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
| 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](https://huggingface.co/ByteDance/Bernini-R)** | |
| (Wan2.2-T2V-A14B + source-id RoPE + APG) for use with | |
| **[ComfyUI-BerniniR](https://github.com/neuregex/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. | |