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OPPOer/
Qwen-Image-Edit-2509-Pruning

Name: Qwen-Image-Edit-2509-Pruning
Availability: InStock

diffusers

safetensors

image-to-image

OPPOer/X2Edit-Dataset

QwenImageEditPlusPipeline

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Qwen-Image-Edit-Pruning

GitHub

Update

2025/10/09: We release Qwen-Image-Edit-2509-Pruning-13B-4steps
2025/09/29: We release Qwen-Image-Edit-2509-Pruning-14B
2025/09/28: We release Qwen-Image-Edit-Pruning-13B-4steps

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.

Quick Start

Install the latest version of diffusers and pytorch

pip install torch
pip install git+https://github.com/huggingface/diffusers

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-2509-Pruning"
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 = "Let the ancient costume beauty in the second picture sit on the sofa in the first picture"
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"))

Qwen-Image-Edit-2509-13B Inference

python

import os
import torch
from PIL import Image
from diffusers import QwenImageEditPlusPipeline
model_name = f"OPPOer/Qwen-Image-Edit-2509-Pruning/Qwen-Image-Edit-2509-13B-4steps"
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 = "Let the ancient costume beauty in the second picture sit on the sofa in the first picture"
inputs = {
    "image": [image1, image2],
    "prompt": prompt,
    "generator": torch.manual_seed(0),
    "true_cfg_scale": 1.0,
    "negative_prompt": " ",
    "num_inference_steps": 4,
    "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"))

Citation

🌟 If you find our work helpful, please consider citing our paper and leaving valuable stars

@misc{ma2025pluggablepruningcontiguouslayer,
      title={Pluggable Pruning with Contiguous Layer Distillation for Diffusion Transformers}, 
      author={Jian Ma and Qirong Peng and Xujie Zhu and Peixing Xie and Chen Chen and Haonan Lu},
      year={2025},
      eprint={2511.16156},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2511.16156}, 
}