🌌 Unipic3-Consistency-Model
📖 Introduction
UniPic3-Consistency-Model is a few-step image editing and multi-image composition model based on Consistency Flow Matching (CM).
The model learns a trajectory-consistent mapping from noisy latent states to clean images, enabling stable generation with strong structural consistency.
It is distilled from UniPic-3 to support fast inference (≤8 steps) while preserving composition correctness.The model is especially suitable for scenarios requiring geometric alignment and semantic coherence, such as multi-image composition and human–object interaction (HOI).
📊 Benchmarks
🧠 Usage
1. Clone the Repository
git clone https://github.com/SkyworkAI/UniPic
cd UniPic-3
2. Set Up the Environment
conda create -n unipic python=3.10
conda activate unipic3
pip install -r requirements.txt
3.Batch Inference
transformer_path = "Skywork/Unipic3-Consistency-Model/ema_transformer"
python -m torch.distributed.launch --nproc_per_node=1 --master_port 29501 --use_env \
qwen_image_edit_fast/batch_inference.py \
--jsonl_path data/val.jsonl \
--output_dir work_dirs/output \
--distributed \
--num_inference_steps 8 \
--true_cfg_scale 4.0 \
--transformer transformer_path \
--skip_existing
📄 License
This model is released under the MIT License.
Citation
If you use Skywork UniPic 3.0 in your research, please cite:
@article{wei2026skywork,
title={Skywork UniPic 3.0: Unified Multi-Image Composition via Sequence Modeling},
author={Wei, Hongyang and Liu, Hongbo and Wang, Zidong and Peng, Yi and Xu, Baixin and Wu, Size and Zhang, Xuying and He, Xianglong and Liu, Zexiang and Wang, Peiyu and others},
journal={arXiv preprint arXiv:2601.15664},
year={2026}
}