#
AllegroTransformer3DModel

A Diffusion Transformer model for 3D data from Allegro was introduced in Allegro: Open the Black Box of Commercial-Level Video Generation Model by RhymesAI.

The model can be loaded with the following code snippet.

    from diffusers import AllegroTransformer3DModel

    vae = AllegroTransformer3DModel.from_pretrained("rhymes-ai/Allegro", subfolder="transformer", torch_dtype=torch.bfloat16).to("cuda")

##
AllegroTransformer3DModel

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class diffusers.AllegroTransformer3DModel

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( patch_size: int = 2 patch_size_t: int = 1 num_attention_heads: int = 24 attention_head_dim: int = 96 in_channels: int = 4 out_channels: int = 4 num_layers: int = 32 dropout: float = 0.0 cross_attention_dim: int = 2304 attention_bias: bool = True sample_height: int = 90 sample_width: int = 160 sample_frames: int = 22 activation_fn: str = 'gelu-approximate' norm_elementwise_affine: bool = False norm_eps: float = 1e-06 caption_channels: int = 4096 interpolation_scale_h: float = 2.0 interpolation_scale_w: float = 2.0 interpolation_scale_t: float = 2.2 )

##
Transformer2DModelOutput

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class diffusers.models.modeling_outputs.Transformer2DModelOutput

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( sample: torch.Tensor )

Parameters

- 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) — The hidden states output conditioned on the `encoder_hidden_states` input. If discrete, returns probability distributions for the unnoised latent pixels.

The output of Transformer2DModel.

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