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! ✌️