Why Image Generation Needs More Than Bigger Models | Fatih Porikli

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.

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📖 CHAPTERS
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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

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