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| <title>Qwen-Image - Advanced Text-to-Image Generation by Alibaba Cloud</title> | |
| <meta name="description" content="Qwen-Image: Part of the Qwen (Tongyi Qianwen) model series by Alibaba Cloud. A powerful text-to-image generative model that creates stunning images from text prompts with high-quality rendering, artistic style control, and exceptional detail." /> | |
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| <div class="nav-logo">QWEN</div> | |
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| <a href="https://wavespeed.ai/models/wavespeed-ai/qwen-image/text-to-image" class="nav-link" target="_blank" rel="noopener noreferrer">Home</a> | |
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| <h1>Qwen-Image</h1> | |
| <p class="subtitle">By Alibaba Cloud Qwen Team</p> | |
| </div> | |
| <div class="announcement-section"> | |
| <p class="announcement">Qwen-Image is now available!</p> | |
| <div class="divider"></div> | |
| <p class="description">Open-source Advanced Text-to-Image Generative Model</p> | |
| </div> | |
| <div class="hero-image"> | |
| <img src="https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-Image/merge3.jpg" alt="Qwen-Image Examples" class="full-width-img"> | |
| </div> | |
| <section class="intro-section"> | |
| <h2>Introduction</h2> | |
| <p>We are thrilled to release Qwen-Image, an image generation foundation model in the Qwen series that achieves significant advances in complex text rendering and precise image editing. Experiments show strong general capabilities in both image generation and editing, with exceptional performance in text rendering, especially for Chinese.</p> | |
| <div class="benchmark-image"> | |
| <img src="https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-Image/bench.png" alt="Qwen-Image Benchmark" class="full-width-img"> | |
| </div> | |
| </section> | |
| <div class="features-section"> | |
| <div class="feature"> | |
| <h3>🚀 Multimodal AI Capabilities</h3> | |
| <p>Part of the Qwen (Tongyi Qianwen) model series, offering powerful text-to-image generation with exceptional understanding of complex prompts</p> | |
| </div> | |
| <div class="feature"> | |
| <h3>🌟 Open Source Innovation</h3> | |
| <p>Part of Alibaba's commitment to open-source AI development, allowing researchers and developers to build upon and extend its capabilities</p> | |
| </div> | |
| <div class="feature"> | |
| <h3>🔍 Comprehensive Model Family</h3> | |
| <p>Works alongside other Qwen models for text, vision, and multimodal applications, providing a complete ecosystem for AI development</p> | |
| </div> | |
| </div> | |
| <section class="quickstart-section"> | |
| <h2>Quick Start</h2> | |
| <p>Choose your preferred Qwen image model:</p> | |
| <h3>Option 1: Using the latest Qwen VLo model</h3> | |
| <p>The new Qwen VLo model supports both text-to-image and image-to-image generation with progressive generation feature.</p> | |
| <div class="code-block"> | |
| <pre><code>pip install dashscope>=1.20.7</code></pre> | |
| </div> | |
| <div class="code-block"> | |
| <pre><code>import dashscope | |
| from dashscope import ImageSynthesis | |
| # Set your API key | |
| dashscope.api_key = "YOUR_API_KEY" | |
| # Text-to-image generation | |
| response = ImageSynthesis.call( | |
| model='qwen-vlo', | |
| prompt='A coffee shop entrance features a chalkboard sign reading "Qwen Coffee 😊 $2 per cup"', | |
| negative_prompt='blurry, low quality', | |
| n=1, # Number of images to generate | |
| size='1024*1024', # Image size | |
| steps=50 # Diffusion steps | |
| ) | |
| # Save the generated image | |
| if response.status_code == 200: | |
| with open('qwen_vlo_result.png', 'wb') as f: | |
| f.write(response.output.images[0].image) | |
| print('Image saved successfully!') | |
| else: | |
| print(f'Failed to generate image: {response.message}')</code></pre> | |
| </div> | |
| <h3>Option 2: Using Qwen-Image with diffusers</h3> | |
| <p>Install the latest version of diffusers</p> | |
| <div class="code-block"> | |
| <pre><code>pip install git+https://github.com/huggingface/diffusers</code></pre> | |
| </div> | |
| <p>The following contains a code snippet illustrating how to use the model to generate images based on text prompts:</p> | |
| <div class="code-block"> | |
| <pre><code>from diffusers import DiffusionPipeline | |
| import torch | |
| model_name = "Qwen/Qwen-Image" | |
| # Load the pipeline | |
| if torch.cuda.is_available(): | |
| torch_dtype = torch.bfloat16 | |
| device = "cuda" | |
| else: | |
| torch_dtype = torch.float32 | |
| device = "cpu" | |
| pipe = DiffusionPipeline.from_pretrained(model_name, torch_dtype=torch_dtype) | |
| pipe = pipe.to(device) | |
| positive_magic = { | |
| "en": "Ultra HD, 4K, cinematic composition.", # for english prompt | |
| "zh": "超清，4K，电影级构图" # for chinese prompt | |
| } | |
| # Generate image | |
| prompt = '''A coffee shop entrance features a chalkboard sign reading "Qwen Coffee 😊 $2 per cup," with a neon light beside it displaying "通义千问". Next to it hangs a poster showing a beautiful Chinese woman, and beneath the poster is written "π≈3.1415926-53589793-23846264-33832795-02384197". Ultra HD, 4K, cinematic composition''' | |
