Qwen Image – The Latest Image Generation Model🔥
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Qwen Image – The Latest Image Generation Model🔥
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Below are some samples generated using the Qwen Image Diffusion Model. Qwen-Image, a 20B MMDiT model for next-generation text-to-image generation, preserves typographic details, layout coherence, and contextual harmony with stunning accuracy. It is especially strong at creating stunning graphic posters with native text. The model is now open-source.  [ 𝚀𝚠𝚎𝚗-𝙸𝚖𝚊𝚐𝚎 : 
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Below are some samples generated using the Qwen Image Diffusion Model. Qwen-Image, a 20B MMDiT model for next-generation text-to-image generation, preserves typographic details, layout coherence, and contextual harmony with stunning accuracy. It is especially strong at creating stunning graphic posters with native text. The model is now open-source.  [ 𝚀𝚠𝚎𝚗-𝙸𝚖𝚊𝚐𝚎 : 
https://huggingface.co/Qwen/Qwen-Image
Qwen/Qwen-Image
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https://huggingface.co/Qwen/Qwen-Image
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⤷ Try the Qwen Image demo here: 
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⤷ Try the Qwen Image demo here: 
https://huggingface.co/spaces/prithivMLmods/Qwen-Image-Diffusion
https://huggingface.co/spaces/prithivMLmods/Qwen-Image-Diffusion
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⤷ Qwen-Image Technical Report : 
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⤷ Qwen-Image Technical Report : 
Qwen-Image Technical Report (2508.02324)
https://huggingface.co/papers/2508.02324
2508.02324
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https://huggingface.co/papers/2508.02324
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⤷ Qwen Image [GitHub] : 
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⤷ Qwen Image [GitHub] : 
https://github.com/QwenLM/Qwen-Image
https://github.com/QwenLM/Qwen-Image
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Even more impressively, it demonstrates a strong ability to understand images. The model supports a wide range of vision-related tasks such as object detection, semantic segmentation, depth and edge (Canny) estimation, novel view synthesis, and image super-resolution. While each task is technically distinct, they can all be viewed as advanced forms of intelligent image editing driven by deep visual understanding. Collectively, these capabilities position Qwen-Image as more than just a tool for generating appealing visuals, it serves as a versatile foundation model for intelligent visual creation and transformation, seamlessly blending language, layout, and imagery. 
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Even more impressively, it demonstrates a strong ability to understand images. The model supports a wide range of vision-related tasks such as object detection, semantic segmentation, depth and edge (Canny) estimation, novel view synthesis, and image super-resolution. While each task is technically distinct, they can all be viewed as advanced forms of intelligent image editing driven by deep visual understanding. Collectively, these capabilities position Qwen-Image as more than just a tool for generating appealing visuals, it serves as a versatile foundation model for intelligent visual creation and transformation, seamlessly blending language, layout, and imagery. 
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Qwen-Image uses a dual-stream MMDiT architecture with a frozen Qwen2.5-VL, VAE encoder, RMSNorm for QK-Norm, LayerNorm elsewhere, and a custom MSRoPE scheme for joint image-text positional encoding.
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Qwen-Image uses a dual-stream MMDiT architecture with a frozen Qwen2.5-VL, VAE encoder, RMSNorm for QK-Norm, LayerNorm elsewhere, and a custom MSRoPE scheme for joint image-text positional encoding.
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To know more about it, visit the model card of the respective model. !!
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To know more about it, visit the model card of the respective model. !!
Qwen Image – The Latest Image Generation Model🔥

Below are some samples generated using the Qwen Image Diffusion Model. Qwen-Image, a 20B MMDiT model for next-generation text-to-image generation, preserves typographic details, layout coherence, and contextual harmony with stunning accuracy. It is especially strong at creating stunning graphic posters with native text. The model is now open-source.  [ 𝚀𝚠𝚎𝚗-𝙸𝚖𝚊𝚐𝚎 : https://huggingface.co/Qwen/Qwen-Image ]

⤷ Try the Qwen Image demo here: https://huggingface.co/spaces/prithivMLmods/Qwen-Image-Diffusion

⤷ Qwen-Image Technical Report : https://huggingface.co/papers/2508.02324
⤷ Qwen Image [GitHub] : https://github.com/QwenLM/Qwen-Image

Even more impressively, it demonstrates a strong ability to understand images. The model supports a wide range of vision-related tasks such as object detection, semantic segmentation, depth and edge (Canny) estimation, novel view synthesis, and image super-resolution. While each task is technically distinct, they can all be viewed as advanced forms of intelligent image editing driven by deep visual understanding. Collectively, these capabilities position Qwen-Image as more than just a tool for generating appealing visuals, it serves as a versatile foundation model for intelligent visual creation and transformation, seamlessly blending language, layout, and imagery. 

Qwen-Image uses a dual-stream MMDiT architecture with a frozen Qwen2.5-VL, VAE encoder, RMSNorm for QK-Norm, LayerNorm elsewhere, and a custom MSRoPE scheme for joint image-text positional encoding.

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To know more about it, visit the model card of the respective model. !!
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