•Number of class images: 12

•Sample batch size: 4

•Max training steps: 800

•Learning rate: 3e-6

•Number of training steps: 800-1200

•Batch size: 2

Please note that these settings might need to be adjusted based on your specific use case and the quality of your training data. For instance, if you’re training on faces, prior preservation is important to avoid overfitting. If the generated images are noisy or the quality is degraded, it likely means overfitting. In such cases, you can use the DDIM scheduler or run more inference steps.

Also, please be aware that fine-tuning the text encoder requires more memory, so a GPU with at least 24 GB of RAM is ideal. However, with techniques like 8-bit Adam, fp16 training or gradient accumulation, it is possible to train on GPUs with less memory like the RTX 3060.

Remember to select clear and high-quality images for training. The face should be clearly visible (avoid noisy or blurry photos) and hairstyles should be similar. Accessories like sunglasses or headbands in some of the images could cause problems.
spam fails_task lang_mismatch pii not_appropriate hate_speech sexual_content quality toxicity humor helpfulness creativity violence