Stable Diffusion

• 1. overview (521 chars)

  Stable Diffusion is an open source AI image generation model developed by Stability AI company and released in 2022. The model is based on Latent Diffusion Models technology and can generate high-quality images through text descriptions. As an open source project, Stable Diffusion provides a free community license and supports local deployment of consumer-grade graphics cards, making an important contribution to the popularization of AI creative tools. However, the model has also faced copyright disputes from artists, and the use of artists' works in its training data has triggered widespread discussion.

• 2. Product background (693 chars)

  Stability AI is a British open source artificial intelligence company founded in 2024 and headquartered in London. The company’s CEO, Emad Mostaque, has extensive experience in the AI ​​field. In the Stable Diffusion project, the core code was actually written by researchers from the University of Munich in Germany and New York University. Stability AI provided computing resource support for the project and conducted product operations.   The company has received more than $101 million in investment from well-known institutions such as Coatue Management and Lightspeed Venture Partners. Stability AI’s partners include EA Games, Warner Music, WPP, Universal Music, HubSpot and other internationally renowned companies. The company launched Brand Studio on April 8, 2026, further expanding its product line.

• 3. Product functions and usage experience (943 chars)

  Stable Diffusion's core capabilities include image generation, image editing, video generation, and 3D model creation. The model supports two main workflows: text-based drawing and picture-based drawing. Users can generate images by inputting text prompts, or perform style migration or partial redrawing based on existing images.   In actual use, Stable Diffusion has demonstrated powerful image generation capabilities, but it still has shortcomings in detail processing. For example, the precision of the relationship between hands, materials, and edges needs to be improved. Sometimes, problems such as incorrect number of fingers and unnatural material performance may occur. In addition, although the model's understanding of semantics is basically accurate, there is still room for improvement in terms of element layout and semantic consistency.   The model supports multiple sampling methods and code implementations, and provides rich parameter configuration options for advanced users to customize. Users can deploy it locally or generate it online through the DreamStudio cloud platform.

• 4. Pricing and business model (777 chars)

  Stable Diffusion comes with a community license, which makes the model free for most users to use for non-commercial use. For commercial use, you need to apply for a commercial license or use the enterprise solutions provided by Stability AI.   DreamStudio is the cloud platform officially provided by Stability AI. It adopts a points-based charging model. Users need to purchase points for image generation. Different versions of models and functions correspond to different points consumption. Enterprise users can contact the sales team through "Book a demo" or "Start your deployment" to obtain customized quotations.   The company also provides Platform API services that allow developers to integrate image generation capabilities into their applications. In addition, Stability AI offers self-hosted licenses, allowing users to deploy models on their own servers for advanced customization and data control.

• 5. User reviews (856 chars)

  User reviews of Stable Diffusion are polarizing. Positive comments mainly focus on the following aspects: open source and free lowering the threshold for AI image generation, supporting local deployment to protect user privacy, providing a highly customized space, and an active community ecology providing a wealth of plug-ins and models.   Negative feedback includes: the threshold for getting started is relatively high, requiring a certain amount of basic technical knowledge, the quality of the pictures produced by the default configuration is not as good as competing products such as Midjourney, the details are not processed carefully enough, and the updates to documentation and tutorials sometimes fail to keep up with version iterations.   From the perspective of market comparison, the competition in the field of AI image generation in 2026 is fierce. Products such as Midjourney V8, FLUX, and DALL-E 4 are constantly evolving. Stable Diffusion is facing competitive pressure from all directions.

• 6. Industry evaluation (811 chars)

  The industry generally believes that Stable Diffusion is an important milestone in the field of open source AI image generation. Zhihu users described it as "the first time I feel that the image generation model is strong enough", but also pointed out that there is still room for improvement in precision and professional commercial scenarios.   From a technological development perspective, AI image generation technology in 2026 is evolving towards higher resolution, faster generation speed, and better semantic understanding. Research by the Shanghai Jiao Tong University and vivo teams has shown that the diffusion model can be comprehensively improved through simple technical improvements, and relevant results have been published in CVPR 2026.   Industry media believe that Stable Diffusion’s open source strategy has played an important role in promoting the popularity of AI creative tools, but it also faces more commercialization challenges.

