ProFusion is a new framework for customized text-to-image generation that preserves fine-grained image details without using regularization, as proposed in the paper. ProFusion includes PromptNet, an encoder network, and Fusion Sampling, a method that generates customized images based on a single user-provided image and text requirements. The paper explains how ProFusion works and provides experiments demonstrating its superior performance compared to existing approaches, while still meeting additional user-defined requirements.
StyleDrop is a technology that generates images in any desired style using text-to-image transformer, Muse. The technology captures nuances of user-provided styles such as design patterns and colour schemes. StyleDrop works by fine-tuning a few trainable parameters and improves the quality of generated images via iterative training with human or automated feedback. The technology can generate high-quality images from text prompts, and style descriptors are added during training and synthesis to improve the results. StyleDrop is easy to use and can be trained with brand assets. It can be used to generate alphabets with consistent styles in a single reference image. StyleDrop on Muse outperforms other methods in style-tuning for text-to-image models.
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