Z-Image Turb Lora Training Guide
Z-Image is a powerful and useful model, but the current Turbo version is not very friendly to training LoRa. Here are a few points to note, shared for those who need them:1. When training with TA, be sure to use the official version released by @通义万相, (Z-Image-Turbo), which allows De-distilled mode.2. Do not use Add Dataset Normalization, as it is not yet supported.3. Regarding the issue of the learning rate not increasing after the loss reaches 0.37, you can control the number of steps to avoid wasting computational resources. Around 5000-6000 steps should reach approximately 0.37; adjust according to your situation and training set.4. I mostly use the default settings for other configurations. Adjust them based on your experience.5. After LoRa is set up, test it with 20 steps and CGF: 1.5-2. Due to the distillation of the model, 9 steps and CGF: 1 may not have any effect.These are some of my experiences training the Z-Image Turbo model LoRa; I hope they are helpful.My Z-IMAGE LoRa link, feel free to test it.
