A 2D Transformer model for image-like data. Parameters: num_attention_heads (`int`, *optional*, defaults to 16): The number of heads

dot.notation access to dictionary attributes

r""" Default processor for performing attention-related computations.

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

Chains list of learning rate schedulers. It takes a list of chainable learning rate schedulers and performs consecutive step() functions belong to

r""" Default processor for performing attention-related computations.

Receives the list of schedulers that is expected to be called sequentially during optimization process and milestone points that provides exact in

The output of [`Transformer2DModel`]. Args: sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` or `(ba

The output of [`UNet2DConditionModel`]. Args: sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):

r""" Default processor for performing attention-related computations.

r""" Default processor for performing attention-related computations.

Two ways to print to console: 1. self.print: correctly handle progress bar 2. rank_zero_info: use the logging module

Decays the learning rate of each parameter group by a small constant factor until the number of epoch reaches a pre-defined milestone: total_iters

Decays the learning rate of each parameter group by linearly changing small multiplicative factor until the number of epoch reaches a pre-defined

r""" Pipeline to generate image variations from an input image using Stable Diffusion. This model inherits from [`DiffusionPipeline`]. Check

Two ways to print to console: 1. self.print: correctly handle progress bar 2. rank_zero_info: use the logging module

r""" A conditional 2D UNet model that takes a noisy sample, conditional state, and a timestep and returns a sample shaped output. This mo