😶‍🌫️llama智能对话@炮老师的小课堂

This is the flagship node of the pack, and it's the reason to install it. Dapao_LlamaChat is a full local multimodal chat inside ComfyUI: model loading, sampling, and conversation all in one box, fed by your own GGUF files. No API key, no cloud round-trip, no Ollama server in the loop - just llama.cpp running a model on your card. In 2026 that's the exact pattern people keep asking for in r/comfyui: any GGUF LLM as a native node instead of a separate process (the KB's LLM-in-ComfyUI essay calls it the standing community want).

It takes text, and if you give it a vision model with a matching mmproj, it takes 8 images + 2 videos + 2 audio tracks simultaneously. That's a lot of context plumbing for a single node, and it's genuinely the selling point.

How it works

Under the hood it's llama-cpp-python talking to a GGUF file from ComfyUI/models/LLM/, with a chat-handler dropdown that tells it what kind of model you loaded: LLaVA-1.5/1.6, Moondream2, MiniCPM, Gemma3/4, Qwen2.5-VL / Qwen3-VL / Qwen3.5 / Qwen3.8, GLM-4.6V, LFM2-VL, Granite-Docling, or None for pure text. Images get scaled to the 📏图像最大边长 cap and packed as tokens, with 🔢图像最小token / 🔢图像最大token (256/1344 by default) controlling the per-image token budget. Video comes in as IMAGE frame batches - no extra video-decode dependency - and 🎞️最大帧数 (default 10) decides how many frames get sampled.

The 💾显存限制(GB) tooltip is the best documentation in the whole pack: it's the VRAM budget the LLM may use, not a reservation. -1 means "try to put everything on GPU" (fastest, but can OOM); a number means partial offload, with a rough table - 8GB card → 6, 12GB → 10, 16GB → 13, 24GB → 20 - leaving ~2GB for ComfyUI, the mmproj, and the context cache. That's the knob to reach for when you share the card with a diffusion model.

🧠思考模式 enables chain-of-thought output for Thinking-series models, and the 🧠Qwen3.8推理强度 dropdown (关闭/自动/低/中等/高) is the Qwen3.8-specific reasoning control - auto and high both mean the model's native xhigh.

Inputs and outputs that matter

• 📝系统提示词 and 💬用户提示词 - the conversation itself.
• 💾保存对话历史 - off by default; flip it on for multi-turn and the 📋完整对话历史 output carries the JSON.
• ⚡推理后卸载模型 - offload the GGUF and mmproj after each run to free VRAM for diffusion; on means the next turn reloads, which is slower but leaves the card empty for sampling.
• Outputs: 💬回复文本 (STRING - wire it into a text node, a preview, or a CLIP encode), 📋完整对话历史 (STRING), and 🔢使用的种子 (INT).

The 🔗队列处理器 input is an any-type pipe, handy for chaining this node in a bigger graph.

Where people get burned

If the model dropdown is empty, you haven't put a .gguf in ComfyUI/models/LLM/ (and the mmproj file must contain "mmproj" in its name or it won't show as a vision option). Images fail to load when you picked a text-only handler or no mmproj. And the install itself is the real trap - see below, because this pack's dependency story is the most common support ticket.

Installing it

cd ComfyUI/custom_nodes
git clone https://github.com/paolaoshi/ComfyUI-llama_Dapao

or search the pack title in ComfyUI Manager. Then the gotcha: the pack pins llama-cpp-python from the JamePeng fork at a tested commit (0.3.47+) because PyPI's stock 0.3.35 can't load Qwen3.8 GGUFs with MTP/NextN layers - you'll see missing tensor 'blk.64.ssm_conv1d.weight' if it's too old. CPU users build from source via pip install -r requirements.txt; NVIDIA users install a matching GPU wheel from the JamePeng releases page with --force-reinstall to override the CPU build, then restart. Don't pick the wheel by the driver's advertised CUDA version alone - the wheel's bundled CUDA runtime has to actually match your environment.

If you mainly want captions, Dapao_LlamaCaption is this node specialized; if you want the full Skill-based chat workbench, that's the DapaoLocalModelLoader + DapaoMultiTurnChatV2 family.