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license: apache-2.0
language:
- en
- zh
- th
base_model: text-to-video-synthesis
pipeline_tag: image-to-video
library_name: gguf
tags:
- image-to-video
- wan2.2
- comfyui
- GGUF
---
# Wan2.2 Custom GGUF (Tesla T4 Optimized)
This repository provides highly optimized **Wan2.2 Image-to-Video (I2V) GGUF+LIGHNINGV2** and custom models. These variants are fine-tuned for running efficiently on memory-constrained environments, such as Google Colab equipped with an NVIDIA Tesla T4 GPU.
---
## ⚡ Optimal Settings for ComfyUI
To achieve perfect video motion without artifacts or image degradation (preventing fried or oversaturated visuals), we strongly recommend using the following parameters:
| Parameter | Recommended Value | Note |
| :--- | :--- | :--- |
| **Sampling Steps** | `4` | When using Wan2.2 Lightning / Distilled V2 |
| **CFG Scale** | `1.0` | Crucial for preventing burnt images |
| **High Noise Steps** | `2` or `3` | To lock in strong motion and structure before the Lightning layer clears noise |
| **low Noise Steps** | `3` or `4 |
| **Sampler / Scheduler** | `euler` + `simple` | Standard diffusion setup |
*(Note for Higher Quality: If you want to achieve higher visual fidelity and enhance micro-details, it is highly recommended to use `wan2.2_i2v_low_noise_14B_fp8_scaled.safetensors` as your final step. This hybrid approach significantly sharpens fine details and effectively eliminates motion blur during camera movements. This multi-step workflow is recommended for NVIDIA Tesla T4 GPUs or higher, and it can be seamlessly combined with any other GGUF High Noise models available in this repository.)
## 💾 Available Model Variants
Choose the right variant based on your creative workflow and VRAM configuration:
### 🔥 High Noise Models (`wan2.2_i2v_high_noise_...`)
* **Best for:** Creative, high-motion generation, and diverse camera movements.
* **Available Quantizations:** `Q4_K_M`, `Q6_K_L`, `Q6_K`, `Q8_H`
### ❄️ Low Noise Models (`wan2.2_i2v_low_noise_...`)
* **Best for:** High fidelity, generation stability, and strictly adhering to the prompt or structural layout of your starting frame.
* **Available Quantizations:** `Q4_K_M`, `Q6_K_L`, `Q6_K`, `Q8_H`, and `fp8_scaled` |