>>109270767
I've done a few krea2 loras with ai-toolkit. I use the following train settings:
train:
batch_size: 1
bypass_guidance_embedding: false
steps: 10000
gradient_accumulation: 1
train_unet: true
train_text_encoder: false
gradient_checkpointing: true
noise_scheduler: "flowmatch"
optimizer: "automagic3"
timestep_type: "sigmoid"
content_or_style: "balanced"
optimizer_params:
weight_decay: 0.0001
unload_text_encoder: false
cache_text_embeddings: false
lr: 0.0001
ema_config:
use_ema: false
ema_decay: 0.99
skip_first_sample: false
force_first_sample: false
disable_sampling: false
dtype: "bf16"
diff_output_preservation: false
diff_output_preservation_multiplier: 1
diff_output_preservation_class: "person"
switch_boundary_every: 1
loss_type: "mse"
Before you say 0.0001 is too high, the optimizer turns it down as it trains. I gen test images every 500 steps and watch to see how well it matches the actual captioned image with the same prompt. If it's a single-character lora I'll use a couple hundred images. I think running as long as I do with just double-digit image count would fry it.