This method works by averaging pre-computed “aesthetically pleasing” embeddings with classifier-free guidance.

These embeddings were actually also used to create the LAION-aesthetic dataset used to train stable-diffusion. You can find saved npy files and more information here:

https://github.com/LAION-AI/aesthetic-predictor/tree/main/vit_l_14_embeddings

Example API Usage:

python3 -m pip install replicate

import replicate

sd_aesthetic_model = replicate.models.get("afiaka87/sd-aesthetic-guidance")

Test various scales/weights for aesthetic guidance:

seed = 42 # use a seed so images don't differ
for aesthetic_rating in range(5, 9):
    for aesthetic_weight in [0.0, 0.1, 0.2, 0.3, 0.4]: # runs the model 16 times
        predictions = sd_aesthetic_model.predict(
            prompt="an oil painting of Mark Hamil, digital art",
            aesthetic_rating=aesthetic_rating,
            aesthetic_weight=aesthetic_weight,
            seed=seed
        )
        print(predictions) # should be a list, you may want to download/display any image URL's