ComfyUI-MultiModal-Prompt-Nodes

Version: 1.0.5
License: GPL-3.0

Advanced multimodal prompt generation nodes for ComfyUI with local GGUF models (Qwen-VL) and cloud API support.

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Important Notes

Language Recommendation for Optimal Results

Based on extensive testing, Wan2.2 and Qwen-Image-Edit respond significantly better to Chinese prompts than English prompts.

Recommendation: Set target_language to "zh" (Chinese) for best results with these models, even if your input is in English. The models will generate more coherent and instruction-following outputs.

Vision Input Compatibility

Vision input support varies by model and llama-cpp-python version. See Installation section for detailed compatibility information. Results may vary based on your specific environment.

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Features

1. Vision LLM Node

• Local GGUF support: Run Qwen2.5-VL and Qwen3-VL models locally
• Multi-image input: Support batch image input via ComfyUI's batch nodes (e.g., Images Batch Multiple)
• Flexible prompting styles:
  • raw: Direct LLM response without system prompt
  • default: Balanced prompt enhancement
  • detailed: Rich visual details (colors, textures, lighting, atmosphere)
  • concise: Minimal keywords, focused on core elements
  • creative: Artistic interpretation with unique perspectives
• Device selection: Simple CPU/GPU dropdown for hardware control
• Auto-detect mmproj: Automatic detection or manual selection for Qwen3-VL

2. Qwen Image Edit Prompt Generator

• Dynamic model selection: Auto-detect local GGUF models and cloud API models
• Image editing prompts: Specialized for Qwen-Image-Edit tasks
• Manual mmproj selection: Choose specific mmproj files or use auto-detect
• Multi-image support: Up to 3 images via optional inputs (image2/image3)
• Unified interface: Consistent parameter ordering and naming
• API key management: Centralized configuration via api_key.txt
• Device control: CPU/GPU selection for local models

3. Wan Video Prompt Generator

• Video generation prompts: Optimized for Wan2.2 text-to-video and image-to-video
• Local Qwen3-VL integration: Use local models for prompt enhancement
• Task-specific optimization: Separate prompts for T2V and I2V workflows
• Extended token limit: 2048 tokens to support longer Chinese prompts (600+ characters)
• Device selection: CPU/GPU dropdown for local model execution
• Optimized for Chinese: Better performance with Chinese language prompts

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Installation

1. Clone Repository

Clone this repository into your ComfyUI custom_nodes folder:

cd ComfyUI/custom_nodes
git clone https://github.com/yourusername/ComfyUI-MultiModal-Prompt-Nodes.git

2. Install Dependencies

cd ComfyUI-MultiModal-Prompt-Nodes
pip install -r requirements.txt

Alternative manual installation:

pip install dashscope pillow numpy

3. Install llama-cpp-python (REQUIRED for local models)

Important: Model compatibility varies by llama-cpp-python version. Based on my testing environment:

Version	Qwen2.5-VL (Text)	Qwen2.5-VL (Vision)	Qwen3-VL
0.3.16 (official)	✅	❌	❌
0.3.21+ (JamePeng fork)	✅	❌*	✅

*Note: Vision input support may vary depending on your environment and configuration. In my setup, I have not been able to get vision input working with Qwen2.5-VL even with the JamePeng fork.

Recommended Installation (JamePeng fork for Qwen3-VL support):

pip install llama-cpp-python==0.3.21 --break-system-packages

Source: https://github.com/JamePeng/llama-cpp-python

My Environment Results:

• Official llama-cpp-python 0.3.16: Qwen2.5-VL text-only, no vision input, Qwen3-VL fails to load
• JamePeng fork 0.3.21+: Qwen3-VL works with vision input, Qwen2.5-VL text works but vision input still unavailable

⚠️ Disclaimer: Your results may differ depending on system configuration, GPU drivers, and other factors. If you encounter issues, please verify your environment setup and consider reporting compatibility details.

4. Place Models

Place your GGUF models in ComfyUI/models/LLM/:

ComfyUI/models/LLM/
├── Qwen3VL-4B-Q4_K_M.gguf
├── Qwen3VL-4B-Q8_0.gguf
├── mmproj-qwen3vl-4b-f16.gguf
└── ...

5. Configure API Key (Optional, for cloud models)

For cloud API usage, create api_key.txt in the node folder:

ComfyUI/custom_nodes/ComfyUI-MultiModal-Prompt-Nodes/api_key.txt

Add your Alibaba Cloud Dashscope API key to this file.

