ComfyUI_DeleteModelPassthrough

A memory management custom node that removes a given model completely from VRAM after use, while passing through any other input (IMAGE, LATENT, CLIP, STRING, INT, CONDITIONING, VAE, etc.) unchanged. This helps free memory in low-RAM & VRAM environments and reduces the risk of out of memory errors in long workflows.

📌 Overview

This custom node provides a memory management utility for ComfyUI.
It allows you to delete a specific model (checkpoint, etc.) completely from VRAM and system RAM after use, while passing through any other input type unchanged (IMAGE, LATENT, CLIP, STRING, INT, CONDITIONING, VAE, etc.).

This is especially useful for low VRAM & low RAM environments, helping to reduce out-of-memory (OOM) errors in long workflows.

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⚙️ Node Details

• Name: Delete Model (Passthrough Any)
• Inputs:
  • data → any input type (IMAGE, LATENT, CLIP, STRING, INT, CONDITIONING, VAE, etc.)
  • model → the MODEL you want to remove
• Outputs:
  • The data input, passed through unchanged
• Effect:
  Deletes the model completely from Python, VRAM, and RAM using:

  del model
  torch.cuda.empty_cache()
  gc.collect()
  
  
  

🛠️ Installation

1. Navigate to your ComfyUI custom_nodes folder:

cd .../ComfyUI/custom_nodes

2. Clone or copy this repository into the folder:

git clone https://github.com/Isi-dev/ComfyUI_DeleteModelPassthrough.git

3. Install dependencies:

pip install -r requirements.txt

📝 Usage

Assume you have a large CLIP model that you want to remove from VRAM (without unloading it into low system RAM) before loading your diffusion model to avoid OOM errors:

• Connect the output from the CLIPTextEncode node into this node’s data input.

• Connect the output from the CLIPLoader node into this node’s model input.

• Connect the output from this node into your sampler node.

The CLIP model will be deleted from memory after use, while your encoded text (data) continues downstream into the workflow.