Prompting is a simple method for style mimicry that utilizes well-known artistic styles in the training data. However, it often fails to replicate underrepresented styles.
Img2Img enhances an image based on a prompt and can create varied styles. Yet, like prompting, it struggles with styles that are not well-represented in the model's training.
Textual inversion allows for the optimization of image prompts by appending new tokens that make generated images closely align with a target style, offering a more flexible but complex approach.
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