How to Train a Consistent Character LoRA for Stable Diffusion (The 2026 Guide)

How to Train a Consistent Character LoRA for Stable Diffusion

The holy grail of AI art isn't just generating a beautiful image; it's generating the SAME beautiful person twice. If you want to create a comic book, a brand mascot, or an influencer, you need a LoRA.

Stable Diffusion is like a dream machine. It knows what "a woman" looks like, but it doesn't know what your specific character looks like. Every time you generate an image, you get a random face. This makes storytelling impossible.

The solution is training a LoRA (Low-Rank Adaptation). Think of a LoRA as a small file (usually 100MB) that "patches" the main AI brain. It teaches the AI one specific concept—your character—without retraining the entire massive model.

The Hardware Requirement: To train a LoRA, you need a GPU with at least 8GB (preferably 12GB+) of VRAM, or you can use cloud services like Google Colab or RunPod.

Step 1: The Dataset (Quality > Quantity)

Garbage in, garbage out. The most critical step is gathering your images. You don't need hundreds of photos. For a character, 15 to 30 high-quality images are better than 100 blurry ones.

Your Checklist:

• Variety: Different angles (front, side, looking up), different lighting, and different distances (close-up, full body).
• Consistency: The character's core features (hair color, eye shape, tattoos) must be visible and consistent.
• Crop: Crop your images to 512x512 or 1024x1024 pixels.

Step 2: Captioning (Teaching the AI)

You need to tell the AI what it is looking at. We use text files associated with each image.

The Golden Rule of Captioning: Describe everything except the character's unique traits. Why? Because you want the "Trigger Word" to contain those unique traits.

Example Image: A photo of your character (let's call her 'sks_girl') wearing a red dress in a forest. BAD CAPTION: "sks_girl with blue hair and green eyes in a forest." (If you tag the blue hair, the AI learns that blue hair is separate from the character.) GOOD CAPTION: "sks_girl, wearing a red dress, standing in a forest, trees, sunlight." (By NOT tagging the hair/eyes, the AI assumes those features are part of the concept 'sks_girl'.)

Step 3: Training Settings (Kohya_ss)

We use a tool called Kohya_ss. It is the industry standard GUI for training. While there are hundreds of settings, here are the ones that matter for 2026:

• Repeats: 10 (How many times the AI looks at each image per epoch).
• Epochs: 10 (How many full cycles).
• Network Rank (Dim): 32 or 128 (Higher captures more detail but risks large file size).
• Alpha: Usually half of the Rank (e.g., 16 or 64).

Comparison: Why LoRA Wins

Before LoRA, we used other methods. Here is why LoRA became the standard.

Method	File Size	Training Time	Consistency
Dreambooth	2GB - 4GB	Slow	High
Textual Inversion	10KB	Fast	Low
LoRA	140MB	Fast (20 mins)	High

Using Your LoRA

Once trained, you get a `.safetensors` file. Place this in your Stable Diffusion models folder. To use it, you simply include the trigger word in your prompt.

Prompt: "Photo of sks_girl, wearing a space suit, on the moon, <lora:my_character:1>"

Boom. The AI now knows exactly who 'sks_girl' is and will render her face perfectly, even though she has never been to the moon.

Conclusion

Training a LoRA is the gateway to professional AI artistry. It transforms you from a "Prompt Engineer" rolling the dice into a "Director" with a consistent cast of actors.

Download January Skills: LoRA Training Config & Dataset Guide