• 7. Controversies and Risks (785 chars)

  The biggest controversy Stable Diffusion faces comes from copyright issues. In September 2022, a large number of artists discovered that Stable Diffusion's training data set contained their own works, and these works were used to generate competitive content for free, triggering collective protests by artists. Some artists said that "using my works will destroy my job" and believed that this seriously damaged the rights and interests of the original author.   In addition, the issue of ownership of the Stable Diffusion source code has also triggered discussions. It was reported that the core Stable Diffusion code that made Stability AI famous actually came from the work of other researchers, a controversy that was reported by Forbes.   In terms of finance, Stability AI, as a start-up company, faces the pressure of continuous cash burning and commercialization, and the company's future profitability is still uncertain.

• 8. Applicable groups and usage suggestions (679 chars)

  Stable Diffusion is suitable for the following user groups: users with a strong technical background and familiarity with programming and AI tools, users who pursue a high degree of customization and privacy protection, users who want to deploy locally for offline use, and developers who rely on the open source community ecosystem.   For ordinary users who pursue out-of-the-box operation and ultimate image quality, it is recommended to consider closed-source solutions such as Midjourney and DALL-E, which have more advantages in terms of ease of use and default output quality.   For enterprise users, it is recommended to evaluate Stability AI’s enterprise solutions or Brand Studio services. These commercial products generally provide more stable technical support and compliance guarantees.

• 9. Summarize (499 chars)

  As one of the pioneers of open source AI image generation, Stable Diffusion has made important contributions in technology popularization and community building. This model lowers the threshold for AI image generation and provides more possibilities for creative expression. However, as market competition intensifies, Stable Diffusion needs to continue to evolve in output quality, ease of use, and business model to maintain a competitive advantage. For users with different needs, the choice should be weighed based on their own technical capabilities, usage scenarios and budget.

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• 

  Jordan_VasquezZ

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  用了两年 SD 的感受就是，前期投入确实大，光显卡就花了五六千，装环境又搭进去一整天，但一旦跑通之后就是完全不一样的体验了。现在批量出图零成本，LoRA 自己训练角色一致性，ControlNet 精准控制构图，这些功能闭源工具根本给不了。唯一遗憾的是底模质量还是不如 Flux，得靠社区模型补。

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  Carol_Jenkins_6628

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  之前一直用 MJ，每个月 $30 说实话不算贵但总觉得不值，后来一狠心买了块 RTX 4070 开始捣鼓 SD，刚开始确实崩溃，什么 checkpoint、LoRA、sampler、CFG scale 这些概念完全懵的，好在 B 站和 Reddit 教程够多，啃了大半个月终于能稳定出图了。现在回头看，当初的投入都值了，关键是想怎么改就怎么改，不需要求人。

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  Christina_Ruiz_X

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  SD 3.5 跟 SDXL 比起来进步挺明显的，特别是文字渲染和复杂 prompt 的理解上，以前写个「穿着红色旗袍的女孩站在樱花树下，背景是模糊的东京塔，黄昏光，电影感」，SDXL 经常把元素搞混或漏掉，3.5 基本能全理解到。不过模型大了之后吃显存也更凶了，12GB 都感觉有点勉强，想流畅跑还是得上 24GB。

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  smallwolf479

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  折腾了三天终于把 ComfyUI 跑起来了，RTX 4060 出图还行吧，就是前置准备太劝退了。

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• 

  LDavis36987

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  SD 最大的优点就是免费，但免费的前提是你得先有一块好显卡，这就已经劝退大部分人了。

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  TEsul

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  从 WebUI 切到 ComfyUI 花了我整整一个周末，但节点式工作流确实比点按钮强太多了。

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  TheCeylanBeşerler_2024

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  用 SDXL 配 DreamShaper 出图效果已经可以媲美 MJ 了，关键是开源免费，商用也不怕被告。

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  BPerez_Plus

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  ControlNet 真的是 SD 的灵魂插件，一张骨架图配几行 prompt 就能精准控制构图，MJ 根本做不到。

