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๐Ÿ’  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.

โœ… This Week's Presentation:

๐Ÿ”น Title: InitNO: Boosting Text-to-Image Diffusion Models via Initial Noise Optimization

๐Ÿ”ธ Presenter: Amir Kasaei

๐ŸŒ€ 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.


๐Ÿ“„ Paper: InitNO: Boosting Text-to-Image Diffusion Models via Initial Noise Optimization

Session Details:
- ๐Ÿ“… Date: Sunday
- ๐Ÿ•’ Time: 5:00 - 6:00 PM
- ๐ŸŒ Location: Online at vc.sharif.edu/ch/rohban


We look forward to your participation! โœŒ๏ธ



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๐Ÿ’  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.

โœ… This Week's Presentation:

๐Ÿ”น Title: InitNO: Boosting Text-to-Image Diffusion Models via Initial Noise Optimization

๐Ÿ”ธ Presenter: Amir Kasaei

๐ŸŒ€ 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.


๐Ÿ“„ Paper: InitNO: Boosting Text-to-Image Diffusion Models via Initial Noise Optimization

Session Details:
- ๐Ÿ“… Date: Sunday
- ๐Ÿ•’ Time: 5:00 - 6:00 PM
- ๐ŸŒ Location: Online at vc.sharif.edu/ch/rohban


We look forward to your participation! โœŒ๏ธ

BY RIML Lab




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