like the prior model, the decoder initializes a VQModel to decode the latents into images and it uses a UNet2DConditionModel:   Copied with ContextManagers(deepspeed_zero_init_disabled_context_manager()):
    vae = VQModel.from_pretrained(
        args.pretrained_decoder_model_name_or_path, subfolder="movq", torch_dtype=weight_dtype
    ).eval()
    image_encoder = CLIPVisionModelWithProjection.from_pretrained(
        args.pretrained_prior_mod