improved Noob training

configs/NOOB_BASE.knf.yaml

@@ -138,12 +138,12 @@ training:
     # into the subset backend entry after the dataset defaults below.
     subsets:
       felkin_anchor:
-        probability: 1          # Sample anchor identity images at steady exposure.
+        probability: 0.5          # Sample anchor identity images at steady exposure.
         crop: false             # Preserve full source composition.
         # repeats: 0            # Optional per-subset repeat count.
         # disabled: false       # Optional per-subset on/off switch.
       fullbody:
-        probability: 8          # Sample full-body subset at high probability.
+        probability: 10          # Sample full-body subset at high probability.
         crop: false             # Preserve full-body composition.
         # repeats: 0
         # disabled: false
@@ -242,7 +242,7 @@ training:
     subset_warmup:
       enabled: true
       subsets: [fullbody, head]
-      steps: 200 # !! First steps of the total run are ONLY listed subsets when enabled!
+      steps: 500 # !! First steps of the total run are ONLY listed subsets when enabled!
 
     # Full-run trainer config. trainer_testrun below overrides selected keys
     # only when knf train prepare/start is called with --testrun.
@@ -254,7 +254,7 @@ training:
       optimizer: adamw_bf16              # BF16 AdamW optimizer.
       learning_rate: 0.0002              # SDXL LoRA learning rates fall between 1e-4 and 5e-4
       lr_scheduler: constant_with_warmup # Ramp up LR to avoid weird color biases.
-      lr_warmup_steps: 100
+      lr_warmup_steps: 200
       seed: 42                           # Training seed.
       gradient_checkpointing: true       # Reduce VRAM by recomputing activations.
       # max_grad_norm: 1.0               # Optional gradient clipping.
@@ -263,7 +263,7 @@ training:
       train_batch_size: 4            # Per-device batch size.
       gradient_accumulation_steps: 1 # Effective batch multiplier.
       num_train_epochs: 0            # Step-based training; max_train_steps controls run length.
-      max_train_steps: 2400          # Current short SDXL run length.
+      max_train_steps: 3000          # Current short SDXL run length.
 
       caption_dropout_probability: 0.0 # Caption dropout for regularization.
       data_backend_sampling: uniform   # Uniform dataloader sampling.
@@ -279,10 +279,10 @@ training:
       validation_disable_unconditional: true # Skip unconditional validation branch.
       validation_resolution: 1024x1024       # Validation image size.
       validation_num_inference_steps: 40     # Full-run validation sampling steps.
-      validation_guidance_real: 5.0          # SDXL validation guidance.
-      # validation_guidance: 5.0             # Alternative CFG field for some SimpleTuner versions.
-      # validation_guidance_rescale: 0.0     # Guidance rescale; 0 disables rescale.
-      # validation_seed: 42                  # Deterministic validation seed.
+      validation_guidance: 5.0               # SDXL CFG guidance used by validation.
+      validation_guidance_real: null         # Remove flow-model real CFG from generated config.
+      validation_guidance_rescale: 0.0       # Guidance rescale; 0 disables rescale.
+      validation_seed: 42                    # Deterministic validation seed.
       num_eval_images: 1                     # Images per validation prompt.
       # validation_negative_prompt: "signature, ugly, cropped, blurry, low-quality"