GoT: Unleashing Reasoning Capability of MLLM for Visual Generation and Editing

Current image generation and editing methods primarily process textual prompts as direct inputs without explicit reasoning about visual composition or operational steps. We present Generation Chain-of-Thought (GoT), a novel paradigm that empowers a Multimodal Large Language Model (MLLM) to first generate an explicit, structured reasoning chain in natural language--detailing semantic relationships, object attributes, and, crucially, precise spatial coordinates--before any image synthesis occurs.