images = pipe([prompt] * 32).images You may see a small performance boost in VAE decoding on multi-image batches, and there should be no performance impact on single-image batches.  Tiled VAE Tiled VAE processing also enables working with large images on limited VRAM (for example, generating 4k images on 8GB of VRAM) by splitting the image into overlapping tiles, decoding the tiles, and then blending the outputs together to compose the final image. You should also used tiled VAE with enable_xformers_memory_efficient_attention() to reduce memory use further if you have xFormers installed. To use tiled VAE processing, call enable_vae_tiling() on your pipeline before inference:   Copied import torch