Instructions to use neuregex/Bernini-R-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Wan2.2
How to use neuregex/Bernini-R-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
metadata
license: apache-2.0
base_model: ByteDance/Bernini-R
tags:
- gguf
- wan2.2
- comfyui
- bernini-r
- text-to-video
- image-editing
Bernini-R — GGUF (high / low noise experts)
GGUF quantizations of ByteDance/Bernini-R
(Wan2.2-T2V-A14B + source-id RoPE + APG) for use with
ComfyUI-BerniniR + ComfyUI-GGUF.
Two experts (Wan 2.2 high/low-noise), quants: Q4_K_M, Q5_K_M, Q8_0.
Load each with UnetLoaderGGUF then BerniniR · Apply Patches. GGUF avoids the fp8
dual-expert memory crash, so both experts run in 24 GB.