ComfyUI-Qwen-Image-Integrated-KSampler

🐋 Qwen Image Integrated KSampler

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QwenImageIntegratedKSampler

This is an integrated ComfyUI Qwen-Image image generation sampler node,support Z-Image. Compared to using the official KSampler, it eliminates the messy wiring, supports both text-to-image and image-to-image generation, solves the offset issues of the official nodes, and integrates prompt input box, automatic image scaling, automatic memory/vRAM cleanup, batch generation, automatic saving and other comprehensive optimization features, so mom no longer has to worry about my messy wiring~~~~

If this project helps you, please give it a ⭐Star — it lets me know there are humans out there using it!

🏆 Features

🎨 Supported Generation Modes

• Z-Image: Support Z-Image Model
• Text-to-Image: Generate images from text prompts
• Image-to-Image: Generate based on reference images, image editing, supports up to 5 images

⚡ Advanced Optimizations

• Optimize Offset Issues: Solves the offset issues of official nodes, and better follows instructions
• Integrated Sampling Algorithm (AuraFlow): Integrates Sampling Algorithm (AuraFlow) node, no additional wiring needed
• CFGNorm Integration: Integrates CFGNorm node, no additional wiring needed

🖼️ Image Processing

• Integrated Prompt Input Box: Integrates prompt input box, no additional wiring needed

• Multiple Reference Images: Supports up to 5 reference images for conditional generation

• Automatic Image Scaling: Maintains aspect ratio while resizing to target dimensions

• Support ControlNet Control: Additional connection to [🐋 Qwen ControlNet Integrated Loader] for pose, depth and other controls

🔧 Productivity Enhancement

• Batch Generation: Generate multiple images in a single operation
• Automatic VRAM Cleanup: Automatic cleanup options for GPU/VRAM memory
• Automatic RAM Cleanup: Automatic cleanup options for RAM memory
• Automatic Save Results: Automatically save generated result images to specified folder
• Completion Sound Notification: Play audio reminder after generation completes

🍧 Comparison Display

🔄 Workflow Complexity Comparison

• ❌ Workflow without using [Qwen Image Integrated KSampler] (complicated, too many nodes, too many wires)
• ✅ Workflow using [Qwen Image Integrated KSampler] (extremely simple, single node done, almost no wires)

🖼️ Generated Image Effect Comparison

• ❌ Workflow without using [Qwen Image Integrated KSampler] (obvious offset, scaling)
• ✅ Workflow using [Qwen Image Integrated KSampler] (completely no offset, scaling)

📦 Installation Method

Method 1: Via ComfyUI Manager (Recommended)

1. Open ComfyUI Manager in the ComfyUI interface
2. Search for “ComfyUI-Qwen-Image-Integrated-KSampler”
3. Click Install

Method 2: Manual Installation

1. Navigate to your ComfyUI custom nodes directory:

   cd /path/to/ComfyUI/custom_nodes
   

2. Clone the repository:

   git clone https://github.com/luguoli/ComfyUI-Qwen-Image-Integrated-KSampler.git
   or gitee repository:
   git clone https://gitee.com/luguoli/ComfyUI-Qwen-Image-Integrated-KSampler.git
   

3. Install dependencies:

   pip install -r requirements.txt
   

4. Restart ComfyUI

🚀 Usage Method

Workflow Example

Basic Text-to-Image Generation

1. Add the “🐋 Qwen Image Integrated KSampler” node to the workflow
2. Set generation_mode to “text-to-image”
3. Connect required inputs:
   • Model (🤖 Model)
   • CLIP (🟡 Clip)
   • VAE (🎨 Vae)
4. Enter positive and negative prompts
5. Set width and height (required for text-to-image)
6. Configure sampling parameters (steps, CFG, sampler, scheduler)
7. Execute the workflow

Image-to-Image Generation

1. Add the node to the workflow
2. Set generation_mode to “image-to-image”
3. Connect at least one reference image (🖼️ Image1)
4. Optionally add up to 4 other reference images
5. Enter positive/negative prompts and instructions
6. Set target width/height for scaling (optional)
7. Configure other parameters as needed
8. Execute the workflow

ControlNet Control

1. Add the [🐋 Qwen ControlNet Integrated Loader] node, connect to [📦 ControlNet Data]

2. Connect pose, depth control images

3. Select ControlNet model, set control type and strength

4. Execute the workflow

Advanced Features

• Memory Management: Enable GPU/CPU cleanup options to improve resource efficiency
• Batch Processing: Set batch_size > 1 for multiple image generation
• Auto-Save: Specify output folder for automatic saving
• AuraFlow Tuning: Adjust auraflow_shift to balance speed and quality
• CFG Enhancement: Stabilizer for CFG

⚠️ Notes

📝 Usage Requirements

• Text-to-Image Mode: Must set width (Width) and height (Height), these are required parameters
• Image-to-Image Mode: Must provide at least one reference image (Image1), supports up to 5 reference images (Image1-Image5)

🎛️ Parameter Setting Suggestions

• Batch Size: Choose between 1-10, adjust according to GPU memory, recommend starting testing from 1
• Resolution (Width/Height): Must be multiples of 8, range 0-16384, recommend starting testing from lower resolutions (like 512x512)
• Sampling Steps: Qwen models recommend 4-20 steps, too high may increase computation time but not necessarily improve quality
• CFG Value: Range 0-100, default 1.0, recommend 1.0-7.0 range
• Denoise Strength: Range 0-1, default 1.0, can lower appropriately in image-to-image mode
• AuraFlow Shift: Range 0-100, default 3.0, used to balance generation speed and quality
• CFG Normalization Strength: Range 0-100, default 1.0, stabilizer for CFG

🔧 Image Processing

• Automatic Scaling: Text-to-image must input width and height parameters, image-to-image fills in to auto-scale reference images while maintaining aspect ratio, setting either width or height to 0 disables scaling
• Reference Image Order: Supports up to 5 reference images, processed in order Image1-Image5, Image1 is the main image
• Image Format: Supports standard image input formats, automatically handles batch dimensions

💾 Memory Management

• GPU Memory Cleanup: Enable enable_clean_gpu_memory option, automatically clean VRAM before/after generation
• CPU Memory Cleanup: Enable enable_clean_cpu_memory_after_finish, clean RAM after generation completes (including file cache, processes, dynamic libraries)
• For continuous large-scale generation, it is recommended to always enable memory cleanup options to prevent memory overflow

💾 Auto-Save

• Output Folder: Set auto_save_output_folder to enable auto-save function, leave blank to disable, supports absolute and relative paths
• File Naming: output_filename_prefix custom prefix, default “auto_save”
• Save format is PNG, filename includes seed and batch number (e.g.: auto_save_123456_00000.png)

🔊 Notification Function

• Sound Notification: Only supported on Windows systems

📝 Update Records

v1.0.6:

• Added Localization Script: Starting from ComfyUI v0.3.68, Chinese language files became invalid. Added automatic localization script, double-click [自动汉化节点.bat] and restart ComfyUI, requires installing ComfyUI-DD-Translation plugin

📞 Contact for Special Customization 📞

• Author: @luguoli（墙上的向日葵）
• Author Email: [email protected]

Made with ❤️ for the ComfyUI community