| --- |
| 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) |
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| This repository provides highly optimized **Wan2.2 Image-to-Video (I2V) GGUF+LIGHTNINGV2** 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. |
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| ## ⚡ Optimal Settings for ComfyUI |
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| To achieve perfect video motion without artifacts or image degradation (preventing fried or oversaturated visuals), we strongly recommend using the following parameters: |
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| | 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` (End Step: `4`) | Fine-tuning phase | |
| | **Sampler / Scheduler** | `euler` + `simple` | Standard diffusion setup | |
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| ### 👑 Note for Higher Quality (Hybrid Workflow & Hardware Restrictions): |
| If you want to achieve higher visual fidelity and enhance micro-details, adopting a hybrid multi-pass approach is highly recommended. This strategy significantly sharpens fine details, effectively eliminates motion blur, and prevents fried visuals. |
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| However, due to severe hardware VRAM limitations and Web GUI overhead, you **MUST** strictly adhere to the following setup configurations based on your execution environment: |
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| #### 💻 1. Via ComfyUI GUI (Web Interface Setup) |
| * 🟢 **NVIDIA L4 (24GB VRAM) or higher:** You can comfortably run high-tier configurations via the Web GUI with these setup options: |
| * **Standard High-Quality Setup:** Use **`Q8_H` (High Noise) + `Q8_H` (Low Noise)**. |
| * **Maximum Fidelity Option:** Use **`Q8_H` (High Noise) + `wan2.2_i2v_low_noise_14B_fp8_scaled.safetensors`** as the final step to achieve ultimate sharpness and micro-details. |
| * ⚠️ **NVIDIA Tesla T4 (15GB VRAM - Free Tier GUI Limits):** **DO NOT use the `fp8_scaled` model or any configurations higher than Q6_K here!** Because the Web GUI consumes a significant amount of VRAM for running its interface, the available memory is extremely limited. Forcing higher settings will result in an immediate **OOM (Out of Memory) crash**. To remain perfectly safe, your setup is strictly limited to: |
| * **Baseline Balanced Setup:** Use **`Q4_K_M` (High Noise) + `Q4_K_M` (Low Noise)**. |
| * **Maximum GUI Ceiling:** You can scale up to **`Q6_K` (Low Noise)** at most. |
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| #### 🚀 2. Via Backdoor (Direct Code / Colab Forms Setup) |
| * 🔥 **NVIDIA Tesla T4 (15GB VRAM - Unlocking Full Potential):** By executing via the backend script directly, you bypass the heavy Web GUI memory overhead entirely, allowing you to forcefully squeeze maximum performance out of your T4 GPU! |
| * **The T4 Backdoor Formulas:** |
| * **Ultimate Quality Setup:** You can successfully execute the top-tier hybrid workflow: **`Q8_H` (High Noise) + `Q8_H` (Low Noise)**. |
| * **Pro Option for Speed:** If you want faster generation times with a minor trade-off, switch to **`Q6_K` (High Noise) + `Q6_K` (Low Noise)** or **`Q6_K` (High Noise) + `Q8_H` (Low Noise)**. This delivers optimized speed while maintaining excellent visual quality compared to full high-quants. |
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| ## 💾 Available Model Variants |
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| Choose the right variant based on your creative workflow and VRAM configuration: |
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| ### 🔥 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` |
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| ### ❄️ 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` |
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| --- |
