import LazyCall as L
TOKENIZER_OPTIONS = {}
def tokenizer_register(key):
def decorator(func):
TOKENIZER_OPTIONS[key] = func
return func
return decorator
@tokenizer_register("cosmos_diffusion_tokenizer_comp8x8x8")
def get_cosmos_tokenizer_comp8x8x8(
resolution: str,
chunk_duration: int,
) -> omegaconf.dictconfig.DictConfig:
assert resolution in ["512", "720"]
pixel_chunk_duration = chunk_duration
temporal_compression_factor = 8
spatial_compression_factor = 8
return L(VideoJITTokenizer)(
name="cosmos_diffusion_tokenizer_comp8x8x8",
enc_fp="checkpoints/Cosmos-Tokenize1-CV8x8x8-720p/encoder.jit",
dec_fp="checkpoints/Cosmos-Tokenize1-CV8x8x8-720p/decoder.jit",
mean_std_fp="checkpoints/Cosmos-Tokenize1-CV8x8x8-720p/mean_std.pt",
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,
)
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