DiTPipeline  
Pipeline for image generation based on a Transformer backbone instead of a UNet.  
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:
transformer ([`DiTTransformer2DModel`]):
A class conditioned `DiTTransformer2DModel` to denoise the encoded image latents.
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
scheduler ([`DDIMScheduler`]):
A scheduler to be used in combination with `transformer` to denoise the encoded image latents.  
- all
- __call__
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/dit.md