r""" A cross attention layer. Parameters: query_dim (`int`): The number of channels in the query. cross_attention_dim (`int`,

Transformer model for image-like data. Takes either discrete (classes of vector embeddings) or continuous (actual embeddings) inputs. Wh

Norm layer modified to incorporate timestep embeddings.

r""" Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0).

r""" A feed-forward layer. Parameters: dim (`int`): The number of channels in the input. dim_out (`int`, *optional*): The num

r""" GELU activation function with tanh approximation support with `approximate="tanh"`.

Norm layer adaptive layer norm zero (adaLN-Zero).

The approximate form of Gaussian Error Linear Unit (GELU) For more details, see section 2: https://arxiv.org/abs/1606.08415

r""" A basic Transformer block. Parameters: dim (`int`): The number of channels in the input and output. num_attention_heads

r""" A variant of the gated linear unit activation function from https://arxiv.org/abs/2002.05202. Parameters: dim_in (`int`): The nu

Apply gaussian smoothing on a 1d, 2d or 3d tensor. Filtering is performed seperately for each channel in the input using a depthwise conv

Spatially conditioned normalization as defined in https://arxiv.org/abs/2209.09002

Args: sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` or `(batch size, num_vector_embeds - 1, num_latent

Args: sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): Hidden states conditioned on `encode

r""" UNet2DConditionModel is a conditional 2D UNet model that takes in a noisy sample, conditional state, and a timestep and returns sample sh