This is a high-fidelity dual-character LoRA trained for Wan 2.2, designed to showcase just how flexible and reliable multi-character generation can be when the training pipeline is done right.
The two characters were intentionally chosen for maximum contrast, making both identity separation and character consistency immediately obvious:
Character A: Goth woman in black fishnet and dark, high-contrast clothing
Character B: Handsome man in a clean, tailored business suit
Even in complex scenes, dynamic poses, or longer prompt chains, each character stays visually and stylistically distinct.
🎬 Why This LoRA Is Special
This isn’t just a “dual character test.”
It’s a demonstration of what you can do when you stop fighting identity bleed and start embracing multi-character storytelling:
Characters can interact naturally
Scene composition stays coherent
Clothing, facial features, and vibes stay locked to the correct character
You can build narrative continuity, not just single images
This opens the door to:
Story-driven image sequences
Multi-character scenes with emotional beats
Consistent recurring characters across generations
Wan 2.2 handles this extremely well when paired with the right training approach — and this LoRA is proof of that.
🎯 Intended Use
Multi-character storytelling
Dual-character scene generation
Testing identity consistency in Wan 2.2
Reference model for advanced LoRA training
Pushing beyond “one character per image” limitations
🔞 SFW / NSFW Notes
This LoRA can be used for both SFW and NSFW generations, depending on your prompts and base model.
Important clarification:
It was not specifically trained on NSFW content
Any explicit output comes from prompting and base model behavior, not the dataset
🛠️ Want to Create Your Own?
This LoRA is an example output of my dual-character training workflow.
If you want to create your own multi-character LoRAs — whether for storytelling, world-building, or more expressive scenes — the full workflow is available on LoboForge:
👉 https://www.loboforge.com/@LoboForge
The workflow covers:
Dataset structure for multiple characters
Caption separation techniques
Identity isolation strategies
Training settings for Wan 2.2
Common failure modes (and how to avoid them)
⚠️ Final Notes
This LoRA is designed to hold up under stress — complex prompts, dynamic scenes, and character interaction
If you’ve struggled with identity bleed before, this will immediately show what’s possible
Clean prompting + solid training = dramatically better results
Description
Prompt:
Ken is a handsome young man in a business suit.
Dakota is a pretty young goth girl with long black hair and fishnet.
Start with one or both of these and then say what they are doing.
Example:
Ken is a handsome young man in a business suit.
Dakota is a pretty young goth girl with long black hair and fishnet.
Ken is feeding Dakota strawberries.