use sliced VAE decode that decodes the batch latents one image at a time.
You likely want to couple this with enable_attention_slicing() or enable_xformers_memory_efficient_attention() to further minimize memory use.
To perform the VAE decode one image at a time, invoke enable_vae_slicing() in your pipeline before inference. For example:


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import torch
from diffusers import StableDiffusionPipeline

pipe = StableDiffusionPipeline.from_pr