--- license: other license_name: flux-1-dev-non-commercial-license license_link: LICENSE.md library_name: diffusers language: - en base_model: - black-forest-labs/FLUX.1-dev pipeline_tag: text-to-image tags: - art - diffusion - aesthetic-poster-generation ---

🎨 PosterCraft:
Rethinking High-Quality Aesthetic Poster Generation in a Unified Framework

[![arXiv](https://img.shields.io/badge/arXiv-2506.10741-red)](https://arxiv.org/abs/2506.10741) [![GitHub](https://img.shields.io/badge/GitHub-Repository-blue)](https://github.com/ephemeral182/PosterCraft) [![HuggingFace](https://img.shields.io/badge/🤗-HuggingFace-yellow)](https://huggingface.co/PosterCraft) [![Website](https://img.shields.io/badge/🌐-Website-green)](https://ephemeral182.github.io/PosterCraft/) [![Video](https://img.shields.io/badge/🎥-Live_Demo-purple)](https://www.youtube.com/watch?v=92wMU4D7qx0) [![HF Demo](https://img.shields.io/badge/🤗-HF_Demo-orange)](https://huggingface.co/spaces/Ephemeral182/PosterCraft) PosterCraft Logo PosterCraft Logo
--- ## 🌟 What is PosterCraft?
What is PosterCraft - Quick Prompt Demo
PosterCraft is a unified framework for **high-quality aesthetic poster generation** that excels in **precise text rendering**, **seamless integration of abstract art**, **striking layouts**, and **stylistic harmony**. ## 🚀 Quick Start ### 🔧 Installation ```bash # Clone the repository git clone https://github.com/ephemeral182/PosterCraft.git cd PosterCraft # Create conda environment conda create -n postercraft python=3.11 conda activate postercraft # Install dependencies pip install -r requirements.txt ``` ### 🚀 Easy Usage PosterCraft is designed as a unified and flexible framework. This makes it easy to use PosterCraft within your own custom workflows or other compatible frameworks. Loading the model is straightforward: ```python import torch from diffusers import FluxPipeline, FluxTransformer2DModel # 1. Define model IDs and settings pipeline_id = "black-forest-labs/FLUX.1-dev" postercraft_transformer_id = "PosterCraft/PosterCraft-v1_RL" device = "cuda" dtype = torch.bfloat16 # 2. Load the base pipeline pipe = FluxPipeline.from_pretrained(pipeline_id, torch_dtype=dtype) # 3. The key step: simply replace the original transformer with our fine-tuned PosterCraft model pipe.transformer = FluxTransformer2DModel.from_pretrained( postercraft_transformer_id, torch_dtype=dtype ) pipe.to(device) # Now, `pipe` is a standard diffusers pipeline ready for inference with your own logic. ``` ### 🚀 Quick Generation For the best results and to leverage our intelligent prompt rewriting feature, we recommend using the provided `inference.py` script. This script automatically enhances your creative ideas for optimal results. Generate high-quality aesthetic posters from your prompt with `BF16` precision, please refer to our [GitHub repository](https://github.com/Ephemeral182/PosterCraft) : ```bash python inference.py \ --prompt "Urban Canvas Street Art Expo poster with bold graffiti-style lettering and dynamic colorful splashes" \ --enable_recap \ --num_inference_steps 28 \ --guidance_scale 3.5 \ --seed 42 \ --pipeline_path "black-forest-labs/FLUX.1-dev" \ --custom_transformer_path "PosterCraft/PosterCraft-v1_RL" \ --qwen_model_path "Qwen/Qwen3-8B" ``` If you are running on a GPU with limited memory, you can use `inference_offload.py` to offload some components to the CPU: ```bash python inference_offload.py \ --prompt "Urban Canvas Street Art Expo poster with bold graffiti-style lettering and dynamic colorful splashes" \ --enable_recap \ --num_inference_steps 28 \ --guidance_scale 3.5 \ --seed 42 \ --pipeline_path "black-forest-labs/FLUX.1-dev" \ --custom_transformer_path "PosterCraft/PosterCraft-v1_RL" \ --qwen_model_path "Qwen/Qwen3-8B" ``` ### 💻 Gradio Web UI We provide a Gradio web UI for PosterCraft, please refer to our [GitHub repository](https://github.com/Ephemeral182/PosterCraft). ```bash python demo_gradio.py ``` ## 📊 Performance Benchmarks
### 📈 Quantitative Results
Method Text Recall ↑ Text F-score ↑ Text Accuracy ↑
OpenCOLE (Open) 0.082 0.076 0.061
Playground-v2.5 (Open) 0.157 0.146 0.132
SD3.5 (Open) 0.565 0.542 0.497
Flux1.dev (Open) 0.723 0.707 0.667
Ideogram-v2 (Close) 0.711 0.685 0.680
BAGEL (Open) 0.543 0.536 0.463
Gemini2.0-Flash-Gen (Close) 0.798 0.786 0.746
PosterCraft (ours) 0.787 0.774 0.735
hpc
--- ## 📝 Citation If you find PosterCraft useful for your research, please cite our paper: ```bibtex @article{chen2025postercraft, title={PosterCraft: Rethinking High-Quality Aesthetic Poster Generation in a Unified Framework}, author={Chen, Sixiang and Lai, Jianyu and Gao, Jialin and Ye, Tian and Chen, Haoyu and Shi, Hengyu and Shao, Shitong and Lin, Yunlong and Fei, Song and Xing, Zhaohu and Jin, Yeying and Luo, Junfeng and Wei, Xiaoming and Zhu, Lei}, journal={arXiv preprint arXiv:2506.10741}, year={2025} } ```