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Usage

Vision LLM Node

Inputs:

• prompt: Text prompt to rewrite/enhance
• style: Prompt rewriting style
  • raw: Direct LLM response without system prompt (useful for custom prompting)
  • default: Balanced prompt enhancement
  • detailed: Rich visual details
  • concise: Minimal, focused keywords
  • creative: Artistic interpretation
• target_language: Output language (auto/en/zh)
• model: Select from auto-detected local GGUF models
• mmproj: mmproj file selection
  • (Auto-detect): Automatically search for matching mmproj
  • (Not required): For Qwen2.5-VL or text-only mode
  • Specific file: Manually select mmproj file
• max_tokens: Maximum tokens to generate (default: 512)
• temperature: Sampling temperature (0.0-2.0, default: 0.7)
• device: CPU or GPU execution
• image (optional): Input image for vision-language processing

Example workflow:

1. Load Vision LLM Node
2. Enter basic prompt: "a cat sitting on a windowsill"
3. Attach image via batch node (optional)
4. Select Qwen3-VL model
5. Choose (Auto-detect) for mmproj or select specific file
6. Select style: default
7. Set device: CPU or GPU
8. Run to get enhanced prompt

Qwen Image Edit Prompt Generator

Inputs:

• image: Primary input image (required)
• prompt: Edit instruction or image description
• prompt_style:
  • Qwen-Image-Edit: For image editing tasks
  • Qwen-Image: For general image understanding
• target_language: Output language (auto/zh/en)
• llm_model: Model selection
  • Local: xxx: Local GGUF models (auto-detected)
  • API models: qwen-vl-max, qwen-plus, etc.
• mmproj: mmproj file (required for local Qwen3-VL)
  • (Auto-detect): Automatic detection
  • (Not required): For API models or Qwen2.5-VL
  • Specific file: Manual selection
• max_retries: Retry attempts for API calls (default: 3)
• device: CPU/GPU selection for local models
• save_tokens: Compress images to save API tokens
• image2/image3 (optional): Additional context images

Use cases:

• Image editing prompt generation
• Multi-image context prompts
• Style transfer descriptions
• Visual question answering

Recommended settings:

• For best results: Set target_language to zh (Chinese)
• Use local models for privacy, API models for quality
• Enable save_tokens when using API models

Wan Video Prompt Generator

Inputs:

• prompt: Video scene description
• task_type:
  • Text-to-Video: Generate video from text description
  • Image-to-Video: Generate video from image + text
• target_language: Output language (auto/zh/en)
• llm_model: Model selection
  • Local: xxx: Local GGUF models
  • API models: qwen-vl-max (for I2V), qwen-plus, etc.
• mmproj: mmproj selection (same as other nodes)
• max_retries: API retry attempts
• device: CPU/GPU for local models
• save_tokens: Image compression for API
• image (optional): Reference frame for I2V tasks

Optimized for:

• Wan2.2 video generation
• Temporal coherence descriptions
• Camera movement instructions
• Scene transitions

Important notes:

• Use Chinese prompts (target_language: zh) for best results
• Supports up to 600+ Chinese characters (2048 tokens)
• For I2V tasks, use qwen-vl-* models

Example T2V workflow:

1. Enter prompt: "一只猫在窗台上看风景" (A cat looking at scenery on a windowsill)
2. Set task_type: Text-to-Video
3. Set target_language: zh
4. Select model (local or API)
5. Run to get optimized video prompt

Example I2V workflow:

1. Attach input image
2. Enter motion description: "镜头慢慢推进" (Camera slowly zooms in)
3. Set task_type: Image-to-Video
4. Set target_language: zh
5. Ensure model supports vision (qwen-vl-*)
6. Run to get I2V prompt

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Model Compatibility

Qwen2.5-VL (Integrated mmproj)

• ✅ Qwen2.5-VL-2B: Text-only in my environment
• ✅ Qwen2.5-VL-7B: Text-only in my environment
• ⚠️ mmproj integrated but vision input unavailable in my setup

Qwen3-VL (Separate mmproj)

• ✅ Qwen3-VL-4B: Full vision support with JamePeng fork
• ✅ Qwen3-VL-7B: Full vision support with JamePeng fork
• ✅ Requires matching mmproj file

Recommended Quantization

• Q4_K_M: Balanced quality/size (recommended for most users)
• Q5_K_M: Higher quality, larger size
• Q8_0: Maximum quality, largest size

Model Sources

• Qwen models: https://huggingface.co/Qwen
• GGUF conversions: https://huggingface.co/models?search=qwen+gguf
• mmproj files: Usually bundled with GGUF conversions

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Configuration

System Requirements

• RAM: 8GB+ (16GB recommended for 7B models)
• Storage: 3-8GB per model (depending on quantization)
• GPU: Optional (CPU execution supported)
  • NVIDIA GPU: CUDA support via llama-cpp-python
  • AMD GPU: ROCm support (requires specific build)
  • Intel Arc: Limited support, CPU recommended

Performance Tips

1. Use Q4_K_M quantization for faster inference and lower memory usage
2. Reduce max_tokens if hitting memory limits
3. Enable GPU if you have compatible hardware (select GPU in device dropdown)
4. Use CPU for stability if encountering GPU issues
5. Batch multiple requests when possible for efficiency
6. Close other applications to free up RAM during inference

Memory Usage Guide

Model	Quantization	RAM Usage
Qwen3-VL-4B	Q4_K_M	~4-5GB
Qwen3-VL-4B	Q8_0	~7-8GB
Qwen3-VL-7B	Q4_K_M	~6-7GB
Qwen3-VL-7B	Q8_0	~12-14GB

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Troubleshooting

Installation Issues

Q: "No module named 'llama_cpp'" error
A: Install llama-cpp-python: pip install llama-cpp-python==0.3.21 --break-system-packages

Q: pip install fails with "externally-managed-environment"
A: Use --break-system-packages flag or create a virtual environment

Q: "Failed to load model" with Qwen3-VL
A: Ensure you're using llama-cpp-python 0.3.21+ (JamePeng fork). Version 0.3.16 doesn't support Qwen3-VL.

