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Node Repo: https://github.com/lonecatone23/ComfyUI_LC_Vision_nodes

LC Vision 🔬📝🎥 — Why This Pack Doesn't Waste Your Time

Introduction: Getting a vision-capable llama.cpp build running is normally a multi-hour fight. Qwen-VL support Qwen3VLChatHandler / Qwen25VLChatHandler) never got merged into upstream llama-cpp-python. It only lives in JamePeng's fork, shipped as raw GitHub Release wheels, one per exact Python version + CUDA version + OS combination. Get the match wrong and you get a bare import error, not a helpful one. This pack does that matching for you, automatically, before you've even opened a workflow.

⚠️ install.py builds itself

- Runs on its own the moment ComfyUI-Manager installs or updates this pack. You run nothing by hand.

- First checks whether a vision-capable llama_cpp is already importable. If it is, it does nothing and gets out of your way.

- If not, it reads your exact Python tag, OS, and CUDA version straight off your own machine, pulls JamePeng/llama-cpp-python's real GitHub Releases, and installs the one wheel that matches (rounding down to the closest CUDA version when there's no exact match).

- Snapshots pip freeze before touching anything, and only ever steps in on a real conflict pip check reports afterward, and only when llama-cpp-python itself is the package at fault, never someone else's pin. Everything else in your environment is left alone.

- Safe to re-run on every update. Idempotent by design, not by luck.

How the Loader actually loads a model

- model_name lists every .gguf + matching mmproj pair already sitting in models/LLM, plus a curated set of one-click downloads prefixed ⬇ Download:.

- Pick a ⬇ Download: entry and it pulls straight from HuggingFace on first use, then lands in that same folder. Next time it's just a normal option in the list.

- 💡 What's curated right now

- Qwen3-VL-4B-abliterated and Qwen3-VL-8B-abliterated, each in Q8_0 (smaller, faster) and f16 (full precision). Four total, hand-picked instead of dumping an entire upstream catalog on you.

- 4B is the video/Moviemaker pick, more headroom for multi-frame batches. 8B is the image/Caption pick, more fidelity.

- mmproj always downloads at f16 regardless of the main model's quant. Vision quality leans on mmproj precision more than the LLM weights do.

- Once loaded, it stays loaded. ComfyUI's own node caching skips re-running the Loader entirely as long as its inputs haven't changed, same as a checkpoint loader. No reload tax between every Caption, Enhancer, or Moviemaker call.

Sell it: other vision-node packs hand you a requirements.txt and a wiki page telling you to go find the right wheel yourself. This one ships the fork detection, the CUDA matching, and the download baked in. Drop the node, pick a model, and it runs.

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LC Vision 🔬📝🎥 — 这个节点包不会浪费你的时间

前言：让支持视觉的 llama.cpp 正常运行起来，通常是一场持续数小时的折腾。Qwen-VL 支持Qwen3VLChatHandler / Qwen25VLChatHandler）从未合并进上游的 llama-cpp-python，只存在于 JamePeng 的 fork 里，以 GitHub Release 的原始 wheel 形式发布，每一个都对应精确的 Python 版本 + CUDA 版本 + 操作系统组合。选错一个，你得到的只是一个莫名其妙的导入错误，而不是任何有用的提示。这个节点包会自动帮你完成这个匹配，在你打开工作流之前就已经搞定。

⚠️ install.py 会自己搭建环境

- 在 ComfyUI-Manager 安装或更新这个节点包时自动运行，你不需要手动执行任何操作。

- 首先检查是否已经可以导入支持视觉的 llama_cpp。如果可以，它什么都不做，不会干扰你。

- 如果不行，它会直接读取你机器上的 Python 版本、操作系统和 CUDA 版本，拉取 JamePeng/llama-cpp-python 的真实 GitHub Releases 列表，安装与之匹配的那一个 wheel（如果没有精确匹配，会向下取最接近的 CUDA 版本）。

- 在做任何改动之前先记录一份 pip freeze 快照，并且只在 pip check 事后报告了真实冲突、且问题确实出在 llama-cpp-python 自身时才会介入，绝不会动其他包的版本锁定。你环境里的其他一切都不会被碰。

- 每次更新时重复运行都是安全的。这是设计上的幂等，不是运气。

Loader 到底是怎么加载模型的

- model_name 会列出 models/LLM 目录下已有的每一对 .gguf 及其匹配的 mmproj，以及一组带有 ⬇ Download: 前缀的精选一键下载选项。

- 选择一个 ⬇ Download: 选项，它会在首次使用时直接从 HuggingFace 下载，并存放到同一个目录下。下次它就会作为普通选项出现在列表里。

- 💡 目前精选的模型

- Qwen3-VL-4B-abliterated 和 Qwen3-VL-8B-abliterated，各自提供 Q8_0（更小更快）和 f16（完整精度）两个版本，一共四个，都是精心挑选的，而不是把上游整个庞大目录甩给你。

- 4B 是视频 / Moviemaker 的首选，为多帧批处理留出更多余量。8B 是图像 / Caption 的首选，保真度更高。

- 无论主模型选择哪种量化，mmproj 始终以 f16 下载。视觉质量更依赖 mmproj 的精度，而不是语言模型权重本身。

- 模型一旦加载就会保持常驻。只要输入没有变化，ComfyUI 自身的节点缓存机制就会完全跳过 Loader 的重新执行，和模型检查点加载器一样。每次调用 Caption、Enhancer 或 Moviemaker 之间都不用重新支付加载成本。

放心用：其他视觉节点包给你的是一份 requirements.txt 和一个让你自己去找对应 wheel 的 wiki 页面。这个节点包把 fork 检测、CUDA 匹配和下载全部内置好了。放下节点，选好模型，直接就能跑。

"True Nothing is. Permitted Everything is"- Yoda Auditore, Assassin's Wars