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from dataclasses import dataclass |
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from ..utils import BaseOutput |
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@dataclass |
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class AutoencoderKLOutput(BaseOutput): |
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""" |
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Output of AutoencoderKL encoding method. |
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Args: |
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latent_dist (`DiagonalGaussianDistribution`): |
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Encoded outputs of `Encoder` represented as the mean and logvar of `DiagonalGaussianDistribution`. |
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`DiagonalGaussianDistribution` allows for sampling latents from the distribution. |
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""" |
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latent_dist: "DiagonalGaussianDistribution" |
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@dataclass |
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class Transformer2DModelOutput(BaseOutput): |
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""" |
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The output of [`Transformer2DModel`]. |
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Args: |
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sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` or `(batch size, num_vector_embeds - 1, num_latent_pixels)` if [`Transformer2DModel`] is discrete): |
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The hidden states output conditioned on the `encoder_hidden_states` input. If discrete, returns probability |
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distributions for the unnoised latent pixels. |
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""" |
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sample: "torch.Tensor" |
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