---
license: other
license_name: stabilityai-community
license_link: https://stability.ai/license
base_model: Qwen/Qwen-Image-Layered
library_name: peft
pipeline_tag: image-to-image
---

<h1 align="center">Stable Layers</h1>

<p align="center">
  <strong>Decomposing Images into Editable RGBA Layers</strong>
</p>

<p align="center">
  <img src="https://stability-ai.github.io/stable-layers.github.io/assets/images/teaser.png" alt="Stable Layers Teaser" width="100%" />
</p>

<p align="center">
  <span><a href="https://arxiv.org/abs/2605.30257">Paper</a></span>&nbsp;&nbsp;&nbsp;
  <span><a href="https://stability-ai.github.io/stable-layers.github.io/">Project Page</a></span>&nbsp;&nbsp;&nbsp;
  <span><a href="https://huggingface.co/StabilityLabs/Stable-Layers">Model (Hugging Face)</a></span>&nbsp;&nbsp;&nbsp;
  <span><a href="https://github.com/Stability-AI/Stable-Layers">Code</a></span>
</p>

## About

**Stable Layers** decomposes a single RGB image into a small stack of back-to-front
RGBA layers (background plus object layers) suitable for compositing and editing.

This repository hosts the LoRA adapter over `Qwen/Qwen-Image-Layered`. This model
was trained using Qwen 3.5 9b as the VLM teacher.

## Model Artifacts

This repository **is** the LoRA adapter (`adapter_config.json`,
`adapter_model.safetensors`). The base model is pulled from Hugging Face
automatically (`Qwen/Qwen-Image-Layered`). See the
[code repository](https://github.com/Stability-AI/Stable-Layers) for the inference
script.

## Installation

```bash
pip install torch diffusers transformers peft pillow numpy
```

Tested with `torch 2.11`, `diffusers 0.37`, `transformers 5.5`, `peft 0.18`.
Requires **one GPU** — the base model is ~40 GB in bf16, so an 80 GB-class card
(A100-80 / H100 / H200) is comfortable.

## Recommended Inference Settings

> ### **Heun sampler · 50 steps · CFG 1.0 · 640 px · 4 layers**

| Setting | Value | Flag |
|---|---|---|
| **Sampler** | **Heun (2nd order)** | always used — not configurable |
| **Steps** | **50** | `--steps 50` |
| **CFG / guidance** | **1.0 (off)** | `--guidance-scale 1.0` |
| Resolution | 640 px (max dim) | `--size 640` |
| Layers | 4 | `--num-layers 4` |

**Note:** using a higher resolution, lower steps, or a non-Heun sampler will garble
the results.

## Quickstart

```bash
# single image
python decompose.py --input photo.png --output results/

# a directory of images
python decompose.py --input images/ --output results/

# RGBA layers with real alpha (for compositing / editors)
python decompose.py --input images/ --output results/ --transparent

# explicit LoRA location
python decompose.py --input photo.png --output results/ --lora ./model
```

## Outputs

```
results/<image_name>/
  source.png       # input, resized
  composite.png    # layers recomposited — compare against source as a sanity check
  layer_0.png      # background (inpainted behind the removed objects)
  layer_1.png      # object layers, back-to-front
  layer_2.png
  layer_3.png
```

Layers are ordered back-to-front: `layer_0` is the background, higher indices sit
on top. Not every image needs all 4 — unused layers come out blank, which is
normal.

By default layers are composited onto **white** (easy to eyeball). Pass
`--transparent` to get **RGBA with real alpha**, which is what you want when
importing into an editor or compositing them yourself.

**Notes:**

- **Reproducible:** noise is seeded per image as `seed + image_index`
  (`--seed 42` by default), so the same inputs give the same outputs.
- **Prompt:** decomposition is driven by the source image; the text prompt only
  nudges guidance. The default (`"a clean, well composed image"`) is fine —
  override with `--prompt` if you want.
- **Aspect ratio** is preserved; the longest side is scaled to `--size` and both
  dimensions are rounded to multiples of 16.

## License

This code and model usage are subject to Stability AI Community License terms.

For individuals or organizations generating annual revenue of USD 1,000,000 (or local
currency equivalent) or more, commercial usage requires an enterprise license from
Stability AI.

- License details: https://stability.ai/license
- Enterprise request: https://stability.ai/enterprise

## Citation

```
@article{stablelayers2026,
  author  = {},
  title   = {Stable Layers: Decomposing Images into Editable RGBA Layers},
  journal = {arXiv preprint arXiv:2605.30257},
  year    = {2026}
}
```