LDMSuperResolutionPipeline  
A pipeline for image super-resolution using latent diffusion.  
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.).  
Parameters:
vqvae ([`VQModel`]):
Vector-quantized (VQ) model to encode and decode images to and from latent representations.
unet ([`UNet2DModel`]):
A `UNet2DModel` to denoise the encoded image.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latens. Can be one of
[`DDIMScheduler`], [`LMSDiscreteScheduler`], [`EulerDiscreteScheduler`],
[`EulerAncestralDiscreteScheduler`], [`DPMSolverMultistepScheduler`], or [`PNDMScheduler`].  
- all
- __call__
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/latent_diffusion.md