MeiGen-AI/PosterOmni_v1

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<h1>[CVPR 2026] 🎨 PosterOmni<br/>Generalized Artistic Poster Creation via Task Distillation and Unified Reward Feedback</h1>

<img src="images/logo_white.png" alt="PosterOmni Logo" width="180"/>

    

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✨ Overview

PosterOmni is a unified image-to-poster framework that bridges two regimes in poster creation:

• —Poster Local Editing: Rescaling, Filling, Extending, Identity-driven
• —Poster Global Creation: Layout-driven, Style-driven
• —Unified Training: Task distillation + unified reward feedback.

<div align="center"> <img src="images/teaser_0209.jpg" alt="PosterOmni Teaser" width="1000"/> </div>

This Hugging Face repository currently provides PosterOmni-v1 transformer weights (component-only). Other components (VAE / text encoder / tokenizer / scheduler / processor) should be loaded from a compatible base pipeline.

🔥 News

• —📄 [2026.02] Paper available on arXiv.
• —🤗 [2026.02] PosterOmni-v1 transformer weights released on Hugging Face.

🚀 Quick Start

1) Installation

git clone https://github.com/Ephemeral182/PosterOmni.git
cd PosterOmni

conda create -n posteromni python=3.11 -y
conda activate posteromni

pip install -r requirements.txt

2) Load with QwenImageEditPlusPipeline (Transformer from this repo)

This repo provides PosterOmni-v1 transformer weights (Diffusers component-only). Please load a compatible base pipeline (e.g., Qwen/Qwen-Image-Edit-Plus) and replace its `transformer` with our weights.

⚠️ Component-only: this repo does NOT include model_index.json and other pipeline components, so ...Pipeline.from_pretrained("MeiGen-AI/PosterOmni_v1") will NOT work.

import torch
from PIL import Image
from diffusers import QwenImageEditPlusPipeline

device = "cuda" if torch.cuda.is_available() else "cpu"
dtype = torch.bfloat16 if device == "cuda" else torch.float32

# 1) Load full base pipeline
base_model = "Qwen/Qwen-Image-Edit-Plus"   # change to your compatible base
pipe = QwenImageEditPlusPipeline.from_pretrained(base_model, torch_dtype=dtype).to(device)
pipe.tokenizer_max_length = 1024  # optional

# 2) Plug PosterOmni transformer from this repo
posteromni_id = "MeiGen-AI/PosterOmni_v1"
pipe.transformer = pipe.transformer.__class__.from_pretrained(posteromni_id, torch_dtype=dtype).to(device)

# 3) Run inference
img = Image.open("your_input.jpg").convert("RGB")

# recommended: make width/height multiples of 16
w, h = img.size
w, h = (w // 16) * 16, (h // 16) * 16

prompt = "Rescale image to 1:1"  # for rescaling, include "to W:H"
generator = torch.Generator(device=device).manual_seed(42)

out = pipe(
    image=[img],
    prompt=prompt,
    negative_prompt="",
    width=w,
    height=h,
    num_inference_steps=40,
    true_cfg_scale=4.0,
    guidance_scale=1.0,
    generator=generator,
).images[0]

out.save("posteromni_test.png")
print("Saved: posteromni_test.png")

Notes

• —Rescaling prompts should include to W:H, e.g. Rescale image to 16:9.
• —For full multi-task CLI examples (rescaling/filling/extending/layout/style/ID-driven), please refer to the GitHub repo.

🧠 Method (High-level)

PosterOmni is trained with a four-stage workflow:

1. 1.Task-specific SFT: train specialized experts for local editing and global creation tasks.
2. 2.Task Distillation: distill expert knowledge into a single multi-task model.
3. 3.Unified Reward Training: learn a universal reward for text fidelity, visual consistency, and aesthetics.
4. 4.Omni-Edit Reinforcement Learning: further align the model with unified reward feedback.

<div align="center"> <img src="images/overview.jpg" alt="PosterOmni Model Architecture" width="1000"/> </div>

📚 PosterOmni Dataset

We introduce a unified data suite with PosterOmni-200K (training) and PosterOmni-Bench (evaluation) for image-to-poster generation. PosterOmni-200K contains 200K+ paired samples covering six tasks—local editing (Rescaling, Filling, Extending, Identity-driven) and global creation (Layout-driven, Style-driven)—and spans six poster themes: Products, Food, Events/Travel, Nature, Education, Entertainment. PosterOmni-Bench provides 540 Chinese and 480 English prompts, evenly distributed across the same six themes for consistent evaluation across tasks.

<div align="center"> <img src="images/posteromni_datapipeline.jpg" alt="PosterOmni Dataset Construction Pipeline" width="1000"/> </div>

📊 Performance Benchmarks

<div align="center"> <img src="images/results.png" alt="PosterOmni Results" width="1000"/> </div>

🧩 Supported Tasks

🔗 Related Project

We also have another text-to-poster work that may interest you:

[ICLR 2026] PosterCraft: Rethinking High-Quality Aesthetic Poster Generation in a Unified Framework    

📌 Model Files

This repository provides:

• —config.json
• —diffusion_pytorch_model-*.safetensors
• —diffusion_pytorch_model.safetensors.index.json

(i.e., Transformer2DModel weights in Diffusers format.)

📬 Contact

Sixiang Chen: schen691@connect.hkust-gz.edu.cn

Jianyu Lai: jlai218@connect.hkust-gz.edu.cn

Jialin Gao: gaojialin04@meituan.com

Hengyu Shi: qq1842084@gmail.com

Zhongying Liu: liuzhongying@meituan.com

📝 Citation

If you find PosterOmni useful for your research, please cite:

@article{chen2026posteromni,
  title={PosterOmni: Generalized Artistic Poster Creation via Task Distillation and Unified Reward Feedback},
  author={Chen, Sixiang and Lai, Jianyu and Gao, Jialin and Shi, Hengyu and Liu, Zhongying and Ye, Tian and Luo, Junfeng and Wei, Xiaoming and Zhu, Lei},
  journal={arXiv preprint arXiv:2602.12127},
  year={2026}
}