ding vector is saved as part of the model after training.
The model can then be run just like any other Flax model:


	Copied
import jax
import numpy as np
from flax.jax_utils import replicate
from flax.training.common_utils import shard
from diffusers import FlaxStableDiffusionPipeline

model_path = "path-to-your-trained-model"
pipe, params = FlaxStableDiffusionPipeline.from_pretrained(model_path, dtype=jax.numpy.bfloat16)

prompt = "A <cat-to