image = vae.decode(latents, return_dict=False)[0]
image = image_processor.postprocess(image, output_type="pil")
image[0].save("split_transformer.png")
```  
By selectively loading and unloading the models you need at a given stage and sharding the largest models across multiple GPUs, it is possible to run inference with large models on consumer GPUs.