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  Dylan234

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  Civitai 上 LoRA 模型多得看不完，从吉卜力风到赛博朋克应有尽有，这才是 SD 最大的护城河。

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  SeanCook

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  inpainting + outpainting 用习惯了回不去别的工具，局部修改比在 PS 里抠图快十倍。

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  DorisTorres_2023

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  坦白讲 SD 的学习曲线太陡了，sampler、CFG、step、denoising 这些参数够新手研究一两个月。

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  aNN473

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  装了一整天才跑通，中途各种依赖冲突，要不是因为有显卡早放弃了。

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  brownswan958

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  最近把 ComfyUI 的生产管线搭起来了，一个工作流模板串联了文生图、高清放大、面部修复、背景替换四个步骤，按一下按钮全自动跑完。以前在 PS 里手动修一套图至少半小时，现在一分多钟搞定。SD 对技术型用户来说确实是当前最强大的工具，但对只想点点鼠标就出大片的人来说还是趁早用 MJ 吧。

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  康博

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  r/StableDiffusion 五百多万成员，啥问题都能搜到答案，这生态不是闭源工具能比的。

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  邹萱欣

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  最香的是零成本商用，接甲方项目直接用 SD 出图，省了每个月给 MJ 交保护费。

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  尹静

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  Flux 质量确实比 SD 3.5 好，但社区模型和 LoRA 资源 SD 还是王者，两者互补着用最舒服。

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  bApow

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  SD 底模质量一般，但配上社区训练的大模型如 Realistic Vision 或 DreamShaper 一下就起飞了。

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  Helen.Patel_Plus

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  MJ 开箱即用确实香，但每个月 $10-$30 一年下来也不少，SD 一次性硬件投入长远看更划算。

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  Ruth_NguyenQ

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  说 SD 不好用的多半是没耐心折腾的，这东西确实不像 MJ 那样开箱即用，但你要想啊，每个月给 MJ 交保护费不说，生成的图还不能商用，想微调一下构图根本没门儿。SD 就不一样了，本地部署零成本无限生成，ControlNet + LoRA 组合拳一出，想做什么风格都行。唯一的问题就是社区模型太多太杂，筛选起来有点累。

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  Abigail_RossSr

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  做企业项目最看重数据不出本机，SD 本地部署离线可用，光这一条就秒杀所有云端方案了。

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  Linda.Torres520

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  断网也能跑图，上次出差在飞机上连 Wi-Fi 都没有，就靠 SD 整了一组产品素材。

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  orangegorilla120

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  秋叶大佬的整合包真的是中文用户的福音，解压即用一键启动，小白也能玩 SD 了。

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  rjyztggo_dev

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  LiblibAI 上国内创作者分享的工作流直接复制到 ComfyUI 就能跑，比从零搭省太多事了。

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  blPAR

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  RTX 4090 上 SD 3.5 出 1024x1024 只需要 3-4 秒，但换到 1660 直接卡成 PPT，显卡差距太大了。

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  Joyce_Carter

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  8GB 显存跑 SDXL 非常勉强，换了 4070 Ti 12GB 后才真正体会到什么叫丝滑。

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  Danielle958

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  WebUI 从 2024 年 7 月最后一次更新后就没动静了，现在都在用 ComfyUI，节点式工作流才是未来。

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  xEeviMakela

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  说实话 ComfyUI 对新手太不友好了，但一旦上手建好自己的工作流模板，效率是 WebUI 的好几倍。

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  Matthew_Barnes_7

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  电商产品图批量生成用 SD 的 inpainting + ControlNet 流水线，一套自动化流程下来一天出几百张没问题。

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  jw2n3

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  很多人说 SD 的安装门槛高，我觉得其实要看怎么装。秋叶的整合包一键解压就用了，ComfyUI 也有官方桌面版，根本不需要命令行。但如果你想自己调参、换模型、装插件，那就确实需要懂点技术了。而且社区模型质量参差不齐是非常现实的问题，Civitai 上下载量高的 checkpoint 基本靠谱，小众的就看运气了。

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  Carolyn_WalkerII612

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  做游戏素材的，SD + LoRA 保持角色一致性简直绝了，比手绘快几十倍而且风格统一。

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