Bernini-R — GGUF (high & low noise experts)
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GGUF quantizations of ByteDance/Bernini-R
(= Wan2.2-T2V-A14B + source-id RoPE + multi-condition APG guidance), for use in ComfyUI with
ComfyUI-BerniniR + 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
- Install ComfyUI-BerniniR and ComfyUI-GGUF.
- Put the
.gguffiles inComfyUI/models/unet/. You also need the Wan VAE (wan_2.1_vae.safetensors) and the UMT5 text encoder (umt5_xxl_fp8_e4m3fn_scaled.safetensors). - t2v / t2i (
source_id=0is identical to stock Wan 2.2): oneUnetLoaderGGUF→ your sampler. - 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.jsonin the node repo.
License: Apache-2.0 (same as the base model).