Remove pertubation seed

networks/lora_flux.py

@@ -29,42 +29,6 @@ logger = logging.getLogger(__name__)
 NUM_DOUBLE_BLOCKS = 19
 NUM_SINGLE_BLOCKS = 38
 
-@contextmanager
-def temp_random_seed(seed, device=None):
-    """
-    Context manager that temporarily sets a specific random seed and then
-    restores the original RNG state afterward.
-    
-    Args:
-        seed (int): The random seed to set temporarily
-        device (torch.device, optional): The device to set the seed for.
-            If None, will detect from the current context.
-    """
-    # Save original RNG states
-    original_cpu_rng_state = torch.get_rng_state()
-    original_cuda_rng_states = None
-    if torch.cuda.is_available():
-        original_cuda_rng_states = torch.cuda.get_rng_state_all()
-    
-    # Determine if we need to set CUDA seed
-    set_cuda = False
-    if device is not None:
-        set_cuda = device.type == 'cuda'
-    elif torch.cuda.is_available():
-        set_cuda = True
-        
-    try:
-        # Set the temporary seed
-        torch.manual_seed(seed)
-        if set_cuda:
-            torch.cuda.manual_seed_all(seed)
-        yield
-    finally:
-        # Restore original RNG states
-        torch.set_rng_state(original_cpu_rng_state)
-        if torch.cuda.is_available() and original_cuda_rng_states is not None:
-            torch.cuda.set_rng_state_all(original_cuda_rng_states)
-
 
 class LoRAModule(torch.nn.Module):
     """
@@ -150,7 +114,6 @@ class LoRAModule(torch.nn.Module):
             self.combined_weight_norms = None
             self.grad_norms = None
             self.perturbation_norm_factor = 1.0 / math.sqrt(org_module.weight.shape[0])
-            self.perturbation_seed = torch.randint(0, 2**32 - 1, (1,)).detach().item()
             self.initialize_norm_cache(org_module.weight)
             self.org_module_shape: tuple[int] = org_module.weight.shape
 
@@ -193,8 +156,8 @@ class LoRAModule(torch.nn.Module):
             lx = self.lora_up(lx)
 
             # LoRA Gradient-Guided Perturbation Optimization
-            if self.training and hasattr(self, 'perturbation_seed') and self.ggpo_sigma is not None and self.ggpo_beta is not None and self.combined_weight_norms is not None and self.grad_norms is not None:
-                with torch.no_grad(), temp_random_seed(self.perturbation_seed):
+            if self.training and self.ggpo_sigma is not None and self.ggpo_beta is not None and self.combined_weight_norms is not None and self.grad_norms is not None:
+                with torch.no_grad():
                     perturbation_scale = (self.ggpo_sigma * torch.sqrt(self.combined_weight_norms ** 2)) + (self.ggpo_beta * (self.grad_norms ** 2))
                     perturbation_scale_factor = (perturbation_scale * self.perturbation_norm_factor).to(self.device)
                     perturbation = torch.randn(self.org_module_shape,  dtype=self.dtype, device=self.device)