A professional and intelligent aspect ratio and resolution selector for ComfyUI. Simplify your workflow by choosing optimal resolutions tailored for specific AI models.

• 🎯 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.

Search for TK_SimpleSize in the ComfyUI Manager and click Install.

1. Open terminal and navigate to your ComfyUI custom_nodes folder:

   cd ComfyUI/custom_nodes/

2. Clone the repository:

   git clone https://github.com/tackcrypto1031/tk_comfyui_simplesize.git

3. Restart ComfyUI.

1. Add Node: Search for TK_SimpleSize under the TK/SimpleSize category.
2. Configure:
   • Select Model Name (e.g., FLUX).
   • Choose Target Ratio (e.g., 16:9).
   • Pick the Resolution from the auto-populated list.
3. Connect:
   • width/height: Connect to resolution inputs.
   • latent: Connect directly to a KSampler or SamplerCustom node.

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。

• 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 are located in the workflow directory of this repository. These show how to integrate the node into standard generation pipelines for different models.

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: tack1031@gmail.com
• GitHub: tackcrypto1031

This project is licensed under the MIT License - see the LICENSE file for details.

Developed with ❤️ by Tack