Think in Strokes, Not Pixels: Process-Driven Image Generation via Interleaved Reasoning

Humans paint images incrementally: they plan a global lay-out, sketch a coarse draft, inspect, and refine details, and most impor-tantly, each step is grounded in the evolving visual states. However, canunified multimodal models trained on text-image interleaved datasetsalso imagine the chain of intermediate states? In this paper, we introduceprocess-driven image generation, a multi-step paradigm that decom-poses synthesis into an interleaved reasoning trajectory of thoughtsand actions. Rather than generating images in a single step, our ap-proach unfolds across multiple iterations, each consisting of 4 stages: tex-tual planning, visual drafting, textual reflection, and visual refinement.The textual reasoning explicitly conditions how the visual state shouldevolve, while the generated visual intermediate in turn constrains andgrounds the next round of textual reasoning. A core challenge of process-driven generation stems from the ambiguity of intermediate states: howcan models evaluate each partially-complete image? We address thisthrough dense, step-wise supervision that maintains two complementaryconstraints: for the visual intermediate states, we enforce the spatial andsemantic consistency; for the textual intermediate states, we preserve theprior visual knowledge while enabling the model to identify and correctprompt-violating elements. This makes the generation process explicit,interpretable, and directly supervisable. To validate proposed method,we conduct experiments under various text-to-image benchmarks.