This repository implements aesthetic gradients, a technique that guides CLIP-conditioned diffusion models toward user-specified aesthetics. It computes embeddings from aesthetic reference images and uses gradient-based optimization during inference to steer the generation process without fine-tuning the base model. Users provide an aesthetic embedding and can tune step count and learning rate to control how strongly the generated image adheres to the target style.