pipe = DiffusionPipeline.from_pretrained(
"DDUF/FLUX.1-dev-DDUF", dduf_file="FLUX.1-dev.dduf", torch_dtype=torch.bfloat16
).to("cuda")
image = pipe(
"photo a cat holding a sign that says Diffusers", num_inference_steps=50, guidance_scale=3.5
).images[0]
image.save("cat.png")
```  
To save a pipeline as a `.dduf` checkpoint, use the [`~huggingface_hub.export_folder_as_dduf`] utility, which takes care of all the necessary file-level validations.  
```py
from huggingface_hub import export_folder_as_dduf