cosmos_predict1/autoregressive/diffusion_decoder/config/base/conditioner.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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# http://www.apache.org/licenses/LICENSE-2.0
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from dataclasses import dataclass
from typing import Dict, Optional

import torch

from cosmos_predict1.diffusion.conditioner import BaseVideoCondition, GeneralConditioner
from cosmos_predict1.diffusion.config.base.conditioner import (
    FPSConfig,
    ImageSizeConfig,
    LatentConditionConfig,
    LatentConditionSigmaConfig,
    NumFramesConfig,
    PaddingMaskConfig,
    TextConfig,
)
from cosmos_predict1.utils.lazy_config import LazyCall as L
from cosmos_predict1.utils.lazy_config import LazyDict


@dataclass
class VideoLatentDiffusionDecoderCondition(BaseVideoCondition):
    # latent_condition will concat to the input of network, along channel dim;
    # cfg will make latent_condition all zero padding.
    latent_condition: Optional[torch.Tensor] = None
    latent_condition_sigma: Optional[torch.Tensor] = None


class VideoDiffusionDecoderConditioner(GeneralConditioner):
    def forward(
        self,
        batch: Dict,
        override_dropout_rate: Optional[Dict[str, float]] = None,
    ) -> VideoLatentDiffusionDecoderCondition:
        output = super()._forward(batch, override_dropout_rate)
        return VideoLatentDiffusionDecoderCondition(**output)


VideoLatentDiffusionDecoderConditionerConfig: LazyDict = L(VideoDiffusionDecoderConditioner)(
    text=TextConfig(),
    fps=FPSConfig(),
    num_frames=NumFramesConfig(),
    image_size=ImageSizeConfig(),
    padding_mask=PaddingMaskConfig(),
    latent_condition=LatentConditionConfig(),
    latent_condition_sigma=LatentConditionSigmaConfig(),
)