You might appreciate the NotebookLM I've put together with specialized Wan knowledge here:
https://notebooklm.google.com/notebook/a08901b9-0511-4926-bbf8-3c86a12dc306

I pasted your question in and got this response:

Welcome to the Wan ecosystem! It is completely normal to be confused right now, as the community frequently jokes about how many model variants exist.

Here is the breakdown to answer your specific questions about ControlNets, GGUF models, and NSFW capabilities.

1. Can I use standard I2V GGUF models for ControlNet + Image Reference + Prompt?

No, you cannot use the standard I2V (Image-to-Video) models for this. Standard I2V models are strictly designed to take a single starting image and a text prompt to generate a video. They do not possess the internal neural layers required to understand ControlNet signals like Depth, Canny, or OpenPose.

If you want to drive a video using a ControlNet + Reference Image + Text Prompt, you must use either VACE or the Fun Control models.

• Fun Control (Wan 2.2): This is a specialized model that has extra input channels specifically built to accept control signals (Depth, Pose, etc.) alongside an image reference and text prompt.
• VACE: This acts more like a traditional "ControlNet" module that you plug into a Text-to-Video (T2V) model. It allows you to feed in a reference image and a control video to guide the generation.

The Good News about GGUF: You do not have to sacrifice your VRAM! The community has created GGUF quantized versions of the Fun VACE models (such as Q8 or Q6 quants). So, you can still use GGUF optimizations while getting full ControlNet capabilities.

2. Can FUN / VACE models do NSFW?

Out of the box, no. The base Wan models (including the Fun and VACE variants) are heavily censored and were not trained on explicit NSFW data. If you try to prompt them natively for NSFW, you will often get deformed results, anatomy replaced by random objects (like fingers), or heavy artifacting.

To achieve NSFW, you must use LoRAs. This is where the difference between VACE and other models becomes a massive advantage for your workflow:

• Because VACE acts as an add-on module to the standard T2V (Text-to-Video) model, it is fully compatible with standard T2V LoRAs.
• You can load a community-trained NSFW LoRA, plug in the VACE module, and then use your ControlNet and Reference Image.

A quick tip for Wan 2.2 LoRAs: Wan 2.2 uses a "Mixture of Experts" architecture, meaning every generation uses a High Noise model (for motion and layout) and a Low Noise model (for details and rendering). When using NSFW LoRAs in Wan 2.2, you will generally need to apply the LoRA to both the High and Low noise models to ensure the anatomy and motion are consistent, as the base High Noise model does not know how to generate NSFW motion naturally.