AuraFlowPipeline  
Args:
tokenizer (`T5TokenizerFast`):
Tokenizer of class
[T5Tokenizer](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5Tokenizer).
text_encoder ([`T5EncoderModel`]):
Frozen text-encoder. AuraFlow uses
[T5](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5EncoderModel), specifically the
[EleutherAI/pile-t5-xl](https://huggingface.co/EleutherAI/pile-t5-xl) variant.
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
transformer ([`AuraFlowTransformer2DModel`]):
Conditional Transformer (MMDiT and DiT) architecture to denoise the encoded image latents.
scheduler ([`FlowMatchEulerDiscreteScheduler`]):
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/aura_flow.md