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# Contributors
This file contains the list of everyone who contributed to the repository
<br>
<table>
<th>Contributors1</th><th>Contributors2</th> <tr>
<td><img src="xxx1">
<br>
<a href="xxx1">xxx1</a></td>
<td><img src="xxx2">
<br>
<a href="xxx2">xxx2</a></td>
</tr>
</table>
<br>
### Thanks to everyone who helped in building this Repository :)
LICENSE 0 → 100755
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README.md
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# Qwen-Image-Edit_hf
## 论文
`Qwen-Image Technical Report`
- https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-Image/Qwen_Image.pdf
## 模型结构
Qwen-Image-Edit 是 Qwen-Image 的图像编辑版本。它基于强大的 20B Qwen-Image 模型构建而成，成功地将 Qwen-Image 独特的文字呈现能力扩展到了图像编辑任务中，实现了精准的文字编辑功能。此外，Qwen-Image-Edit 同时将输入的图像传递给 Qwen2.5-VL（用于视觉语义控制）和 VAE 编码器（用于视觉外观控制），从而在语义和外观编辑方面都具备了相应的能力。
<div align=center>
<img src="./doc/MMDIT.png"/>
</div>
## 算法原理
Qwen-Image-Edit的核心是扩散模型，并创新地使用了一个强大的多模态大语言模型(Qwen2.5-VL)来深度理解复杂的图文指令，从而在生成高质量图像的同时，实现了业界领先的、尤其是在中英文上的精准文字渲染能力。
<div align=center>
<img src="./doc/qwen-image.png"/>
</div>
## 环境配置
### 硬件需求
DCU型号：K100_AI,节点数量：1台,卡数：2张。
### Docker（方法一）
```bash
docker pull image.sourcefind.cn:5000/dcu/admin/base/custom:vllm0.8.5-ubuntu22.04-dtk25.04-rc7-das1.5-py3.10-20250612-fixpy-rocblas0611-rc2
docker run -it --shm-size 200g --network=host --name {docker_name} --privileged --device=/dev/kfd --device=/dev/dri --device=/dev/mkfd --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -u root -v /path/your_code_data/:/path/your_code_data/ -v /opt/hyhal/:/opt/hyhal/:ro {imageID} bash
cd /your_code_path/qwen-image-edit_hf
pip install -r requirements.txt
# 需要安装torch2.5.1或以上版本
wget https://download.sourcefind.cn:65024/directlink/4/pytorch/DAS1.6/torch-2.5.1+das.opt1.dtk25041-cp310-cp310-manylinux_2_28_x86_64.whl
pip install torch-2.5.1+das.opt1.dtk25041-cp310-cp310-manylinux_2_28_x86_64.whl
```
### Dockerfile（方法二）
此处提供dockerfile的使用方法
```bash
cd docker
docker build --no-cache -t qwen-image-edit:latest .
docker run -it --shm-size 200g --network=host --name {docker_name} --privileged --device=/dev/kfd --device=/dev/dri --device=/dev/mkfd --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -u root -v /path/your_code_data/:/path/your_code_data/ -v /opt/hyhal/:/opt/hyhal/:ro {imageID} bash
cd /your_code_path/qwen-image-edit_hf
pip install -r requirements.txt
# 需要安装torch2.5.1或以上版本
wget https://download.sourcefind.cn:65024/directlink/4/pytorch/DAS1.6/torch-2.5.1+das.opt1.dtk25041-cp310-cp310-manylinux_2_28_x86_64.whl
pip install torch-2.5.1+das.opt1.dtk25041-cp310-cp310-manylinux_2_28_x86_64.whl
```
### Anaconda（方法三）
关于本项目DCU显卡所需的特殊深度学习库可从[光合](https://developer.sourcefind.cn/tool/)开发者社区下载安装。
```bash
DTK: 25.04
python: 3.10
torch: 2.5.1+das.opt2.dtk25041
```
`Tips：以上dtk驱动、pytorch等DCU相关工具版本需要严格一一对应`
其它非深度学习库安装方式如下：
```bash
pip install -r requirements.txt
```
## 数据集
暂无
## 训练
暂无
## 推理
### diffusers推理方法
```bash
## 设置双卡推理
export HIP_VISIBLE_DEVICES=6,7
## 代码内部请修改model_name路径
python ./infer/infer_hf.py
```
## result
```bash
Processing task 1:
......
......
......
All inference tasks completed.
```
### 精度
```bash
# 运行acc.py之前，请分别在DCU和GPU上运行infer_hf.py，并将代码中folder_gpu变量和folder_dcu变量替换成生成的文件夹路径
开始对比文件夹 './output_images_A800' (基准) 和 './output_images_K100AI' (测试)...
