VisualCloze: A Universal Image Generation Framework via Visual In-Context Learning

Understand

Recent progress in diffusion models significantly advances various image generation tasks.

• However, the current mainstream approach remains focused on building task-specific models, which have limited efficiency when supporting a wide range of different needs.
• While universal models attempt to address this limitation, they face critical challenges, including generalizable task instruction, appropriate task distributions, and unified architectural design.
• To tackle these challenges, we propose VisualCloze, a universal image generation framework, which supports a wide range of in-domain tasks, generalization to unseen ones, unseen unification of multiple tasks, and reverse generation.