Consistency Decoder Consistency decoder can be used to decode the latents from the denoising UNet in the StableDiffusionPipeline. This decoder was introduced in the DALL-E 3 technical report. The original codebase can be found at openai/consistencydecoder. Inference is only supported for 2 iterations as of now. The pipeline could not have been contributed without the help of madebyollin and mrsteyk from this issue.  ConsistencyDecoderVAE  class diffusers.ConsistencyDecoderVAE  < source > ( scaling_factor: float = 0.18215 latent_channels: int = 4 encoder_act_fn: str = 'silu' encoder_block_out_channels: Tuple = (128, 256, 512, 512) encoder_double_z: bool = True encoder_down_block_types: Tuple = ('DownEncoderBlock2D', 'DownEncoderBlock2D', 'DownEncoderBlock2D', 'DownEncoderBlock2D') encoder_in_channels: int = 3 encoder_layers_per_block: int = 2 encoder_norm_num_groups: int = 32 encoder_out_channels: int = 4 decoder_add_attention: bool = False decoder_block_out_channels: Tuple = (320, 640, 1024, 1024) decoder_down_block_types: Tuple = ('ResnetDownsampleBlock2D', 'ResnetDownsampleBlock2D', 'ResnetDownsampleBlock2D', 'ResnetDownsampleBlock2D') decoder_downsample_padding: int = 1 decoder_in_channels: int = 7 decoder_layers_per_block: int = 3 decoder_norm_eps: float = 1e-05 decoder_norm_num_groups: int = 32 decoder_num_train_timesteps: int = 1024 decoder_out_channels: int = 6 decoder_resnet_time_scale_shift: str = 'scale_shift' decoder_time_embedding_type: str = 'learned' decoder_up_block_types: Tuple = ('ResnetUpsampleBlock2D', 'ResnetUpsampleBlock2D', 'ResnetUpsampleBlock2D', 'ResnetUpsampleBlock2D')  )    The consistency decoder used with DALL-E 3.  Examples:   Copied >>> import torch