Style-Friendly SNR Sampler for Style-Driven Generation
Jooyoung Choi, Chaehun Shin, Yeongtak Oh, Heeseung Kim, Sungroh Yoon
2024-11-24
Summary
This paper introduces the Style-Friendly SNR Sampler, a new method that helps large diffusion models generate high-quality images in unique artistic styles by improving how they learn from reference images.
What's the problem?
Large diffusion models are great at creating high-quality images, but they struggle to learn and replicate new, personalized artistic styles. When these models are fine-tuned with reference images, they often use methods that don't effectively capture the unique features of those styles, leading to poor results.
What's the solution?
The authors propose the Style-Friendly SNR Sampler, which adjusts the signal-to-noise ratio (SNR) during the training process. By focusing on higher noise levels where stylistic features become clear, this method allows the model to better capture and generate unique styles. The sampler helps the models learn from reference images more effectively, enabling them to create a variety of artistic outputs, such as watercolor paintings, cartoons, and memes.
Why it matters?
This research is important because it enhances the ability of AI models to create personalized and diverse artistic content. By improving how these models learn from different styles, artists and creators can use this technology to produce unique visual works more easily, expanding the possibilities for digital art and content creation.
Abstract
Recent large-scale diffusion models generate high-quality images but struggle to learn new, personalized artistic styles, which limits the creation of unique style templates. Fine-tuning with reference images is the most promising approach, but it often blindly utilizes objectives and noise level distributions used for pre-training, leading to suboptimal style alignment. We propose the Style-friendly SNR sampler, which aggressively shifts the signal-to-noise ratio (SNR) distribution toward higher noise levels during fine-tuning to focus on noise levels where stylistic features emerge. This enables models to better capture unique styles and generate images with higher style alignment. Our method allows diffusion models to learn and share new "style templates", enhancing personalized content creation. We demonstrate the ability to generate styles such as personal watercolor paintings, minimal flat cartoons, 3D renderings, multi-panel images, and memes with text, thereby broadening the scope of style-driven generation.