RIML Lab

💠 Compositional Learning Journal Club

Join us this week for an in-depth discussion on Compositional Learning in the context of cutting-edge text-to-image generative models. We will explore recent breakthroughs and challenges, focusing on how these models handle compositional tasks and where improvements can be made.

✅ This Week's Presentation:

🔹 Title: InitNO: Boosting Text-to-Image Diffusion Models via Initial Noise Optimization

🔸 Presenter: Amir Kasaei

🌀 Abstract:
Recent advancements in diffusion models, like Stable Diffusion, have shown impressive image generation capabilities, but ensuring precise alignment with text prompts remains a challenge. This presentation introduces Initial Noise Optimization (InitNO), a method that refines initial noise to improve semantic accuracy in generated images. By evaluating and guiding the noise using cross-attention and self-attention scores, the approach effectively enhances image-prompt alignment, as demonstrated through rigorous experimentation.


📄 Paper: InitNO: Boosting Text-to-Image Diffusion Models via Initial Noise Optimization

Session Details:
- 📅 Date: Sunday
- 🕒 Time: 5:00 - 6:00 PM
- 🌐 Location: Online at vc.sharif.edu/ch/rohban


We look forward to your participation! ✌️

arXiv.org

InitNO: Boosting Text-to-Image Diffusion Models via Initial Noise...

Recent strides in the development of diffusion models, exemplified by advancements such as Stable Diffusion, have underscored their remarkable prowess in generating visually compelling images....

www.tgoop.com/RIMLLab/139

1.7K viewsAmir Kasaei, Oct 19 at 17:51

💠 Compositional Learning Journal Club

Join us this week for an in-depth discussion on Compositional Learning in the context of cutting-edge text-to-image generative models. We will explore recent breakthroughs and challenges, focusing on how these models handle compositional tasks and where improvements can be made.

✅ This Week's Presentation:

🔹 Title: InitNO: Boosting Text-to-Image Diffusion Models via Initial Noise Optimization

🔸 Presenter: Amir Kasaei

🌀 Abstract:
Recent advancements in diffusion models, like Stable Diffusion, have shown impressive image generation capabilities, but ensuring precise alignment with text prompts remains a challenge. This presentation introduces Initial Noise Optimization (InitNO), a method that refines initial noise to improve semantic accuracy in generated images. By evaluating and guiding the noise using cross-attention and self-attention scores, the approach effectively enhances image-prompt alignment, as demonstrated through rigorous experimentation.


📄 Paper: InitNO: Boosting Text-to-Image Diffusion Models via Initial Noise Optimization

Session Details:
- 📅 Date: Sunday
- 🕒 Time: 5:00 - 6:00 PM
- 🌐 Location: Online at vc.sharif.edu/ch/rohban


We look forward to your participation! ✌️

BY RIML Lab