Update README.md
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naykun
	
							
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        README.md
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            </p>
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            <p align="center">
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                <img src="https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-Image/ | 
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            <p>
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            ## Introduction
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            ## News
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            - 2025.08.04: We released [Qwen-Image  | 
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            - 2025.08.04: We released Qwen-Image weights! Check at [huggingface](https://huggingface.co/Qwen/Qwen-Image) and [Modelscope](https://modelscope.cn/models/Qwen/Qwen-Image)!
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            - 2025.08.04: We released Qwen-Image! Check our [blog](https://qwenlm.github.io/blog/qwen-image) for more details!
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            pipe = DiffusionPipeline.from_pretrained(model_name, torch_dtype=torch_dtype)
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            pipe = pipe.to(device)
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            positive_magic =  | 
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            # Generate image
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            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'''
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            width, height = aspect_ratios["16:9"]
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            image = pipe(
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                prompt=prompt + positive_magic,
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                width=width,
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                height=height,
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                num_inference_steps=50,
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            </p>
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            <p align="center">
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                <img src="https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-Image/merge3.png" width="1600"/>
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            <p>
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            ## Introduction
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            ## News
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            - 2025.08.04: We released the [Technical Report](https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-Image/Qwen_Image.pdf) of Qwen-Image!
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            - 2025.08.04: We released Qwen-Image weights! Check at [huggingface](https://huggingface.co/Qwen/Qwen-Image) and [Modelscope](https://modelscope.cn/models/Qwen/Qwen-Image)!
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            - 2025.08.04: We released Qwen-Image! Check our [blog](https://qwenlm.github.io/blog/qwen-image) for more details!
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            pipe = DiffusionPipeline.from_pretrained(model_name, torch_dtype=torch_dtype)
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            pipe = pipe.to(device)
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            positive_magic = [
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                "en": "Ultra HD, 4K, cinematic composition." # for english prompt,
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                "zh": "超清,4K,电影级构图" # for chinese prompt,
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            ]
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            # Generate image
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            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'''
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            width, height = aspect_ratios["16:9"]
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            image = pipe(
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                prompt=prompt + positive_magic["en"],
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                negative_prompt=negative_prompt,
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                width=width,
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                height=height,
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                num_inference_steps=50,
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