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
metadata
tags:
- diffusion
- image-decomposition
- rgba
- layered-image-editing
SynLayers Stage 2
This repository contains the Stage 2 checkpoints and runtime assets for SynLayers, our real-world image layer decomposition system.
The main assets in this repo are:
SynLayers_checkpoints/FLUX.1-devSynLayers_checkpoints/FLUX.1-dev-Controlnet-Inpainting-AlphaSynLayers_ckpt/step_120000ckpt/trans_vae/0008000.ptckpt/pre_trained_LoRAckpt/prism_ft_LoRA
These assets are used by the public SynLayers demo: SynLayers/synlayers
The full pipeline uses:
- Stage 1 bbox + whole-caption prediction from
SynLayers/Bbox-caption-8b - Stage 2 layer decomposition from this repository
This repo is intended for the SynLayers decomposition pipeline rather than a single generic DiffusionPipeline(prompt) model.
If you want to see more details of our implementation, please check our paper: https://arxiv.org/abs/2605.15167
If you find our work useful, please consider citing:
@misc{wu2026doessyntheticlayereddesign,
title={Does Synthetic Layered Design Data Benefit Layered Design Decomposition?},
author={Kam Man Wu and Haolin Yang and Qingyu Chen and Yihu Tang and Jingye Chen and Qifeng Chen},
year={2026},
eprint={2605.15167},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2605.15167},
}