feat(lora): per-layer select stack modes klora and estlora

Two-network subject+style sets select a winner per layer instead of
summing: scores are top-K magnitude sums (klora) or Frobenius energies
(estlora), and a timestep ramp shifts layers from the subject network
toward the style network across sampling, reduced to at most one
precomputed flip per layer per pass. On sub-8-bit SDNQ the pair rides
the side-channel as separate segments flipped in place; other layers
recompute the winner from the pristine backup, so select modes force
backup mode. Selection resets per pass from the callback setup and is
gated off under model compile. estlora's measured style-discrepancy
term is exposed as an option. Adds XYZ axes for the stack settings.

scripts/xyz/xyz_grid_classes.py

@@ -104,6 +104,10 @@ class SharedSettingsStackHelper():
     todo_ratio = None
     teacache_thresh = None
     extra_networks_default_multiplier = None
+    lora_stack_mode = None
+    lora_stack_density = None
+    lora_stack_alpha = None
+    lora_stack_discrepancy = None
     disable_apply_metadata = None
     disable_apply_params = None
     sdnq_quant_mode = None
@@ -136,6 +140,10 @@ class SharedSettingsStackHelper():
         self.sd_unet = shared.opts.sd_unet
         self.sd_text_encoder = shared.opts.sd_text_encoder
         self.extra_networks_default_multiplier = shared.opts.extra_networks_default_multiplier
+        self.lora_stack_mode = shared.opts.lora_stack_mode
+        self.lora_stack_density = shared.opts.lora_stack_density
+        self.lora_stack_alpha = shared.opts.lora_stack_alpha
+        self.lora_stack_discrepancy = shared.opts.lora_stack_discrepancy
         self.teacache_thresh = shared.opts.teacache_thresh
         self.disable_apply_metadata = shared.opts.disable_apply_metadata
         self.disable_apply_params = shared.opts.disable_apply_params
@@ -148,6 +156,10 @@ class SharedSettingsStackHelper():
         shared.opts.data["disable_apply_metadata"] = self.disable_apply_metadata
         shared.opts.data["disable_apply_params"] = self.disable_apply_params
         shared.opts.data["extra_networks_default_multiplier"] = self.extra_networks_default_multiplier
+        shared.opts.data["lora_stack_mode"] = self.lora_stack_mode
+        shared.opts.data["lora_stack_density"] = self.lora_stack_density
+        shared.opts.data["lora_stack_alpha"] = self.lora_stack_alpha
+        shared.opts.data["lora_stack_discrepancy"] = self.lora_stack_discrepancy
         shared.opts.data["prompt_attention"] = self.prompt_attention
         shared.opts.data["schedulers_solver_order"] = self.schedulers_solver_order
         shared.opts.data["schedulers_sigma_adjust"] = self.schedulers_sigma_adjust
@@ -205,6 +217,10 @@ axis_options = [
     AxisOption("[Prompt] Prompt parser", str, apply_setting("prompt_attention"), choices=lambda: ["native", "compel", "xhinker", "a1111", "fixed"]),
     AxisOption("[Network] LoRA", str, apply_lora, cost=0.5, choices=list_lora),
     AxisOption("[Network] LoRA strength", float, apply_lora_strength, cost=0.6),
+    AxisOption("[Network] LoRA stack mode", str, apply_setting("lora_stack_mode"), cost=0.6, choices=lambda: ["sum", "ties", "dare_ties", "dare_linear", "magnitude_prune", "klora", "estlora"]),
+    AxisOption("[Network] LoRA stack density", float, apply_setting("lora_stack_density"), cost=0.6),
+    AxisOption("[Network] LoRA stack ramp", float, apply_setting("lora_stack_alpha"), cost=0.6),
+    AxisOption("[Network] LoRA stack discrepancy", float, apply_setting("lora_stack_discrepancy"), cost=0.6),
     AxisOption("[Network] Styles", str, apply_styles, choices=lambda: [s.name for s in shared.prompt_styles.styles.values()]),
     AxisOption("[Param] Width", int, apply_field("width")),
     AxisOption("[Param] Height", int, apply_field("height")),