Runtime Issues

Q: "mmproj not specified" error
A: Select an mmproj file (or choose (Auto-detect)) in the mmproj dropdown for Qwen3-VL models

Q: "No models found" in model dropdown
A:

1. Place GGUF models in ComfyUI/models/LLM/
2. Restart ComfyUI
3. Verify file extensions are .gguf

Q: Vision input not working with Qwen2.5-VL
A: This is a known issue in my environment. Qwen2.5-VL currently only supports text input. Use Qwen3-VL for vision capabilities.

Q: Out of memory errors
A:

1. Use smaller quantization (Q4_K_M instead of Q8_0)
2. Reduce max_tokens parameter
3. Close other applications
4. Use a smaller model (4B instead of 7B)

Q: Slow inference on CPU
A: Normal for large models. Consider:

1. Q4_K_M quantization (faster than Q8_0)
2. Smaller models (4B faster than 7B)
3. GPU acceleration if available

Q: "API_KEY is not set" error with local models
A: This error should only appear when using API models. If using local models (starting with "Local:"), this is a bug - please report it.

Output Quality Issues

Q: Wan2.2 output is incoherent or doesn't follow instructions
A: Set target_language to zh (Chinese). Wan2.2 performs significantly better with Chinese prompts, even if your input is in English.

Q: Qwen-Image-Edit not understanding my edits
A:

1. Use target_language: zh for better results
2. Be specific in edit instructions
3. Try using reference examples in your prompt

Q: Output is cut off or incomplete
A: Increase max_tokens parameter (Vision LLM Node) or note that other nodes have fixed limits (512 for Qwen, 2048 for Wan)

Device Selection Issues

Q: How to choose between CPU and GPU?
A:

• GPU: Faster inference, requires compatible hardware (NVIDIA with CUDA)
• CPU: Universal compatibility, slower but stable
• Recommendation: Start with CPU, switch to GPU if available and working

Q: GPU selected but still using CPU
A: Your GPU may not be compatible with llama-cpp-python. Check:

1. NVIDIA GPU with CUDA support
2. llama-cpp-python built with CUDA support
3. Driver installation

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API Key Management

For Cloud API Models

1. Create api_key.txt in the node directory:

ComfyUI/custom_nodes/ComfyUI-MultiModal-Prompt-Nodes/api_key.txt

2. Add your Alibaba Cloud Dashscope API key (single line, no quotes)

3. The key will be automatically loaded by Qwen and Wan nodes when using cloud API models

Security Notes

• Never commit api_key.txt to version control
• The file is listed in .gitignore by default
• API keys are only loaded when using cloud API models
• Local models don't require API keys

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Examples

See the examples/ directory for:

• Basic prompt enhancement workflows
• Multi-image vision processing
• Image editing prompt generation
• Video prompt generation (T2V and I2V)
• Style-specific optimizations

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License

This project is licensed under the GNU General Public License v3.0.

Copyright (C) 2026 kantan-kanto
GitHub: https://github.com/kantan-kanto

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see https://www.gnu.org/licenses/.

Note: GPL-3.0 is required due to llama-cpp-python dependency.

For full details, see the LICENSE file and AUTHORS.md.

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Credits

Original Authors

• ComfyUI-QwenPromptRewriter: lihaoyun6 (GPL-3.0)
• ComfyUI-QwenVL: 1038lab (GPL-3.0)

Dependencies

• llama-cpp-python: Andrei Betlen
• Qwen3-VL support: JamePeng's fork
• Qwen models: Alibaba Cloud Qwen Team
• Dashscope API: Alibaba Cloud

For full attribution, see AUTHORS.md

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Contributing

Contributions are welcome! Please see CONTRIBUTING.md for guidelines.

Areas needing help:

• Testing on different hardware configurations
• Documenting vision input compatibility across environments
• Additional workflow examples
• Performance optimizations

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Support

• Issues: Report bugs or request features via GitHub Issues
• Documentation: See CHANGELOG.md for version history
• Examples: Check examples/ for workflow templates

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Changelog

See CHANGELOG.md for detailed version history.

Current Version: 1.0.5

• Device selection: CPU/GPU dropdown
• Raw style for Vision LLM Node
• Unified interface across all nodes
• Extended token limit for Wan (2048)
• API key management via api_key.txt only
• mmproj auto-detect improvements