Class Transformer2DModelOutput

models/flowsep/diffusers/models/transformer_2d.py:30–38

Args: sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` or `(batch size, num_vector_embeds - 1, num_latent_pixels)` if [`Transformer2DModel`] is discrete): Hidden states conditioned on `encoder_hidden_states` input. If discrete, returns prob

@dataclass
class Transformer2DModelOutput(BaseOutput):
    """
    Args:
        sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` or `(batch size, num_vector_embeds - 1, num_latent_pixels)` if [`Transformer2DModel`] is discrete):
            Hidden states conditioned on `encoder_hidden_states` input. If discrete, returns probability distributions
            for the unnoised latent pixels.
    """

    sample: torch.FloatTensor


class Transformer2DModel(ModelMixin, ConfigMixin):