Bernini R GGUF

--- 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_l...