u/FitEgg603

If you’ve been endlessly morphing faces or relying solely on tools like FaceFusion, you already know the results can be inconsistent. Face swapping works—but if you’re aiming for high-fidelity, production-level LoRAs, there’s a far more reliable approach.
After 100+ LoRA training experiments, I’ve refined a workflow that consistently delivers clean identity retention, better proportions, and more realistic outputs—especially with Z Image Turbo and Z Image Base.
Here’s the method 👇

🧠 Core Idea
Instead of repeatedly morphing images, you:
Build a controlled dataset of faces
Combine it with high-quality face-swapped bodies
Train a balanced LoRA that understands both identity and structure

📸 Step 1: Prepare Clean Face Data
Start with ~50 images of your character (Character X)
Crop/edit each image so that:
Only the face + slight neck is visible
No distractions, backgrounds, or full body
Higher resolution = better results
Low-res can work, but only if you later use ADetailer (outputs will be average otherwise)

🔁 Step 2: Controlled Face Swapping
Use front-facing or near-front headshots of Character X
In FaceFusion:
Choose images where the face is aligned with the camera
Swap onto well-lit, clean base images
This step ensures identity consistency before training.

🎯 Step 3: Match Face Proportions (Critical Step)
This is where most people fail.
Use platforms like Instagram to find:
Models with similar face size and proportions
Your goal:
Match face scale (≈70%–100% similarity) with Character X
Experiment with:
Different source faces
Slight variations in angles
👉 The more precise this step, the better your final LoRA.

⚙️** FaceFusion Settings (Recommended**)
Enhancer: ESRGAN 1.4
Strength: 0.60 – 0.75
Face Swap Model:
FP16 version (512px model)
Occlusion: Enabled
These settings give a good balance between sharpness and realism.

🧪 Step 4: Generate Training Data
Create 20–30 face-swapped images
Vary clothing, lighting, backgrounds
Combine with:
Your original 50 cropped face images
📁 Final dataset:
~80 images total (50 faces + 30 swapped)

🏷️** Step 5: Captioning & Dataset Pre**p
Caption all images properly (important for identity learning)
Maintain consistency in naming and tagging

🤖 Step 6: Train the LoRA
Using your preferred toolkit (e.g., AI Toolkit):
Dataset: 80 images
Steps: ~8000
Use your stable, tested config
Train and bake the LoRA
👉 I personally prefer using the final LoRA rather than intermediate checkpoints.

🔥 Step 7: Post-Processing (Must-Do)
Always use:
ADetailer
This significantly improves:
Facial accuracy
Eye symmetry
Overall realism

⚠️** Key Takeaway**s
Face proportion matching is everything
Dataset balance (faces + swapped bodies) = better generalization
Don’t skip captioning
ADetailer is non-negotiable

💬 Final Thoughts
This workflow is based on extensive hands-on testing (100+ LoRAs) and consistently produces:
Better identity preservation
More natural body proportions
Less “midget” or distorted outputs
If you’ve struggled with inconsistent LoRAs—this method is worth trying.

🤝 Feedback & Samples
I’d love to hear your results, tweaks, or improvements.
If you want sample outputs, feel free to DM.