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[提交译文][news]: 20220907 Image Generation Model Stable Diffusion Is Now Open Source.md
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[#]: subject: "Image Generation Model Stable Diffusion Is Now Open Source"
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[#]: via: "https://www.opensourceforu.com/2022/09/image-generation-model-stable-diffusion-is-now-open-source/"
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[#]: author: "Laveesh Kocher https://www.opensourceforu.com/author/laveesh-kocher/"
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[#]: collector: "lkxed"
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[#]: translator: "lkxed"
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[#]: reviewer: " "
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[#]: publisher: " "
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Image Generation Model Stable Diffusion Is Now Open Source
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======
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The pre-trained model weights for Stable Diffusion, a text-to-image AI model, were made available to the general public by Stability AI. When given a written prompt, Stable Diffusion can produce 512×512 pixel graphics that are photorealistic and represent the scene it describes.
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Following the earlier code release and a restricted release of the model weights to the research community, the model weights have now been made available to the general public. With the most recent version, anyone can download and utilise Stable Diffusion on hardware designed for common consumers. The model not only supports text-to-image generation but also image-to-image style transfer and upscaling. Along with the release, Stable AI also made available a beta version of DreamStudio, an API and web UI for the model. Stable AI states that:
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“Stable Diffusion is a text-to-image model that will empower billions of people to create stunning art within seconds. It is a breakthrough in speed and quality meaning that it can run on consumer GPUs…This will allow both researchers and…the public to run this under a range of conditions, democratizing image generation. We look forward to the open ecosystem that will emerge around this and further models to truly explore the boundaries of latent space.”
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Latent diffusion models are a method of image production that Stable Diffusion is built on (LDMs). LDMs create images by iteratively “de-noising” input in a latent representation space, then decoding the representation into a complete image, in contrast to other well-known image synthesis techniques like generative adversarial networks (GANs) and the auto-regressive approach employed by DALL-E. The recent IEEE / CVF Computer Vision and Pattern Recognition Conference featured a paper on LDM, which was created by the Ludwig Maximilian University of Munich’s Machine Vision and Learning research group (CVPR). An further diffusion-based picture generating AI that InfoQ reported earlier this year was Google’s Imagen model.
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Numerous operations can be supported by the Stable Diffusion model. Similar to DALL-E, it may generate a high-quality image that exactly fits a text description of a desired image. A straightforward sketch and a textual description of the desired image can also be used to create a realistic-looking image. Similar image-to-image abilities may be found in the Make-A-Scene model from Meta AI, which was just just released.
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Examples of created photos from Stable Diffusion have been shared openly by several people, and Katherine Crowson, lead developer at Stability AI, has shared numerous photographs on Twitter. The effects that AI-based picture synthesis will have on artists and the art world worry some observers. Stable Diffusion was released the same week that an AI-generated piece of art took home the top honour in the Colorado State Fair’s art competition. Stable Diffusion’s source code is accessible on [GitHub][1].
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--------------------------------------------------------------------------------
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via: https://www.opensourceforu.com/2022/09/image-generation-model-stable-diffusion-is-now-open-source/
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作者：[Laveesh Kocher][a]
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选题：[lkxed][b]
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译者：[译者ID](https://github.com/译者ID)
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校对：[校对者ID](https://github.com/校对者ID)
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本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
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[a]: https://www.opensourceforu.com/author/laveesh-kocher/
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[b]: https://github.com/lkxed
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[1]: https://github.com/CompVis/stable-diffusion
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[#]: subject: "Image Generation Model Stable Diffusion Is Now Open Source"
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[#]: via: "https://www.opensourceforu.com/2022/09/image-generation-model-stable-diffusion-is-now-open-source/"
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[#]: author: "Laveesh Kocher https://www.opensourceforu.com/author/laveesh-kocher/"
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[#]: collector: "lkxed"
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[#]: translator: "lkxed"
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[#]: reviewer: " "
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[#]: publisher: " "
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[#]: url: " "
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图像生成模型 Stable Diffusion 现已开源
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======
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Stable Diffusion 是一个“文本到图像”的人工智能模型。近日，Stable AI. 公司向公众开放了它的预训练模型权重。当输入一个文字描述时，Stable Diffusion 可以生成 512×512 像素的图像，这些图像如相片般真实，反映了文字描述的场景。
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这个项目先是经历了早期的代码发布，而后又向研究界有限制地发布了模型权重，现在模型权重已经向公众开放。对于最新版本，任何人都可以在为普通消费者设计的硬件上下载和使用 Stable Diffusion。该模型不仅支持文本到图像的生成，而且还支持图像到图像的风格转换和放大。与之一同发布的还有 DreamStudio 测试版，这是一个用于该模型的 API 和 Web 用户界面。
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Stable AI 公司表示：
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“Stable Diffusion 是一个文本到图像的模型，它将使数十亿人在几秒钟内创造出令人惊叹的艺术。它在速度和质量上的突破意味着它可以在消费者级的 GPU 上运行。这将允许研究人员和公众在一系列条件下运行它，并使图像生成民主化。我们期待着有围绕这个模型和其他模型的开放生态系统出现，以真正探索潜伏空间的边界。”
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Latent Diffusion 模型（LDMs）是 Stable Diffusion 模型建立的一种图像生成方法。LDMs 通过在潜伏表示空间中迭代“去噪 ”输入来创建图像，然后将表示解码为完整的图像，这与其他著名的图像合成技术，如生成对抗网络（GANs）和 DALL-E 采用的自动回归方法不同。最近的 IEEE/CVF 计算机视觉和模式识别会议（CVPR）上有一篇关于 LDM 的论文，它是由慕尼黑路德维希-马克西米利安大学的机器视觉和学习研究小组创建的。今年早些时候，InfoQ 也报道过另一个基于扩散的图片生成 AI，它是谷歌的 Imagen 模型。
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Stable Diffusion 可以支持众多的操作。与 DALL-E 类似，它可以生成一个高质量的图像，并使其完全符合所需图像的文字描述。我们也可以使用一个直观的草图和所需图像的文字描述，从而创建一个看起来很真实的图像。类似的“图像到图像”的能力可以在 Meta AI 的 Make-A-Scene 模型中找到，该模型刚发布不久。
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一些人公开分享了 Stable Diffusion 创建的照片的例子，Stable AI. 的首席开发人员 Katherine Crowson 也在 Twitter 上分享了许多照片。毫无疑问，基于人工智能的图片合成技术将对艺术家和艺术界产生影响，这令一些观察家感到担忧。值得注意的是，在 Stable Diffusion 发布的同一周，一幅由人工智能生成的作品在科罗拉多州博览会的艺术竞赛中获得了最高荣誉。
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Stable Diffusion 的源代码可以在 [GitHub][1] 上查阅。
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--------------------------------------------------------------------------------
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via: https://www.opensourceforu.com/2022/09/image-generation-model-stable-diffusion-is-now-open-source/
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作者：[Laveesh Kocher][a]
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选题：[lkxed][b]
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译者：[lkxed](https://github.com/lkxed)
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校对：[校对者ID](https://github.com/校对者ID)
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本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
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[a]: https://www.opensourceforu.com/author/laveesh-kocher/
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[b]: https://github.com/lkxed
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[1]: https://github.com/CompVis/stable-diffusion
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