MiniCPM‑V‑4 (GGUF) for ComfyUI

Modular ComfyUI nodes to run the vision-language model MiniCPM‑V‑4 in GGUF format, powered by llama‑cpp‑python.

By mamorett·Created 12 months ago·Updated 10 months ago· 1

mamorett/ComfyUI_minicpmv4

Nodes2

On cloudLocal install

CategoryLLM/Multimodal

Stars1

Updated10 months ago

Nodes (2)

Readme

Modular ComfyUI nodes to run the vision-language model MiniCPM‑V‑4 in GGUF format, powered by llama‑cpp‑python.

Loader: choose a quant, auto-download from Hugging Face, and load with GPU acceleration.
Prompt Builder: compose prompts with optional chat tags and automatic <image> token.
Vision Inference: send one or more images + prompt, with streaming token output.

Repo of models:

Dropdown of official GGUF quantizations
Auto-download (with resume) to ComfyUI/models/llama
Optional checksum verification (fill in KNOWN_SHA256)
Multi-image input (image batch) — passed as images=[PIL...]
Token streaming to the ComfyUI console
Configurable context length, GPU layers, threads, sampler params, stop sequences

Files:

CPU only:
```
pip install "llama-cpp-python>=0.2.90"
```
NVIDIA CUDA (Windows/Linux):
```
pip install --upgrade --force-reinstall --no-binary :all: llama-cpp-python
```
Make sure CUDA toolchain is available for wheel build, or use prebuilt CUDA wheels if available for your platform.
macOS (Apple Silicon): Metal is used automatically by default wheels.

Pick a quant (e.g., “Q4_K_M (2.19 GB) [balanced]”).
On first run, the file is downloaded to ComfyUI/models/llama.
Adjust:
- n_ctx (context length), n_gpu_layers (0 = CPU), threads, seed.
Outputs:
- LLAMA handle, a boolean suggesting <image> prepend default, and the model path string.

Compose your prompt (system/user).
Enable “prepend_image_token” if image is ignored.
“chat_style” wraps prompts with <|im_start|> tags (add stop at "<|im_end|>" in Infer if you want).

Connect the LLAMA handle, the image (or image batch), and the built prompt.
Options:
- max_tokens, temperature, top_p, top_k, repeat_penalty
- stop_1, stop_2
- force_prepend_image_token (safety net)
- stream_tokens (prints tokens as they arrive)
Multi-image:
Provide an IMAGE batch (B x H x W x C). All images in the batch are passed in order.
Reference them in the prompt if needed (e.g., “Consider all images; describe differences.”).

If the image seems to have no effect:
Ensure <image> appears before the prompt (use Prompt Builder or force toggle in Infer).
Update llama‑cpp‑python to the latest version.
Some models expect chat formatting plus the image token.
Performance
n_gpu_layers: more layers on GPU = higher speed, requires VRAM.
8–12 GB VRAM: try 35–60 for MiniCPM‑V‑4 class.
Quant:
Q4_K_M is a great speed/quality balance.
Q5_K_M for better quality if VRAM allows.
n_ctx: 4096 is safe for single-turn; increase for longer interactions (more RAM/VRAM).
CPU tuning
Set threads > 0 to control llama.cpp threading on CPU.

The loader supports optional SHA‑256 verification. Fill KNOWN_SHA256 in loader.py with the hash for the file(s) you want to enforce.
Even without hashes, downloads use Hugging Face resumable transfers.

“Invalid model handle”:
Ensure you connected the Loader to Infer.
CUDA not used / slow:
Your wheel may be CPU-only. Reinstall with CUDA (see install section).
Reduce n_gpu_layers or quant level if you OOM.
API mismatch errors:
We support both llama(prompt=...) and llama.create_completion(...) signatures.
For streaming, we use create_completion(..., stream=True) which is broadly compatible.

Optional remote inference node (HTTP) for people who can’t modify the ComfyUI env.
Add more stop sequences and presets for popular chat formats.
Progress callback during HF download (when huggingface_hub exposes hooks).

Enjoy! PRs/ideas welcome.