Summary of Fbsdiff: Plug-and-play Frequency Band Substitution Of Diffusion Features For Highly Controllable Text-driven Image Translation, by Xiang Gao et al.
FBSDiff: Plug-and-Play Frequency Band Substitution of Diffusion Features for Highly Controllable Text-Driven Image Translation
by Xiang Gao, Jiaying Liu
First submitted to arxiv on: 2 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Large-scale text-to-image diffusion models have been a revolutionary milestone in generative AI and multimodal technology, enabling the generation of wonderful images from natural-language text prompts. However, their lack of controllability restricts their practical applicability for real-life content creation. To address this issue, researchers have focused on leveraging reference images to control text-to-image synthesis, which is also known as text-driven image-to-image translation. This paper presents a novel approach that adapts pre-trained large-scale text-to-image diffusion models to the image-to-image paradigm in a plug-and-play manner, achieving high-quality and versatile text-driven I2I translation without model training, fine-tuning, or online optimization. The proposed method decomposes diverse guiding factors with different frequency bands of diffusion features in the DCT spectral space and devises a novel frequency band substitution layer that realizes dynamic control over the reference image to the T2I generation result. Our approach allows flexible control over both guiding factor and intensity by tuning the type and bandwidth of substituted frequency bands, respectively. Extensive experiments verify the superiority of our method in I2I translation visual quality, versatility, and controllability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us generate wonderful images from text prompts using a pre-trained large-scale text-to-image diffusion model. It’s like having a magic pen that can bring words to life! The researchers found a way to make this process more controlled and practical for real-life uses. They did this by looking at the reference image and adjusting it based on the text prompt, kind of like editing a picture with words. |
Keywords
» Artificial intelligence » Diffusion » Diffusion model » Fine tuning » Image synthesis » Optimization » Prompt » Translation
