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README.md

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 # 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.
+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](https://github.com/comfyanonymous/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.
+
+---
+
+## ⚙️ 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:
+  ```python
+  del model
+  torch.cuda.empty_cache()
+  gc.collect()
+
+
+🛠️ Installation
+
+1. Navigate to your ComfyUI custom_nodes folder:
+  ```python
+  cd .../ComfyUI/custom_nodes
+  ```
+
+2. Clone or copy this repository into the folder:
+  ```python
+  git clone https://github.com/Isi-dev/ComfyUI_DeleteModelPassthrough.git
+  ```
+3. Install dependencies:
+  ```python
+  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.
+