next run z-image

configs/Z_IMAGE.knf.yaml

@@ -150,7 +150,7 @@ training:
         resolution: 1024        # Preserve detail in high-res head crops.
         crop: false             # Preserve detail framing; many samples are close crops.
       genitals:
-        probability: 5   # Sample anatomy subset at normal probability.
+        probability: 6   # Sample anatomy subset at normal probability.
         crop: false      # Preserve anatomy crop framing.
         # repeats: 0
         # disabled: false
@@ -238,9 +238,9 @@ training:
       hub_model_id: ladybird-z_image-1-lora               # Optional Hub model id if push_to_hub is enabled.
 
       optimizer: adamw_bf16        # BF16 AdamW optimizer.
-      learning_rate: 0.00006        # 1.2e-4; BFL recommends 8e-5 to 1e-4 for LoRA.
+      learning_rate: 0.00009        # 1.2e-4; BFL recommends 8e-5 to 1e-4 for LoRA.
       lr_scheduler: constant_with_warmup # Ramp up LR to avoid weird color biases.
-      lr_warmup_steps: 200
+      lr_warmup_steps: 400
       seed: 42                     # Training seed.
       gradient_checkpointing: true # Reduce VRAM by recomputing activations.
       max_grad_norm: 0.1           # Clip gradients for stability. Simpletuner recommends 0.01 for z-image
@@ -249,7 +249,7 @@ training:
       train_batch_size: 4             # Per-device batch size; 32 GB VRAM should handle this first.
       gradient_accumulation_steps: 1  # Keep optimizer steps direct when VRAM allows batch 2.
       num_train_epochs: 0             # Step-based training; max_train_steps controls run length.
-      max_train_steps: 4800           # First full Z-Image character run.
+      max_train_steps: 3200           # First full Z-Image character run.
 
       caption_dropout_probability: 0.0  # Disable caption dropout; keep trigger behavior stable.
       data_backend_sampling: auto-weighting  # Auto-balance dataloader sampling based on subset probabilities and sizes.