新智元报道  

编辑：润

【新智元导读】Stability AI放出了号称能暴打闭源模型的Stable Diffusion 3的技术报告，采用DiT构架的新模型在灵活性和性能上都达到了新的高度。

论文深入分析了Stable Diffusion 3的核心技术——改进版的Diffusion模型和一个基于DiT的文生图全新架构！

报告地址：

通过人类评价测试，Stable Diffusion 3在字体设计和对提示的精准响应方面，超过了DALL·E 3、Midjourney v6和Ideogram v1。

Stability AI新开发的多模态扩散Transformer（MMDiT）架构，采用了分别针对图像和语言表示的独立权重集，与SD 3的早期版本相比，显著提升了对文本的理解和文字的拼写能力。

性能评估

在人类反馈的基础之上，技术报告将SD 3于大量开源模型SDXL、SDXL Turbo、Stable Cascade、Playground v2.5 和 Pixart-α，以及闭源模型DALL·E 3、Midjourney v6 和 Ideogram v1进行了详细的对比评估。

评估员根据与给定提示的一致性、文本的清晰度以及图像的整体美观度选择了每个模型的最佳输出：

测试结果显示，无论是在遵循提示的准确性、文本的清晰呈现还是图像的视觉美感方面，Stable Diffusion 3都达到或超过了当前文生图生成技术的最高水平。

完全没有针对硬件进行过优化的SD 3模型具有8B参数，能够在24GB显存的RTX 4090消费级GPU上运行，并且在使用50个采样步骤的情况下，生成1024x1024分辨率的图像需耗时34秒。

此外，Stable Diffusion 3在发布时将提供多个版本，参数范围从8亿到80亿，从而能以进一步降低使用的硬件门槛。

架构细节曝光

在文生图的过程中，模型需同时处理文本和图像这两种不同的信息。所以作者将这个新框架称之为MMDiT。

在文本到图像生成的过程中，模型需同时处理文本和图像这两种不同的信息类型。这就是作者将这种新技术称为MMDiT（多模态Diffusion Transformer的简称）的原因。

与Stable Diffusion之前的版本一样，SD 3采用了预训练模型来提取适合的文本和图像的表达形式。

具体而言，他们利用了三种不同的文本编码器——两个CLIP模型和一个T5 ——来处理文本信息，同时使用了一个更为先进的自编码模型来处理图像信息。

SD 3的架构是在Diffusion Transformer（DiT）的基础上建立的。由于文本和图像信息的差异，SD 3为这两种信息各自设置了独立的权重。

这种设计相当于为每种信息类型配备了两个独立的Transformer，但在执行注意力机制时，会将两种信息的数据序列合并，这样就可以在各自的领域内独立工作的同时，能保持够相互参考和融合。

