cosmos_predict1/autoregressive/diffusion_decoder/config/registry.py

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# SPDX-License-Identifier: Apache-2.0
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# Licensed under the Apache License, Version 2.0 (the "License");
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from hydra.core.config_store import ConfigStore

from cosmos_predict1.autoregressive.diffusion_decoder.config.base.conditioner import (
    VideoLatentDiffusionDecoderConditionerConfig,
)
from cosmos_predict1.autoregressive.tokenizer.discrete_video import DiscreteVideoFSQJITTokenizer
from cosmos_predict1.diffusion.module.pretrained_vae import JITVAE, JointImageVideoSharedJITTokenizer, VideoJITTokenizer
from cosmos_predict1.utils.lazy_config 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_predict1_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_predict1_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-Tokenize1-CV8x8x8-720p/.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-Tokenize1-DV8x16x16-720p/.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)