Qwen Image Edit 2509

---
license: apache-2.0
language:

• en

• zh

<p align="center">
<img src="https://qianwen-res.oss-accelerate-overseas.aliyuncs.com/Qwen-Image/edit2509/edit2509_top.jpg" width="1600"/>
<p>

Introduction

• Multi-image Editing Support: For multi-image inputs, Qwen-Image-Edit-2509 builds upon the Qwen-Image-Edit architecture and is further trained via image concatenation to enable multi-image editing. It supports various combinations such as "person + person," "person + product," and "person + scene." Optimal performance is currently achieved with 1 to 3 input images.

• Enhanced Single-image Consistency: For single-image inputs, Qwen-Image-Edit-2509 significantly improves editing consistency, specifically in the following areas:

• Native Support for ControlNet: Including depth maps, edge maps, keypoint maps, and more.

Quick Start

Install the latest version of diffusers

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

The following contains a code snippet illustrating how to use Qwen-Image-Edit-2509:

import os
import torch
from PIL import Image
from diffusers import QwenImageEditPlusPipeline
pipeline = QwenImageEditPlusPipeline.from_pretrained("Qwen/Qwen-Image-Edit-2509", torch_dtype=torch.bfloat16)
print("pipeline loaded")
pipeline.to('cuda')
pipeline.set_progress_bar_config(disable=None)
image1 = Image.open("input1.png")
image2 = Image.open("input2.png")
prompt = "The magician bear is on the left, the alchemist bear is on the right, facing each other in the central park square."
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"))

The primary update in Qwen-Image-Edit-2509 is support for multi-image inputs.

Another major update in Qwen-Image-Edit-2509 is enhanced consistency.**

First, regarding person consistency, Qwen-Image-Edit-2509 shows significant improvement over Qwen-Image-Edit. Below are examples generating various portrait styles:
!Portrait Styles Example

For instance, changing a person’s pose while maintaining excellent identity consistency:
!Pose Change with Identity Consistency

Leveraging this improvement along with Qwen-Image’s unique text rendering capability, we find that Qwen-Image-Edit-2509 excels at creating meme images:
!Meme Image Example

Of course, even with longer text, Qwen-Image-Edit-2509 can still render it while preserving the person’s identity:
!Long Text with Identity Preservation

Person consistency is also evident in old photo restoration. Below are two examples:
!Old Photo Restoration 1
!Old Photo Restoration 2

Naturally, besides real people, generating cartoon characters and cultural creations is also possible:
!Cartoon & Cultural Creation

Second, Qwen-Image-Edit-2509 specifically enhances product consistency. We find that the model can naturally generate product posters from plain-background product images:
!Product Poster Example

Or even simple logos:
!Logo Generation Example

Third, Qwen-Image-Edit-2509 specifically enhances text consistency and supports editing font type, font color, and font material:
!Text Font Type
!Text Font Color
![Text Font Material](https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-Image/edit2509/%