Guide: Train Your First FLUX LoRA in Under 6 Hours

LoRA (Low-Rank Adaptation) lets you fine-tune a model on your own images without training from scratch. This guide covers building a character or style LoRA for FLUX Dev using Kohya SS — the most reliable training tool available.

#Hardware Requirements

Minimum: 10GB VRAM with 8-bit Adam optimizer.

#Step 1 — Dataset Preparation

Quality of training data is 80% of your result.

Image Requirements

• →Quantity: 15–50 images (character LoRA), 50–200 (style LoRA)
• →Resolution: 1024x1024 minimum, consistent aspect ratio
• →Variety: Different angles, lighting, expressions (character), or diverse examples (style)
• →Quality: Sharp, no compression artifacts, no watermarks

Folder Structure

The number before the underscore (10_) is the repeat count — how many times each image is shown per epoch. For small datasets (15–20 images), use 10–15. For larger datasets (50+), use 3–5.

#Step 2 — Auto-Captioning

Every image needs a text caption. Use WD14 tagger for automatic captioning.

Edit the captions — Add your trigger word to the start of every caption:

Your trigger word (mycharcterv1) is what you type in prompts to activate the LoRA later.

#Step 3 — Install Kohya SS

#Step 4 — Training Configuration

Create flux_lora_config.toml:

Key Parameters Explained

network_dim (rank) — Controls LoRA size. Higher = more capacity, more VRAM.

• →Character LoRA: 32
• →Style LoRA: 16–64
• →Concept LoRA: 8–16

network_alpha — Usually set to half of dim. Controls learning rate scaling.

learning_rate — Start at 1e-4. If results are too strong, lower to 5e-5.

max_train_epochs — 8–12 for character, 5–8 for style.

#Step 5 — Run Training

RTX 5080 Performance

Watch the loss curve — it should decrease steadily. If it plateaus early, lower the learning rate.

#Step 6 — Evaluate Your LoRA

Test your LoRA in ComfyUI after each saved checkpoint:

Good signs:

• →Subject is recognizable at weight 0.7–0.9
• →Prompt still controls other elements (background, lighting)
• →No artifacts or distortion

Bad signs:

• →Only activates at weight 1.0+ (undertrained)
• →Breaks non-subject elements (overtrained)
• →Flickering or artifacts (learning rate too high)

#Step 7 — Export and Use

Your trained LoRA is saved as a .safetensors file in your output directory.

#Common Issues

OOM at start — Enable gradient_checkpointing = true and reduce batch_size to 1.

Loss not decreasing — Check your captions are correct and trigger word is consistent.

Subject not activating — Increase LoRA weight, or train more epochs.

Overfit (everything looks like subject) — Reduce epochs or increase dataset variety.

Slow training — Ensure cache_latents = true and mixed_precision = "bf16".