StableDiffusionLatentUpscalePipeline  
Pipeline for upscaling Stable Diffusion output image resolution by a factor of 2.  
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, etc.).  
The pipeline also inherits the following loading methods:
- [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files  
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
text_encoder ([`~transformers.CLIPTextModel`]):
Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
tokenizer ([`~transformers.CLIPTokenizer`]):
A `CLIPTokenizer` to tokenize text.
unet ([`UNet2DConditionModel`]):
A `UNet2DConditionModel` to denoise the encoded image latents.
scheduler ([`SchedulerMixin`]):
A [`EulerDiscreteScheduler`] to be used in combination with `unet` to denoise the encoded image latents.  
- all
- __call__
- enable_sequential_cpu_offload
- enable_attention_slicing
- disable_attention_slicing
- enable_xformers_memory_efficient_attention
- disable_xformers_memory_efficient_attention
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_diffusion/latent_upscale.md