QWEN Vision-to-Prompt Generator | Universal Image & Video Analysis
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Model description
🎨 QWEN Vision-to-Prompt Generator | Universal Image & Video Analysis
Transform any image or video into ultra-detailed, model-optimized prompts using Qwen3-VL
📋 Overview
This workflow leverages Qwen3-VL (Qwen Vision Language Model) to analyze images or videos and generate comprehensive, highly-detailed prompts optimized for your specific AI model. Whether you're working with FLUX, SDXL, WAN 2.1/2.2, or any other generative model, this workflow creates prompts that capture every nuance of your reference material.
Perfect for:
Creating detailed prompts from reference images
Analyzing video frames for consistent prompt generation
Reverse-engineering successful generations
Building comprehensive training datasets
Generating model-specific prompt optimizations
⚙️ Requirements
ComfyUI Custom Nodes
ComfyUI-QwenVL - Vision language model integration
pythongosssss Custom Scripts (ShowText node)
Core ComfyUI - LoadImage, LoadVideo, GetVideoComponents
Model Options (VRAM Considerations)
Recommended Models:
Qwen3-VL-8B-Instruct (Default) - 8GB+ VRAM
Qwen2.5-VL-7B-Instruct - 6GB+ VRAM (Lower VRAM alternative)
Qwen2-VL-2B-Instruct - 4GB+ VRAM (Budget-friendly option)
Quantization Settings:
8-bit (Balanced) - Recommended for most users
4-bit - For lower VRAM systems (3-4GB)
Full Precision - Best quality but requires 12GB+ VRAM
🚀 How to Use
Basic Workflow
Choose Your Input Type:
For Image Analysis: Use the LoadImage node, BYPASS the LoadVideo and GetVideoComponents nodes
For Video Analysis: Use the LoadVideo node, BYPASS the LoadImage node
Configure the QWEN Vision Node:
Select your model size based on available VRAM
Choose quantization level (8-bit recommended)
Set attention mode (sdpa is default)
Customize Your Prompt Request:
CRITICAL: Update the custom question field to specify your target model
Examples:
"Create an ultra detailed prompt optimized for FLUX""Create an ultra detailed prompt optimized for SDXL""Create an ultra detailed prompt optimized for WAN 2.1""Create an ultra detailed prompt optimized for ZImage""Create an ultra detailed prompt optimized for Pony Diffusion"
Generate & Review:
Run the workflow
View the generated prompt in the ShowText node
Copy the output for use in your generation workflows
💡 Usage Tips
Image Prompts
Best for: Character references, scene composition, style analysis
Supports: PNG, JPG, WebP
Tip: Use high-resolution reference images for more detailed descriptions
Video Prompts
Best for: Motion analysis, sequential consistency, character movement
Supports: MP4, AVI, MOV, WebM
Tip: QWEN analyzes the entire video sequence for comprehensive prompts
Note: Longer videos may take more time to process
Model-Specific Optimization
Always specify your target model in the custom question! Different models respond better to different prompt structures:
FLUX: Loves detailed scene descriptions, natural language
SDXL: Responds well to structured prompts with technical details
WAN 2.1/2.2: Benefits from motion descriptors and temporal elements
ZImage: Optimized for specific style keywords and artistic direction
Performance Optimization
Lower VRAM (4-6GB): Use Qwen2-VL-2B with 4-bit quantization
Mid-Range (8-12GB): Use Qwen3-VL-8B with 8-bit quantization
High-End (16GB+): Use full precision for maximum detail
Memory Issues: Reduce max tokens from 1024 to 512 or 256
🎯 Workflow Features
Dual Input Support: Seamlessly switch between image and video analysis
Model Flexibility: Choose from multiple QWEN models based on VRAM
Quantization Options: Balance quality vs. performance
Customizable Output: Tailor prompts to specific model requirements
Real-time Preview: ShowText node displays results immediately
📊 Example Output
The workflow generates comprehensive prompts including:
Subject description (facial features, clothing, pose)
Lighting conditions (direction, quality, atmosphere)
Background context (environment, depth, composition)
Technical specifications (camera angle, depth of field, color grading)
Style references (artistic direction, mood, tone)
Model-specific keywords (optimized for your target generator)
⚠️ Important Notes
BYPASS nodes appropriately: Don't run both LoadImage and LoadVideo simultaneously
Specify target model: Always update the custom question with your intended generation model
VRAM management: Start with lower settings if you experience crashes
Video processing: Longer videos require more VRAM and processing time
Prompt refinement: Use generated prompts as a starting point; adjust based on results
🔧 Troubleshooting
Out of Memory Errors:
Switch to a smaller model (2B or 7B)
Enable 4-bit quantization
Reduce max tokens to 512 or lower
Close other applications
Slow Processing:
Use 8-bit quantization instead of full precision
Reduce video length or resolution
Check attention mode (sdpa is fastest)
Generic Outputs:
Make sure custom question is updated with target model
Try increasing max tokens for more detail
Use higher resolution reference images
📈 Workflow Integration
This workflow pairs perfectly with:
Multi-phase SDXL workflows (use generated prompts in Phase 1)
WAN video generation (create consistent prompt sets)
LoRA training prep (generate detailed captions for training data)
Contest entries (reverse-engineer winning generations)
🙏 Credits
Qwen VL Models by Alibaba Cloud AI Research
ComfyUI-QwenVL by AIrjen
Workflow Design optimized for production content generation
Happy prompting! 🚀
Found this useful? Give it a ❤️ and share your generated prompts in the comments!