ComfyUI-TeleStyle

ComfyUI-TeleStyle

<img width="842" height="553" alt="image" src="https://github.com/user-attachments/assets/ebc5b3e6-eaa9-4a8e-a4a5-fdce5608ea70" />

An unofficial, streamlined, and highly optimized ComfyUI implementation of TeleStyle.

This node is specifically designed for Video Style Transfer using the Wan2.1-T2V architecture and TeleStyle custom weights. Unlike the original repository, this implementation strips away all heavy image-editing components (Qwen weights) to focus purely on video generation with speed/quality.

Requirements

• GPU VRAM: 6GB minimum
• Disk Space: ~6GB for models and weights

✨ Key Features

• High Performance:

  • Acceleration: Built-in support for Flash Attention 2 and SageAttention for faster inference.
  • Fast Mode: Optimized memory management with aggressive cache cleanup to prevent conflicts between CPU offloading and GPU processing.

• Simplified Workflow: No need for complex external text encoding nodes. The model uses pre-computed stylistic embeddings (prompt_embeds.pth) for maximum efficiency.

<table style="width: 100%;"> <tr> <td style="width: 50%; text-align: center;"> <video src="https://github.com/user-attachments/assets/e10c2435-fc72-46d4-bfa0-5203a74b2a93" width="100%" controls></video> </td> <td style="width: 50%; text-align: center;"> <video src="https://github.com/user-attachments/assets/3d6310fa-f1c4-4c04-bf34-1fe0c05d3457" width="100%" controls></video> </td> <td style="width: 50%; text-align: center;"> <video src="https://github.com/user-attachments/assets/e65001fc-f181-4da7-bd97-4f36e7700ffe" width="100%" controls></video> <td style="width: 50%; text-align: center;"> <video src="https://github.com/user-attachments/assets/11c504e5-bfee-4847-846f-87f1a567dfb8" width="100%" controls></video> </td> </td> </tr> </table>

📦 Installation

Navigate to your ComfyUI custom nodes directory:

cd ComfyUI/custom_nodes/

Clone this repository:

git clone https://github.com/neurodanzelus-cmd/ComfyUI-TeleStyle.git

Install dependencies:

pip install -r requirements.txt

Note: For SageAttention support, you may need to install sageattention manually.

📂 Model Setup

This node requires specific weights placed in the ComfyUI/models/telestyle_models/ directory.

The weights are downloaded automatically at the first run

Directory Structure:

ComfyUI/
└── models/
    └── telestyle_models/
        ├── weights/
        │   ├── dit.ckpt            # Main Video Transformer weights
        │   └── prompt_embeds.pth   # Pre-computed style embeddings
        └── Wan2.1-T2V-1.3B-Diffusers/
        │   ├── transformer_config.json
        │   ├── vae/
        │   │   │   ├── diffusion_pytorch_model.safetensors
        │   │   │   └── config.json
        │   └── scheduler/
        │       └── scheduler_config.json

Where to get weights:

https://huggingface.co/Danzelus/TeleStyle_comfy/tree/main

🚀 Usage

1. TeleStyle Model Loader

This node loads the necessary model components.

| Parameter | Description | |-----------|-------------| | dtype | Choose between bf16 (best quality), fp16 |

2. TeleStyle Video Transfer

The main inference node.

| Parameter | Description | |-----------|-------------| | model | Connect the output from the Loader | | video_frames | Input video batch (from Load Video or VHS_LoadVideo) | | style_image | A reference image to guide the style transfer | | steps | Inference steps (default: 12) | | cfg | Guidance scale (default: 1) | | scheduler | Choose your sampler (FlowMatchEuler, DPM++) | | fast_mode | Keep True for speed. Set to False for low-VRAM offloading (slower) | | acceleration | default - Standard PyTorch attention<br>flash_attn - Faster, requires compatible GPU<br>sage_attn - Ultra-fast, requires sageattention library |

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To-Do List

• [x] Initial release
• [ ] More samplers
• [ ] Consistency for very long videos

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Guys, I’d really appreciate any support right now. I’m in a tough spot:

📜 Credits

This project is an unofficial implementation based on the amazing work by the original authors. Please refer to their repository for the original research and model weights.