all models to CPU using accelerate, significantly reducing memory usage. When called, the pipeline’s
models have their state dicts saved to CPU and then are moved to a torch.device('meta') and loaded to GPU only when their specific submodule has its forward` method called.

class diffusers.UnCLIPImageVariationPipeline

<
source
>
(
decoder: UNet2DConditionModel
text_encoder: CLIPTextModelWithProjection
tokenizer: CLIPTokenizer
text_proj: UnCLIP