Buckets:
| ## LatteTransformer3DModel | |
| Model Diffusion Transformer untuk data 3D dari [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) | |
| ``` | |
| [Sumber](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) | |
| ``` | |
| [Sumber](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/transformers/latte_transformer_3d.py#L166) | |
| **Parameter:** | |
| hidden_states (`torch.Tensor` dengan bentuk `(batch size, channel, num_frame, height, width)`) : Input `hidden_states`. | |
| timestep ( `torch.LongTensor`, *opsional*) : Digunakan untuk menunjukkan langkah denoising. Timestep opsional yang diterapkan sebagai embedding di `AdaLayerNorm`. | |
| encoder_hidden_states ( `torch.FloatTensor` dengan bentuk `(batch size, sequence len, embed dims)`, *opsional*) : Embedding kondisional untuk lapisan cross attention. Jika tidak diberikan, cross-attention secara default menggunakan self-attention. | |
| encoder_attention_mask ( `torch.Tensor`, *opsional*) : Mask cross-attention yang diterapkan pada `encoder_hidden_states`. Dua format didukung: * Mask `(batcheight, sequence_length)` True = simpan, False = buang. * Bias `(batcheight, 1, sequence_length)` 0 = simpan, -10000 = buang. Jika `ndim == 2`: akan diinterpretasikan sebagai mask, kemudian dikonversi menjadi bias yang konsisten dengan format di atas. Bias ini akan ditambahkan ke skor cross-attention. | |
| enable_temporal_attentions : (`bool`, *opsional*, defaultnya `True`): Apakah akan mengaktifkan temporal attentions. | |
| return_dict (`bool`, *opsional*, defaultnya `True`) : Apakah akan mengembalikan `~models.unet_2d_condition.UNet2DConditionOutput` alih-alih tuple biasa. | |
| **Mengembalikan:** | |
| Jika `return_dict` bernilai True, sebuah `~models.transformer_2d.Transformer2DModelOutput` akan dikembalikan, selain itu sebuah `tuple` di mana elemen pertama adalah tensor sampel. | |
| Metode forward dari [LatteTransformer3DModel](/docs/diffusers/main/id/api/models/latte_transformer3d#diffusers.LatteTransformer3DModel). | |
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