del pipe.transformer
flush()
```  
<Tip>  
Notice that while initializing `pipe`, you're setting `text_encoder` to `None` so that it's not loaded.  
</Tip>  
Once the latents are computed, pass it off to the VAE to decode into a real image:  
```python
with torch.no_grad():
image = pipe.vae.decode(latents / pipe.vae.config.scaling_factor, return_dict=False)[0]
image = pipe.image_processor.postprocess(image, output_type="pil")[0]
image.save("cat.png")
```