sion = DiffusionPipeline.from_pretrained(
    "runwayml/stable-diffusion-v1-5", variant="fp16", torch_dtype=torch.float16
)
# load non_ema variant
stable_diffusion = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", variant="non_ema")
To save a checkpoint stored in a different floating point type or as a non-EMA variant, use the DiffusionPipeline.save_pretrained() method and specify the variant argument. You should try and sav