File size: 2,861 Bytes
c12813d a5975f0 c12813d 42a27fc c12813d a5975f0 c12813d a5975f0 c12813d a5975f0 c12813d a5975f0 c12813d a5975f0 c12813d a5975f0 c12813d a5975f0 c12813d 331f981 a5975f0 c12813d a5975f0 c12813d a5975f0 c12813d a5975f0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 | ---
library_name: diffusers
tags:
- image-decomposition
- layered-image-editing
- diffusion
- flux
- lora
- image-to-image
- transparent-rgba
- arxiv:2605.15167
---
# 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](https://huggingface.co/spaces/SynLayers/synlayers)
The full SynLayers system has two stages:
1. bbox + whole-caption prediction from [`SynLayers/Bbox-caption-8b`](https://huggingface.co/SynLayers/Bbox-caption-8b)
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`](https://huggingface.co/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`:
```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:
```bash
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:
```yaml
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](https://arxiv.org/abs/2605.15167)
If you find our work useful, please consider citing:
```bibtex
@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.
|