Standalone ComfyUI plugin with a complete Wan 2.2 Bernini pipeline that does not depend on ComfyUI core Bernini nodes (PR #14216).

中文文档 → README_ZH.md

1. Clone into ComfyUI/custom_nodes/:

   cd ComfyUI/custom_nodes
   git clone https://github.com/AIMixer/ComfyUI-Bernini.git

2. Install Python dependencies (use the same Python environment as your ComfyUI):

   cd ComfyUI-Bernini
   pip install -r requirements.txt

   On Windows portable builds, prefer ComfyUI's bundled Python, for example:

   ..\..\python_embeded\python.exe -m pip install -r requirements.txt

3. Restart ComfyUI. Nodes appear under the Bernini category.

4. Download models — quantized GGUF / FP8 + workflows: comfyit.cn/article/489; original Kijai FP8 only: HuggingFace Bernini. Put weights under ComfyUI/models/ (diffusion_models/, vae/, text_encoders/). See Quantized models.

5. Run a workflow: Model Loader → Context Embeds → Sampler → Decode. Example JSON workflows: Comfyit article 489.

Download: Comfyit article 489 — Bernini models & workflows (GGUF quantizations, scaled FP8, VAE, T5, and example JSON).

Wan 2.2 Bernini diffusion weights are available in GGUF and FP8 safetensors quantizations. Place files under ComfyUI/models/diffusion_models/ and load both HIGH and LOW in Bernini Model Loader (GGUF is supported).

FP8 safetensors (scaled, same naming as Kijai Bernini pack):

• Wan22_Bernini_LOW_fp8_e4m3fn_scaled.safetensors
• Wan22_Bernini_HIGH_fp8_e4m3fn_scaled.safetensors

VRAM tips (Bernini Director, HIGH + LOW): Q4 → 8 GB min; Q5 → 10 GB; Q6 → 12 GB; Q8 → 16 GB. Enable Block Swap on both loaders; use T5 disk cache or fp8 text encoder where possible.

All quantized builds above are included in the resource pack at comfyit.cn/article/489. For the original non-GGUF FP8 pack from Kijai, see HuggingFace.

BerniniModelLoader · BerniniVAELoader · BerniniTextEncodeCached · BerniniContextEmbeds · BerniniContextOptions · BerniniSamplerExtraArgs · BerniniScheduler · BerniniSampler · BerniniDecode · BerniniDirector · BerniniDirectorOfficial

All-in-one node with an embedded timeline editor: upload video and reference images inside the node, split segments, set per-segment prompts / task_type, then run the full Bernini HIGH/LOW pipeline in one queue. Uses Bernini Model Loader (GGUF + Block Swap) for lower VRAM.

Example workflows: see Example workflows below (all from Comfyit article 489).

Same timeline UI as Bernini Director, but execution goes through ComfyUI native Bernini (VAELoader / UNETLoader ×2 / CLIPLoader → BerniniConditioning → dual-stage KSamplerAdvanced), aligned with PR #14216.

Typical v2v workflow: load bernini_director_official_core_v2v_2.json → connect VAE / UNET×2 / CLIP → upload source video → Equal Split into segments → global prompt → queue once, export all segments.

Download Bernini model weights + example JSON workflows from Comfyit: Bernini models & workflows (article 489):

After download: merge models/ into ComfyUI/models, install plugins/deps, drag the JSON into ComfyUI. Details on the article page.

The engine/ layer is adapted from kijai/ComfyUI-WanVideoWrapper (Apache-2.0). Deep respect and gratitude to kijai and all contributors to the WanVideo ecosystem.

This project is licensed under the Apache License, Version 2.0.

The engine/ layer is adapted from kijai/ComfyUI-WanVideoWrapper (also Apache-2.0). See Acknowledgements.

Comfyit is a one-stop ComfyUI tools & learning platform. For environment setup, models, workflows, and tutorials that complement this plugin, see the Product Center (ComfyUI Manager, LoRA Trainer, Prompt Master) and free resources: packages · models · workflows · learning center.

Full details in README_ZH.md.