Bernini-R 1.3B — ComfyUI bundle (everything you need)
The small, accessible Bernini-R for ComfyUI. This repo is self-contained — the 1.3B renderer plus the VAE and text encoder it needs — so you can edit images/video on modest GPUs. It's the lightweight sibling of the 14B GGUF repo (that one is full quality for 24 GB cards).
Bernini-R 1.3B is single-expert (fine-tuned from Wan2.1-T2V-1.3B — no high/low MoE), so it's tiny (~2.6 GB) and runs almost anywhere. It performs close to the 14B on simpler edits (style transfer, watermark/subtitle removal, local editing) and lags on the hardest tasks.
Files
| File | What | Size | Folder in ComfyUI |
|---|---|---|---|
bernini_r_1.3B-bf16.safetensors | renderer (native, full precision) | ~2.8 GB | models/diffusion_models/ |
bernini_r_1.3B-Q8_0.gguf | renderer (GGUF, near-bf16) | ~1.5 GB | models/unet/ |
bernini_r_1.3B-Q6_K.gguf | renderer (GGUF, high quality) | ~1.1 GB | models/unet/ |
bernini_r_1.3B-Q5_K_M.gguf | renderer (GGUF, balanced) | ~1.0 GB | models/unet/ |
bernini_r_1.3B-Q4_K_M.gguf | renderer (GGUF, smallest) | ~0.8 GB | models/unet/ |
vae/wan_2.1_vae.safetensors | Wan 2.1 VAE | ~0.25 GB | models/vae/ |
text_encoders/umt5-xxl-encoder-Q5_K_M.gguf | UMT5 text encoder (GGUF — used by the workflows) | ~4.1 GB | models/text_encoders/ |
text_encoders/umt5_xxl_fp8_e4m3fn_scaled.safetensors | UMT5 text encoder (fp8 — optional fallback) | ~6.4 GB | models/text_encoders/ |
Usage in ComfyUI
- Install ComfyUI-BerniniR and ComfyUI-GGUF (required — the graphs load the text encoder with
CLIPLoaderGGUF). - Drop each file in the folder from the table above.
- Load the workflow
workflows/bernini_i2i_1.3B.json(or grab it from the node repo). It's a single-expert graph — nomodel_low. Put your image, run.- Renderer — native (
bf16): BerniniR · Load Model (native). GGUF:UnetLoaderGGUF→ BerniniR · Apply Patches → Source Stream → Guider (leavemodel_lowempty). - Text encoder:
CLIPLoaderGGUF(type = wan) →umt5-xxl-encoder-Q5_K_M.gguf. The graphs default to the GGUF encoder because the fp8.safetensorsone triggers a Windows / torch-2.8 access violation under memory pressure; the fp8 file is kept only as an optional fallback.
- Renderer — native (
UMT5 GGUF text encoder quantized by city96.
Need full quality on 24 GB?
Use the 14B dual-expert GGUFs: neuregex/Bernini-R-GGUF.
Same nodes, just wire both experts into the guider (model = high, model_low = low).
License: Apache-2.0 (same as Bernini-R and the Wan base).