- 对比 Add_a_small_wooden_output_5.png: MAE=2.0897, PSNR=32.24dB, SSIM=0.9842
- 对比 Change_the_word_output_2.png: MAE=0.4869, PSNR=39.33dB, SSIM=0.9954
- 对比 Replace_the_background_output_4.png: MAE=1.7056, PSNR=31.50dB, SSIM=0.9824
- 对比 Replace_the_woman_output_6.png: MAE=0.3702, PSNR=50.32dB, SSIM=0.9975
- 对比 Take_a_Breather_output_1.png: MAE=0.4357, PSNR=41.63dB, SSIM=0.9963
==================================================
--- 批量对比平均结果 ---
成功对比图片对数: 5
平均绝对误差 (MAE): 1.0176
平均峰值信噪比 (PSNR): 39.00 dB
平均结构相似性 (SSIM): 0.9912
==================================================
```
DCU(K100AI)与GPU(A800)在BF16精度下推理Qwen-Image-Edit模型，结果精度一致，推理框架：diffusers。
## 应用场景
### 算法类别
`图像生成`
### 热点应用行业
`制造,金融,教育`
## 预训练权重
- [Qwen/Qwen-Image-Edit](https://hf-mirror.com/Qwen/Qwen-Image-Edit)
## 源码仓库及问题反馈
- http://developer.sourcefind.cn/codes/modelzoo/qwen-image-edit_hf.git
## 参考资料
- https://qwenlm.github.io/blog/qwen-image-edit/
Qwen-Image-Edit是 Qwen-Image 的图像编辑版本，将文字渲染能力拓展到了图像编辑任务中，实现了精准的文字编辑。
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FROM image.sourcefind.cn:5000/dcu/admin/base/custom:vllm0.8.5-ubuntu22.04-dtk25.04-rc7-das1.5-py3.10-20250612-fixpy-rocblas0611-rc2
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icon.png

67.5 KB

infer/acc.py 0 → 100755
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import numpy as np
from PIL import Image
from skimage.metrics import peak_signal_noise_ratio as psnr
from skimage.metrics import structural_similarity as ssim
import os
import csv
def calculate_mae(image_a, image_b):
"""计算两张图片的平均绝对误差 (Mean Absolute Error, MAE)"""
image_a = image_a.astype(np.float32)
image_b = image_b.astype(np.float32)
mae_value = np.mean(np.abs(image_a - image_b))
return mae_value
def compare_images(image_path1, image_path2):
"""加载两张图片并计算它们的MAE, PSNR, 和 SSIM"""
try:
img1_pil = Image.open(image_path1).convert('RGB')
img2_pil = Image.open(image_path2).convert('RGB')
except FileNotFoundError as e:
print(f"错误: 无法找到文件。 {e}")
return None
img1_np = np.array(img1_pil)
img2_np = np.array(img2_pil)
if img1_np.shape != img2_np.shape:
print(f"错误: 图片 '{os.path.basename(image_path1)}' 尺寸不匹配。")
print(f" - 图片1尺寸: {img1_np.shape}")
print(f" - 图片2尺寸: {img2_np.shape}")
return None
mae_value = calculate_mae(img1_np, img2_np)
psnr_value = psnr(img1_np, img2_np, data_range=255)
try:
ssim_value = ssim(img1_np, img2_np, data_range=255, channel_axis=-1, win_size=7)
except TypeError:
ssim_value = ssim(img1_np, img2_np, data_range=255, multichannel=True, win_size=7)
return {"MAE": mae_value, "PSNR": psnr_value, "SSIM": ssim_value}
if __name__ == "__main__":
# *****************************************************************
# * 请在这里修改你的文件夹路径 *
# *****************************************************************
folder_gpu = "./output_images_A800" # 基准文件夹 (GPU推理结果)
folder_dcu = "./output_images_K100AI" # 待测试文件夹 (DCU推理结果)
report_filename = "comparison_report.csv"
if not os.path.isdir(folder_gpu) or not os.path.isdir(folder_dcu):
print(f"错误：请确保文件夹 '{folder_gpu}' 和 '{folder_dcu}' 都存在。")
exit()
print(f"开始对比文件夹 '{folder_gpu}' (基准) 和 '{folder_dcu}' (测试)...")
