ComfyUI-MiniCPM

A custom ComfyUI node for MiniCPM vision-language models, supporting v4, v4.5, and v4 GGUF formats, enabling high-quality image captioning and visual analysis.

🎉 Now supports MiniCPM-V-4.5! The latest model with enhanced capabilities.

News & Updates

2025/08/28: Update ComfyUI-MIniCPM to v1.1.1 ( update.md )
2025/08/27: Update ComfyUI-MIniCPM to v1.1.0 ( update.md )
Added support for MiniCPM-V-4.5 models (Transformers)

Features

MiniCPM-V-4 GGUF
MiniCPM-V-4 Batch Images
MiniCPM-V-4 video
Supports MiniCPM-V-4.5 (Transformers) and MiniCPM-V-4.0 (GGUF) models
Latest MiniCPM-V-4.5 with enhanced capabilities via Transformers
Multiple caption types to suit different use cases (Describe, Caption, Analyze, etc.)
Memory management options to balance VRAM usage and speed
Auto-downloads model files on first use for easy setup
Customizable parameters: max tokens, temperature, top-p/k sampling, repetition penalty
Advanced node with full parameter control
Legacy node for backward compatibility
Comprehensive GGUF quantization options for V4.0 models

Installation

Clone the repo into your ComfyUI custom nodes folder:

cd ComfyUI/custom_nodes
git clone https://github.com/1038lab/comfyui-minicpm.git

Install required dependencies:

cd ComfyUI/custom_nodes/comfyui-minicpm
ComfyUI\python_embeded\python pip install -r requirements.txt
ComfyUI\python_embeded\python llama_cpp_install.py

Note

llama-cpp-python CUDA Installation for ComfyUI Portable

llama_cpp_install.md

Supported Models

Transformers Models

Model	Description
MiniCPM-V-4.5	🌟 Latest V4.5 version with enhanced capabilities
MiniCPM-V-4.5-int4	🌟 V4.5 4-bit quantized version, smaller memory footprint
MiniCPM-V-4	V4.0 full precision version, higher quality
MiniCPM-V-4-int4	V4.0 4-bit quantized version, smaller memory footprint

https://huggingface.co/openbmb/MiniCPM-V-4_5
https://huggingface.co/openbmb/MiniCPM-V-4_5-int4
https://huggingface.co/openbmb/MiniCPM-V-4 https://huggingface.co/openbmb/MiniCPM-V-4-int4

GGUF Models

Note: MiniCPM-V-4.5 GGUF models are temporarily unavailable due to llama-cpp-python compatibility issues. Please use MiniCPM-V-4.5 Transformers models or MiniCPM-V-4.0 GGUF models.

MiniCPM-V-4.0 (Fully Supported)

Model	Size	Description
MiniCPM-V-4 (Q4_K_M)	~2.19GB	Recommended balance of quality/size
MiniCPM-V-4 (Q4_0)	~2.08GB	Standard 4-bit quantization
MiniCPM-V-4 (Q4_1)	~2.29GB	4-bit quantization improved
MiniCPM-V-4 (Q4_K_S)	~2.09GB	4-bit K-quants small
MiniCPM-V-4 (Q5_0)	~2.51GB	5-bit quantization
MiniCPM-V-4 (Q5_1)	~2.72GB	5-bit quantization improved
MiniCPM-V-4 (Q5_K_M)	~2.56GB	5-bit K-quants medium
MiniCPM-V-4 (Q5_K_S)	~2.51GB	5-bit K-quants small
MiniCPM-V-4 (Q6_K)	~2.96GB	Very high quality
MiniCPM-V-4 (Q8_0)	~3.83GB	Highest quality quantized

https://huggingface.co/openbmb/MiniCPM-V-4-gguf

The models will be automatically downloaded on first run. Manual download and placement into models/LLM (transformers) or models/LLM/GGUF (GGUF) is also supported.

Available Nodes

1. MiniCPM-4-V-Transformers

Basic transformers-based node with essential parameters
Supports image and video input
Memory management options
Preset prompt types

2. MiniCPM-4-V-Transformers Advanced

Full-featured transformers-based node
All parameters customizable
System prompt support
Advanced video processing options

3. MiniCPM-4-V-GGUF

GGUF-based node with essential parameters
Optimized for performance

4. MiniCPM-4-V-GGUF Advanced

Full-featured GGUF-based node
All parameters customizable

5. MiniCPM (Legacy)

Original node for backward compatibility
Basic functionality

Usage

Add the MiniCPM node from the 🧪AILab category in ComfyUI.
Connect an image or video input node to the MiniCPM node.
Select the model variant (default is MiniCPM-V-4-int4 for transformers).
Choose caption type and adjust parameters as needed.
Execute your workflow to generate captions or analysis.

Configuration Defaults

{
  "context_window": 4096,
  "gpu_layers": -1,
  "cpu_threads": 4,
  "default_max_tokens": 1024,
  "default_temperature": 0.7,
  "default_top_p": 0.9,
  "default_top_k": 100,
  "default_repetition_penalty": 1.10,
  "default_system_prompt": "You are MiniCPM-V, a helpful, concise and knowledgeable vision-language assistant. Answer directly and stay on task."
}

Caption Types

Describe: Describe this image in detail.
Caption: Write a concise caption for this image.
Analyze: Analyze the main elements and scene in this image.
Identify: What objects and subjects do you see in this image?
Explain: Explain what's happening in this image.
List: List the main objects visible in this image.
Scene: Describe the scene and setting of this image.
Details: What are the key details in this image?
Summarize: Summarize the key content of this image in 1-2 sentences.
Emotion: Describe the emotions or mood conveyed by this image.
Style: Describe the artistic or visual style of this image.
Location: Where might this image be taken? Analyze the setting or location.
Question: What question could be asked based on this image?
Creative: Describe this image as if writing the beginning of a short story.

Memory Management Options

Keep in Memory: Model stays loaded for faster subsequent runs
Clear After Run: Model is unloaded after each run to save memory
Global Cache: Model is cached globally and shared between nodes

Tips

VRAM Requirements

4-6GB VRAM: Use MiniCPM-V-4-int4 or GGUF Q4 models
8GB VRAM: Use MiniCPM-V-4.5-int4 (recommended)
12GB+ VRAM: Can use full MiniCPM-V-4.5
CUDA OOM Error: Try int4 quantized models or CPU mode

General Tips

🌟 Try MiniCPM-V-4.5 Transformers first - enhanced capabilities over V4.0
For best balance: use MiniCPM-V-4 (Q4_K_M) GGUF model
For highest quality: use MiniCPM-V-4.5 Transformers
For low VRAM: use MiniCPM-V-4.5-int4 or MiniCPM-V-4 (Q4_0) GGUF
Adjust temperature (0.6–0.8) for balancing creativity and coherence.
Use top-p (0.9) and top-k (80) sampling for natural output diversity.
Lower max tokens or precision (bf16/fp16) for faster generation on less powerful GPUs.
Memory modes help optimize VRAM usage: default, balanced, max savings.
Transformers models offer better quality but use more memory.
GGUF models are more memory-efficient but may have slightly lower quality.

License

GPL-3.0 License

1038lab/ComfyUI-MiniCPM