{
 "generated": "2026-11-02",
 "node": {
  "id": "model:primal-vae",
  "type": "Model",
  "name": "Primal VAE（2x1 / 2x2）",
  "slug": "primal-vae",
  "date": "2026-10-31",
  "category": "video-world",
  "category_zh": "图像 · 视频 · 世界",
  "access": "open",
  "license": "Apache-2.0",
  "eps": [
   110
  ],
  "page": "/graph/models/#m-primal-vae",
  "size": "图像 VAE（扩散模型的自编码器），两个变体各约 80M 参数：2x1 是空间下采样 2 倍、1 通道，2x2 是 2 倍、2 通道；扩散模型自带的 4×4 分块之后分别等于 Flux.1 VAE（16 通道 @ 8 倍）和 Flux.2 VAE（32 通道 @ 8 倍）的形状；图像边长要是 16 的倍数",
  "avail": "开放权重，不设门槛（HF 未设 gated）：ostris/primal_vae，用 diffusers 的 AutoModel 加载（trust_remote_code）；也可以从 ai-toolkit 加载自己训练的检查点",
  "license_full": "Apache-2.0（模型卡；LICENSE 文件是 UTC 10-31 10:09 的提交补上的）",
  "facts": [
   "设计：潜变量训练成范数和噪声一致，所以没有缩放因子、没有平移、不需要潜变量归一化；编码器和解码器是镜像的卷积 U-Net，4 级（模型卡）",
   "Ostris 称它在重建上优于 Flux.2 VAE（开发者自述，X 帖）。第一个试验：用 2x1 配 64 倍空间压缩来训 MOSS Image，同样算力下原生出 4096×4096，而 MOSS Image 默认是 1024×1024（开发者自述，10-31 X 帖，是过夜训练的中间结果）",
   "社区评论（Reddit r/StableDiffusion）指出卡里缺“拿它训扩散模型收敛快不快”的对比，重建好不等于好训（独立，评论者意见）"
  ],
  "sources": [
   {
    "label": "HF 模型页（含模型卡）",
    "url": "https://huggingface.co/ostris/primal_vae",
    "kind": "official"
   },
   {
    "label": "Ostris 发布帖（X）",
    "url": "https://x.com/ostrisai/status/2116285109573988712",
    "kind": "official"
   }
  ],
  "ep_tags": {
   "110": "开发者 X 帖 UTC 10-30 21:44 · 北京 10-31 05:44"
  },
  "developer_text": "Ostris（Jaret Burkett，ai-toolkit 作者）",
  "degree": 0.0011
 },
 "page": "https://xiaopingfeng.com/graph/e/model/primal-vae/",
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   "sentence": "Primal VAE的开发方是Ostris",
   "direction": "out",
   "id": "org:p-ostris",
   "name": "Ostris",
   "unit": "",
   "role": "primary",
   "status": "base",
   "evidence": [
    {
     "kind": "ledger",
     "label": "本站新模型台账",
     "text": "新模型台账：开发者栏写作“Ostris（Jaret Burkett，ai-toolkit 作者）”；HF 模型页（含模型卡）",
     "url": "https://huggingface.co/ostris/primal_vae"
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  {
   "type": "placed_in",
   "sentence": "Primal VAE出现在 Landscape 的「图像、视频与世界模型」",
   "direction": "out",
   "id": "layer:ai-models--5",
   "name": "图像、视频与世界模型",
   "status": "derived",
   "evidence": [
    {
     "kind": "own",
     "label": "本站页面",
     "text": "AI 新模型台账：Primal VAE（2x1 / 2x2）（图像、视频与世界模型）",
     "url": "https://xiaopingfeng.com/graph/models/#m-primal-vae"
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}