SynLayers/synlayers

SynLayers Stage 2 Checkpoints

This repository hosts the Stage 2 checkpoints and runtime assets for SynLayers, our real-world image layer decomposition system.

The main assets in this repo include:

• —SynLayers_checkpoints/FLUX.1-dev
• —SynLayers_checkpoints/FLUX.1-dev-Controlnet-Inpainting-Alpha
• —SynLayers_ckpt/step_120000
• —ckpt/trans_vae/0008000.pt
• —ckpt/pre_trained_LoRA
• —ckpt/prism_ft_LoRA

These assets are used by our public Space: SynLayers/synlayers

The full SynLayers system has two stages:

1. 1.bbox + whole-caption prediction from `SynLayers/Bbox-caption-8b`
2. 2.layer decomposition into transparent RGBA outputs using this repository

This repository is intended for the SynLayers decomposition pipeline. It is not meant to be loaded as a single generic DiffusionPipeline(prompt) model.

Stage 2 Inference

The standalone Stage 2 entrypoint is:

• —infer/infer.py
• —infer/infer.yaml

Stage 2 expects images plus a JSONL file containing the whole-image caption and bounding boxes. The easiest way to get those inputs is to run Stage 1 first with `SynLayers/Bbox-caption-8b`, or use the public Space for the full two-stage pipeline.

After preparing your inputs, update these fields in infer/infer.yaml:

data_dir: "path/to/your/work_dir"
image_dir: "path/to/your/images"
test_jsonl: "path/to/caption_bbox_infer.jsonl"
save_dir: "path/to/save/results"

Then run:

python infer/infer.py \
  --config_path infer/infer.yaml

The default checkpoint paths in infer/infer.yaml are repo-relative and point to the assets in this repository:

pretrained_model_name_or_path: "SynLayers_checkpoints/FLUX.1-dev"
pretrained_adapter_path: "SynLayers_checkpoints/FLUX.1-dev-Controlnet-Inpainting-Alpha"
lora_ckpt: "SynLayers_ckpt/step_120000/transformer"
layer_ckpt: "SynLayers_ckpt/step_120000"
adapter_lora_dir: "SynLayers_ckpt/step_120000/adapter"

For most users, the public Space is the recommended interface because it runs both Stage 1 and Stage 2 in one workflow.

For more details, please check our paper: https://arxiv.org/abs/2605.15167

If you find our work useful, please consider citing:

@article{wu2026does,
  title={Does Synthetic Layered Design Data Benefit Layered Design Decomposition?},
  author={Wu, Kam Man and Yang, Haolin and Chen, Qingyu and Tang, Yihu and Chen, Jingye and Chen, Qifeng},
  journal={arXiv preprint arXiv:2605.15167},
  year={2026}
}

Thanks for trying SynLayers.