A residual block that can optionally change the number of channels. :param channels: the number of input channels. :param emb_channels: t

A sequential module that passes timestep embeddings to the children that support it as an extra input.

An attention block that allows spatial positions to attend to each other. Originally ported from here, but adapted to the N-d case. https

A downsampling layer with an optional convolution. :param channels: channels in the inputs and outputs. :param use_conv: a bool determini

Transformer block for image-like data. First, project the input (aka embedding) and reshape to b, t, d. Then apply standard transform

An upsampling layer with an optional convolution. :param channels: channels in the inputs and outputs. :param use_conv: a bool determinin

A module which performs QKV attention and splits in a different order.

Adapted from CLIP: https://github.com/openai/CLIP/blob/main/clip/model.py

A module which performs QKV attention. Matches legacy QKVAttention + input/ouput heads shaping

The full UNet model with attention and timestep embedding. :param in_channels: channels in the input Tensor. :param model_channels: base

A Karras et al. preconditioner for denoising diffusion models.

The half UNet model with attention and timestep embedding. For usage, see UNet.

L_simple with the Karras et al. preconditioner.

Any module where forward() takes timestep embeddings as a second argument.

Learned 2x upsampling without padding