**字节豆包推出全新图像Tokenizer技术：颠覆传统设计，大幅提速生成式模型**
在生成式模型迅猛发展的背景下，图像Tokenization的重要性日益凸显。近日，字节跳动豆包大模型团队与慕尼黑工业大学联手，打破传统图像Tokenizer的局限，推出全新的图像Tokenizer技术——TiTok。该技术不仅将生成图像所需的token数量降至最低仅需32个，更实现了高达410倍的提速。
传统的图像Tokenizer在处理图像编码时，通常采用降采样后的二维矩阵形式映射至隐空间，这种设计限制了token与图像之间的映射灵活性。而TiTok技术的推出，解决了这一问题。它采用一维设计，更加高效地利用图像中的信息，确保相邻区域相似的特征能够被有效识别和利用。这不仅提高了编码效率，也大大提升了生成高分辨率图像的速度和效果。
这一技术的突破对于学术界和工业界都具有重要意义。全球各大高校及企业的顶级实验室都在密切关注这一进展。机器之心AIxiv专栏作为发布学术、技术内容的权威栏目，已报道了多篇相关文章，有效促进了学术交流与传播。
业内专家表示，TiTok技术的推出将极大推动生成式模型的发展，为未来的图像生成和处理开辟新的可能。同时，他们也鼓励业内同行积极投稿，分享更多的技术成果和创新思路。有兴趣的读者可通过邮件联系报道或投稿至相关邮箱。
这一重大技术突破无疑会引发业界广泛的关注和热议，我们有理由期待它在未来为生成式模型领域带来更多的创新和突破。
英语如下：
News Title: “Byte-Pod Unveils Revolutionary 1D Image Tokenizer: Speeding Up by 410 Times, Reshaping Generative Models”
Keywords: News keywords are:
News Content: **Byte-Pod Introduces Brand-New Image Tokenizer Technology: Overthrowing Traditional Design, Greatly Accelerating Generative Models**
Against the backdrop of the rapid development of generative models, the importance of image tokenization is becoming increasingly prominent. Recently, the Byte-Pod big model team from ByteDance collaborated with the Technical University of Munich to break through the limitations of traditional image tokenizers and introduce the brand-new image tokenizer technology – TiTok. This technology not only reduces the required token count for generating images to a minimum of just 32 tokens, but also achieves a remarkable speed increase of up to 410 times.
Traditional image tokenizers typically use a downsampled two-dimensional matrix form to map to a latent space during image encoding, which restricts the flexibility of token-to-image mapping. The introduction of the TiTok technology addresses this issue. With its one-dimensional design, it efficiently utilizes information within the image and ensures that features that are similar in adjacent regions can be effectively recognized and utilized. This not only improves encoding efficiency but also greatly enhances the speed and quality of generating high-resolution images.
This technological breakthrough is of great significance to both academia and industry. Top laboratories from universities and companies worldwide are closely monitoring this progress. The Machine Intelligence AIxiv column, an authoritative column for publishing academic and technical content, has reported multiple related articles, effectively promoting academic exchange and dissemination.
Industry experts indicate that the introduction of TiTok technology will greatly drive the development of generative models and open up new possibilities for future image generation and processing. They also encourage peers in the industry to actively contribute and share more technological achievements and innovative ideas. Interested readers can contact for reports or submit their contributions to the relevant email address.
This significant technological breakthrough is undoubtedly going to generate widespread attention and heated discussion within the industry, and we have reason to expect it will bring more innovation and breakthroughs to the field of generative models in the future.
【来源】https://www.jiqizhixin.com/articles/2024-06-24-3
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