开源项目解析
ComfyUI-WanVideoWrapper
把 Wan 系列模型接进 ComfyUI 的节点包。社区里跑 Wan 的事实标准封装。
社区跑 Wan 的事实标准封装块交换省显存1281 个 issue 开着
读懂层
没有由源码生成的图。 DeepWiki 没有索引这个仓库(2026-08-16 查,页面返回 not found)。所以这一页只有 README 与 issue 两层,没有由源码生成的图。
包在 Wan 官方实现外面的一层 ComfyUI 节点。核心是显存策略:把 DiT 的块在显存与内存之间轮换,LoRA 权重挂进块里跟着一起换,并支持 torch.compile。README 大半篇幅在讲这些开关怎么互相影响。
它管哪几个决定
自持能力
权重在不在本地可下载到本地
种子能不能填可填
换掉它要动几处几处
本地权重;显存策略由节点参数控制
动手层
文档代码结构 未做踩过的坑实测 未做
装它要过哪几关
- 1装进 custom_nodes用 ComfyUI-Manager 装最省事
- 2定块交换的块数这是主要旋钮。显存不够就多换几块,代价是慢
- 3决定用不用 torch.compile不用也能跑 —— README 说已经降低了对它的依赖
改哪个文件能改什么
节点参数 · blocks to swap显存与速度的主要权衡点triton 缓存目录compile 后首次显存暴涨时要手动清,README 给了默认路径坑在哪一处
带 issue 编号的来自:github issues · 核验 2026-08-16
升级后老工作流爆显存README 明写 LoRA 权重的加载方式改了:不用块交换的人会看到显存上涨,用块交换的人块变大了,要多换几块补回来
torch.compile 首跑显存暴涨README:多见于 Windows,多半是旧 pytorch/triton 或旧 triton 缓存。清掉缓存目录,再跑一次就正常
issue 太多找不到自己那条1281 个开着 —— 搜之前先把 Wan 变体名和显卡型号一起当关键词
教程
没找到官方口径的教程 —— 官方只给了 README。
项目自述
以下是仓库 README 的开头部分(原文引用,抓取 2026-08-17)。读全文 ↗
ComfyUI wrapper nodes for WanVideo and related models. Memory use update (again) I've made everythign less reliant on torch.compile for VRAM efficiency, so things should work better even without it. Also figured workaround for some issues when using compile that made first run use drastically more VRAM, issue I battled with myself a lot. Update notification that can affect memory use in old workflows In a recent update I changed how unmerged LoRA weights are handled: Previously mostly due to my laziness they were always loaded from RAM when used, this was of course inefficient and also made using torch.compile for LoRA applying difficult, thus forcing a graph break when using unmerged LoRAs. Now the LoRA weights are assigned as buffers to the corresponding modules, so they are part of the blocks and obey the block swapping unifying the offloading and allowing LoRA weights to benefit from the prefetch feature for async offoading. Downside is that this means if you did not use block swap, you will see increased memory use as the LoRAs are part of the model and all on VRAM. If you use block swap, the LoRAs are swapped along the rest of the block, but the block size is now larger, this means you may have to compensate with couple of more blocks swapped. Example situation: you use 1GB LoRA unmerged and swap 20 blocks on 14B model, we can divide the LoRA size by block count, single block grows by 25MB, 20 blocks grow by 500MB, so your VRAM usage would be 500MB more than before, to compensate you swap 2 more blocks. Unrelated other VRAM issue with torch.compile After any update that modifies the model code and when using torch.compile it's common to run into issues with VRAM, this can be caused by using older pytorch/triton version without latest compile fixes, and/or from old triton caches, mostly in Windows. This manifests in the issue that first run of new input size may have drastically increased memory use, which can clear from simply running it again, and once cached, not manifest again. Again I've only seen this happen in Windows. To clear your Triton cache you can delete the contents of following (default) folders: C:\Users\\.triton C:\Users\\AppData\Local\Temp\torchinductor_ Note: Due to the stupid amount of bots or people thinking this is some of video generation service, I've blocked new accounts from posting issues for now. WORK IN PROGRESS (perpetually) Why should I use custom nodes when WanVideo works natively? Short answer: Unless it's a model/feature not available yet on native, you shouldn't. Long answer: Due to the complexity of ComfyUI core code, and my lack of coding experience, in many cases it's far easier and faster to implement new models and features to a standalone wrapper, so this is a way to test things relatively quickly. I consider this my personal sandbox (which is obviously open for everyone) to play with without having to worry about compability issues etc, but as such this code is always work in progress and prone to have issues. Also not all new models end up being worth the trouble to implement in core Comfy, though I've also made some patcher nodes to allow using them in native workflows, such as ……(后略,见原文)
判断
优势
- 社区里跑 Wan 的事实标准封装,多数 Wan 工作流都基于它
- 显存调度做得细:块交换 + 异步预取 + LoRA 挂进块,同一张卡能跑更大的配置
- 作者持续跟进 Wan 的新变体
- Apache-2.0
局限
- 1281 个 issue 开着 —— 这个数字本身说明它站在显存、torch.compile、triton 缓存的交叉口上
- 已经 3 个月没更新
- README 明说改过 LoRA 的加载方式:老工作流不改块数会比以前多占显存
- torch.compile 相关的显存异常需要手动清 triton 缓存,README 给了目录但这不是个自动的事
不适用
不用 ComfyUI 的人;以及想读一份稳定文档的人 —— 它的 README 更像一份持续更新的变更说明。
同管这些决定的其它项目
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github README + GitHub API(DeepWiki 未索引) · 核验 2026-10-19 | 判断栏可报错。