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:
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