---
license: apache-2.0
base_model:
- Qwen/Qwen-Image-Edit
language:
- en
- zh
library_name: diffusers
pipeline_tag: image-to-image
datasets:
- OPPOer/X2Edit-Dataset
---
<div align="center">
  <h1>Qwen-Image-Edit-Pruning</h1>
<a href='https://github.com/OPPO-Mente-Lab/Qwen-Image-Pruning'><img src="https://img.shields.io/badge/GitHub-OPPOer-blue.svg?logo=github" alt="GitHub"></a>
</div>

## Update
- 2025/09/29: We release **Qwen-Image-Edit-2509-Pruning**
- 
## Introduction
This open-source project is based on Qwen-Image-Edit and has attempted model pruning, removing 20 layers while retaining the weights of 40 layers, resulting in a model size of 13.6B parameters. The pruned version will continue to be iterated upon. Please stay tuned.

<div align="center">
  <img src="bench.png">
</div>
<div align="center">
  <img src="bench-2509.png">
</div>

## Quick Start

Install the latest version of diffusers and pytorch
```
pip install torch
pip install git+https://github.com/huggingface/diffusers
```

### Qwen-Image-Edit-13B Inference
```python
from diffusers import QwenImageEditPipeline
import os
from PIL import Image
import time
import torch
model_name = "OPPOer/Qwen-Image-Edit-Pruning/Qwen-Image-Edit-13B"
pipe = QwenImageEditPipeline.from_pretrained(model_name, torch_dtype=torch.bfloat16)
pipe = pipe.to('cuda')
output_path = 'outputs'
os.makedirs(output_path, exist_ok=True)
for file_name in os.listdir('examples'):
    prompt = file_name.replace('_in.jpg', '')
    subject_img = Image.open(os.path.join('examples', file_name)).convert('RGB')
    t1 = time.time()
    inputs = {
        "image": subject_img,
        "prompt": prompt,
        "generator": torch.manual_seed(42),
        "true_cfg_scale": 1,
        "num_inference_steps": 4,
    }
    with torch.inference_mode():
        output = pipe(**inputs)
        output_image = output.images[0]
        output_image.save(os.path.join(output_path, f'{prompt}.jpg'))
    print(time.time()-t1)
```

### Qwen-Image-Edit-2509-14B Inference
```python
import os
import torch
from PIL import Image
from diffusers import QwenImageEditPlusPipeline

model_name = f"OPPOer/Qwen-Image-Edit-Pruning/Qwen-Image-Edit-2509-14B"
pipeline = QwenImageEditPlusPipeline.from_pretrained(model_name, torch_dtype=torch.bfloat16)
print("pipeline loaded")

pipeline.to('cuda')
pipeline.set_progress_bar_config(disable=None)
image1 = Image.open("input1.jpg")
image2 = Image.open("input2.jpg")
prompt = "美女坐在沙发上"
inputs = {
    "image": [image1, image2],
    "prompt": prompt,
    "generator": torch.manual_seed(0),
    "true_cfg_scale": 4.0,
    "negative_prompt": " ",
    "num_inference_steps": 40,
    "guidance_scale": 1.0,
    "num_images_per_prompt": 1,
}
with torch.inference_mode():
    output = pipeline(**inputs)
    output_image = output.images[0]
    output_image.save("output_image_edit_plus.png")
    print("image saved at", os.path.abspath("output_image_edit_plus.png"))
```