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.
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
).
t2v / t2i
(
source_id=0
is identical to stock Wan 2.2): one
UnetLoaderGGUF
→ 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.json
in the node repo.
License: Apache-2.0 (same as the base model).
Runs of chfm Bernini-R-GGUF on huggingface.co
245
Total runs
9
24-hour runs
17
3-day runs
188
7-day runs
199
30-day runs
More Information About Bernini-R-GGUF huggingface.co Model
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Bernini-R-GGUF is an open source model from GitHub that offers a free installation service, and any user can find Bernini-R-GGUF on GitHub to install. At the same time, huggingface.co provides the effect of Bernini-R-GGUF install, users can directly use Bernini-R-GGUF installed effect in huggingface.co for debugging and trial. It also supports api for free installation.