通过这种独特的构架，图像和文本信息之间可以相互流动和交互，从而在生成的结果中提高对内容的整体理解和视觉表现。

而且，这种架构未来还可以轻松扩展到其他包括视频在内的多种模态。

得益于SD 3在遵循提示方面的进步，模型能够精确生成集中于多种不同主题和特性的图像，同时在图像风格上也保持了极高的灵活性。

通过重赋权法改进Rectified Flow

除了推出的全新Diffusion Transformer构架之外，SD 3对于Diffusion模型也进行了重大的改进。

SD 3采用了Rectified Flow（RF）策略，将训练数据和噪声沿着直线轨迹连接起来。

这种方法让模型的推理路径更加直接，因此可以通过更少的步骤完成样本的生成。

作者在训练流程中引入了一种创新的轨迹采样计划，特别增加了对轨迹中间部分的权重，这些部分的预测任务更具挑战性。

通过与其他60种扩散轨迹（例如 LDM、EDM 和 ADM）进行比较，作者发现尽管之前的RF方法在少步骤采样中表现更佳，但随着采样步骤增多，性能会慢慢下降。

为了避免这种情况的出现，作者提出的加权RF方法，就能够持续提升模型性能。

扩展RF Transformer模型

Stability AI训练了多个不同规模的模型，从 15 个模块、450M参数到38个模块、8B参数，发现模型大小和训练步骤都能平滑地降低验证损失。

为了验证这是否意味着模型输出有实质性的改进，他们还评估了自动图像对齐指标和人类偏好评分。

结果表明，这些评估指标与验证损失强相关，说明验证损失是衡量模型整体性能的有效指标。

此外，这种扩展趋势没有达到饱和点，让我们对未来能够进一步提升模型性能持乐观态度。

作者在256 *256像素分辨率下，在4096的批大小下，用不同参数数对模型进行了500k步训练。

上图说明了长时间训练较大模型对样本质量的影响。

上表显示了GenEval的结果。当使用作者提出的训练方法并提高训练图像的分辨率时，最大的模型在大多数类别中都表现出色，在总分上超过了 DALL·E 3。

根据作者对不同构架模型的测试对比，MMDiT效果非常好，超过了DiT，Cross DiT，UViT，MM-DiT。

灵活的文本编码器

通过在推理阶段去除占用大量内存的4.7B参数的T5文本编码器，SD 3的内存需求得到了大幅降低，而性能损失微乎其微。

去除这个文本编码器不会影响图像的视觉美感（不使用T5的胜率为 50%），只会略微降低文本的准确遵循能力（胜率为46%）。

然而，为了充分发挥SD 3在生成文字的能力，作者还是建议使用T5编码器。

因为作者发现在没有它的情况下，排版生成文字的性能会有更大的下降（胜率为 38%）。

网友热议

网友们对Stability AI不断撩拨用户但是不让用的行为显得有些不耐烦了，纷纷催促赶快上线让大家使用。

看了技术报考后，网友说看来现在生图圈子要成第一个开源碾压闭源的赛道了！

参考资料：

https://stability.ai/news/stable-diffusion-3-research-paper

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and (prefers-color-scheme:dark){body:not(.pages_skin_pc) .wx-root[data-weui-mode=care]:not([data-weui-theme=light]),body:not(.pages_skin_pc) body[data-weui-mode=care]:not([data-weui-theme=light]){--weui-BG-0: #111;--weui-BG-1: #1e1e1e;--weui-BG-2: #191919;--weui-BG-3: #202020;--weui-BG-4: #404040;--weui-BG-5: #2c2c2c;--weui-FG-0: rgba(255, 255, 255, .85);--weui-FG-HALF: rgba(255, 255, 255, .65);--weui-FG-1: rgba(255, 255, 255, .55);--weui-FG-2: rgba(255, 255, 255, .35);--weui-FG-3: rgba(255, 255, 255, .1);--weui-FG-4: rgba(255, 255, 255, .15);--weui-FG-5: rgba(255, 255, 255, .1);--weui-RED: #fa5151;--weui-ORANGERED: #ff6146;--weui-ORANGE: #c87d2f;--weui-YELLOW: #cc9c00;--weui-GREEN: #74a800;--weui-LIGHTGREEN: #3eb575;--weui-BRAND: #07c160;--weui-BLUE: #10aeff;--weui-INDIGO: #1196ff;--weui-PURPLE: #8183ff;--weui-WHITE: rgba(255, 255, 255, .8);--weui-LINK: #7d90a9;--weui-TEXTGREEN: #259c5c;--weui-FG: #fff;--weui-BG: #000;--weui-TAG-TEXT-RED: rgba(250, 81, 81, .6);--weui-TAG-BACKGROUND-RED: rgba(250, 81, 81, .1);--weui-TAG-TEXT-ORANGE: rgba(250, 157, 59, .6);--weui-TAG-BACKGROUND-ORANGE: rgba(250, 157, 59, .1);--weui-TAG-TEXT-GREEN: rgba(6, 174, 86, .6);--weui-TAG-BACKGROUND-GREEN: rgba(6, 174, 86, .1);--weui-TAG-TEXT-BLUE: rgba(16, 174, 255, .6);--weui-TAG-BACKGROUND-BLUE: rgba(16, 174, 255, .1);--weui-TAG-TEXT-BLACK: rgba(255, 255, 255, .5);--weui-TAG-BACKGROUND-BLACK: rgba(255, 255, 255, .05)}}@media screen and (min-width:1024px) and (prefers-color-scheme:dark){body:not(.pages_skin_pc) .wx-root:not([data-weui-theme=light]),body:not(.pages_skin_pc) body:not([data-weui-theme=light]){--weui-BG-0: #111;--weui-BG-1: #1e1e1e;--weui-BG-2: #191919;--weui-BG-3: #202020;--weui-BG-4: #404040;--weui-BG-5: #2c2c2c;--weui-FG-0: rgba(255, 255, 255, .8);--weui-FG-HALF: rgba(255, 255, 255, .6);--weui-FG-1: rgba(255, 255, 255, .5);--weui-FG-2: rgba(255, 255, 255, .3);--weui-FG-3: rgba(255, 255, 255, .1);--weui-FG-4: rgba(255, 255, 255, .15);--weui-FG-5: rgba(255, 255, 255, .1);--weui-RED: #fa5151;--weui-ORANGERED: #ff6146;--weui-ORANGE: #c87d2f;--weui-YELLOW: #cc9c00;--weui-GREEN: #74a800;--weui-LIGHTGREEN: #3eb575;--weui-BRAND: #07c160;--weui-BLUE: #10aeff;--weui-INDIGO: #1196ff;--weui-PURPLE: #8183ff;--weui-WHITE: rgba(255, 255, 255, .8);--weui-LINK: #7d90a9;--weui-TEXTGREEN: #259c5c;--weui-FG: #fff;--weui-BG: #000;--weui-TAG-TEXT-RED: rgba(250, 81, 81, .6);--weui-TAG-BACKGROUND-RED: rgba(250, 81, 81, .1);--weui-TAG-TEXT-ORANGE: rgba(250, 157, 59, .6);--weui-TAG-BACKGROUND-ORANGE: rgba(250, 157, 59, .1);--weui-TAG-TEXT-GREEN: rgba(6, 174, 86, .6);--weui-TAG-BACKGROUND-GREEN: rgba(6, 174, 86, .1);--weui-TAG-TEXT-BLUE: rgba(16, 174, 255, .6);--weui-TAG-BACKGROUND-BLUE: rgba(16, 174, 255, .1);--weui-TAG-TEXT-BLACK: rgba(255, 255, 255, .5);--weui-TAG-BACKGROUND-BLACK: rgba(255, 255, 255, .05)}}@media screen and (min-width:1024px) and (prefers-color-scheme:dark){body:not(.pages_skin_pc) 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#10aeff;--weui-INDIGO: #1485ee;--weui-PURPLE: #6467f0;--weui-WHITE: #fff;--weui-LINK: #576b95;--weui-TEXTGREEN: #06ae56;--weui-FG: #000;--weui-BG: #fff;--weui-TAG-TEXT-RED: rgba(250, 81, 81, .6);--weui-TAG-BACKGROUND-RED: rgba(250, 81, 81, .1);--weui-TAG-TEXT-ORANGE: #fa9d3b;--weui-TAG-BACKGROUND-ORANGE: rgba(250, 157, 59, .1);--weui-TAG-TEXT-GREEN: #06ae56;--weui-TAG-BACKGROUND-GREEN: rgba(6, 174, 86, .1);--weui-TAG-TEXT-BLUE: #10aeff;--weui-TAG-BACKGROUND-BLUE: rgba(16, 174, 255, .1);--weui-TAG-TEXT-BLACK: rgba(0, 0, 0, .5);--weui-TAG-BACKGROUND-BLACK: rgba(0, 0, 0, .05)}.wx-root,body{--weui-BTN-ACTIVE-MASK: rgba(0, 0, 0, .1)}.wx-root,body{--weui-BTN-DEFAULT-ACTIVE-BG: #e6e6e6}.wx-root,body{--weui-DIALOG-LINE-COLOR: rgba(0, 0, 0, .1)}.wx-root,body{--weui-BG-COLOR-ACTIVE: #ececec}@media(prefers-color-scheme:dark){.wx-root:not([data-weui-theme=light]),body:not([data-weui-theme=light]){--weui-BTN-ACTIVE-MASK: rgba(255, 255, 255, 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59, .6);--weui-TAG-BACKGROUND-ORANGE: rgba(250, 157, 59, .1);--weui-TAG-TEXT-GREEN: rgba(6, 174, 86, .6);--weui-TAG-BACKGROUND-GREEN: rgba(6, 174, 86, .1);--weui-TAG-TEXT-BLUE: rgba(16, 174, 255, .6);--weui-TAG-BACKGROUND-BLUE: rgba(16, 174, 255, .1);--weui-TAG-TEXT-BLACK: rgba(255, 255, 255, .5);--weui-TAG-BACKGROUND-BLACK: rgba(255, 255, 255, .05)}}@media(prefers-color-scheme:dark){.wx-root:not([data-weui-theme=light]),body:not([data-weui-theme=light]){--discussInput-BG: rgba(255, 255, 255, .03)}}@media(prefers-color-scheme:dark){.wx-root:not([data-weui-theme=light]),body:not([data-weui-theme=light]){--nickName-FG: #959595}}@media(prefers-color-scheme:dark){.wx-root:not([data-weui-theme=light]),body:not([data-weui-theme=light]){--weui-BG-0: #111;--weui-BG-1: #1e1e1e;--weui-BG-2: #191919;--weui-BG-3: #202020;--weui-BG-4: #404040;--weui-BG-5: #2c2c2c;--weui-FG-0: rgba(255, 255, 255, .8);--weui-FG-HALF: rgba(255, 255, 255, .6);--weui-FG-1: rgba(255, 255, 255, .5);--weui-FG-2: rgba(255, 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.05)}}@media(prefers-color-scheme:dark){.wx-root:not([data-weui-theme=light]),body:not([data-weui-theme=light]){--appmsgExtra-BG: #121212}}@media(prefers-color-scheme:dark){body:not([data-weui-theme=light]) .rich_media_content img:not(.wx_img_placeholder){filter:brightness(.8)}}.rich_media_content p{clear:both;min-height:1em}td p{margin:0;padding:0}@media screen and (min-width:1024px){body:not(.pages_skin_pc) :root{--appmsgPageGap: 20px;--appmsgPageBottomGap: 40px}}:root{--articleFontsize: 17px}:root{--sab: env(safe-area-inset-bottom)}:root{--wxBorderAvatarRatio: 3}:root{--discussPageGap: 20px}:root{--immersive-safe-bottom: env(safe-area-inset-bottom)}:root{--appmsgPageGap: 20px;--appmsgPageBottomGap: 40px}*{margin:0;padding:0}.rich_media_content *{max-width:100%!important;box-sizing:border-box!important;-webkit-box-sizing:border-box!important;word-wrap:break-word!important}

div.autoTypeSetting24psection > p,div.autoTypeSetting24psection > section{margin-bottom: 24px;}