Summary of Fashionsd-x: Multimodal Fashion Garment Synthesis Using Latent Diffusion, by Abhishek Kumar Singh et al.
FashionSD-X: Multimodal Fashion Garment Synthesis using Latent Diffusion
by Abhishek Kumar Singh, Ioannis Patras
First submitted to arxiv on: 26 Apr 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 The novel generative pipeline combines latent diffusion models with ControlNet and LoRA fine-tuning to transform the fashion design process. The approach generates high-quality images from multimodal inputs like text and sketches, leveraging state-of-the-art virtual try-on datasets such as Multimodal Dress Code and VITON-HD by integrating sketch data. The model outperforms traditional stable diffusion models in terms of FID, CLIP Score, and KID, showcasing its potential to revolutionize fashion design workflows. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research creates a new way for designers to make clothes using computers. It uses special kinds of AI called latent diffusion models to turn text and sketches into pictures of clothing. The team tested this method with lots of data from real-life fashion shows and found that it worked much better than other methods. This is important because it means that computers can help people design more creative and personalized clothes, making the fashion industry more interactive and exciting. |
Keywords
» Artificial intelligence » Diffusion » Fine tuning » Lora
