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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.
"""The default image and video tokenizer configs."""
from cosmos_predict1.tokenizer.modules import (
ContinuousFormulation,
Decoder3DType,
DecoderType,
DiscreteQuantizer,
Encoder3DType,
EncoderType,
)
continuous_image = dict(
# The attention resolution for res blocks.
attn_resolutions=[32],
# The base number of channels.
channels=128,
# The channel multipler for each resolution.
channels_mult=[2, 4, 4],
dropout=0.0,
in_channels=3,
# The spatial compression ratio.
spatial_compression=16,
# The number of layers in each res block.
num_res_blocks=2,
out_channels=3,
resolution=1024,
patch_size=4,
patch_method="haar",
# The output latent dimension (channels).
latent_channels=16,
# The encoder output channels just before sampling.
# Which is also the decoder's input channels.
z_channels=16,
# A factor over the z_channels, to get the total channels the encoder should output.
# For a VAE for instance, we want to output the mean and variance, so we need 2 * z_channels.
z_factor=1,
name="CI",
# What formulation to use, either "AE" or "VAE".
# Chose VAE here, since the pre-trained ckpt were of a VAE formulation.
formulation=ContinuousFormulation.AE.name,
# Specify type of encoder ["Default", "LiteVAE"]
encoder=EncoderType.Default.name,
# Specify type of decoder ["Default"]
decoder=DecoderType.Default.name,
)
continuous_image_8x8_360p = dict(continuous_image)
continuous_image_8x8_360p["patch_size"] = 2
continuous_image_8x8_360p["spatial_compression"] = 8
continuous_image_16x16_360p = dict(continuous_image)
continuous_image_16x16_360p["patch_size"] = 2
continuous_image_16x16_360p["spatial_compression"] = 16
discrete_image = dict(
# The attention resolution for res blocks.
attn_resolutions=[32],
# The base number of channels.
channels=128,
# The channel multipler for each resolution.
channels_mult=[2, 4, 4],
dropout=0.0,
in_channels=3,
# The spatial compression ratio.
spatial_compression=16,
# The number of layers in each res block.
num_res_blocks=2,
out_channels=3,
resolution=1024,
patch_size=4,
patch_method="haar",
# The encoder output channels just before sampling.
z_channels=256,
# A factor over the z_channels, to get the total channels the encoder should output.
# for discrete tokenization, often we directly use the vector, so z_factor=1.
z_factor=1,
# The quantizer of choice, VQ, LFQ, FSQ, or ResFSQ.
quantizer=DiscreteQuantizer.FSQ.name,
# The embedding dimension post-quantization, which is also the input channels of the decoder.
# Which is also the output
embedding_dim=6,
# The number of levels to use for fine-scalar quantization.
levels=[8, 8, 8, 5, 5, 5],
# The number of quantizers to use for residual fine-scalar quantization.
num_quantizers=4,
name="DI",
# Specify type of encoder ["Default", "LiteVAE"]
encoder=EncoderType.Default.name,
# Specify type of decoder ["Default"]
decoder=DecoderType.Default.name,
)
discrete_image_8x8_360p = dict(discrete_image)
discrete_image_8x8_360p["patch_size"] = 2
discrete_image_8x8_360p["spatial_compression"] = 8
discrete_image_16x16_360p = dict(discrete_image)
discrete_image_16x16_360p["patch_size"] = 2
discrete_image_16x16_360p["spatial_compression"] = 16
continuous_video = dict(
attn_resolutions=[32],
channels=128,
channels_mult=[2, 4, 4],
dropout=0.0,
in_channels=3,
num_res_blocks=2,
out_channels=3,
resolution=1024,
patch_size=4,
patch_method="haar",
latent_channels=16,
z_channels=16,
z_factor=1,
num_groups=1,
legacy_mode=False,
spatial_compression=8,
temporal_compression=8,
formulation=ContinuousFormulation.AE.name,
encoder=Encoder3DType.FACTORIZED.name,
decoder=Decoder3DType.FACTORIZED.name,
name="CV",
)
continuous_video_8x8x8_720p = dict(continuous_video)
continuous_video_8x8x8_720p["temporal_compression"] = 8
continuous_video_8x8x8_720p["spatial_compression"] = 8
continuous_video_4x8x8_360p = dict(continuous_video)
continuous_video_4x8x8_360p["temporal_compression"] = 4
continuous_video_4x8x8_360p["spatial_compression"] = 8
continuous_video_4x8x8_360p["patch_size"] = 2
discrete_video = dict(
attn_resolutions=[32],
channels=128,
channels_mult=[2, 4, 4],
dropout=0.0,
in_channels=3,
num_res_blocks=2,
out_channels=3,
resolution=1024,
patch_size=4,
patch_method="haar",
z_channels=16,
z_factor=1,
num_groups=1,
legacy_mode=False,
spatial_compression=16,
temporal_compression=8,
quantizer=DiscreteQuantizer.FSQ.name,
embedding_dim=6,
levels=[8, 8, 8, 5, 5, 5],
encoder=Encoder3DType.FACTORIZED.name,
decoder=Decoder3DType.FACTORIZED.name,
name="DV",
)
discrete_video_8x16x16_720p = dict(discrete_video)
discrete_video_8x16x16_720p["temporal_compression"] = 8
discrete_video_8x16x16_720p["spatial_compression"] = 16
discrete_video_4x8x8_360p = dict(discrete_video)
discrete_video_4x8x8_360p["z_channels"] = 256
discrete_video_4x8x8_360p["temporal_compression"] = 4
discrete_video_4x8x8_360p["spatial_compression"] = 8
discrete_video_4x8x8_360p["patch_size"] = 2
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