Diffusers 文档
AllegroTransformer3D模型
并获取增强的文档体验
开始使用
AllegroTransformer3D模型
来自 Allegro 的用于 3D 数据的扩散 Transformer 模型在 Allegro: Open the Black Box of Commercial-Level Video Generation Model (由 RhymesAI 提出) 中被介绍。
该模型可以使用以下代码片段加载。
from diffusers import AllegroTransformer3DModel
vae = AllegroTransformer3DModel.from_pretrained("rhymes-ai/Allegro", subfolder="transformer", torch_dtype=torch.bfloat16).to("cuda")AllegroTransformer3D模型
class diffusers.AllegroTransformer3DModel
< 源代码 >( 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
class diffusers.models.modeling_outputs.Transformer2DModelOutput
< 源代码 >( sample: torch.Tensor )
参数
- sample (
torch.Tensor，形状为(batch_size, num_channels, height, width)或(batch size, num_vector_embeds - 1, num_latent_pixels)，如果 Transformer2DModel 是离散的) — 以encoder_hidden_states输入为条件的隐藏状态输出。 如果是离散的，则返回未噪声潜像素的概率分布。
Transformer2DModel 的输出。