LDMTextToImagePipeline  
Pipeline for text-to-image generation 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.
bert ([`LDMBertModel`]):
Text-encoder model based on [`~transformers.BERT`].
tokenizer ([`~transformers.BertTokenizer`]):
A `BertTokenizer` to tokenize text.
unet ([`UNet2DConditionModel`]):
A `UNet2DConditionModel` to denoise the encoded image latents.
scheduler ([`SchedulerMixin`]):
A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].  
- all
- __call__
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/latent_diffusion.md