Can We Make AI Image Generation More Controllable?

Text-to-image models have become remarkably good at producing realistic images. But realism isn’t the same as correctness. Ask for several distinct people, a specific composition, or a high-resolution image generated locally, and today’s models still struggle in surprising ways. In this episode, Fatih Porikli, Vice President of Technology at Qualcomm, joins me to discuss what remains unsolved in image generation and several approaches his team presented at CVPR to address those challenges. We explore why better training objectives can improve controllability, how separating scene planning from rendering may lead to more reliable image generation, techniques for generating 16-megapixel images efficiently on edge devices, and new methods for eliminating the visible artifacts that often appear in AI-powered image editing. Along the way, we discuss reinforcement learning for image generation, agentic image generation pipelines, on-device AI, and what the next phase of progress in generative vision systems is likely to look like. 🗒️ Full show notes including references: https://twimlai.com/go/773. 🔔 Subscribe to our channel for more great content just like this: https://youtube.com/twimlai?sub_confirmation=1 📖 CHAPTERS =============================== 00:00 - Introduction 02:04 - What’s Still Unsolved in Image Generation 05:18 - Disco: Improving Identity Diversity 09:09 - Better Training Objectives and Reinforcement Learning 13:09 - Are We Asking One Model to Do Too Much? 16:06 - Agentic Image Generation Pipelines 18:42 - Evaluating Diversity with the DiverseHumans Benchmark 22:14 - Ar2Can: Separating Scene Planning from Rendering 33:07 - PixelRush: Efficient High-Resolution Image Generation 42:50 - PixelRush: Generating 16-Megapixel Images with Limited Memory 48:30 - InvertFill: Artifact-Free Image Editing 53:31 - PyramidalWan, ReHyAtt, and Attention Surgery for Efficient Video Generation 55:18 - Closing Remarks 🗣️ CONNECT WITH US! =============================== Subscribe to the TWIML AI Podcast: https://twimlai.com/podcast/twimlai/ Follow us on Twitter: https://twitter.com/twimlai Follow us on LinkedIn: https://www.linkedin.com/company/twimlai/ Join our Slack Community: https://twimlai.com/community/ Subscribe to our newsletter: https://twimlai.com/newsletter/ Want to get in touch? Send us a message: https://twimlai.com/contact/