# ComfyUI-TeleStyle

# An unofficial, streamlined, and highly optimized ComfyUI implementation of [TeleStyle](https://github.com/Tele-AI/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. ##

## 📦 Installation Navigate to your ComfyUI custom nodes directory: ```bash cd ComfyUI/custom_nodes/ ``` Clone this repository: ```bash git clone https://github.com/neurodanzelus-cmd/ComfyUI-TeleStyle.git ``` Install dependencies: ```bash 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
`flash_attn` - Faster, requires compatible GPU
`sage_attn` - Ultra-fast, requires sageattention library | ___ ## To-Do List - [x] Initial release - [ ] More samplers - [ ] Consistency for very long videos ___ Guys, I’d really appreciate any support right now. I’m in a tough spot: [![Boosty](https://img.shields.io/badge/Boosty-Support-orange?style=for-the-badge)](https://boosty.to/danzelus) [![Ko-fi](https://ko-fi.com/img/githubbutton_sm.svg)](https://ko-fi.com/danzelus) ## 📜 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.

TeleStyle Model Loader

Inputs (1)

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