📌 Recommended LoRA Training Settings by Dataset Size

This table provides optimal training settings for LoRA fine-tuning based on the number of training images.

🔹 Recommended Training Parameters

# of Images	Network Rank (network_dim)	Repeats	Epochs	Batch Size	Network Alpha (alpha)
≤ 50	8~16	7~10	15~20	2~4	Rank / 2 (e.g., 16/8)
50~100	16	6~8	12~18	4	Rank / 2 (e.g., 16/8)
100~200	16~32	5~7	12~15	4~8	Rank / 2 (e.g., 16/8 or 32/16)
200~300	16~32	5	10~12	4~8	Rank / 2 (e.g., 16/8 or 32/16)
300~500	32	4~5	8~12	8	Rank / 2 (e.g., 32/16)
500+	32~64	3~4	6~10	8~16	Rank / 2 (e.g., 32/16 or 64/32)

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🔹 Explanation of Each Parameter

• network_dim (Network Rank) → Higher values capture more detail but may overfit.
• Repeats → More repeats are needed for smaller datasets to reinforce learning.
• Epochs → Smaller datasets need more epochs, larger datasets need fewer to prevent overfitting.
• Batch Size → Lower for small datasets, higher for large datasets for faster training.
• Network Alpha (alpha) → Rank / 2 keeps the LoRA balanced, preventing excessive influence.

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🚀 TL;DR~Quick Recommendations

• If you have 50-100 images, use:

1. network_dim = 16
2. Repeats = 6~8
3. Epochs = 12~18
4. Batch Size = 4

• If you have 200-300 images, use:

1. network_dim = 16~32
2. Repeats = 5
3. Epochs = 10~12
4. Batch Size = 4~8

• If you have 500+ images, use:

1. network_dim = 32~64
2. Repeats = 3~4
3. Epochs = 6~10
4. Batch Size = 8~16

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💡 Notes:

• These values are a guideline. If results aren’t optimal, adjust accordingly.
• If the LoRA is too weak, increase Repeats or Epochs.
• If the LoRA overfits (generates only training images), reduce Repeats or increase dataset size.
• Testing different Network Ranks (network_dim) can improve results based on dataset diversity.

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