schedulers remove false positives

Signed-off-by: Vladimir Mandic <mandic00@live.com>

.github/skills/check-schedulers/SKILL.md

@@ -95,6 +95,7 @@ Special attention:
 - flow-matching schedulers: shift/base_shift/max_shift/use_dynamic_shifting
 - DPM families: algorithm_type/solver_order/solver_type/final_sigmas_type
 - compatibility-only keys that are intentionally ignored should be documented, not silently assumed
+- detect false positives from runtime config pruning such as `if 'EDM' in name` or `name in {'IPNDM', 'CMSI', 'VDM Solver'}` in `DiffusionSampler`: verify whether unsupported keys are removed intentionally before constructor invocation
 
 ### 4. Validate SamplerData Mapping Correctness
 

pipelines/lumina_dimmo/lumina_dimoo.py

@@ -370,7 +370,7 @@ class LayerNormBase(nn.Module):
         elif tensor.device.type == "cpu" and torch.is_autocast_cpu_enabled():
             return tensor.to(dtype=dtype if dtype is not None else torch.get_autocast_cpu_dtype())
         else:
-           return tensor
+            return tensor
 
     def reset_parameters(self):
         if self.weight is not None:
@@ -890,7 +890,7 @@ class LLaDASequentialBlock(LLaDABlock):
         if self._activation_checkpoint_fn is not None:
             x = self._activation_checkpoint_fn(self.act, x)  # type: ignore
         else:
-           x = self.act(x)
+            x = self.act(x)
         x = self.ff_out(x)
         x = self.dropout(x)
         x = og_x + x