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Model: EmpathicRobotics/vla-1.7b-qwen3-v2
Source: Original Platform

tools/decode/vendor/cosmos_tokenizer/networks/configs.py

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+# SPDX-FileCopyrightText: Copyright (c) 2024 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_tokenizer.modules import (
+    ContinuousFormulation,
+    DiscreteQuantizer,
+    EncoderType,
+    DecoderType,
+    Encoder3DType,
+    Decoder3DType,
+)
+
+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,
+)
+
+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,
+)
+
+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",
+)
+
+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",
+)