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(ICCV 2025) FlowTok: Flowing Seamlessly Across Text and Image Tokens
This repository provides a PyTorch re-implementation of FlowTok for the text-to-image generation task.
Compared to the original paper, this implementation extends the generation capability to 512×512 resolution.
FlowTok: Flowing Seamlessly Across Text and Image Tokens
If you use our work in your research, please use the following BibTeX entries.
@article{he2025flowtok,
author = {Ju He and Qihang Yu and Qihao Liu and Liang-Chieh Chen},
title = {FlowTok: Flowing Seamlessly Across Text and Image Tokens},
journal = {ICCV},
year = {2025}
}
@article{liu2025crossflow,
author = {Qihao Liu and Xi Yin and Alan Yuille and Andrew Brown and Mannat Singh},
title = {Flowing from Words to Pixels: A Noise-Free Framework for Cross-Modality Evolution},
journal = {CVPR},
year = {2025}
}
@article{kim2025democratizing,
author = {Dongwon Kim and Ju He and Qihang Yu and Chenglin Yang and Xiaohui Shen and Suha Kwak and Liang-Chieh Chen},
title = {Democratizing Text-to-Image Masked Generative Models with Compact Text-Aware One-Dimensional Tokens},
journal = {ICCV},
year = {2025}
}
@article{yu2024an,
author = {Qihang Yu and Mark Weber and Xueqing Deng and Xiaohui Shen and Daniel Cremers and Liang-Chieh Chen},
title = {An Image is Worth 32 Tokens for Reconstruction and Generation},
journal = {NeurIPS},
year = {2024}
}
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PyTorch re-implementation of FlowTok: Flowing Seamlessly Across Text and Image Tokens