RIML Lab

🚨Open Position: Visual Compositional Generation Research 🚨

We are excited to announce an open research position for a project under Dr. Rohban at the RIML Lab (Sharif University of Technology). The project focuses on improving text-to-image generation in diffusion-based models by addressing compositional challenges.

🔍 Project Description:

Large-scale diffusion-based models excel at text-to-image (T2I) synthesis, but still face issues like object missing and improper attribute binding. This project aims to study and resolve these compositional failures to improve the quality of T2I models.

Key Papers:
- T2I-CompBench: A Comprehensive Benchmark for Open-world Compositional T2I Generation
- Attend-and-Excite: Attention-Based Semantic Guidance for T2I Diffusion Models
- If at First You Don’t Succeed, Try, Try Again: Faithful Diffusion-based Text-to-Image Generation by Selection
- ReNO: Enhancing One-step Text-to-Image Models through Reward-based Noise Optimization

🎯 Requirements:

- Must: PyTorch, Deep Learning,
- Recommended: Transformers and Diffusion Models.
- Able to dedicate significant time to the project.


🗓 Important Dates:

- Application Deadline: 2024/10/12 (23:59 UTC+3:30)

📌 Apply here:
Application Form

For questions:
📧 [email protected]
💬 @amirkasaei

@RIMLLab
#research_application
#open_position

www.tg-me.com/sg/RIML Lab/com.RIMLLab/138

3.7K viewsAmir Kasaei, Oct 6, 2024 at 08:25

🚨Open Position: Visual Compositional Generation Research 🚨

We are excited to announce an open research position for a project under Dr. Rohban at the RIML Lab (Sharif University of Technology). The project focuses on improving text-to-image generation in diffusion-based models by addressing compositional challenges.

🔍 Project Description:

Large-scale diffusion-based models excel at text-to-image (T2I) synthesis, but still face issues like object missing and improper attribute binding. This project aims to study and resolve these compositional failures to improve the quality of T2I models.

Key Papers:
- T2I-CompBench: A Comprehensive Benchmark for Open-world Compositional T2I Generation
- Attend-and-Excite: Attention-Based Semantic Guidance for T2I Diffusion Models
- If at First You Don’t Succeed, Try, Try Again: Faithful Diffusion-based Text-to-Image Generation by Selection
- ReNO: Enhancing One-step Text-to-Image Models through Reward-based Noise Optimization

🎯 Requirements:

- Must: PyTorch, Deep Learning,
- Recommended: Transformers and Diffusion Models.
- Able to dedicate significant time to the project.


🗓 Important Dates:

- Application Deadline: 2024/10/12 (23:59 UTC+3:30)

📌 Apply here:
Application Form

For questions:
📧 [email protected]
💬 @amirkasaei

@RIMLLab
#research_application
#open_position

BY RIML Lab