PosterReward: Unlocking Accurate Evaluation for High-Quality Graphic Design Generation
[](https://alexlai2860.github.io/mypaper/posterreward/PosterReward_Arxiv_released.pdf)
[](https://alexlai2860.github.io/PosterReward/)
[](https://github.com/MeiGen-AI/PosterReward)
[](https://arxiv.org/abs/2603.29855)
## Overview
PosterReward is a dedicated reward modeling framework for poster assessment. It builds a **70k poster preference dataset** from multi-MLLM consensus and introduces specialized models for poster quality evaluation across five dimensions:
1. **Foundational Visual Quality**
2. **AI Artifacts**
3. **Textual Accuracy**
4. **Prompt Fidelity**
5. **Aesthetic Value**
## Available Models
This repository contains three model variants, all built on **Qwen3-VL-8B**:
| Model | Path | Type | Description |
|-------|------|------|-------------|
| **PosterReward Analyser** | `PosterReward_analyser/` | Generative VLM | Generates detailed multi-dimensional analysis of poster images |
| **PosterReward Scorer** | `PosterReward_scorer/` | Scalar Reward Model | Takes analysis + image and produces a scalar reward score |
| **PosterReward-Lite** | `PosterReward-Lite/` | Scalar Reward Model | Simplified pointwise scorer that omits the analysis module for faster inference |
### PosterReward (Full Pipeline)
The full PosterReward pipeline is a two-stage `analysis -> scoring` process:
1. **PosterReward Analyser** generates a detailed textual analysis across five quality dimensions.
2. **PosterReward Scorer** takes the analysis together with the image and outputs a scalar reward score.
### PosterReward-Lite
A simplified variant that directly predicts a scalar reward from the image and prompt, without requiring a separate analysis step. Faster inference at the cost of slightly lower accuracy.
## Benchmark Data
This repository also hosts the **PosterRewardBench** benchmark images:
| File | Description | Size |
|------|-------------|------|
| `PRB_basic_images.tar.gz` | PosterRewardBench-Basic images (1,034 images from Flux, Flux-Krea, SD3.5-L) | ~1.1 GB |
| `PRB_advanced_images.tar.gz` | PosterRewardBench-Advanced images (2,446 images from Seedream-3.0, Seedream-4.0, Qwen-Image-Lightning) | ~736 MB |
Download and extract these archives into the `poster_reward_bench/` directory of the [code repository](https://github.com/MeiGen-AI/PosterReward).
## Quick Start
### Environment Setup
```bash
git clone https://github.com/MeiGen-AI/PosterReward.git
cd PosterReward
cd swift && pip install -e . && cd ..
pip install msgspec "qwen_vl_utils>=0.0.14" torchvision diffusers pillow
```
### PosterReward-Lite (Fast Pointwise Scoring)
```python
from swift.llm import PtEngine, InferRequest
model_path = "path/to/PosterReward-Lite" # or download from this repo
engine = PtEngine(model_path, max_batch_size=64, task_type='seq_cls', num_labels=1)
messages = [
{"role": "user", "content": "