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MeiGen-AI/
PosterReward_v1

image-quality-assessment

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PosterReward: Unlocking Accurate Evaluation for High-Quality Graphic Design Generation

PosterReward Logo

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:

Foundational Visual Quality
AI Artifacts
Textual Accuracy
Prompt Fidelity
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:

PosterReward Analyser generates a detailed textual analysis across five quality dimensions.
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.

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": "<image>Your poster description prompt here."},
    {"role": "assistant", "content": ""}
]
request = InferRequest(messages=messages, images=["path/to/poster.png"])

resp_list = engine.infer([request])
score = resp_list[0].choices[0].message.content
print(f"Reward Score: {score}")

Full PosterReward (Two-Stage Pipeline)