| negative_prompt = " " | |
| # Generate with different aspect ratios | |
| aspect_ratios = { | |
| "1:1": (1328, 1328), | |
| "16:9": (1664, 928), | |
| "9:16": (928, 1664), | |
| "4:3": (1472, 1140), | |
| "3:4": (1140, 1472) | |
| } | |
| width, height = aspect_ratios["16:9"] | |
| image = pipe( | |
| prompt=prompt + positive_magic["en"], | |
| negative_prompt=negative_prompt, | |
| width=width, | |
| height=height, | |
| num_inference_steps=50, | |
| true_cfg_scale=4.0, | |
| generator=torch.Generator(device="cuda").manual_seed(42) | |
| ).images[0] | |
| image.save("example.png")</code></pre> | |
| </div> | |
| </section> | |
| <section class="showcase-section"> | |
| <h2>Show Cases</h2> | |
| <div class="showcase-item-full"> | |
| <div class="showcase-description-full"> | |
| <h3>Superior Text Rendering</h3> | |
| <p>One of its standout capabilities is high-fidelity text rendering across diverse images. Whether it's alphabetic languages like English or logographic scripts like Chinese, Qwen-Image preserves typographic details, layout coherence, and contextual harmony with stunning accuracy. Text isn't just overlaid—it's seamlessly integrated into the visual fabric.</p> | |
| </div> | |
| <div class="showcase-image-full"> | |
| <img src="https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-Image/s1.jpg" alt="Text Rendering Example" class="showcase-img-full"> | |
| </div> | |
| </div> | |
| <div class="showcase-item-full"> | |
| <div class="showcase-description-full"> | |
| <h3>Artistic Style Support</h3> | |
| <p>Beyond text, Qwen-Image excels at general image generation with support for a wide range of artistic styles. From photorealistic scenes to impressionist paintings, from anime aesthetics to minimalist design, the model adapts fluidly to creative prompts, making it a versatile tool for artists, designers, and storytellers.</p> | |
| </div> | |
| <div class="showcase-image-full"> | |
| <img src="https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-Image/s2.jpg" alt="Artistic Styles Example" class="showcase-img-full"> | |
| </div> | |
| </div> | |
| <div class="showcase-item-full"> | |
| <div class="showcase-description-full"> | |
| <h3>Advanced Image Editing</h3> | |
| <p>When it comes to image editing, Qwen-Image goes far beyond simple adjustments. It enables advanced operations such as style transfer, object insertion or removal, detail enhancement, text editing within images, and even human pose manipulation—all with intuitive input and coherent output. This level of control brings professional-grade editing within reach of everyday users.</p> | |
| </div> | |
| <div class="showcase-image-full"> | |
| <img src="https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-Image/s3.jpg" alt="Image Editing Example" class="showcase-img-full"> | |
| </div> | |
| </div> | |
| <div class="showcase-item-full"> | |
| <div class="showcase-description-full"> | |
| <h3>Image Understanding</h3> | |
| <p>But Qwen-Image doesn't just create or edit—it understands. It supports a suite of image understanding tasks, including object detection, semantic segmentation, depth and edge (Canny) estimation, novel view synthesis, and super-resolution. These capabilities, while technically distinct, can all be seen as specialized forms of intelligent image editing, powered by deep visual comprehension.</p> | |
| </div> | |
| <div class="showcase-image-full"> | |
| <img src="https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-Image/s4.jpg" alt="Image Understanding Example" class="showcase-img-full"> | |
| </div> | |
| </div> | |
| <div class="showcase-conclusion"> | |
| <p>Together, these features make Qwen-Image not just a tool for generating pretty pictures, but a comprehensive foundation model for intelligent visual creation and manipulation—where language, layout, and imagery converge.</p> | |
| </div> | |
| </section> | |
| <div class="resource-links-section"> | |
| <h2>Resources</h2> | |
| <div class="resource-links"> | |
| <a href="https://huggingface.co/Qwen/Qwen-Image" target="_blank" rel="noopener noreferrer" class="resource-link">Qwen-Image on Hugging Face</a> | |
| <a href="https://github.com/QwenLM/Qwen" target="_blank" rel="noopener noreferrer" class="resource-link">Qwen GitHub</a> | |
| <a href="https://www.alibabacloud.com/en/solutions/generative-ai/qwen" target="_blank" rel="noopener noreferrer" class="resource-link">Alibaba Cloud Qwen</a> | |
| <a href="https://modelscope.cn/models/qwen/Qwen-Image" target="_blank" rel="noopener noreferrer" class="resource-link">ModelScope</a> | |
| <a href="https://help.aliyun.com/zh/dashscope/developer-reference/qwen-vlo-quick-start" target="_blank" rel="noopener noreferrer" class="resource-link">Qwen VLo Documentation</a> | |
| <a href="https://www.alibabacloud.com/help/en/model-studio/vision/" target="_blank" rel="noopener noreferrer" class="resource-link">Qwen-VL Documentation</a> | |
| </div> | |
| </div> | |
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