README.md

metadata

license: apache-2.0
base_model: ByteDance/Bernini-R-1.3B-Diffusers
pipeline_tag: image-text-to-video
library_name: gguf
tags:
  - comfyui
  - bernini-r
  - wan2.1
  - text-to-video
  - image-editing
  - video-editing
  - gguf

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

💬 Discord — updates, roadmaps, projects, or just to chat

💻 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

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

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).