| ---
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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-Layered
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| pipeline_tag: image-text-to-image
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| library_name: ggml
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| tags:
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| - gguf
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| - quantized
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| - unsloth
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| - qwen
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| - lora
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| - psd
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| - photoshop
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| - image-decomposition
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| ---
|
|
|
| > [!NOTE]
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| > This repository features **Qwen-Image-Layered (Designer PSD Fine-Tune)** — adapted and quantized for designer workflows.
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| > - **Custom LoRA Fine-Tuning**: Trained on paired designer PSD layouts to deliver clean object separation, distinct text/graphics isolation, and reliable alpha channels.
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| > - **End-to-End PSD Packaging**: Bundled with a packaging pipeline that exports generated layers directly into a multi-layer Adobe Photoshop (`.psd`) file.
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| > - **GGUF Quantization & Web Studio**: Optimized with [Unsloth Dynamic 2.0](https://docs.unsloth.ai/basics/unsloth-dynamic-2.0-ggufs) GGUFs for lightweight local inference, accompanied by a local **Layer Studio** web UI.
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|
|
| <div>
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| <div style="display: flex; gap: 5px; align-items: center; ">
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| <a href="https://github.com/unslothai/unsloth/">
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| <img src="https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png" width="133">
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| </a>
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| <a href="https://discord.gg/unsloth">
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| <img src="https://github.com/unslothai/unsloth/raw/main/images/Discord%20button.png" width="173">
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| </a>
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| <a href="https://docs.unsloth.ai/basics/unsloth-dynamic-2.0-ggufs">
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| <img src="https://raw.githubusercontent.com/unslothai/unsloth/refs/heads/main/images/documentation%20green%20button.png" width="143">
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| </a>
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| </div>
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| </div>
|
|
|
| ---
|
|
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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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|
|
| <p align="center">
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| 🤗 <a href="https://huggingface.co/Qwen/Qwen-Image-Layered">Original Base Model</a> |
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| 📑 <a href="https://arxiv.org/abs/2512.15603">Research Paper</a> |
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| 📑 <a href="https://qwenlm.github.io/blog/qwen-image-layered/">Blog</a> |
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| 🤗 <a href="https://huggingface.co/spaces/Qwen/Qwen-Image-Layered">Official Demo</a>
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| </p>
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|
|
| ## Introduction
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|
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| This model is a **custom-adapted, designer-focused version of Qwen-Image-Layered**, enhanced to convert flat visual designs into fully editable, production-ready layered assets.
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|
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| While the base Qwen-Image-Layered model introduces the ability to decompose an image into multiple RGBA layers, we have tailored and extended the ecosystem with several key custom additions:
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|
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| ### 🎨 Custom Modifications & Key Features
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|
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| 1. **Designer-Layer LoRA Fine-Tuning**:
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| - **Targeted Training**: Fine-tuned using a transformer LoRA adapter on curated, permissioned multi-layer graphic designs and campaign materials.
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| - **Semantic Separation**: Specialized in distinguishing foreground text, product imagery, badges, decorative elements, and clean background plates into separate, logical layers.
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| - **Edge Quality & Transparency**: Focuses on preserving crisp alpha masks and canvas alignment, minimizing artifact bleeding when individual layers are moved or hidden.
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|
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| 2. **Automated Photoshop PSD Packaging**:
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| - Includes custom downstream packaging tools (`package_psd.py` / `run_layered_diffusers.py`) that bundle decomposed RGBA layers into standard, editable **Adobe Photoshop (`.psd`)** documents.
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| - Preserves layer stack order, naming conventions, exact canvas coordinates, and blend properties ready for professional editing workflows.
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|
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| 3. **GGUF Quantization & Local Inference**:
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| - Provides quantized GGUF weights (Q5_0, Q5_1, Q5_K_M, Q5_K_S, Q6_K) leveraging the **Unsloth Dynamic 2.0** methodology to enable fast, low-VRAM local execution on consumer GPUs.
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|
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| 4. **Interactive Layer Studio UI**:
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| - Accompanied by a lightweight local web interface (**Editable-Image-Layers-REA**) and REST API, allowing users to upload images, preview live decompositions, inspect individual layers, and export PSD or ZIP archives with a single click.
|
|
|
| ---
|
|
|
| ## Quick Start
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|
|
| ### 1. Using Diffusers with the Designer LoRA Adapter
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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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|
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| # Load base pipeline
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| pipeline = QwenImageLayeredPipeline.from_pretrained("Qwen/Qwen-Image-Layered", torch_dtype=torch.bfloat16)
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| pipeline = pipeline.to("cuda")
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|
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| # (Optional) Load custom designer LoRA adapter
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| # pipeline.load_lora_weights("path/to/designer-lora")
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|
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| image = Image.open("input_design.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(42),
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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, # Number of layers to extract
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| "resolution": 640, # Recommended resolution bucket (640 or 1024)
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| "cfg_normalize": True,
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| "use_en_prompt": True,
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| }
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|
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| with torch.inference_mode():
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| output = pipeline(**inputs)
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| layers = output.images[0]
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|
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| # Save individual layer PNGs
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| for i, layer in enumerate(layers):
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| layer.save(f"layer_{i:02d}.png")
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| ```
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|
|
| ### 2. Exporting to Layered Photoshop PSD
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|
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| You can package the exported layer PNGs directly into a layered `.psd` file:
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|
|
| ```bash
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| python package_psd.py --composite source.png --layers layer_00.png layer_01.png layer_02.png layer_03.png --output decomposition.psd
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| ```
|
|
|
| ---
|
|
|
| ## Capabilities
|
|
|
| ### Layer Decomposition in Application
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| Given an input design, the model decomposes it into several RGBA layers:
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| - **Independent Manipulation**: Edits can be applied exclusively to targeted layers, physically isolating changes from the rest of the canvas.
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| - **Subject & Text Replacement**: Replace specific layer elements (e.g. swap subjects or text) without altering backgrounds.
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| - **Recoloring & Styling**: Recolor specific graphics, badges, or backgrounds while preserving unaffected elements.
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| - **Elementary Operations**:
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| - **Clean Object Removal**: Erase elements cleanly by simply hiding or deleting layers.
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| - **Repositioning & Resizing**: Move or resize objects freely on the canvas without distortion or background hole-filling issues.
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|
|
| ### Flexible and Iterative Decomposition
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| - **Variable Layer Counts**: Supports decomposing images into arbitrary layer counts (e.g., 2, 4, 6, 8+ layers) depending on complexity.
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| - **Recursive Decomposition**: Any decomposed layer can be recursively passed back into the model to break it down further into sub-components.
|
|
|
| ---
|
|
|
| ## License Agreement
|
|
|
| Qwen-Image-Layered is licensed under Apache 2.0.
|
|
|
| ## Citation
|
|
|
| ```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},
|
| 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},
|
| }
|
| ```
|
| |
