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--- |
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license: apache-2.0 |
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language: |
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- en |
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- zh |
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base_model: |
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- Qwen/Qwen-Image |
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pipeline_tag: image-text-to-image |
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library_name: diffusers |
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--- |
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<p align="center"> |
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<img src="https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-Image/layered/qwen-image-layered-logo.png" width="800"/> |
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<p> |
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<p align="center">  🤗 <a href="https://huggingface.co/Qwen/Qwen-Image-Layered">HuggingFace</a>   |   🤖 <a href="https://modelscope.cn/models/Qwen/Qwen-Image-Layered">ModelScope</a>   |    📑 <a href="https://arxiv.org/abs/2512.15603">Research Paper</a>    |    📑 <a href="https://qwenlm.github.io/blog/qwen-image-layered/">Blog</a>    |    🤗 <a href="https://huggingface.co/spaces/Qwen/Qwen-Image-Layered">Demo</a>    |
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</p> |
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<p align="center"> |
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<img src="https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-Image/layered/layered.JPG" width="1024"/> |
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<p> |
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## Introduction |
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We are excited to introduce **Qwen-Image-Layered**, a model capable of decomposing an image into multiple RGBA layers. This layered representation unlocks **inherent editability**: each layer can be independently manipulated without affecting other content. Meanwhile, such a layered representation naturally supports **high-fidelity elementary operations**-such as resizing, reposition, and recoloring. By physically isolating semantic or structural components into distinct layers, our approach enables high-fidelity and consistent editing. |
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## Quick Start |
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1. Make sure your transformers>=4.51.3 (Supporting Qwen2.5-VL) |
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2. Install the latest version of diffusers |
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``` |
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pip install git+https://github.com/huggingface/diffusers |
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pip install python-pptx |
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``` |
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```python |
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from diffusers import QwenImageLayeredPipeline |
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import torch |
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from PIL import Image |
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pipeline = QwenImageLayeredPipeline.from_pretrained("Qwen/Qwen-Image-Layered") |
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pipeline = pipeline.to("cuda", torch.bfloat16) |
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pipeline.set_progress_bar_config(disable=None) |
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image = Image.open("asserts/test_images/1.png").convert("RGBA") |
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inputs = { |
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"image": image, |
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"generator": torch.Generator(device='cuda').manual_seed(777), |
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"true_cfg_scale": 4.0, |
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"negative_prompt": " ", |
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"num_inference_steps": 50, |
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"num_images_per_prompt": 1, |
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"layers": 4, |
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"resolution": 640, # Using different bucket (640, 1024) to determine the resolution. For this version, 640 is recommended |
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"cfg_normalize": True, # Whether enable cfg normalization. |
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"use_en_prompt": True, # Automatic caption language if user does not provide caption |
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} |
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with torch.inference_mode(): |
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output = pipeline(**inputs) |
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output_image = output.images[0] |
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for i, image in enumerate(output_image): |
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image.save(f"{i}.png") |
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``` |
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## Showcase |
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### Layered Decomposition in Application |
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Given an image, Qwen-Image-Layered can decompose it into several RGBA layers: |
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After decomposition, edits are applied exclusively to the target layer, physically isolating it from the rest of the content, and thereby fundamentally ensuring consistency across edits. |
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For example, we can recolor the first layer and keep all other content untouched: |
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We can also replace the second layer from a girl to a boy (The target layer is edited using Qwen-Image-Edit): |
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Here, we revise the text to "Qwen-Image" (The target layer is edited using Qwen-Image-Edit): |
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Furthermore, the layered structure naturally supports elemetary operations. For example, we can delete unwanted objects cleanly: |
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We can also resize an object without distortion: |
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After layer decomposition, we can move objects freely within the canvas: |
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### Flexible and Iterative Decomposition |
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Qwen-Image-Layered is not limited to a fixed number of layers. The model supports variable-layer decomposition. For example, we can decompose an image into either 3 or 8 layers as needed: |
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Moreover, decomposition can be applied recursively: any layer can itself be further decomposed, enabling infinite decomposition. |
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## License Agreement |
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Qwen-Image-Layered is licensed under Apache 2.0. |
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## Citation |
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We kindly encourage citation of our work if you find it useful. |
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```bibtex |
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@misc{yin2025qwenimagelayered, |
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title={Qwen-Image-Layered: Towards Inherent Editability via Layer Decomposition}, |
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author={Shengming Yin, Zekai Zhang, Zecheng Tang, Kaiyuan Gao, Xiao Xu, Kun Yan, Jiahao Li, Yilei Chen, Yuxiang Chen, Heung-Yeung Shum, Lionel M. Ni, Jingren Zhou, Junyang Lin, Chenfei Wu}, |
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year={2025}, |
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eprint={2512.15603}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CV}, |
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url={https://arxiv.org/abs/2512.15603}, |
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} |
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``` |
