TK SimpleSize - Resolution Selector
A professional and intelligent aspect ratio and resolution selector for ComfyUI, with multi-model optimization and integrated latent output.
TK ComfyUI SimpleSize
A professional and intelligent aspect ratio and resolution selector for ComfyUI. Simplify your workflow by choosing optimal resolutions tailored for specific AI models.

🌟 Key Features
- 🎯 Multi-Model Optimization: Native support for FLUX, SDXL, SD1.5, QwenImage, Zimage, and WAN.
- 🧠 Intelligent Filtering: Dynamically filters resolution presets based on the selected model and aspect ratio (e.g., 1:1, 16:9, 9:21).
- ⚡ Integrated Latent Output: Automatically generates a standard empty latent (1/8 scale) matching the selected resolution—no extra nodes required.
- 📱 Responsive Ratios: Comprehensive support for modern aspect ratios including standard, cinematic, and mobile-friendly formats.
- 🎨 Visual Clarity: A clean, dropdown-based UI that prevents selection errors and ensures pixel-perfect generations.
🛠️ Installation
Method 1: ComfyUI Manager (Recommended)
Search for TK_SimpleSize in the ComfyUI Manager and click Install.
Method 2: Manual Installation
- Open terminal and navigate to your ComfyUI
custom_nodesfolder:cd ComfyUI/custom_nodes/ - Clone the repository:
git clone https://github.com/tackcrypto1031/tk_comfyui_simplesize.git - Restart ComfyUI.
📖 How to Use
- Add Node: Search for
TK_SimpleSizeunder theTK/SimpleSizecategory. - Configure:
- Select Model Name (e.g.,
FLUX). - Choose Target Ratio (e.g.,
16:9). - Pick the Resolution from the auto-populated list.
- Select Model Name (e.g.,
- Connect:
width/height: Connect to resolution inputs.latent: Connect directly to aKSamplerorSamplerCustomnode.
🇹🇼 繁體中文說明
TK ComfyUI SimpleSize 是一款為 ComfyUI 設計的專業解析度與長寬比選擇器。它能自動根據您選擇的模型(如 FLUX, SDXL)提供最佳的解析度預設,避免解析度設定錯誤導致的圖像崩壞。
核心優勢
- 自動過濾:根據模型特性與比例,自動顯示最合適的像素組合。
- 內建 Latent:節省節點空間,直接輸出對應尺寸的 Empty Latent。
- 支援廣泛:全面支援從 SD1.5 到最新的 FLUX 與 WAN 模型。
📂 Example Workflows
Example workflows are located in the workflow directory of this repository. These show how to integrate the node into standard generation pipelines for different models.
🤝 Contributing & Feedback
Contributions are what make the open-source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.
- Author: Tack
- Email: [email protected]
- GitHub: tackcrypto1031
📄 License
This project is licensed under the MIT License - see the LICENSE file for details.
Developed with ❤️ by Tack