UnCLIPPipeline  
Pipeline for text-to-image generation using unCLIP.  
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.).  
Args:
text_encoder ([`~transformers.CLIPTextModelWithProjection`]):
Frozen text-encoder.
tokenizer ([`~transformers.CLIPTokenizer`]):
A `CLIPTokenizer` to tokenize text.
prior ([`PriorTransformer`]):
The canonical unCLIP prior to approximate the image embedding from the text embedding.
text_proj ([`UnCLIPTextProjModel`]):
Utility class to prepare and combine the embeddings before they are passed to the decoder.
decoder ([`UNet2DConditionModel`]):
The decoder to invert the image embedding into an image.
super_res_first ([`UNet2DModel`]):
Super resolution UNet. Used in all but the last step of the super resolution diffusion process.
super_res_last ([`UNet2DModel`]):
Super resolution UNet. Used in the last step of the super resolution diffusion process.
prior_scheduler ([`UnCLIPScheduler`]):
Scheduler used in the prior denoising process (a modified [`DDPMScheduler`]).
decoder_scheduler ([`UnCLIPScheduler`]):
Scheduler used in the decoder denoising process (a modified [`DDPMScheduler`]).
super_res_scheduler ([`UnCLIPScheduler`]):
Scheduler used in the super resolution denoising process (a modified [`DDPMScheduler`]).  
- all
- __call__
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/unclip.md