Instructions to use ussoewwin/WAN2.2_14B_GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Wan2.2
How to use ussoewwin/WAN2.2_14B_GGUF with Wan2.2:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
Model Files
wan2.2_i2v_high_noise_14B_fp16.gguf: High-noise model in FP16 format (not quantized)wan2.2_i2v_low_noise_14B_fp16.gguf: Low-noise model in FP16 format (not quantized)wan2.2_t2v_high_noise_14B_fp16.gguf: High-noise model in FP16 format (not quantized)wan2.2_t2v_low_noise_14B_fp16.gguf: High-noise model in FP16 format (not quantized)
Format Details
- Important: These are NOT quantized models but FP16 precision models in GGUF container format
- Base model: Wan-AI/Wan2.2-I2V-A14B -Base model: Wan-AI/Wan2.2-T2V-A14B
- Format: GGUF container with FP16 precision (unquantized)
- Original model size: ~27B parameters (14B active per step)
- File sizes:
- high: 28.6 GB for FP16 (SHA256: 3a7d4e...)
- low: 28.6 GB (SHA256: 1b4e28...)
Why FP16 in GGUF?
While GGUF is typically used for quantized models, ComfyUI-GGUF extension supports:
- Loading FP16 models in GGUF container format
- This provides compatibility with ComfyUI workflow
- Downloads last month
- 123
Hardware compatibility
Log In to add your hardware
We're not able to determine the quantization variants.
Model tree for ussoewwin/WAN2.2_14B_GGUF
Base model
Wan-AI/Wan2.2-I2V-A14B