Image-to-Image
Diffusers
Safetensors
image-decomposition
layered-image-editing
diffusion
flux
lora
transparent-rgba
Instructions to use SynLayers/synlayers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use SynLayers/synlayers with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("fill-in-base-model", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("SynLayers/synlayers") prompt = "Turn this cat into a dog" input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png") image = pipe(image=input_image, prompt=prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
Upload README.md with huggingface_hub
Browse files
README.md
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---
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tags:
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- diffusion
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- image-decomposition
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- rgba
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- layered-image-editing
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---
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# SynLayers Stage 2
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This repository contains the Stage 2 checkpoints and runtime assets for SynLayers, our real-world image layer decomposition system.
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The main assets in this repo are:
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- `SynLayers_checkpoints/FLUX.1-dev`
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- `SynLayers_checkpoints/FLUX.1-dev-Controlnet-Inpainting-Alpha`
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- `SynLayers_ckpt/step_120000`
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- `ckpt/trans_vae/0008000.pt`
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- `ckpt/pre_trained_LoRA`
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- `ckpt/prism_ft_LoRA`
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These assets are used by the public SynLayers demo:
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[SynLayers/synlayers](https://huggingface.co/spaces/SynLayers/synlayers)
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The full pipeline uses:
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1. Stage 1 bbox + whole-caption prediction from [`SynLayers/Bbox-caption-8b`](https://huggingface.co/SynLayers/Bbox-caption-8b)
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2. Stage 2 layer decomposition from this repository
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This repo is intended for the SynLayers decomposition pipeline rather than a single generic `DiffusionPipeline(prompt)` model.
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If you want to see more details of our implementation, please check our paper:
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[https://arxiv.org/abs/2605.15167](https://arxiv.org/abs/2605.15167)
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If you find our work useful, please consider citing:
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```bibtex
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@misc{wu2026doessyntheticlayereddesign,
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title={Does Synthetic Layered Design Data Benefit Layered Design Decomposition?},
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author={Kam Man Wu and Haolin Yang and Qingyu Chen and Yihu Tang and Jingye Chen and Qifeng Chen},
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year={2026},
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eprint={2605.15167},
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archivePrefix={arXiv},
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primaryClass={cs.CV},
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url={https://arxiv.org/abs/2605.15167},
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}
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```
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