| --- |
| license: apache-2.0 |
| base_model: ByteDance/Bernini-R |
| pipeline_tag: text-to-video |
| library_name: gguf |
| tags: |
| - gguf |
| - wan2.2 |
| - comfyui |
| - bernini-r |
| - text-to-video |
| - image-editing |
| - video-editing |
| --- |
| |
| # Bernini-R — GGUF (high & low noise experts) |
|
|
| **[💻 Code / ComfyUI nodes](https://github.com/neuregex/ComfyUI-BerniniR)** |
| > **[💬 Discord — updates, roadmaps, projects, or just to chat](https://discord.gg/HxfP9TnctJ)** |
| > **[🧬 Bernini-R](https://huggingface.co/ByteDance/Bernini-R)** |
| > **[Wan2.2-T2V-A14B](https://huggingface.co/Wan-AI/Wan2.2-T2V-A14B)** |
|
|
| GGUF quantizations of **[ByteDance/Bernini-R](https://huggingface.co/ByteDance/Bernini-R)** |
| (= Wan2.2-T2V-A14B + source-id RoPE + multi-condition APG guidance), for use in ComfyUI with |
| **[ComfyUI-BerniniR](https://github.com/neuregex/ComfyUI-BerniniR)** + [`city96/ComfyUI-GGUF`](https://github.com/city96/ComfyUI-GGUF). |
|
|
| Bernini-R is a **dual-expert** model (Wan 2.2): a high-noise expert sets the composition and a |
| low-noise expert refines detail. Both are quantized here. GGUF carries no fp8 tensors, so the two |
| experts coexist in **24 GB VRAM** without the offload crash the fp8 build hits. |
|
|
| ## Files |
|
|
| | File | Expert | Quant | Size | |
| |------|--------|-------|------| |
| | `bernini_r_high_noise_14B-Q4_K_M.gguf` | high-noise | Q4_K_M | 9.66 GB | |
| | `bernini_r_high_noise_14B-Q5_K_M.gguf` | high-noise | Q5_K_M | 10.8 GB | |
| | `bernini_r_high_noise_14B-Q8_0.gguf` | high-noise | Q8_0 | 15.4 GB | |
| | `bernini_r_low_noise_14B-Q4_K_M.gguf` | low-noise | Q4_K_M | 9.66 GB | |
| | `bernini_r_low_noise_14B-Q5_K_M.gguf` | low-noise | Q5_K_M | 10.8 GB | |
| | `bernini_r_low_noise_14B-Q8_0.gguf` | low-noise | Q8_0 | 15.4 GB | |
| |
| `Q5_K_M` is the recommended balance; `Q8_0` for best quality, `Q4_K_M` for the lowest VRAM. |
|
|
| ## Usage in ComfyUI |
|
|
| 1. Install **[ComfyUI-BerniniR](https://github.com/neuregex/ComfyUI-BerniniR)** and **[ComfyUI-GGUF](https://github.com/city96/ComfyUI-GGUF)**. |
| 2. Put the `.gguf` files in `ComfyUI/models/unet/`. You also need the Wan VAE (`wan_2.1_vae.safetensors`) and the UMT5 text encoder (`umt5_xxl_fp8_e4m3fn_scaled.safetensors`). |
| 3. **t2v / t2i** (`source_id=0` is identical to stock Wan 2.2): one `UnetLoaderGGUF` → your sampler. |
| 4. **Editing (i2i / v2v), both experts:** load each GGUF with `UnetLoaderGGUF`, send each through |
| **BerniniR · Apply Patches**, then into **BerniniR · Guider** (`model` = high, `model_low` = low). |
| The guider switches expert by timestep (t=875) and runs the APG guidance. |
| Ready-made graph: `workflows/ui/bernini_i2i_gguf_dual.json` in the node repo. |
|
|
| License: Apache-2.0 (same as the base model). |
|
|
