---
license: apache-2.0
---
# Qwen-Image-Layered

## Model Introduction

This model is trained based on the model [Qwen/Qwen-Image-Layered](https://modelscope.cn/models/Qwen/Qwen-Image-Layered) using the dataset [artplus/PrismLayersPro](https://modelscope.cn/datasets/artplus/PrismLayersPro), enabling text-controlled extraction of segmented layers.

For more details about training strategies and implementation, feel free to check our [technical blog](https://modelscope.cn/learn/4938).

## Usage Tips

* The model architecture has been changed from multi-image output to single-image output, producing only the layer relevant to the provided text description.
* The model was trained exclusively on English text, but retains Chinese language understanding capabilities inherited from the base model.
* The native training resolution is 1024x1024; however, inference at other resolutions is supported.
* The model struggles to separate multiple entities that are heavily occluded or overlapping, such as the cartoon skeleton head and hat in the examples.
* The model excels at decomposing poster-like graphics but performs poorly on photographic images, especially those involving complex lighting and shadows.
* The model supports negative prompts—users can specify content they wish to exclude via negative prompt descriptions.

## Demo Examples

**Some images contain white text on light backgrounds. ModelScope users should click the "☀︎" icon in the top-right corner to switch to dark mode for better visibility.**

### Example 1

<div style="display: flex; justify-content: space-between;">

<div style="width: 30%;">

|Input Image|
|-|
|![](./assets/image_1_input.png)|

</div>

<div style="width: 66%;">

|Prompt|Output Image|Prompt|Output Image|
|-|-|-|-|
|A solid, uniform color with no distinguishable features or objects|![](./assets/image_1_0_0.png)|Text 'TRICK'|![](./assets/image_1_4_0.png)|
|Cloud|![](./assets/image_1_1_0.png)|Text 'TRICK OR TREAT'|![](./assets/image_1_3_0.png)|
|A cartoon skeleton character wearing a purple hat and holding a gift box|![](./assets/image_1_2_0.png)|Text 'TRICK OR'|![](./assets/image_1_7_0.png)|
|A purple hat and a head|![](./assets/image_1_5_0.png)|A gift box|![](./assets/image_1_6_0.png)|

</div>

</div>

### Example 2

<div style="display: flex; justify-content: space-between;">

<div style="width: 30%;">

|Input Image|
|-|
|![](./assets/image_2_input.png)|

</div>

<div style="width: 66%;">

|Prompt|Output Image|Prompt|Output Image|
|-|-|-|-|
|Blue sky, white clouds, a garden with colorful flowers|![](./assets/image_2_0_0.png)|Colorful, intricate floral wreath|![](./assets/image_2_2_0.png)|
|Girl, wreath, kitten|![](./assets/image_2_1_0.png)|Girl, kitten|![](./assets/image_2_3_0.png)|

</div>

</div>

### Example 3

<div style="display: flex; justify-content: space-between;">

<div style="width: 30%;">

|Input Image|
|-|
|![](./assets/image_3_input.png)|

</div>

<div style="width: 66%;">

|Prompt|Output Image|Prompt|Output Image|
|-|-|-|-|
|A clear blue sky and a turbulent sea|![](./assets/image_3_0_0.png)|Text "The Life I Long For"|![](./assets/image_3_2_0.png)|
|A seagull|![](./assets/image_3_1_0.png)|Text "Life"|![](./assets/image_3_3_0.png)|

</div>

</div>

## Inference Code

Install DiffSynth-Studio:

```
git clone https://github.com/modelscope/DiffSynth-Studio.git  
cd DiffSynth-Studio
pip install -e .
```

Model inference:

```python
from diffsynth.pipelines.qwen_image import QwenImagePipeline, ModelConfig
from PIL import Image
import torch, requests

pipe = QwenImagePipeline.from_pretrained(
    torch_dtype=torch.bfloat16,
    device="cuda",
    model_configs=[
        ModelConfig(model_id="DiffSynth-Studio/Qwen-Image-Layered-Control", origin_file_pattern="transformer/diffusion_pytorch_model*.safetensors"),
        ModelConfig(model_id="Qwen/Qwen-Image", origin_file_pattern="text_encoder/model*.safetensors"),
        ModelConfig(model_id="Qwen/Qwen-Image-Layered", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
    ],
    processor_config=ModelConfig(model_id="Qwen/Qwen-Image-Edit", origin_file_pattern="processor/"),
)
prompt = "A cartoon skeleton character wearing a purple hat and holding a gift box"
input_image = requests.get("https://modelscope.oss-cn-beijing.aliyuncs.com/resource/images/trick_or_treat.png", stream=True).raw
input_image = Image.open(input_image).convert("RGBA").resize((1024, 1024))
input_image.save("image_input.png")
images = pipe(
    prompt,
    seed=0,
    num_inference_steps=30, cfg_scale=4,
    height=1024, width=1024,
    layer_input_image=input_image,
    layer_num=0,
)
images[0].save("image.png")
```