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Training Tips & Best Practices

Get the best results from your custom model training with these expert tips.

Getting Great Results from Training

Follow these best practices to ensure your custom models produce high-quality outputs.

Image Quality Matters

Resolution

  • Minimum: 512 x 512 pixels

  • Recommended: 1024 x 1024 or higher

  • Higher resolution = more detail captured

Focus & Clarity

  • Sharp, well-focused images

  • Avoid motion blur

  • Good exposure (not too dark or bright)

File Format

  • JPG or PNG recommended

  • Avoid heavily compressed files

  • No watermarks or overlays

How Many Images?

You only need a minimum of 1 image to train a model. We recommend uploading several for better results.

Which images matter most?

  • Style models: The first 5 images have the highest impact

  • Person, Object & Scene models: The first 3 images have the highest impact

Image order matters — if you're not satisfied with results, try reordering your training images.

Variety in Training Sets

For Person Models:

  • Different angles: front, side, 3/4

  • Various expressions

  • Indoor and outdoor

  • Different outfits

For Style Models:

  • Different subjects

  • Same consistent style

  • Various compositions

For Object Models:

  • Multiple angles

  • Different scales

  • Various lighting setups

For Scene Models:

  • Wide and close views

  • Different perspectives

  • Various times of day

Common Mistakes to Avoid

  1. Low variety: Same pose/angle in every image

  2. Inconsistent subjects: Mixing different people/objects

  3. Poor quality: Blurry, dark, or compressed images

  4. Wrong model type: Choosing style when you mean object

  5. Busy backgrounds: Distracting elements in training images

Checking Training Results

After training (takes around 20 seconds):

  1. Generate test images with different prompts

  2. Try various styles and settings

  3. Check for consistency in outputs

  4. If unsatisfied, try reordering your training images or adding new ones

When to Retrain

  • Results don't capture the subject accurately

  • Output is inconsistent

  • Model doesn't respond well to prompts

  • You have better training images available

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