neuregex / Bernini-1.3B-ComfyUI

Bernini-R 1.3B — ComfyUI bundle (everything you need)

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💻 Code / ComfyUI nodes

🧬 Bernini-R 1.3B

14B GGUF (full quality)

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

Usage in ComfyUI

1. Install ComfyUI-BerniniR and ComfyUI-GGUF (required — the graphs load the text encoder with CLIPLoaderGGUF ).
2. Drop each file in the folder from the table above.
3. Load the workflow workflows/bernini_i2i_1.3B.json (or grab it from the node repo). It's a single-expert graph — no model_low . Put your image, run.
   • Renderer — native ( bf16 ): BerniniR · Load Model (native) . GGUF: UnetLoaderGGUF → BerniniR · Apply Patches → Source Stream → Guider (leave model_low empty).
   • Text encoder: CLIPLoaderGGUF ( type = wan ) → umt5-xxl-encoder-Q5_K_M.gguf . The graphs default to the GGUF encoder because the fp8 .safetensors one triggers a Windows / torch-2.8 access violation under memory pressure; the fp8 file is kept only as an optional fallback.

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).