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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_hf # Qwen-Image_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，一个20B的MMDiT模型。这是通义千问系列中首个图像生成基础模型，其在复杂文本渲染和精确图像编辑方面取得了显著进展。其主要特性包括：
- 卓越的文本渲染能力: Qwen-Image 在复杂文本渲染方面表现出色，支持多行布局、段落级文本生成以及细粒度细节呈现。无论是英语还是中文，均能实现高保真输出。
- 一致性的图像编辑能力: 通过增强的多任务训练范式，Qwen-Image 在编辑过程中能出色地保持编辑的一致性。
- 强大的跨基准性能表现: 在多个公开基准测试中的评估表明，Qwen-Image 在各类生成与编辑任务中均获得SOTA，是一个强大的图像生成基础模型。
<div align=center>
<img src="./doc/MMDIT.png"/>
</div>
## 算法原理
Qwen-Image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_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: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_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
正在加载模型... 这可能需要一些时间。
......
模型加载成功！
所有图片将保存在 'generated_images_DCU' 文件夹中。
正在处理第 1/11 个prompt...
Prompt: 'A cute capybara wearing a top hat, sitting in a library....'
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 50/50 [01:20<00:00, 1.61s/it]
图片已成功保存为: 'generated_images_DCU/prompt_0.png'
......
所有prompt处理完毕！
```
### 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 '/home/zwq/project/shangchaun/external/qwen-image_hf/infer/generated_images_GPU' (基准) 和 '/home/zwq/project/shangchaun/external/qwen-image_hf/infer/generated_images_DCU' (测试)...
- 对比 prompt_0.png: MAE=1.0268, PSNR=40.89dB, SSIM=0.9944
- 对比 prompt_1.png: MAE=2.7091, PSNR=28.35dB, SSIM=0.9760
- 对比 prompt_10.png: MAE=1.5839, PSNR=36.26dB, SSIM=0.9844
- 对比 prompt_2.png: MAE=2.8871, PSNR=29.00dB, SSIM=0.9636
- 对比 prompt_3.png: MAE=2.2327, PSNR=30.49dB, SSIM=0.9750
- 对比 prompt_4.png: MAE=2.3045, PSNR=31.78dB, SSIM=0.9778
- 对比 prompt_5.png: MAE=2.0608, PSNR=30.85dB, SSIM=0.9819
- 对比 prompt_6.png: MAE=2.9847, PSNR=27.91dB, SSIM=0.9639
- 对比 prompt_7.png: MAE=0.9722, PSNR=37.80dB, SSIM=0.9933
- 对比 prompt_8.png: MAE=1.8605, PSNR=33.35dB, SSIM=0.9839
- 对比 prompt_9.png: MAE=2.8696, PSNR=29.13dB, SSIM=0.9718
==================================================
--- 批量对比平均结果 ---
成功对比图片对数: 11
平均绝对误差 (MAE): 2.1356
平均峰值信噪比 (PSNR): 32.35 dB
平均结构相似性 (SSIM): 0.9787
==================================================
详细报告已保存至: comparison_report.csv
```
DCU(K100AI)与GPU(A800)在BF16精度下推理Qwen-Image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](https://huggingface.co/Qwen/Qwen-Image)
## 源码仓库及问题反馈
- https://developer.sourcefind.cn/codes/modelzoo/qwen-image_hf
## 参考资料
- https://github.com/QwenLM/Qwen-Image/tree/main
Qwen-Image 是阿里巴巴开源的20B参数MMDiT图像生成基础模型
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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

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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 = "/home/zwq/project/shangchaun/external/qwen-image_hf/infer/generated_images_GPU" # 基准文件夹 (GPU推理结果)
folder_dcu = "/home/zwq/project/shangchaun/external/qwen-image_hf/infer/generated_images_DCU" # 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}")
\ No newline at end of file
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filename,MAE,PSNR,SSIM
prompt_0.png,1.026849,40.8854127728873,0.9943677756820654
prompt_1.png,2.709065,28.352697000945955,0.9760256669877224
prompt_10.png,1.583917,36.26208399922048,0.9844140780231716
prompt_2.png,2.887137,29.003646998886367,0.9636331249621533
prompt_3.png,2.2327194,30.48617502196941,0.9749565736788535
prompt_4.png,2.3044817,31.78111178764969,0.9778486737634221
prompt_5.png,2.0608156,30.85081818532004,0.9819459349119617
prompt_6.png,2.984743,27.910236859607608,0.963862190680128
prompt_7.png,0.9722118,37.803413121475515,0.9932747064085926
prompt_8.png,1.8604748,33.35498014242068,0.983864947926822
prompt_9.png,2.8695865,29.129936691854404,0.9717987060529074
,,,
Average,2.1356366,32.347319325657956,0.9787265799161637
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