🎨 QWEN Vision-to-Prompt Generator | Universal Image & Video Analysis

Transform any image or video into ultra-detailed, model-optimized prompts using Qwen3-VL

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📋 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

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⚙️ 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

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🚀 How to Use

Basic Workflow

1. 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

2. 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)

3. 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"

4. Generate & Review:

   • Run the workflow

   • View the generated prompt in the ShowText node

   • Copy the output for use in your generation workflows

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💡 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

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🎯 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

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📊 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)

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⚠️ 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

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🔧 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

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📈 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)

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🙏 Credits

• Qwen VL Models by Alibaba Cloud AI Research

• ComfyUI-QwenVL by AIrjen

• Workflow Design optimized for production content generation

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Happy prompting! 🚀

Found this useful? Give it a ❤️ and share your generated prompts in the comments!