simpleposter: a simple baseline for product poster generation

TL;DR

SimplePoster is a streamlined inpainting-based framework for product poster generation that ensures faithful subject preservation and precise, controllable text layout without external modules.

Contribution

It introduces a simple, effective inpainting approach with zero-cost position encoding, outperforming complex methods in subject preservation and text accuracy.

Findings

Achieves 98.7% subject preservation rate, higher than competitors.

Improves text rendering accuracy in product posters.

Eliminates the need for external controllers like ControlNet.

Abstract

Product poster generation poses distinct challenges beyond general poster design, requiring both faithful preservation of product appearance and precise control over dense, multi-line text layouts. Prior methods typically adopt inpainting frameworks augmented with auxiliary modules such as ControlNet and OCR encoders. However, these approaches introduce architectural complexity and computational overhead while still suffering from text errors and subject extension artifacts. We present SimplePoster, a simple yet effective inpainting-based framework that achieves faithful subject preservation and accurate, position-controllable text rendering without external controllers. Our approach builds on two observations: (1) full-parameter fine-tuning of the base model effectively suppresses subject extension, outperforming ControlNet-based alternatives; and (2) a zero-cost character-level position encoding enables geometry-aware text generation without dedicated layout modules. Experiments show that SimplePoster achieves a $98.7\%$ subject preservation rate, compared to $55.2\%$ for SeedEdit 3.0 and $85.3\%$ for PosterMaker, while also improving text rendering accuracy. Code, models, benchmark and a part of training data will be available at https://github.com/Alibaba-YuFeng/SIMPLEPOSTER