Buckets:
| ## LatteTransformer3DModel | |
| Un modelo Diffusion Transformer para datos 3D de [Latte](https://github.com/Vchitect/Latte). | |
| ## LatteTransformer3DModel[[diffusers.LatteTransformer3DModel]] | |
| #### diffusers.LatteTransformer3DModel[[diffusers.LatteTransformer3DModel]] | |
| ```python | |
| diffusers.LatteTransformer3DModel(num_attention_heads: int = 16, attention_head_dim: int = 88, in_channels: int | None = None, out_channels: int | None = None, num_layers: int = 1, dropout: float = 0.0, cross_attention_dim: int | None = None, attention_bias: bool = False, sample_size: int = 64, patch_size: int | None = None, activation_fn: str = 'geglu', num_embeds_ada_norm: int | None = None, norm_type: str = 'layer_norm', norm_elementwise_affine: bool = True, norm_eps: float = 1e-05, caption_channels: int = None, video_length: int = 16) | |
| ``` | |
| [Fuente](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/transformers/latte_transformer_3d.py#L27) | |
| #### forward[[diffusers.LatteTransformer3DModel.forward]] | |
| ```python | |
| forward(hidden_states: Tensor, timestep: typing.Optional[torch.LongTensor] = None, encoder_hidden_states: typing.Optional[torch.Tensor] = None, encoder_attention_mask: typing.Optional[torch.Tensor] = None, enable_temporal_attentions: bool = True, return_dict: bool = True) | |
| ``` | |
| [Fuente](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/transformers/latte_transformer_3d.py#L166) | |
| **Parámetros:** | |
| hidden_states (`torch.Tensor` de forma `(batch size, channel, num_frame, height, width)`) : `hidden_states` de entrada. | |
| timestep ( `torch.LongTensor`, *opcional*) : Se utiliza para indicar el paso de desruido. Paso temporal opcional que se aplica como un embedding en `AdaLayerNorm`. | |
| encoder_hidden_states ( `torch.FloatTensor` de forma `(batch size, sequence len, embed dims)`, *opcional*) : Incrustaciones condicionales para la capa de atención cruzada. Si no se proporcionan, la atención cruzada predeterminada es la autoatención. | |
| encoder_attention_mask ( `torch.Tensor`, *opcional*) : Máscara de atención cruzada aplicada a `encoder_hidden_states`. Se admiten dos formatos: * Máscara `(batcheight, sequence_length)` True = conservar, False = descartar. * Sesgo `(batcheight, 1, sequence_length)` 0 = conservar, -10000 = descartar. Si `ndim == 2`: se interpretará como una máscara y luego se convertirá en un sesgo coherente con el formato anterior. Este sesgo se sumará a las puntuaciones de atención cruzada. | |
| enable_temporal_attentions : (`bool`, *opcional*, valor predeterminado `True`): Si se habilitan las atenciones temporales. | |
| return_dict (`bool`, *opcional*, valor predeterminado `True`) : Indica si se debe devolver un `~models.unet_2d_condition.UNet2DConditionOutput` en lugar de una tupla simple. | |
| **Devuelve:** | |
| Si `return_dict` es True, se devuelve un `~models.transformer_2d.Transformer2DModelOutput`; en caso contrario, se devuelve una `tuple` cuyo primer elemento es el tensor de la muestra. | |
| El método forward de [LatteTransformer3DModel](/docs/diffusers/main/es/api/models/latte_transformer3d#diffusers.LatteTransformer3DModel). | |
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