all_metrics = []
base_filenames = sorted(os.listdir(folder_gpu))
for filename in base_filenames:
if not filename.lower().endswith(('.png', '.jpg', '.jpeg', '.bmp')):
continue
path1 = os.path.join(folder_gpu, filename)
path2 = os.path.join(folder_dcu, filename)
if not os.path.exists(path2):
print(f" [跳过] 在 '{folder_dcu}' 中未找到对应的文件: {filename}")
continue
results = compare_images(path1, path2)
if results:
print(f" - 对比 {filename}: "
f"MAE={results['MAE']:.4f}, "
f"PSNR={results['PSNR']:.2f}dB, "
f"SSIM={results['SSIM']:.4f}")
all_metrics.append({
'filename': filename,
'MAE': results['MAE'],
'PSNR': results['PSNR'],
'SSIM': results['SSIM']
})
# --- 计算并打印平均结果 ---
if not all_metrics:
print("\n未找到任何可以对比的图片对。请检查文件夹内容和文件名。")
else:
# 使用Numpy高效计算平均值
avg_mae = np.mean([m['MAE'] for m in all_metrics])
avg_psnr = np.mean([m['PSNR'] for m in all_metrics])
avg_ssim = np.mean([m['SSIM'] for m in all_metrics])
print("\n" + "="*50)
print("--- 批量对比平均结果 ---")
print(f"成功对比图片对数: {len(all_metrics)}")
print(f"平均绝对误差 (MAE): {avg_mae:.4f}")
print(f"平均峰值信噪比 (PSNR): {avg_psnr:.2f} dB")
print(f"平均结构相似性 (SSIM): {avg_ssim:.4f}")
print("="*50)
# --- 将详细结果写入CSV文件 ---
try:
with open(report_filename, 'w', newline='', encoding='utf-8') as csvfile:
fieldnames = ['filename', 'MAE', 'PSNR', 'SSIM']
writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
writer.writeheader()
writer.writerows(all_metrics)
# 写入平均值
writer.writerow({})
writer.writerow({'filename': 'Average', 'MAE': avg_mae, 'PSNR': avg_psnr, 'SSIM': avg_ssim})
print(f"\n详细报告已保存至: {report_filename}")
except Exception as e:
print(f"\n保存报告失败: {e}")
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filename,MAE,PSNR,SSIM
Add_a_small_wooden_output_5.png,2.0897036,32.23572562876897,0.984223018079311
Change_the_word_output_2.png,0.48687163,39.33201774772982,0.9954169983939914
Replace_the_background_output_4.png,1.7055854,31.501655221325425,0.9824052945937866
Replace_the_woman_output_6.png,0.3701708,50.317291097024544,0.9975423187586948
Take_a_Breather_output_1.png,0.43572536,41.63346109329262,0.9963357851640264
,,,
Average,1.0176113,39.00403015762827,0.991184682997962
infer/infer.json 0 → 100644
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[
{
"input_image":"./input_image/Take_a_Breather.png",
"prompt":"Change the phrase 'Take a Breather' on the note to 'Relax and Recharge'"
},
{
"input_image":"./input_image/Change_the_word.png",
"prompt":"Change the word 'HEALTH INSURANCE' to '明天 会更好' on the Scrabble tiles."
},
{
"input_image":"./input_image/Replace_the_background.png",
"prompt":"Replace the background with a sunnybeach scene featuring fine sand,gentle waves, and a clear blue sky"
},
{
"input_image":"./input_image/Add_a_small_wooden.png",
"prompt":"Add a small wooden sign in the foreground in front of the penguins with the text 'Welcome to Penguin Beach"
},
{
"input_image":"./input_image/Replace_the_woman.png",
"prompt":"Replace the woman's polka-dot blouse with a light blue button-up shirt."
}
]
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infer/infer_hf.py 0 → 100644
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import os
import json
from PIL import Image
import torch
from diffusers import QwenImageEditPipeline
output_dir = "./output_images_A800"
os.makedirs(output_dir, exist_ok=True)
pipeline = QwenImageEditPipeline.from_pretrained("/home/zwq/model/Qwen-Image-Edit")
print("pipeline loaded")
pipeline.to(torch.bfloat16)
pipeline.to("cuda")
pipeline.set_progress_bar_config(disable=None)
json_file_path = "./infer.json"
try:
with open(json_file_path, 'r', encoding='utf-8') as f:
inference_data = json.load(f)
except FileNotFoundError:
print(f"Error: JSON file not found at {json_file_path}")
exit()
except json.JSONDecodeError:
print(f"Error: Could not decode JSON from {json_file_path}. Please check file format.")
exit()
print(f"Loaded {len(inference_data)} inference tasks from {json_file_path}")
for i, task in enumerate(inference_data):
input_image_path = task.get("input_image")
prompt = task.get("prompt")
if not input_image_path or not prompt:
print(f"Skipping task {i+1} due to missing 'input_image' or 'prompt'. Task data: {task}")
continue
try:
image = Image.open(input_image_path).convert("RGB")
print(f"\nProcessing task {i+1}:")
print(f" Input Image: {input_image_path}")
print(f" Prompt: {prompt}")
inputs = {
"image": image,
"prompt": prompt,
"generator": torch.manual_seed(0),
"true_cfg_scale": 4.0,
"negative_prompt": " ",
"num_inference_steps": 50,
}
with torch.inference_mode():
output = pipeline(**inputs)
output_image = output.images[0]
base_name = os.path.splitext(os.path.basename(input_image_path))[0]
output_image_name = f"{base_name}_output_{i+1}.png"
output_image_path = os.path.join(output_dir, output_image_name)
output_image.save(output_image_path)
print(f" Image saved at {os.path.abspath(output_image_path)}")
except FileNotFoundError:
print(f"Error: Input image not found at {input_image_path}. Skipping this task.")
except Exception as e:
print(f"An error occurred while processing task {i+1} (Image: {input_image_path}, Prompt: {prompt}): {e}")
print("\nAll inference tasks completed.")
\ No newline at end of file
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